Generated by All in One SEO Pro v4.9.6.2, this is an llms-full.txt file, used by LLMs to index the site. # DEDICATTED Driving your business forward with the power of Cloud & AI ## Posts ### [Insights](https://dedicatted.com/insights) **Published:** February 21, 2025 **Author:** Dedicatted --- ### [Inside a WAF Challenge Rule: How We Stop DDoS Attacks Without AWS Shield Alone](https://dedicatted.com/insights/inside-a-waf-challenge-rule-how-we-stop-ddos-attacks-without-aws-shield-alone) **Published:** September 10, 2026 **Author:** Andriy Kopachevskyy **Content:** At 7:31 PM, a server crossed 90% CPU. At 7:32:30, an engineer who’d never seen this specific traffic pattern before had already opened the right dashboard. By 7:36, the attack was being filtered at the edge, and by 7:37 the load was back to normal. Nobody outside the incident channel ever knew it happened. That’s the whole article, really. Everything below is just how those five minutes actually work, and how you can tell whether your own infrastructure would survive them. One of our customers is an e-commerce business that gets hit with DDoS attempts on a regular basis. Revenue depends entirely on uptime, so every attempt is a direct threat to the business, not just an engineering headache. The attempts don’t stop, which is exactly why we built a process that catches them at the alert stage instead of at the “why is the site down” stage. This article is about that: how good incident management actually works, with real examples from our own [Managed Services](https://dedicatted.com/what-we-do/devops/devops-as-a-service) work. ## Why five minutes is the bar, not an exaggeration ***A DDoS attack***, distributed denial-of-service, is when someone floods your infrastructure with traffic until real users can’t get through. It doesn’t need to be sophisticated. A flood of requests to one product page, one login endpoint, one API, is often enough to slow a server to a crawl or knock it over completely. Cloudflare’s [2026 threat report ](https://blog.cloudflare.com/ddos-threat-report-2026-h1)clocks roughly 5,300 network-layer DDoS attacks per hour globally, and over 90% of them finish inside ten minutes. Our research shows Layer 7 attacks specifically are up 104% since 2023, mostly because DDoS-for-hire platforms have turned “launch an attack” into a checkout flow: pick a duration, pick a bandwidth tier, pay in crypto, done. It’s not a rare event anymore. It’s background noise on the internet, and at some point it will point at you. Here’s the technical detail that matters more than either stat: [AWS’s own DDoS resiliency guidance](https://docs.aws.amazon.com/pdfs/whitepapers/latest/aws-best-practices-ddos-resiliency/aws-best-practices-ddos-resiliency.pdf#aws-best-practices-ddos-resiliency) is blunt about this. AWS Shield Standard, the protection that’s automatically on for every AWS resource, handles the classic infrastructure-layer floods, SYN floods, UDP reflection, that kind of volumetric noise, at no cost and with no configuration. But it explicitly does not stop a Layer 7 attack dressed up as normal traffic. A flood of well-formed HTTP requests to a real product page, at a rate a human might plausibly generate, doesn’t trip any network-layer defense. Nothing about the packets is malformed. The only thing wrong with it is the *pattern*, and pattern-based detection is a different layer of the stack entirely. That gap, between “AWS blocks this for free” and “you have to configure this yourself,” is exactly where our incident started. ### 1. Observability tuned to how you actually run things A generic “is the server up” dashboard tells you something is wrong. It rarely tells you what to do about it. Real observability is built around the specific stack, traffic patterns, and failure modes of the system it’s watching, which means it looks different from one implementation to the next. > > ***How this varies across our projects.** For a customer running a serverless AWS stack, we build mainly on native AWS tooling around CloudWatch, alarms on Lambda concurrency, [API Gateway](https://dedicatted.com/services/glossary-of-terms#term-api-gateway) error rates, and similar signals. For a customer running a fleet of EC2 instances, we maintain a golden image with monitoring agents baked in, so a new instance joins observability automatically the moment Auto Scaling spins it up. For one e-commerce customer who gets DDoSed regularly, observability runs on Grafana, Prometheus, and Loki, with HAProxy logs feeding a dashboard that breaks traffic down by path, not just by volume. That per-path view is what turns “load is high” into “load is high on this one product page,” which is the difference between an investigation and an instant answer.* The common thread isn’t the specific tools. It’s that the setup is chosen deliberately for what could actually go wrong, instead of bought once and left generic. [![tVAQq4bds3 cKnfcCoJdNA5zkLu41x dNxnsBTRUMp02OKUhagJSlecCfcG9HWc8XokPaIBJxQ46Q4QFegV2L7T6loOtKj 2vKD6xlIfyLhXvyRF9Dx3MyB0OdpYzf6tCcFRKcwAZD6xngtackK2sZ 6qSNsEtByoV8XLv4rYhuthmRX8NLf3VU7 9j5p wH](https://dedicatted.com/wp-content/uploads/2026/09/tVAQq4bds3-cKnfcCoJdNA5zkLu41x-dNxnsBTRUMp02OKUhagJSlecCfcG9HWc8XokPaIBJxQ46Q4QFegV2L7T6loOtKj-2vKD6xlIfyLhXvyRF9Dx3MyB0OdpYzf6tCcFRKcwAZD6xngtackK2sZ_6qSNsEtByoV8XLv4rYhuthmRX8NLf3VU7_9j5p-wH-1024x519.jpeg)](https://dedicatted.com/wp-content/uploads/2026/09/tVAQq4bds3-cKnfcCoJdNA5zkLu41x-dNxnsBTRUMp02OKUhagJSlecCfcG9HWc8XokPaIBJxQ46Q4QFegV2L7T6loOtKj-2vKD6xlIfyLhXvyRF9Dx3MyB0OdpYzf6tCcFRKcwAZD6xngtackK2sZ_6qSNsEtByoV8XLv4rYhuthmRX8NLf3VU7_9j5p-wH-1024x519.jpeg)### 2. Escalation that filters noise instead of paging everyone The second piece is making sure the right person actually sees the alert, quickly, without burning out the team on false alarms. A flat “page everyone for everything” approach fails in both directions: real incidents get lost in the noise, and people stop trusting the pager. ***The pattern we use routes by severity**. A short-lived, minor spike lands quietly in a chat channel for whoever’s on shift to check. A harder signal, a disk hitting its IOPS limit when it normally sits around 30%, triggers escalation loud enough to guarantee someone acknowledges it. On projects with 24/7 coverage, a high-priority alert typically follows a ladder like this: it hits chat and the escalation system at once; if it hasn’t auto-resolved within a minute (filtering out the noise of things that fix themselves), the on-duty engineer gets an SMS and a push notification; if that goes unacknowledged for another minute, the system calls their phone directly. Each rung exists to fail safe against a human being asleep or slow to check their phone, without paging someone at 2 AM over something that would’ve resolved on its own.* ### 3. Runbooks instead of improvising under pressure The third piece is what happens once a person is actually looking at the problem. Under time pressure, improvising is slow and inconsistent, even for a good engineer. A runbook removes that variable. Every runbook we write follows the same four-part shape: 1. **Trigger** – the alert or request that leads you here. 2. **Validation** – how to confirm what’s actually going on. 3. **Steps to resolution** – the specific actions to take. 4. **Escalation criteria** – when and how to bring in more help. It sounds almost too simple to matter, but that’s the point. A runbook doesn’t ask an engineer to rediscover, mid-incident, what usually causes this alert on this project. It tells them, based on the project’s own history, and sends them straight to the most likely cause instead of a generic top-to-bottom investigation. ## The examples: what DDos attack looks like in practice **A DDoS attack on a single product page.** On the e-commerce customer mentioned above, a P1 alert fired at 7:30 PM: one instance had jumped to 90% CPU. It didn’t auto-resolve within a minute, so the on-duty engineer got the escalation push and acknowledged it a minute and a half after the first alert. The runbook’s validation step pointed straight at traffic distribution per path, because on this project, DoS and DDoS attempts are by far the most common cause of this specific alert. The HAProxy dashboard showed nearly all the traffic hitting one product page. Steps to resolution: add that page’s path to a regex set already wired into an AWS WAF rule with a **Challenge** action attached. [![VrcWyk5LLS1Tj7nbVT1YKH3C yaqhvgFcGi2vJsX1QjADYN6DUlFOsLDpxi1T779bcZdZAiT0PAL6Dism8Vy3pEHF27n7hyysJrZFDPYYp6rDPIUpPCC4jxL1EqYZhHHCZDoc9yKwTXON3OljRnICuFAm5fu4mJv7FpDtrzannbdkkYW10RxdZynhWwIPX8m](https://dedicatted.com/wp-content/uploads/2026/09/VrcWyk5LLS1Tj7nbVT1YKH3C-yaqhvgFcGi2vJsX1QjADYN6DUlFOsLDpxi1T779bcZdZAiT0PAL6Dism8Vy3pEHF27n7hyysJrZFDPYYp6rDPIUpPCC4jxL1EqYZhHHCZDoc9yKwTXON3OljRnICuFAm5fu4mJv7FpDtrzannbdkkYW10RxdZynhWwIPX8m-1024x495.jpeg)](https://dedicatted.com/wp-content/uploads/2026/09/VrcWyk5LLS1Tj7nbVT1YKH3C-yaqhvgFcGi2vJsX1QjADYN6DUlFOsLDpxi1T779bcZdZAiT0PAL6Dism8Vy3pEHF27n7hyysJrZFDPYYp6rDPIUpPCC4jxL1EqYZhHHCZDoc9yKwTXON3OljRnICuFAm5fu4mJv7FpDtrzannbdkkYW10RxdZynhWwIPX8m-1024x495.jpeg)Here’s what that one line actually does. Challenge issues a JavaScript-based proof-of-work puzzle and requires a valid `aws-waf-token` cookie before a request proceeds. A real browser solves it invisibly, in milliseconds, no CAPTCHA, no friction a shopper would ever notice. A basic bot script, the kind behind most low-effort DDoS-for-hire traffic, can’t execute JavaScript or manage cookies properly, so it never gets through. Add one path to one regex set, and the traffic sorts itself. - **Response time:** 1.5 minutes, first alert to acknowledgment. - **Resolution time:** 3 minutes, acknowledgment to mitigated traffic. - **End to end:** under 5 minutes, zero downtime. > **Why not just block the IPs?** Botnet traffic is often spread across thousands of addresses, residential proxies, and compromised IoT devices, so a static IP blocklist is a losing game of whack-a-mole, and it risks catching real visitors on shared or rotating IPs. A behavioral challenge doesn’t care what IP a request came from. It cares whether the client can act like a browser, which is a much harder thing for a cheap bot to fake. ### The same pattern at 3 AM On another project, an unnatural traffic surge started building at 3:25 AM. It crossed the alert threshold at 3:56, and the on-duty team got a call. By 4:03 the customer had been notified, by 4:04 the root cause was traced to a specific path being hit by an unusual concentration of US traffic, and by 4:08 a WAF challenge rule was live and the load was already dropping. The customer found out from the incident report the next morning, not from a dead site at 4 AM. ### When the fix isn’t DDoS related at all The same proactive posture shows up outside traffic attacks, too. On another engagement, we found that a customer’s development and staging environments were open to anyone, anywhere, no restrictions at all. Real vulnerability, zero tickets filed, because nobody outside the infrastructure team would have known to look. We introduced a VPN, restricted access to the customer’s own offices and authorized individuals, and ran the knowledge transfer needed so the dev team’s workflow didn’t skip a beat. The customer didn’t have to notice the gap, let alone request the fix. That’s really the point of all three examples together: the specific fix changes every time, but the shape of the response doesn’t. ### What happens after the load drops Resolving the incident isn’t the finish line. Once traffic is back to normal, the team holds an incident review, writes up a report, and defines follow-up work: new alerts that would catch this faster next time, or an evaluation of what could be automated further without a human in the loop. Every incident is supposed to make the next one cheaper to handle. That compounding is what separates a proactive process from a merely fast one. ## If you’re reading this and honestly don’t know where you stand Most businesses aren’t lying awake worrying about DDoS. They’re just not sure where they stand, and not sure is its own kind of risk, because you find out the hard way, mid-incident, with customers watching. A few honest questions worth asking yourself: - **If your traffic spiked right now, would anyone know inside a minute, or would you find out from a customer complaint?** - **Could you tell, in under thirty seconds, which specific page or endpoint is taking the abnormal load, or would you be staring at one “load is high” graph with no way to drill down?** - **Is there a documented, tested action your team can take without waiting for a senior engineer to wake up?** - **Has anyone actually checked whether your traffic patterns need WAF rules on top of the automatic protection AWS already gives you, or are you assuming Shield Standard covers everything?** - **After your last traffic spike, did anything get written down and fixed, or did everyone just move on once it passed?** If two or more of those made you wince, that’s a normal starting point, not a failure. Infrastructure usually gets built to handle expected load, not adversarial load, and DDoS mitigation tends to get added only after an attack, or after someone finally asks the right question first. The fix isn’t necessarily “hire a 24/7 team tomorrow.” It’s often smaller: get visibility into traffic by path, not just by volume; put one WAF rule in front of your highest-value endpoints; write down the first three steps of what to do when the alert fires, even a rough version. Each of those closes a real gap on its own. ## Where Dedicatted fits Dedicatted has been building and running infrastructure like this for over 9 years, with around 80 engineers and 140+ projects delivered. [As an AWS Premier Tier Services Partner](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/Dedicatted) and a top 2% global AWS partner with MSP designation, we combine deep cloud expertise with hands-on engineering delivery. We’re SOC 2 and ISO 27001 certified, with Managed Services SLAs specific down to the minute rather than “same day”: Platinum customers get a 15-minute response on P1 incidents, Gold gets 30 minutes, Silver gets an hour, all with 24/7 coverage. The point of [Managed Services](https://dedicatted.com/what-we-do/devops/devops-as-a-service), in plain terms, is that we take full ownership of the infrastructure so the business doesn’t have to think about it, the same way an accountant handles your taxes so you don’t have to become one. You still get full visibility: an incident report for every incident, regular operational and business reporting, and SLA monitoring you can actually check against. The work doesn’t disappear. It just stops being your problem. ## Want to see where your own setup stands? If your business depends on being available, and it probably does, we’d be glad to look at your infrastructure together and tell you honestly where the gaps are: in monitoring, in alerting, or in what happens after the alert fires. Try the MSP calculator at [msp.dedicatted.com,](https://msp.dedicatted.com/) or just start a conversation with the engineers who’d be on the other end of that phone call. **Categories:** Blog **Services:** Cloud Operations, Managed Services, Security Compliance **Industry:** Financial Services, Healthcare, Retail --- ### [Agentic Voice Automation for the Clinic Revenue Cycle at Spike Technologies](https://dedicatted.com/insights/agentic-voice-automation-for-the-clinic-revenue-cycle) **Published:** September 8, 2026 **Author:** Dedicatted **Content:** ## About project #### Working time: 2024 – ongoing #### Industry: HealthTech #### The service: Agentic AI, Cloud Security, Managed Services #### Tags: HealthTech / Agentic AI / Generative AI / Cloud Security / Managed Services #### Overview **Spike Technologies, Inc.** is a New York healthcare technology company. Its product, Health OPS, is an agentic AI voice and workflow automation platform for outpatient rehabilitation clinics: physical therapy, occupational therapy and speech-language pathology. Its agents call payers and patients, verify eligibility, obtain and renew prior authorizations, follow up on claims and run patient outreach, writing every outcome back into the systems the clinic already uses. A rehabilitation clinic’s revenue cycle runs on the telephone. Eligibility has to be verified before the first visit, and prior authorization has to be obtained before treatment and renewed as the authorised visit count is consumed, because a plan of care is a sequence of visits and each has to be covered. Industry figures for these practices put front-desk time at more than 10 minutes per patient on prior authorization alone, against an average claim denial rate of about 13 per cent, with nearly three-quarters of denials requiring an appeal. Dedicatted built the platform on Amazon Bedrock AgentCore inside a HIPAA-eligible boundary in us-east-1, and has operated it under a managed DevOps service since it went live on 8 December 2025. Eleven clinics were live by June 2026, at about 9,500 calls a month. ## **The Challenge** **The expensive part of the work is a conversation.** Scripted IVR navigation and portal scraping retrieve a fraction of eligibility answers and break whenever a payer changes a page. The costly work is the call with a payer representative about one patient’s authorization: IVR navigation, hold time, a conversation that follows no script, and then re-keying the answer into the EMR. **A product company with no platform.** Spike was a venture-backed startup with the domain knowledge and the product thesis, a small engineering team, and no cloud platform or security function. It needed to run many concurrent telephone conversations carrying protected health information and to orchestrate calls into EMR, billing and payer systems safely, without consuming the engineering capacity the product itself needed. **Protected health information cannot leave the boundary.** Each clinic is a HIPAA covered entity, and Spike is its business associate. Every clinic’s compliance officer reads the data-flow diagram before signing, so protected health information had to stay inside HIPAA-eligible services under a business associate agreement, verifiable from the architecture rather than promised. **A confident wrong answer is worse than no answer.** When an agent tells a patient how many visits remain or what the copay is, the clinic acts on it. A fluent completion of a payer’s response that is not in it is a denial waiting to happen, so nothing the agent says or records could originate in the model. ## **Our Approach** 1. **Reuse the landing zone, and sign the paperwork before the first call.** The platform was built into the AWS Control Tower landing zone Dedicatted had delivered for Spike in 2024, with its management, log archive and audit accounts and preventive service control policies. The AWS Business Associate Addendum was accepted in AWS [Artifact](https://dedicatted.com/services/glossary-of-terms#term-artifact) at the Organization level on 20 November 2025, before any protected health information entered the account. 2. **Keep every component that touches PHI inside HIPAA-eligible services.** Telephony, speech, models, agent runtime, state and storage were each selected on eligibility as well as capability, so the whole data-flow diagram sits inside one business associate agreement, one audit record and one set of keys. 3. **Take the agent platform as a managed service instead of building one.** Session isolation between clinics, memory with a retention policy, a credential vault, a tool registry and tracing are what Amazon Bedrock AgentCore provides, and they are exactly what a small team should not own. 4. **Set a turn-latency budget in milliseconds and design against it.** A voice agent that pauses gets interrupted or hung up on. Two models, streaming on every hop and private network paths were chosen against a budget of 2 seconds at the 95th percentile. 5. **Ground every fact the agent states or records.** Every statement the agent makes or records comes from a tool result or a transcript, is grounding-checked, and is read back to the payer representative before the call ends. ## **Our Solution** **Telephony and speech.** Calls enter and leave through Amazon Connect, which provides contact flows, telephone numbers and carrier connectivity and encrypts the call recordings with a customer-managed AWS KMS key. Amazon Transcribe performs streaming speech recognition with content redaction for PII and PHI, so the stored transcript carries redaction tokens where identifiers were spoken, and Amazon Polly neural voices synthesise the agent’s side of the conversation. **Agents.** Each call or workflow task is an Amazon Bedrock AgentCore [Runtime](https://dedicatted.com/services/glossary-of-terms#term-runtime) session: isolated per session, so one clinic’s call cannot share state with another’s, and long-lived enough to survive a payer’s hold. AgentCore Memory holds in-call and cross-call state under a retention policy. AgentCore Gateway exposes AWS Lambda tools under one schema, for the clinic EMR API, the clearinghouse eligibility API with its X12 270/271 transactions, and the scheduling system, logging every tool call with its arguments and result. Prior authorization requests go to the clearinghouse as X12 278 transactions where the payer accepts them, and by voice where it does not. AgentCore Identity holds the OAuth credentials for those APIs, so the agent never sees a raw secret. AgentCore [Observability](https://dedicatted.com/services/glossary-of-terms#term-observability) traces every reasoning step into Amazon CloudWatch, which is what makes an agent’s action auditable. **Models.** Reasoning runs on Anthropic Claude models on Amazon Bedrock, with a smaller model managing the conversational turn and a larger one reasoning over the case and drafting the record, both selected by configuration. Amazon Bedrock Guardrails applies sensitive-information filters and contextual grounding checks, so a statement about coverage is checked against the payer response it was derived from. Amazon SageMaker runs the evaluation pipeline over transcripts and tool-call traces and hosts a custom call-outcome classifier that labels every completed call. **Application, state and network.** The clinic-facing application and the workflow API run on Amazon ECS on AWS Fargate with Graviton, in private subnets across three Availability Zones. Live call state sits in Amazon DynamoDB, where a turn needs single-digit millisecond reads; durable case and authorization records in Amazon Aurora PostgreSQL; recordings, transcripts and generated documents in Amazon S3 under a customer-managed key per data class. Interface endpoints keep traffic to Bedrock, AgentCore and Transcribe on the AWS network, and outbound reach to EMR, clearinghouse and payer destinations is allow-listed. **Governance and operations.** AWS CloudTrail with S3 data events, Amazon GuardDuty with runtime monitoring on the tasks, AWS Config with the Operational Best Practices for HIPAA Security conformance pack, AWS Security Hub, Amazon Inspector on the container images, and CloudWatch Logs data protection policies that mask PHI patterns make up the governance plane. All infrastructure is [Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform) applied through the CI/CD pipeline, and Dedicatted operates the account under a managed DevOps service as a subcontractor business associate. ## **The Results** Baselines were measured at the first two clinics in November 2025, the month before go-live. The results are from May 2026, across all eleven clinics. **Front-desk staff time per prior authorization fell from 12 minutes to 4**, against a target of under 5. The 4 minutes is the average across all authorizations and is human review of the exceptions; the routine case is completed by the agent with no staff time. **Eligibility checks completed by the agent with no human involvement rose from 0 to 78 per cent**, against a target of 70 per cent, over 6,200 checks in May 2026. The remaining 22 per cent reach a person with the payer’s response already attached. **Conversational turn latency is 1.1 seconds at the 50th percentile and 1.9 seconds at the 95th**, inside the budget set at design time. **The blended cost is USD 0.31 per handled call**, against a pre-implementation estimate of USD 0.38. Every tier of the platform is consumption-priced, so a new clinic group adds calls rather than infrastructure. The platform handles about 9,500 calls a month with a peak of 40 concurrent calls. On 15 July 2026 Spike Technologies was acquired by Raintree Systems, an EHR and practice management vendor for rehabilitation providers, whose stated purpose was to bring this platform to its own customer base. Dedicatted is an AWS Premier Tier Services Partner. We build and operate agentic AI workloads that handle protected health information on AWS, inside the compliance boundary the customer’s own customers audit. **Categories:** Case study **Services:** Agentic AI, Cloud Security, Generative AI, Managed Services **Industry:** Healthcare --- ### [Self-Service Clinical Analytics on a FHIR Data Store for a Cardiology Network](https://dedicatted.com/insights/self-service-clinical-analytics-on-a-fhir-data-store-for-a-cardiology-network) **Published:** September 8, 2026 **Author:** Dedicatted **Content:** ## About project #### Working time: 2024 – ongoing #### Industry: Healthcare #### The service: Data Architecture, Analytics, Cloud Security #### Tags: Healthcare / Data Architecture / Analytics / Generative AI / Security [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) #### Overview **Cardio Study** is a privately owned network of eight diagnostic cardiology clinics, six in Ontario and two in British Columbia. About 60 clinicians, technologists and administrators handle around 40,000 patient encounters a year. The clinics perform Holter monitoring, ambulatory blood pressure monitoring, exercise stress testing, cardiology consultations, and telemedicine consultations through the Ontario Telemedicine Network. The operating model is referral-based: a physician refers the patient, the clinic fits the device and collects the recording, a cardiologist interprets it, and a report goes back to the referrer. Every study produces clinically rich data: heart rate statistics, arrhythmia burden including atrial fibrillation, daytime and night-time blood pressure averages, and the cardiologist’s interpretation. None of it could be queried across the network. Dedicatted built the network one governed clinical record, HL7 FHIR R4 in AWS HealthLake, with serverless analytics over it: dashboards for the recurring questions and natural-language questions for the rest. Everything runs in the ca-central-1 Region, so personal health information stays in Canada. The platform has been in production since 4 March 2026. ## **The Challenge** **Three copies of the record, and none of them queryable.** The clinical data existed in three disconnected forms: inside each device vendor’s proprietary analysis software, as PDF reports sent to referring physicians, and as files accumulating in Amazon S3 with no data model and no query layer over them. Clinic records were held per site, so the network had eight partial views of itself. **One cohort question cost a working day.** The reference question is the simplest one a cardiology network can ask: how many patients with atrial fibrillation were treated across all sites in the last 30 days. Answering it meant opening the device software at each of the eight clinics, exporting, filtering, counting by hand, and reconciling the sites in a spreadsheet. That was 6 hours of work, and the answer arrived the next day. The harder questions the cardiologists wanted to ask, such as which patients show a different pattern at night than by day, were not being asked at all. **Operational reporting was manual and late.** The monthly cross-site report on study volumes, test mix and turnaround from device fitting to signed report took 14 hours of administrator time to assemble from per-site exports. A multi-site clinical operation was being managed on numbers that were already stale when they arrived. **A custodian has to account for health information it cannot see.** Cardio Study is a health information custodian under Ontario’s Personal Health Information Protection Act. Personal health information spread across vendor software, PDF attachments and unstructured storage is harder to protect, to log and to produce on request than the same information held in one encrypted, access-controlled store. ## **Our Approach** 1. **Decide the residency boundary before anything else.** The whole solution was placed in ca-central-1, the Canadian Region where AWS HealthLake became available in October 2025. The FHIR data store was created there in November 2025, and no health data was ever held in any other Region. 2. **Make ingestion the clinics’ least interesting task.** Each site pushes its device exports and clinic records nightly over SFTP, with its own user and its own IP allow-list. No clinic had to install software or change how its technologists work. 3. **Model the data as FHIR, and decide per element whether it is structured or narrative.** Summary values from the Holter and ABPM analyses are mapped to LOINC-coded Observations with explicit day and night components. The natural language processing over the cardiologist’s interpretation is enrichment on top of that, never a substitute for a value the source already provides in a field. 4. **Put the analytics on top of the data store rather than beside it.** The FHIR store publishes its own analytics tables, so there is no second copy of the clinical record, no ETL job to maintain and no capacity to provision before anyone asks a question. 5. **Curate the natural-language topics with the cardiologists.** Clinicians ask in clinical shorthand and about windows relative to the study date, not the calendar. Synonyms, a default date field and verified answers were built with the cardiologists as a delivery milestone with its own acceptance, not as tuning after go-live. ## **Our Solution** **Ingestion.** Each of the eight clinics pushes its Holter and ABPM analysis exports, as vendor CSV and PDF files and HL7 v2 ORU messages, and its clinic records nightly through AWS Transfer Family over SFTP, under a per-site user with key-based authentication and an IP allow-list. Files land in an Amazon S3 landing bucket encrypted with a customer-managed AWS KMS key, versioned, with public access blocked and a bucket policy that denies any request that is not TLS. Amazon GuardDuty Malware Protection scans every object on arrival. **Transformation.** An object-created event routed through Amazon EventBridge triggers AWS Lambda functions that parse each vendor export and clinic record into FHIR R4 resources: Patient, Practitioner, Organization, Encounter, LOINC-coded Observation for the ECG and blood pressure readings, DiagnosticReport for the Holter and ABPM reports, and DocumentReference carrying the signed PDF and the free-text interpretation. AWS Step Functions runs the nightly bulk import into the data store, records the outcome and raises an alarm on failure. **System of record.** AWS HealthLake holds the FHIR R4 record, encrypted with a customer-managed key, with integrated natural language processing enabled on the clinical notes, so that a finding written in narrative becomes a coded resource a cohort query can reach. A self-managed FHIR server was rejected: it would have handed a clinic network with no platform team an application, a database, a search index and a patching schedule, with the clinical NLP still to be built. **Analytics.** HealthLake writes the imported data to its SQL analytics tables in Apache Iceberg format in S3, one table per FHIR resource type, registered in the AWS Glue Data Catalog and queried with Amazon Athena in a dedicated workgroup whose results are encrypted with their own key. AWS Lake Formation grants are issued per clinic, so the data-access boundary sits below the reporting tool rather than inside it. **Consumption.** Amazon QuickSight Enterprise carries the dashboards: study volumes by site and test type, turnaround from device fitting to signed report, atrial fibrillation prevalence, and day-night blood pressure patterns. Row-level security maps every user to the clinics and the role they are entitled to, column-level security removes direct identifiers from the operational datasets, and SPICE is encrypted with a customer-managed key. Amazon Q in QuickSight answers questions typed in clinical vocabulary against three curated topics: patient cohorts, monitoring throughput and referral turnaround. **Identity and governance.** Human access is federated through AWS IAM Identity Center, for the console and for QuickSight alike, with no IAM users and no long-lived access keys. AWS CloudTrail records management events and S3 data events into a log bucket under S3 Object Lock. AWS Config evaluates the Operational Best Practices for HIPAA Security conformance pack, AWS Security Hub aggregates control state, and Amazon GuardDuty provides threat detection. The estate is defined in [Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform). There is no VPC-hosted compute, no database instance and no operating system anywhere in it. ## **The Results** Two KPIs were agreed with the customer before the build and measured against baselines taken before the go-live of 4 March 2026. **Time to answer a clinical cohort question fell from 6 hours to 4 minutes**, against a target of under 10 minutes. The figure is the median across the 12 recurring cohort questions the clinical lead tracks, measured in April 2026, each question run by the person who asks it rather than by a report writer. **Preparation of the monthly cross-site operations report fell from 14 hours per month to 2.5 hours**, against a target of under 4 hours, measured in May 2026 from the hours logged by the administrator who owns the report. All eight clinics have fed the data store nightly since go-live, at the network’s rate of about 40,000 patient encounters a year. The QuickSight subscription serves 4 authors and 26 readers, 19 of them weekly active in June 2026. An AWS Well-Architected Framework review of the estate on 4 September 2026 closed with zero high-risk issues, both on the framework lens and on the Healthcare Industry Lens. Dedicatted is an AWS Premier Tier Services Partner. We design, build and operate regulated healthcare workloads on AWS, from FHIR data foundations to the analytics and the governance around them. **Categories:** Case study **Services:** Cloud Security, Data Architecture, Generative AI, Security Compliance **Industry:** Healthcare --- ### [How does Agentic AI differ from Generative AI?](https://dedicatted.com/insights/genai-vs-agentic-ai-vs-ai-agents) **Published:** July 3, 2025 **Author:** Serhii Semenchenko **Content:** Artificial intelligence is entering an exciting new chapter. Generative AI has already transformed the way businesses create content, communicate, and extract insights – helping teams work smarter and faster. Now, a new breakthrough is expanding those possibilities even further: Agentic AI. The momentum is building fast: Deloitte predicts nearly half of Gen AI users will explore agentic AI pilots by 2027, while investment in enterprise agentic AI startups has already reached $2 billion. At the same time, public interest is surging, with search trends for “agentic AI” peaking at the end of 2024. Whether you’re a developer, decision-maker, or simply curious about the latest advances in artificial intelligence, this analysis will help you understand how these technologies are redefining our relationship with automated systems and opening new possibilities for innovation. [![Graphic titled "Curious how this works in real-world use cases in retial " with the image of the first page of the whitepaper](https://dedicatted.com/wp-content/uploads/2025/07/Group-146-2-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/07/Group-146-2-scaled.png)## **What is generative AI?** [Generative AI](https://dedicatted.com/services/generative-ai) is a class of AI systems designed to create original content – including text, images, code, audio, and video – that closely resembles human output. These models, typically powered by large language models (LLMs) and deep learning architectures like transformers, are trained on massive datasets to recognize patterns and generate coherent, creative results. Using learning approaches such as unsupervised, semi-supervised, and reinforcement learning with human feedback (RLHF), they form the backbone of tools like GPT-4, Claude, DALL·E, and others. The real impact of Generative AI becomes clear when applied to complex, document-heavy industries. For example, [**Dedicatted’s GenAI-powered claims processing solution**](https://dedicatted.com/insights/genai-powered-claims-processing) for an insurance company automated data extraction and validation across thousands of claims. By integrating large language models into workflow orchestration, the system reduced manual handling time by 70% and improved accuracy in fraud detection. This case perfectly illustrates how GenAI transforms traditional back-office processes into intelligent, self-improving systems – a crucial first step toward agentic autonomy. [![Diagram showing the three steps of reinforcement learning with human feedback (RLHF). Step 1: Collect demonstration data and train a supervised policy. A prompt is sampled, a human labeler demonstrates the desired behavior, and the data fine-tunes GPT-3.5 with supervised learning. Step 2: Collect comparison data and train a reward model. Multiple model outputs are sampled for a prompt, a labeler ranks them from best to worst, and this data trains the reward model. Step 3: Optimize a policy with PPO reinforcement learning. A new prompt is sampled, the policy generates an output, the reward model scores it, and the reward is used to update the policy](https://dedicatted.com/wp-content/uploads/2025/07/AI-2025-07-01T151658.492.png)](https://dedicatted.com/wp-content/uploads/2025/07/AI-2025-07-01T151658.492.png)## **What does Agentic mean in** AI? As AI matures beyond content generation, a new class of intelligent systems is taking the spotlight: [Agentic AI](https://dedicatted.com/services/artificial-intelligence/agentic-ai) . Unlike generative models that rely on prompts to produce text, images, or code, Agentic AI systems are designed to act independently toward specific goals. They perceive their environment, reason through decisions, take real-time actions, and learn from the results – all with minimal human supervision. What is agentic ai definition: At the heart of Agentic AI is a continuous decision loop: perceive, reason, act, and learn. These systems gather real-time data from various sources (like APIs, documents, or sensors), analyze it using advanced reasoning techniques – often coordinated by large language models (LLMs) – and then execute tasks directly through software integrations or human interaction. Once an action is taken, the AI evaluates the outcome, applies reinforcement learning or feedback loops, and improves over time. This adaptive autonomy enables businesses to scale operations, respond faster to change, and reduce the burden of repetitive decision-making. Agentic AI takes intelligence one step further – from creating to deciding and acting. A striking real-world example is [**Dedicatted’s collaboration with Cassen Laboratories**](https://dedicatted.com/insights/smarter-science-how-dedicatted-brought-agentic-ai-to-chemical-reporting-at-cassen-laboratories), a Canadian chemical lab. We helped deploy one of **Canada’s first multi-agent AI systems for environmental testing**. Built on AWS with Amazon Bedrock and Nova models, the solution automated complex chemical profiling, expanded analytical capacity threefold, and introduced consumer-ready reporting layers without compromising compliance. [![Diagram showing how an AI agent handles a user request through perception (camera, text, audio, sensors), cognition (memory, knowledge base, decision making), and action (real-world tasks and monitoring).](https://dedicatted.com/wp-content/uploads/2025/07/AI-2025-07-01T151845.041-1.png)](https://dedicatted.com/wp-content/uploads/2025/07/AI-2025-07-01T151845.041-1.png)## Agentic AI vs Agent AI difference It’s also important to clarify a common confusion: Agentic AI vs. AI agents. ***Agentic AI*** refers to the *capability* – the intelligent framework that powers autonomy and learning. ***AI agents***, on the other hand, are the *applications* – the individual entities or tools that perform tasks in specific domains. Not all AI agents are agentic. Many still rely on simple rules or predefined flows. In a practical context, a complex agentic AI system might orchestrate several specialized agents. A research agent might be responsible for collecting relevant information, while an analysis agent would process and interpret this data. A decision agent would then determine the most appropriate actions to undertake, and an execution agent would finally implement these actions in a concrete manner. ## **Difference between Agentic AI and Generative** AI Although generative AI, agentic AI and AI agents represent significant advances in the field of artificial intelligence, they differ fundamentally on several crucial aspects. [![Comparison table of Generative AI, Agentic Ai and AI Agents on the 5 comparative criteria : primary role, goal orientation, adaptability, enterprise use cases, human input management](https://dedicatted.com/wp-content/uploads/2025/07/table-12-1-1024x743.png)](https://dedicatted.com/wp-content/uploads/2025/07/table-12-1-1024x743.png)## Fundamental architectural differences The difference between agentic AI vs traditional AI isn’t just in what they do – it’s in how they’re built. These architectural foundations shape how each system behaves, scales, and delivers value to your business. [Generative AI](https://dedicatted.com/services/generative-ai) systems are streamlined by design. They rely primarily on a large pre-trained model focused on delivering high-quality content based on static input. There’s minimal infrastructure for memory, perception, or interaction with external environments – making them powerful creators, but limited in autonomous function or adaptability. [![Dedicatted is a first and inly generative AI and MSP partner in Canada](https://dedicatted.com/wp-content/uploads/2025/07/plashka-4-1-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/07/plashka-4-1-scaled.png)[Agentic AI](https://dedicatted.com/services/artificial-intelligence/agentic-ai) introduces a layered, modular architecture built for real-world action. At the core is a decision-making engine (often a large language model), but it’s enhanced by dynamic data ingestion modules, memory systems that preserve long-term context, integrated tools for taking action, and feedback loops for learning. This complexity is intentional – it enables agentic systems to perceive, decide, act, and evolve across time and tasks. AI agents can be thought of as the operational layer – the front-line actors that embody these architectures. Their structure depends on the intelligence behind them: from lightweight task bots built on rules, to sophisticated autonomous agents orchestrated by agentic frameworks. The more advanced the architecture, the more these agents can operate independently, adapt on the fly, and deliver strategic impact across business functions. [![Comparison table of Generative AI, Agentic AI a and AI Agents](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-7.jpg)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-7.jpg)A crucial point to understand is that agentic AI and generative AI are not mutually exclusive. In fact, majority of the agentic AI systems incorporate generative capabilities as one of their components. For example, an AI agent in a customer service context could use generative AI to write personalized responses while leveraging its agentic capabilities to decide when to escalate to a human. It could also employ knowledge base access tools to retrieve relevant information and advanced analysis functions to continuously evaluate customer satisfaction. This synergy allows combining the best of both approaches: the creativity of generative AI and the decisional autonomy of agentic AI. ## Concrete applications: Gen AI, Agentic AI & AI Agents in action ### **Customer Service: From Replies to Resolution** ***Generative AI*** helps brands scale personalized communication. It can draft customer responses, improve tone and clarity, and enrich self-service resources like FAQs and help center articles. By analyzing past tickets, it can suggest the most relevant language to increase satisfaction – but it stops short of taking action. ***Agentic A***I elevates the customer experience by identifying user intent and managing entire resolution workflows. It can fetch CRM data, update order status, or even issue refunds without waiting for a manual trigger. When an issue gets too complex, it intelligently knows when to escalate – closing more cases without sacrificing quality. ***AI Agents*** are the deployed interface: from live chatbots answering Tier 1 inquiries to voice assistants triaging support calls. When built on agentic principles, these agents move beyond scripts and act as decision-makers within customer-facing systems. ### **Software Development: From Generating Code to Managing Delivery** ***Generative AI*** speeds up development by generating code from plain language, writing unit tests, and documenting APIs. It’s an effective co-pilot for developers, reducing repetitive work and helping identify potential bugs through pattern matching. ***Agentic AI*** acts as a project-aware collaborator – interpreting context from user stories, tracking dependencies, and suggesting architecture choices. It automates testing, orchestrates CI/CD pipelines, and learns from delivery performance to improve efficiency over time. ***AI Agents*** in dev environments might auto-assign tasks, monitor system health, or run test suites based on predefined triggers. When paired with an agentic framework, they shift from support roles to intelligent co-owners of engineering workflows ### **Finance & Investment: From Reports to Real-Time Action** ***Generative AI*** provides powerful reporting support – summarizing portfolio performance, creating investor-ready documents, or interpreting economic trends in digestible language. It enhances transparency and speeds up financial communication. ***Agentic AI*** monitors markets continuously, analyzing real-time signals and executing trades aligned with predefined strategies. It adjusts portfolios autonomously in response to volatility, and applies learned behavior to refine risk management decisions. ***AI Agents*** might automate KYC processes, run compliance checks, or initiate alerts based on triggers. Advanced agents can link directly to trading platforms, execute transactions, and adapt risk thresholds as economic indicators shift. [![Graphic titled "Is you business ready for AI?" and the button on the right](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-8-1024x234.png)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-8-1024x234.png)## Conclusion Generative AI and agentic AI represent two complementary paradigms in the evolution of artificial intelligence. While GenAI unlocks speed and creativity, Agentic AI brings autonomy, decision-making, and goal execution into play. As these paradigms converge, we’re entering an era where AI won’t just generate content – it will independently act on it, optimize it, and continuously learn from the results. Success with AI agents isn’t a given. To realize the huge benefits they offer, you need access to deep technical expertise and industry knowledge. This is exactly what we offer at [Dedicatted](https://dedicatted.com/contact). Our AI experts will guide you along the entire AI adoption path, helping you implement agentic AI effectively and responsibly. [Book a free AI-readiness consultation ](https://shorturl.at/I3fJ6) **Categories:** Blog **Services:** Agentic AI, Generative AI, Managed Services **Industry:** Financial Services, Healthcare, Manufacturing, Retail --- ### [Intelligence that acts: Agentic AI for businesses of tomorrow.](https://dedicatted.com/insights/intelligence-that-acts-agentic-ai-for-businesses-of-tomorrow) **Published:** July 25, 2025 **Author:** Dedicatted **Content:** What if you could delegate complex, time-consuming tasks to an intelligent system that not only executes them but also learns and adapts over time? This is the promise of AI agents. These autonomous or semi-autonomous systems are reforming various industries. We’re looking at what might be the biggest shift in how AI works since, well… ever. And it’s going to completely transform how we think about work, productivity, and human-machine collaboration. In view of the rapid expansion of artificial intelligence across industries and applications, should we be concerned? Apprehensive? Excited? Enthusiastic? Perhaps all of the above. Book our **[free AI Discovery Session](#contact-form "free AI Discovery Session")** – a conversation designed to help you understand the practical impact of emerging AI capabilities ## ****What Exactly is Agentic AI?**** The fundamental distinction between AI agents and conventional LLM-powered systems lies in their ability to break down and manage multi-step tasks. Standard chatbots, for example, struggle to process requests that require decomposition into smaller tasks and sequential reasoning. Rather than relying on rigid instructions, AI agents built on foundation models can adapt to different scenarios, similar to how LLMs generate meaningful responses to new prompts. [![Diagram titled 'AI agents bridge the gap' showing the transition from current AI systems to future AI systems. Current AI systems are described as requiring strong supervision, executing simple tasks, and displaying static behavior. Future AI systems are described as acting autonomously, executing complex goals, and having adaptable behavior. In the middle, 'AI agents' connect the two, bridging the gap. Dedicatted logo is in the top right corner.](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-21.jpg)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-21.jpg)**[Agentic AI](https://dedicatted.com/services/artificial-intelligence/agentic-ai)?** That’s like having a super-intelligent assistant who doesn’t just answer questions – they actually take initiative. They can: - Set their own goals and figure out how to achieve them - Break down complex tasks into smaller, manageable pieces - Use tools and resources to get things done - Learn from their mistakes and get better over time - Work independently for hours or even days to solve problems Think of it this way: if traditional AI is like a really advanced calculator, Agentic AI is like having a brilliant intern who can actually run with projects and deliver results without you micromanaging every step. [![Promotional banner asking ](https://dedicatted.com/wp-content/uploads/2025/07/subscribe-1024x149.png)](https://dedicatted.com/wp-content/uploads/2025/07/subscribe-1024x149.png)## **********The Secret Sauce: Agentic AI – Intelligence That Plans, Acts, and Learns********** Agentic AI represents a leap beyond traditional automation. It doesn’t just follow instructions – it thinks, plans, acts, and learns, operating with a degree of autonomy that makes it a true partner in complex work environments like HR, IT, operations, and beyond. Unlike task-specific tools or static automation scripts, agentic AI works through a dynamic loop of perception, reasoning, action, and learning, all guided by a central planning mechanism. Let’s break down what that really means: **1. Perception: Understanding the [Environment](https://dedicatted.com/services/glossary-of-terms#term-environment)** Agentic AI continuously monitors diverse data sources – employee communications, behavioral signals, system logs, or customer requests. It interprets context in real time to recognize patterns, detect anomalies, and identify areas that require attention. This proactive awareness makes it capable of catching issues before they escalate. **2. Reasoning & Planning: Strategic Thinking** When given a goal like “launch a new product” or “resolve this payroll issue”, agentic AI doesn’t rely on templates. Instead, it applies reasoning to: - Understand the specific context - Break the goal into smaller, actionable tasks - Prioritize the steps logically - Determine what tools and data are needed - Construct a multi-step execution plan This “planning engine” is what gives agentic AI its name – it can think ahead and organize action sequences much like a skilled project manager or analyst would. **3. Action: Intelligent Execution** Beyond providing recommendations, agentic AI takes action. It executes tasks directly – whether by routing service requests, generating content, analyzing data, triggering automations, or interacting with APIs and enterprise tools. These systems are designed to function as capable digital collaborators that can drive outcomes with minimal oversight. [![Banner with the text ](https://dedicatted.com/wp-content/uploads/2025/07/Data-services-1-1024x233.png)](https://dedicatted.com/wp-content/uploads/2025/07/Data-services-1-1024x233.png)**4. Memory: Personalized, Contextual Assistance** [Agentic AI](https://dedicatted.com/services/artificial-intelligence/agentic-ai) systems are equipped with advanced memory structures that go beyond simple data storage: - Working memory helps it manage current tasks - Episodic memory recalls past events and how they were handled - Procedural memory learns and stores effective strategies This means the AI improves over time – not just in general, but specifically for you. It remembers your preferences, style, and patterns, offering increasingly personalized support. **5. Learning & Reflection: Continuous Improvement** Following task completion, agentic AI reflects on performance by analyzing outcomes, identifying what worked and what didn’t, and refining its approach. This feedback loop ensures that the system continuously improves, avoiding repetition of errors and becoming increasingly effective at handling similar tasks in the future. **Ready to put AI to work for you – intelligently?** With memory and learning capabilities tailored to your unique needs, agentic AI doesn’t just support your business – it evolves with it. [Book a call](#contact-form) to explore how a smarter, more personalized AI strategy can drive lasting impact for your organization. ## ****Real-World Examples: Where This Is Already Happening**** By integrating GenAI into their adjudication workflow on AWS, we cut manual document handling by 70%, dramatically reducing labor and accelerating throughIn the last decade, [robotic process automation (RPA) and AI ](https://dedicatted.com/services/artificial-intelligence/agentic-ai)have firmly established themselves in multiple industries. Now, a new wave of transformation is coming with the growing adoption of agentic AI, which is poised to find many uses across industries. put. In collaboration with **Cassen Testing Laboratories**, an ISO 17025-certified Canadian chemical and environmental lab, Dedicatted implemented a **multi-agent AI platform** that automates compliance checks, accelerates chemical profiling, and supports B2B and B2C reporting models. Running on AWS Bedrock and Nova models, it can now analyze over 150 markers per run – a 3× improvement in throughput, while ensuring full traceability and adherence to safety standards. This project illustrates a key truth about intelligent systems: when engineered responsibly, they don’t just optimize workflows – they unlock entirely new markets. **[Read the full Cassen Laboratories case study.](https://dedicatted.com/insights/smarter-science-how-dedicatted-brought-agentic-ai-to-chemical-reporting-at-cassen-laboratories)** [![Graphic titled "Industries that can benefit from agentic AI" with the datils of agentic ai usage in finance, healthcare. retail , manufacturing and etc](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-22-1024x576.jpg)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-22-1024x576.jpg)**Customer Service [Automation](https://dedicatted.com/services/glossary-of-terms#term-automation)** Modern AI agents go far beyond answering basic questions. Today’s AI-powered chatbots and virtual assistants manage complex queries across multiple channels – web, email, and phone These agents learn from interactions, using past conversations to personalize responses, recommend solutions, and predict customer needs. They help resolve issues faster, offer tailored discounts, and boost satisfaction, all while reducing operational costs. **Autonomous Workflow [Orchestration](https://dedicatted.com/services/glossary-of-terms#term-orchestration)** AI agents are not just for automating tasks – they’re for orchestrating entire workflows. From supply chain to operations, they manage multi-step processes involving multiple teams and systems. For example, in supply chain management, AI agents monitor inventory, trigger reorders, optimize logistics, and proactively flag issues. With adaptive capabilities, they adjust workflows on the fly, identify inefficiencies, and propose real-time improvements. **Decision Intelligence** AI agents support better decision-making by gathering and analyzing data from various sources. They simulate outcomes, recommend actions, and in some cases, autonomously implement decisions. In financial services, AI agents can assess creditworthiness, analyze market trends, and guide investment strategies, all in real time, with minimal human intervention. As decision-makers face growing pressure to act faster with greater accuracy, agentic AI offers a practical advantage – not just in insights, but in execution. It’s not about replacing human judgment, but enhancing it with real-time, data-driven support across the business. If you’re exploring where AI can make the biggest impact in your organization, let’s talk. [**Book a call** ](#contact-form)with our team to discuss your goals and how agentic AI can help you get there faster. **Predictive Analytics** AI agents use historical data to forecast future outcomes and act on those insights. In areas like supply chain, they anticipate demand, adjust stock levels, and recommend optimal ordering schedules. These agents not only generate predictions – they automate actions based on them, enabling enterprises to respond faster and smarter to shifting conditions. **Risk Management** AI agents enhance risk detection and prevention by continuously monitoring systems and analyzing real-time data. They identify anomalies, predict threats, and take preemptive action , such as adjusting firewalls or flagging suspicious activity. Their learning capabilities ensure constant improvement, making risk management more agile, accurate, and proactive. **Personalization** AI agents deliver highly personalized experiences by learning from individual behavior, preferences, and interactions. In ecommerce, they recommend products, adjust prices, and offer loyalty incentives- often before the user even asks. Unlike static recommendation engines, AI agents anticipate needs and refine suggestions as they gather more data, driving engagement and conversion. These use cases illustrate the transformative potential of agentic AI across business functions- but what does it look like in a specific industry? Let’s take retail as an example. In this highly competitive space, AI agents are already redefining how brands connect with customers, manage operations, and stay top of mind in AI-driven recommendation engines. Below is a breakdown of how agentic AI can help retailers navigate emerging challenges and build scalable, intelligent systems that maintain relevance and customer loyalty. [![Graphic titled 'What are the competitive challenges for retailers?' showing three questions in colored boxes: 'Will your products consistently appear in AI recommendations?', 'If customers never directly visit your channels, will your relationships suffer?', and 'Will your brand be commoditized and lose its unique identity?'. Below, a blue banner states: 'The answer is a flexible, scalable strategy for agentic AI.' A list of five strategies follows: optimize for AI discovery with structured data and content, maintain excellence with detailed content and visuals, enable agent-to-agent interaction across systems, build flexible infrastructure to adapt to trends, and preserve brand value and proposition despite AI filtering.](https://dedicatted.com/wp-content/uploads/2025/07/Screenshot-307.png)](https://dedicatted.com/wp-content/uploads/2025/07/Screenshot-307.png)Retailers that want to stay relevant must act now. A well-defined agentic AI strategy ensures your brand isn’t just found – it’s chosen. Whether it’s through AI-powered personalization, smart inventory planning, or seamless cross-channel experiences, future-ready retail leaders will be those who embed AI intelligence across every layer of their operations. This whitepaper shows how AI on AWS transforms the retail journey and fixes the digital gaps costing you sales.- [**read the full version**](https://dedicatted.com/insights/genai-for-retail-the-secret-weapon-of-customer-first-brands) ## **Conclusion** Agentic AI will soon become an everyday technology. Gartner has found that, thanks to agentic AI, as many as 80% of common customer services issues will be resolved entirely autonomously by 2029. As a result, it is absolutely critical to get any agentic AI implementation right, through a combination of: - Balancing immediate value with long-term flexibility - Using a product-centric approach to identify the use cases with the highest value - Making architectural decisions based on organizational culture, risk appetite, and long-term goals - Focusing on enablement and change management from the outset If you can get this right, then your organization stands to realize significant cost savings through automating complex tasks; deliver faster and more accurate service that boosts customer experience and perception; and build internal capabilities to adapt to new AI technologies and more AI-centric customer relationships. Working with an expert partner like Dedicatted maximizes your chances of success. We can assess your organizational readiness, help you identify high-value use cases, and use these insights to select the ideal architectural approach for your needs. From there, we implement targeted solutions that quickly demonstrate value, build on that success through continuous evaluation, and develop a comprehensive AI Center of Excellence. Want a tailored roadmap for your use case? Request your custom [**AI architecture review**](#contact-form) **Categories:** Blog **Services:** Agentic AI, Managed Services **Industry:** Automotive, Financial Services, Insurance, Manufacturing --- ### [Dedicatted Achieves the AWS Agentic AI Specialization](https://dedicatted.com/insights/dedicatted-achieves-the-aws-agentic-ai-specialization) **Published:** November 30, 2025 **Author:** Andriy Kopachevskyy **Content:** **November 30, 2025** – [**Dedicatted**](https://dedicatted.com/ "Dedicatted"), a cloud and AI consulting company that specializes in building enterprise ready autonomous AI systems on AWS, announced today that it has achieved the Amazon Web Services (AWS) Agentic AI Specialization, a new category launched within the AWS AI Competency. This specialization recognizes Dedicatted as an AWS Partner that enables customers to deploy smart, self-operating AI systems that can think, plan, and work independently to execute complex business processes. The AWS AI Specialization in Agentic AI distinguishes Dedicatted as an AWS Partner with proven technical expertise and customer success in delivering production-ready autonomous AI systems that reason, plan, collaborate, utilize tools, execute tasks, and continuously improve. Dedicatted excels in [**implementing Amazon Bedrock Agents**](https://dedicatted.com/insights/smarter-science-how-dedicatted-brought-agentic-ai-to-chemical-reporting-at-cassen-laboratories "implementing Amazon Bedrock Agents") and other [**leading AWS compatible frameworks**](https://dedicatted.com/insights/ai-that-understands-you-taw9eels-bilingual-shopping-assistant-delivers-results "leading AWS compatible frameworks"), enabling customers to move beyond AI experimentation to deploy autonomous systems that deliver tangible returns on investment. “Our position as an inaugural launch partner for the AWS Agentic AI Specialization demonstrates our leadership in autonomous AI deployment. This recognition confirms our role in driving industry transformation, helping customers implement agent-based systems that deliver measurable production impact.” #### Denys Yermakov, CEO of Dedicatted Dedicatted distinguishes itself in the Cloud and AI consulting market through demonstrated success in delivering production-ready [**Generative**](https://dedicatted.com/services/generative-ai "Generative") and [**Agentic AI**](https://dedicatted.com/services/artificial-intelligence/agentic-ai "Agentic AI") solutions. The company builds autonomous systems that integrate securely with enterprise environments and address specific operational needs. “In my professional opinion, they may very well be the best in the world at what they do. Dedicatted delivered a multilingual agentic assistant that understands our product catalog data, integrates with our ecommerce workflows and interacts with our customers, delivering a unique shopping experience.” #### Kirk Kirkpatrick, CTO of Tawseel Holding This Specialization ensures customers can confidently select partners who demonstrate validated expertise in building and implementing enterprise-grade AI agents. These specialized partners help organizations deploy autonomous AI systems that can handle end-to-end business processes across diverse use cases including enterprise knowledge operations, intelligent process automation, autonomous customer operations, financial operations automation, and supply chain optimization. This expansion of the AWS AI Specialization now includes partners that demonstrate advanced capabilities delivering enterprise-ready generative AI and agentic AI systems to customers. ## **About Dedicatted** Achieving the AWS AI Competency distinction in the categories of [**Generative AI**](https://dedicatted.com/services/generative-ai "Generative AI"), [**Agentic AI**](https://dedicatted.com/services/artificial-intelligence/agentic-ai "Agentic AI"), and [**Data and Analytics**](https://dedicatted.com/services/data/architecture "Data and Analytics") reinforces Dedicatted as a trusted partner for building, scaling, and operationalizing advanced AI solutions on AWS. **Categories:** Blog **Services:** Agentic AI **Industry:** Automotive, Financial Services, High Tech Software, Manufacturing, Retail --- ### [OMO Systems: Sub-300ms Device Control for Smart Buildings on AWS IoT](https://dedicatted.com/insights/smart-building-aws-iot) **Published:** September 5, 2026 **Author:** Dedicatted **Content:** ## About project #### Working time: Platform rebuild delivered in 2025; under Dedicatted managed service since October 2025 #### Industry: Real Estate and Property Technology #### The service: IoT platform engineering, edge computing, managed services #### Tags: IoT Platform, Managed Services, Cloud Modernization, DevOps Consulting, Cloud and Infrastructure Security, Real Estate #### Overview **OMO Systems** runs a smart building platform that connects the locks, valves, thermostats, sensors and cameras inside hotel and residential buildings, serving two very different groups at once: the operators who run the building, and the residents and guests who live in it. The legacy platform was slow and fragile, and a command from an operator or a guest took more than two seconds to reach a device. Dedicatted rebuilt the platform on AWS around AWS IoT Core, AWS IoT Greengrass v2 running at the building edge, and AWS IoT Device Management for fleet operations. Median command latency is now under 300 milliseconds, device control and alerting hold 99.98 per cent uptime, and more than 3,000 devices are in service across the estate. Dedicatted is an approved AWS Managed Service Provider and holds the AWS IoT Core Service Delivery and AWS IoT Greengrass Service Delivery designations, and the platform remains under Dedicatted’s managed service. ## **Challenges** Two seconds means very different things depending on which device is at the other end. On a thermostat it is an annoyance a user forgives. On a lock at a door it is a guest in a corridor pressing the button again because nothing happened. On a valve under a detected leak it is water still running while the platform decides. The legacy platform applied the same delay uniformly to all of them. The customer came into the engagement with a specific and well founded objection: that cloud hosted MQTT would be too slow for door locks. The second question followed immediately, and it was the better one. What happens if the internet drops in the middle of a command? The legacy platform had no good answer. Every control path, including the safety critical ones, depended on a round trip to a central service, so anything that degraded the network degraded the building, and a dropped link left the operator unable to tell whether a command had been applied. To rebuild the platform, the engagement had to deliver: - Sub-second device command latency end to end, with predictable behaviour under packet loss - Safety automations that execute with no internet connection at all - Secure identity for every device, so that one device cannot act for another - Over-the-air updates across thousands of devices in buildings nobody visits - A per-device cost that lets the operator grow the estate without growing the operations team ## **Our Approach** ### **Device identity and cloud control plane** Device identity and state live in AWS IoT Core. Every lock, valve, thermostat, sensor and camera is a registered device with its own device shadow, and the AWS IoT rules engine routes messages into the rest of the platform. Devices connect using MQTT over TLS, each with its own X.509 certificate, and policies are scoped so a device can publish and subscribe only on its own topics. One device cannot speak on behalf of another. ### **Local autonomy at the building edge** The decisive part of the design is at the edge. AWS IoT Greengrass v2 runs on the OMO Hub installed in each building and handles local execution, buffering, secrets and edge inference. The principle we designed to is that the building keeps working when the internet does not. - Safety automations such as “leak detected, close the valve” execute locally on the hub with no cloud round trip, so a safety action does not wait on the wide area link. - Access windows are cached in a device shadow, so a credential that is valid right now still opens the door while the building is offline. - [Telemetry](https://dedicatted.com/services/glossary-of-terms#term-telemetry) is buffered to disk in a capped ring buffer and replayed in order once the link returns, so an outage costs the operator visibility during the outage and nothing after it. The cap is deliberate: a hub that stays offline for a long period degrades predictably instead of filling its own storage. Camera video is handled by Amazon Kinesis Video Streams. Everything else, from the operator and resident APIs to the data the platform holds about buildings, units and devices, runs on a serverless backend of Amazon [API Gateway](https://dedicatted.com/services/glossary-of-terms#term-api-gateway), AWS Lambda, Amazon DynamoDB and Amazon S3. ### **Fleet operations at scale** More than 3,000 devices are in service, spread across buildings nobody visits to run a software update. AWS IoT Device Management carries that load. Fleet provisioning issues each device its own certificate and registers it as it comes online, and fleet indexing makes the estate queryable, so a question such as which devices are on an older firmware version, or which have not reported recently, is answered directly rather than reconstructed from logs. Firmware and component updates go out through AWS IoT Jobs in staged waves, a canary wave first and then progressively larger ones. Each device runs pre-flight checks before applying an update, and firmware is written to A/B partitions, so a device that fails its health check afterwards rolls back automatically to the version that was working. Rollouts are governed at fleet level too: if the failure rate inside a wave crosses its threshold, the rollout halts on its own rather than waiting for someone to notice. Shipping firmware to a physical fleet becomes a routine operation instead of a scheduled risk. ## **Outcome** Key achievements, all figures from the customer’s 2025 case study of this platform: - Command latency down from more than 2 seconds to a median under 300 milliseconds - 99.98 per cent uptime for device control and alerting - Operating expenditure down approximately 58 per cent - Cost per device down from approximately USD 150 to approximately USD 45 - Return on investment achieved in under 10 months - Approximately USD 1.6 million net saving over five years - Firmware update overhead down 60 per cent - Development cycle time down 40 per cent - More than 3,000 devices in service across the estate Two further figures come from network impairment drills rather than from a production measurement window, and are reported here as drill results: - Under 20 per cent injected packet loss, 95th percentile command latency was 1.2 to 1.5 seconds - After a simulated wide area outage, shadow convergence recovered inside 1.5 seconds at the 95th percentile for locks and valves ## **Why It Matters** For a building operator, the difference between a two second command and a sub-300 millisecond one is the difference between a system staff work around and a system they trust. The difference between a cloud dependent platform and an edge resilient one is whether a connectivity problem is an IT incident or a safety incident. OMO Systems can now answer the question its own customers ask first, what happens when the internet goes down, by describing exactly what still works: locks open for valid credentials, leak automations close valves, and telemetry is replayed rather than lost. Fleet wide updates that once carried real operational risk are now a staged, self halting routine, so the software in a building keeps improving without anyone going to the building. **Categories:** Case study **Services:** Cloud and Infrastructure Security, Cloud Modernization, DevOps Consulting, IoT Platform, Managed Services **Industry:** Real Estate --- ### [GenAI-Powered Claims Processing](https://dedicatted.com/insights/genai-powered-claims-processing) **Published:** December 3, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: May 2025 to November 2025, two phases #### Industry: Travel Insurance / Travel Services #### The service: Generative AI #### Tags: Travel & Hospitality, Generative AI, Agentic AI #### Overview **Xodus Travel Services Inc.** administers travel insurance programs and supports travellers who fall ill or need help abroad: claims processing, emergency assistance, medical case management and cost containment against foreign providers. It works exclusively in the travel market. Dedicatted built the GenAI-powered adjudication platform described in this case study. The claims this platform processes are medical claims. Canadian travel insurance is predominantly emergency medical coverage, so the evidence that reaches Xodus is clinical before it is financial: itemised hospital invoices from foreign providers, physician notes, diagnostic and medical reports, and pharmacy receipts, often in the treating country’s language and arriving as photographs or scans. The platform extracts the facts of the claim from that medical evidence, aligns them to the policy clauses that govern medical necessity, emergency status and pre-existing conditions, and attaches the clause it relied on to the decision. The same platform supports medical case management for travellers who fall ill or are injured abroad: a coordinator sees the patient’s current clinical status, the internal activity on the case, the communication with the traveller and the family, and the next action, whether that is a guarantee of payment to a treating hospital, a repatriation or a cost containment negotiation with the provider. All personal health information stays inside the ca-central-1 Region in Canada, encrypted under customer-managed AWS KMS keys, and no part of the solution processes it outside that Region. Xodus is a member of the Travel Health Insurance Association of Canada in the Claims Administrators, Cost Containment, and Assistance and Service Providers categories. ## The Challenge One of Xodus’s key business processes is claims adjudication. Claims are evaluated against individual insurance policies for regulatory reasons. The process requires accuracy and attention to detail, which makes it slow and hard to scale. Because of this, Xodus decided to build a GenAI solution to streamline the process. Claims arrive as unstructured evidence: foreign hospital invoices, physician notes, receipts, itineraries and policy schedules, often in more than one language and often as photographs. An experienced adjudicator could process three to five claims per day. Volume spikes around peak travel seasons, which created backlogs and breached commitments to insurer partners. Adjudication capacity was tied to headcount, so every new insurer program meant more hiring. And all claim and policyholder data had to stay in Canada. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ******Manual Claims Processing Bottleneck****** Adjudicators spent hours reviewing documents, verifying claims, and updating CRM systems. ****Lengthy Processing**** The labor-intensive approach led to lengthy processing of claims and extended wait times for the end customers. **********Customer Experience Impacted********** Backlogs in claim assessments delayed transaction processing, affecting customer satisfaction and retention. ************[Scalability](https://dedicatted.com/services/glossary-of-terms#term-scalability) Limitations************ Growing transaction volumes strained existing workflows, making it difficult to scale efficiently. ## ****Business Flow & Key Features**** [![AI-powered insurance claim review workflow: customer sends claims for review → AI processes the claim and matches it with the policy → AI prepares an assessment → AI checks eligibility, scores the claim, and generates a report → AI delivers a structured conclusion and report.](https://media.dedicatted.com/wp-content/uploads/2025/03/Group-141-18-1.png)](https://media.dedicatted.com/wp-content/uploads/2025/03/Group-141-18-1.png)Dedicatted designed the solution to integrate directly into **Xodus**‘s internal claims adjudication flow, ensuring minimal disruption to existing operations while enhancing efficiency and scalability. - **Seamless CRM Integration** – The AI-powered adjudication system is fully embedded within Xodus’s internal CRM, enabling adjudicators to review AI-processed claims without switching platforms. - **Dynamic Policy & Vendor Management** – Automated pipelines allow real-time addition and versioning of insurance policies and vendor data, ensuring adaptability to evolving business needs. - **Configurable AI Rules & Thresholds** – Business users can fine-tune confidence thresholds and adjudication rules without requiring manual code changes. - **Scalable & Modular Architecture** – Built on AWS cloud-native services (Amazon Bedrock, Amazon OpenSearch Service, Amazon ECS on AWS Fargate, Amazon QuickSight), the solution scales with growing transaction volumes. ## **Technology & Approach** To transform claims processing, we implemented a modular Generative AI-powered automation system, implementing separate AI flows for each key process step. The stack runs on Amazon Bedrock for the generative AI flows, Amazon OpenSearch Service for policy and claim retrieval, Amazon ECS on AWS Fargate for the processing services, and Amazon QuickSight for adjudication analytics. This approach ensures high accuracy, scalability, and seamless integration with existing infrastructure. [![AWS architecture diagram showing interconnected services including API Gateway, Lambda, Bedrock Agents, S3 storage, OpenSearch, and security components for assessment processing workflow](https://media.dedicatted.com/wp-content/uploads/2025/03/Gen_AI_claims_architecture.webp)](https://media.dedicatted.com/wp-content/uploads/2025/03/Gen_AI_claims_architecture.webp)### ****Key Components**** - **Policy Preprocessing & Claim Validation** AI-driven validation ensures claims align with individual policy terms by extracting key conditions and coverage details. - **Claim Preprocessing** Structured data extraction and classification streamline claims before adjudication, reducing manual effort. - **Intelligent Document Processing (IDP)** Extract relevant details from medical reports, invoices, and other claim-supporting documents. - **AI-Powered Claim Confidence Scoring** A rules-based AI engine evaluates claim eligibility and documentation compliance, assigning confidence scores based on extracted data. - **Automated Workflows & Decisions** Platform orchestrates claim assessments, automating repetitive tasks while integrating human validation where needed. ## **Architecture on AWS** - Deployed entirely in the AWS Canada (Central) Region, ca-central-1, so all claim and policyholder data stays in Canada. - Claims arrive through a REST API on Amazon [API Gateway](https://dedicatted.com/services/glossary-of-terms#term-api-gateway), protected by AWS WAF with managed rule groups. The TLS certificate is issued and rotated by AWS Certificate Manager. - The adjudication workflow runs on Amazon ECS on AWS Fargate in private subnets across three Availability Zones, reached through an API Gateway VPC Link, with one NAT Gateway per zone. - Three Amazon Bedrock agents do the work: one extracts the facts of the claim from the submitted documents, one aligns those facts to the governing policy clauses, and one scores the decision and surfaces the evidence behind it. - Amazon OpenSearch Service holds the policy rules and their embeddings, so every decision points back to the specific clause it relied on. - Claim and policy documents are stored in Amazon S3. Amazon QuickSight reports on throughput, accuracy and confidence distribution. - Operator access is through AWS Systems Manager Session Manager: no bastion hosts, no long-lived keys. [Monitoring](https://dedicatted.com/services/glossary-of-terms#term-monitoring) runs through Amazon CloudWatch. - Every decision carries a confidence score and a reference to the policy clause it relied on, so an adjudicator can accept, question or override it. - Decisions are written back into the CRM the adjudication team already uses. ## ****Results & Business Impact**** The GenAI-powered solution transformed claims processing, turning it from a bottleneck into a competitive advantage. By automating routine assessments, claims adjudicators can now focus on complex cases that require more attention while customers benefit from faster and more consistent claim resolutions. **Key Outcomes** - Manual document processing effort reduced by approximately 70%, measured by time sampling across a representative set of claims before and after. - Adjudicator throughput up 85%: from 4.0 to 7.4 completed adjudications per adjudicator per working day, reported from the QuickSight operational dashboard, same month before and after go-live to hold the seasonal profile constant. - Policy interpretation standardised, reducing variation between adjudicators on materially similar claims. - Confidence scoring on every decision gives adjudicators a basis to accept, question or override. - All claim and policyholder data resident in Canada. - Xodus contracted and launched a second, larger phase two months after the first went live. ****Business Impact**** 1. Scalable framework that adapts to increasing transaction volumes. 2. Continuous learning enhances claim assessment accuracy over time. 3. A standardized evaluation process ensures compliance and reduces risk. 4. Advanced data analytics unlocks new insights for process optimization. **Categories:** Case study **Services:** Cloud Modernization, Cloud Security, Generative AI, Security Compliance **Industry:** Healthcare, Insurance --- ### [The 2026 Enterprise Data Readiness Benchmark: Your Foundation for Successful AI](https://dedicatted.com/insights/the-2026-enterprise-data-readiness-benchmark-your-foundation-for-successful-ai) **Published:** September 4, 2026 **Author:** Dedicatted **Content:** Every enterprise is running the same experiment right now: pour more budget into AI, wait for the return. Most are getting an unsatisfying result. Not because the models are weak, and not because the use cases are wrong, but because the data underneath was never built for a system that has to *act*, not just *report*. Three different organizations reached the same conclusion this year from three different angles. [Accenture found that only **7% of companies**](https://www.accenture.com/in-en/insights/ai-data/ai-ready-data?utm_source=chatgpt.com) have built the data foundations required to scale advanced AI. [McKinsey](https://www.mckinsey.com/capabilities/mckinsey-technology/our-insights/ai-data-readiness-the-key-to-scaling-impact?utm_source=chatgpt.com#/) found that only **7% of companies** have fully scaled AI across their organization and pinned the bottleneck on the same root cause. We also surveyed IT leaders and found that **72% don’t have data of the right quality, overlaid with the governance to support advanced AI**, despite 85% believing their data strategy is “clearly defined.” That’s not statistical coincidence. It’s the same wall, measured from three different sides. Somewhere between 92% and 93% of enterprises run AI initiatives on a data foundation that was never built for the job. And the cost of that gap shows up every quarter in AI projects that get paused, revised, or quietly deprioritized because the data underneath them can’t be trusted, accessed, or explained. This article is a benchmark: where the average enterprise actually stands right now, what the leaders are doing differently, and most importantly, what to do about it, with real examples of companies that have already closed the gap. ## “Good enough for reporting” doesn’t mean adequate for AI For two decades, a good data program meant complete, accurate, consistent, and governed information sitting in structured tables ,built for a human analyst to query, and a dashboard to render. That standard isn’t going away. But it’s stopped being sufficient, and the reason is worth sitting with. ### Traditional data was built to inform a decision. AI-ready data has to be built for a system to act on it. Our research puts it bluntly: AI-ready data is data designed for machines and agents to act. Consider the example from our experience: a global hotel chain ran its reservations for years through a system that treated rooms as broad categories. Guest preferences:a high floor, an ocean view, a corner balcony were captured as free-text notes typed by a reservations agent. A human could read those notes and make a judgment call. An AI system couldn’t reliably act on them at all: there was no structured field to query, no way to price a preference, no way to guarantee it got honored. Once the hotel chain restructured those preferences into proper bookable data fields, two things happened simultaneously: guest experience improved, and the chain unlocked incremental revenue it hadn’t been able to price before. Same information. Different scaffolding. Only one version was machine-usable. That’s the shift in miniature. AI-ready data means: - **A referential corpus, not just transaction tables.** The unstructured material: contracts, call transcripts, engineering diagrams, internal wikis, tribal knowledge that’s never been written down has to become as usable as the structured data sitting in the ERP. - **Explicit semantic meaning.** What does “active customer” mean? If a person querying SQL, a person searching a knowledge base, and an AI agent retrieving a vector embedding all get different answers to that question, nothing built on top of it can be trusted. - **Real-time freshness.** A document can be perfectly accurate in full and still produce an incorrect AI answer if the fragment retrieved was superseded, outdated, or taken out of context. Static, batch-refreshed data increasingly isn’t fast enough. [![A2aka98eHx2btiHtZlgL9HVCYVNPAWsO4pdm0sNtfef9NnyVRk72gM8mTBYjFzW9zvm WDnHWzIN6h8vX8dNqCTkVd76QA9MECeAHY2YxAoqAvEXLRRV20v7zf3rFx CD9Dxj40wSrt RrDeCqBwcjlYg9kwzYUmFajKHRNru0Z6yX1RpdlBHmgrKWY6gOxo](https://dedicatted.com/wp-content/uploads/2026/09/A2aka98eHx2btiHtZlgL9HVCYVNPAWsO4pdm0sNtfef9NnyVRk72gM8mTBYjFzW9zvm-WDnHWzIN6h8vX8dNqCTkVd76QA9MECeAHY2YxAoqAvEXLRRV20v7zf3rFx-CD9Dxj40wSrt_RrDeCqBwcjlYg9kwzYUmFajKHRNru0Z6yX1RpdlBHmgrKWY6gOxo-1024x443.jpeg)](https://dedicatted.com/wp-content/uploads/2026/09/A2aka98eHx2btiHtZlgL9HVCYVNPAWsO4pdm0sNtfef9NnyVRk72gM8mTBYjFzW9zvm-WDnHWzIN6h8vX8dNqCTkVd76QA9MECeAHY2YxAoqAvEXLRRV20v7zf3rFx-CD9Dxj40wSrt_RrDeCqBwcjlYg9kwzYUmFajKHRNru0Z6yX1RpdlBHmgrKWY6gOxo-1024x443.jpeg)### Where the average enterprise actually stands today Strip away the survey jargon and a consistent, uncomfortable picture emerges. **Confidence is high. Control is not.** 84% of IT leaders told us they’re confident in the accuracy and completeness of their organization’s data. But only 18% say their data is *fully* governed: the rest are operating with meaningful blind spots. 79% say their AI-backed initiatives are actively hindered because they can’t access 100% of the data they need, across every environment it lives in. That’s not a minority problem; that’s four out of five organizations building AI on a foundation with known holes in it. **The fuel for AI is the data being migrated last.** Among organizations moving data to the cloud, 55% are moving structured operational data: the CRM and ERP records that have always been easy to move. Only 39% are migrating unstructured data: the emails, PDFs, contracts, and knowledge articles that advanced AI depends on most heavily. Just 2% of organizations have fully integrated data and AI to support real-time insight. Everyone else is running gen AI and agentic workloads against a data estate where the most important input: the messy, human-generated, high-context material is still sitting on the sidelines. **Data quality is why ROI disappoints.** When we asked IT leaders why AI initiatives fell short of expected ROI, the top answer wasn’t a weak model or a bad use case – it was data quality, followed by cost overruns and weak integration into existing workflows. Break it down by industry and the pattern sharpens: software/technology and public-sector organizations blame data quality most directly; healthcare, manufacturing, and financial services companies instead point to weak workflow integration the data might be fine, but it never gets to the point of use in a form the workflow can consume. **Regulated industries carry the widest gap and the highest stakes.** Telecommunications organizations report the strongest position: 89% say they have complete visibility into where their data resides, and 84% can access 100% of their organization’s data on demand, in any format. Financial services and the public sector trail badly: roughly 30% and 16% respectively report the same level of access. That gap is the direct cost of operating under heavier compliance obligations without the modernized governance layer that would make compliance and accessibility compatible. It also means the industries with the most to gain from AI-driven efficiency – claims processing, KYC, citizen services are the ones furthest from being able to use it safely at scale. **Cloud infrastructure tells the same story from underneath.** Recent assessment of 216 enterprise cloud estates found that 59% of workloads have seen little to no meaningful cloud movement: they’re still on-premises, under-maintained, or running years past their intended lifespan. A third are modernized just enough to keep the lights on. Only 8% are being used to actively experiment with advanced technology. Just one in five companies has migrated 80% or more of its applications. The easy migrations are done. What’s left: mainframes, monoliths, regulated core systems is exactly the complex, high-value infrastructure that would matter most to modernize, and exactly what most organizations have been avoiding. ## Proof it’s solvable: four companies that closed the gap Our research isn’t diagnostic, rather full of examples of organizations that made the shift and can show what it bought them. *One of our clients* needed infrastructure that could scale globally while still meeting local regulatory requirements in each market: a problem financial services companies everywhere recognize. Instead of patching together market-by-market systems, we built Analytics + Data + AI (ADA), a single cloud-native data and AI platform serving as the operating foundation across every market it’s in. The result: legacy complexity eliminated, real-time insight enabled, and ,critically, a foundation for *decentralized* data ownership, meaning individual business units can now experiment and build without waiting on a central team to unblock them. **[Kwiksave](https://dedicatted.com/insights/production-grade-genai-lead-enrichment-on-aws-for-kwiksave-logistics-operations) – turning operational exhaust into a commercial signal.** One of Canada’s largest logistics operators, Kwiksave generates enormous amounts of delivery data every day and buried inside that operational exhaust were signals about new potential customers that nobody had the bandwidth to extract. The data itself was a mess by AI standards: metadata structures varied client to client, recipient information was often incomplete, and every record had been designed for operations, not commercial insight. Kwiksave’s first attempt at solving this with fully autonomous AI agents ran straight into the problem McKinsey warns about: the agents sometimes generated low-confidence or outright fabricated data, with operating costs that were impossible to predict. So Kwiksave, working with Dedicatted, deliberately didn’t go fully autonomous. It built a hybrid system on AWS: deterministic validation first, GenAI enrichment only where it added real value, and a human approval step before anything touched the CRM. The platform normalizes messy delivery metadata, filters out existing customers and irrelevant vehicle types, and blocks duplicate or low-confidence leads before they ever reach a salesperson. As a result, what used to take up to 45 minutes of manual research per lead became a short, structured review, with governed, campaign-ready leads flowing straight into HubSpot and Mailchimp. It’s a clean example of the AI-ready data principle in practice – the fix was restructuring inconsistent operational data into something an AI system could act on safely, with cost and trust built in from the start rather than bolted on after the fact. **[Tawseel ](https://dedicatted.com/insights/ai-that-understands-you-taw9eels-bilingual-shopping-assistant-delivers-results)– making unstructured intent searchable, in two languages at once.** Tawseel, one of the most technologically advanced e-commerce players in the Middle East, ran into a version of the semantic-meaning problem McKinsey describes: keyword search simply couldn’t handle how people actually shop. A query like “What do I need for a beach day?” or its Arabic equivalent, with all the contextual nuance that carries has no clean keyword match, and the mismatch was showing up as longer browsing sessions, lower conversion, and missed cross-sell opportunities, while competitors like Amazon were already moving toward AI-driven shopping assistants. Dedicatted built Tawseel a Rufus-style generative AI shopping assistant on Amazon Bedrock, using a Model-Agent-Search architecture orchestrated through custom MCP servers: one agent retrieves SKUs directly, another runs semantic and hybrid search over the catalog, another pulls in curated web sources when the catalog alone falls short, and a fourth layers in co-bought and trending signals so the assistant can explain why it’s recommending something, not just what. That’s the referential-corpus idea in action: structured catalog data, semantic search, and external context all feeding one system that reasons across languages without losing meaning in the switch. The result was a shopping assistant completing product discovery 20-25% faster, saving shoppers roughly two minutes per session, with projected positive ROI within the first year and operating costs held under 1% of the incremental revenue it drove. ## The six disciplines that actually separate leaders from laggards McKinsey’s research identifies the specific data disciplines that have to be rebuilt, not replaced, when data has to serve autonomous systems instead of dashboards. 1. **[Observability](https://dedicatted.com/services/glossary-of-terms#term-observability)**, extended past pipeline monitoring into the AI layer itself: tracking whether retrieved content is stale, whether retrieval logic is drifting, and whether generated answers still align with current source material. 2. **Data quality management** is applied continuously across extraction, chunking, and retrieval, not just checked once at ingestion, since a document can be fully accurate and still produce a wrong answer if the wrong fragment gets pulled. 3. **Metadata management**, upgraded to a real control layer: ownership, sensitivity, and allowed usage defined at the level of the *extracted object* (a clause, a speaker turn, a table), not just the source file. 4. **Data lineage**, extended to trace which version of a document was indexed, how it was segmented, which chunks were retrieved, and how the final prompt was assembled: the full dynamic chain. 5. **Governance and controls**, moved to runtime: policy has to be enforced at the point of embedding and retrieval, because a document can be access-restricted in storage and still leak sensitive fragments through a prompt if the embedding layer doesn’t inherit the same rule. 6. **Platform and tooling architecture**, standardized so extraction, embedding, and retrieval infrastructure gets built once and reused, the exact discipline behind the $10–20M cost-avoidance example above. Companies executing these well don’t spread their effort evenly, either. Accenture’s data reinventors are **nearly 2x more likely** than peers to concentrate their resourcing on the one or two domains that matter most in their industry’s value chain: research and discovery in life sciences, network operations in telecom, core banking in financial services, rather than funding a long tail of low-value pilots that never individually justify the infrastructure investment. ## The math: what readiness is actually worth Worldwide data reinventors carry an estimated **4.5 percentage point EBIT margin advantage** over industry peers – a margin uplift of up to **1.6x** measured over the last three years. That’s not a projection; it’s what’s already showing up on the income statements of the 7% who got the foundation right. The inverse carries a cost too, and it’s more common than the upside. More than 80% of organizations delay, limit, or alter AI initiatives at least occasionally because of data-related risk. Each delay costs a quarter of lost compounding – competitors who solved the foundation problem keep extending their lead while the rest wait for the data team to catch up. Not every organization is starting from the same place, and pretending otherwise leads to bad advice. Our cloud research groups companies into three practical profiles and the framing works just as well for data maturity more broadly. Here’s what to actually do at each stage. ### If you’re a Stabilizer (roughly 60% of companies) You’re still working through legacy constraints: on-premises systems, partial migrations, minimal automation, controls that fragment across cloud and on-prem. The instinct to chase a big AI initiative here is the wrong one – it will fail on the foundation. Instead: - **Tie every cloud and data investment explicitly to a business objective**: cost control, resilience, or a specific compliance requirement, so funding decisions stop being made on faith. One global food company hit a post-migration cost shock when consumption spiked and it exhausted its cloud budget 40% early, with no visibility into why. Establishing clear ownership, spend tagging, and product-level cost transparency delivered an immediate 15% cost reduction and identified another 50% in storage savings – funding the next phase of modernization instead of stalling it. - **Pick a handful of high-impact, customer-visible systems and modernize those first**, making them fully observable in real time before touching anything else. Don’t attempt a wholesale migration; fix the systems that generate the most operational pain and the most visible wins. - **Define data products with lineage from day one**, even at small scale, so the small amount of AI-ready data you do build is trustworthy rather than another pile of unmanaged files. ### If you’re an Optimizer (roughly a third of companies) You’ve completed the core migration and built a stable cloud estate, but it was built for continuity, not innovation. Data integration and AI-driven analytics are basically working, but compliance and data sprawl are still the top blockers to going further. The mistake at this stage is declaring victory too early. Instead: - **Pick one revenue-critical process: pricing, claims, parts availability and rebuild it end-to-end on a modern platform**, tying performance, cost, and business outcome together explicitly. Then codify what worked into templates, controls, and runbooks so the *next* process scales faster and more safely. This is the “prototype once, template everywhere” discipline that separates Optimizers who eventually become Innovators from Optimizers who stay stuck shipping incremental features forever. - **Build a governed cloud layer that makes both structured and unstructured data accessible**, with explicit business meaning attached, not just a data lake with better search. - **Introduce AI FinOps on top of existing cost management**, so the cost and value of each AI use case is forecast and measured over time, rather than discovered after the fact in a monthly cloud bill. ### If you’re an Innovator (roughly 8% of companies) Advanced technology already runs across most of your workloads, with strong observability and automation. Your remaining gap is integration: only about a quarter of Innovators have fully connected data and AI for real-time insight, and even fewer have full automation across cloud operations. The next moves should target the board, not the backlog: - **Redesign a mission-critical workflow for autonomous, AI-paced decision-making**: not as a pilot bolted onto the existing process, but as a genuine reinvention where AI agents handle routine decisions and humans focus only on exceptions and strategy. - **Deploy agents with tight scope and clear guardrails**: define explicitly what “good” looks like, when the system escalates to a human, and how to roll back a bad decision. Track revenue impact and risk (accuracy, bias, security) side by side; trust beyond the pilot team depends on both. - **Turn the capability itself into a product or partner offering.** Utilities are already packaging predictive pricing signals as “price-smart power” plans. Automotive companies are offering fleets guaranteed repair windows and parts commitments based on predictive maintenance data. If your AI-driven decisions are good enough to trust internally, they’re often good enough to sell externally. ## Five moves every organization should make now, regardless of stage Fund and measure based on reuse, not experimentation. The organizations pulling ahead aren’t the ones running the most pilots, they’re the ones building a foundation once and using it fifteen times, at a fraction of the marginal cost. Capture unstructured and tacit knowledge as a first-class asset, not an afterthought bolted onto a structured-data roadmap that was designed years before generative AI existed. Federate instead of centralize. Build one enterprise-wide logical view of data that business units can build on independently, not a single team that becomes the bottleneck for every new AI use case. [![co6fkMJYG0lgczsNlwSWI120sUMiASPiqxTtamXDg3 LAsniWXiLvhmqN5bx NeOrXJR0xGbNyDXTFxLAQvrZNQB3MJ3aEVDpg75lgAAlngUDEnwmFMnua cHCRPVcL5ayHFWP2xm4LNAGyc4s5JhSQkxZlr8L8bRKGO4TI1gWVfWogHUx606LChv2cLwb9F](https://dedicatted.com/wp-content/uploads/2026/09/co6fkMJYG0lgczsNlwSWI120sUMiASPiqxTtamXDg3_LAsniWXiLvhmqN5bx-NeOrXJR0xGbNyDXTFxLAQvrZNQB3MJ3aEVDpg75lgAAlngUDEnwmFMnua-cHCRPVcL5ayHFWP2xm4LNAGyc4s5JhSQkxZlr8L8bRKGO4TI1gWVfWogHUx606LChv2cLwb9F-1024x568.jpeg)](https://dedicatted.com/wp-content/uploads/2026/09/co6fkMJYG0lgczsNlwSWI120sUMiASPiqxTtamXDg3_LAsniWXiLvhmqN5bx-NeOrXJR0xGbNyDXTFxLAQvrZNQB3MJ3aEVDpg75lgAAlngUDEnwmFMnua-cHCRPVcL5ayHFWP2xm4LNAGyc4s5JhSQkxZlr8L8bRKGO4TI1gWVfWogHUx606LChv2cLwb9F-1024x568.jpeg)Productize data. Treat both structured and unstructured content as a reusable, quality-checked product with a defined owner, not a one-off extract pulled for a single project and thrown away afterward. Push governance and lineage all the way to the point of retrieval and generation. This is the single most common failure mode McKinsey identifies: access controls that work perfectly on the source document and fail silently the moment content is chunked and embedded. ## Standing still is a decision, not a pause Every organization in this research had the option to wait. Almost all of them are, whether they’d describe it that way or not: 92 to 93% haven’t yet built the foundation advanced AI actually requires. But waiting isn’t neutral. It’s a strategic choice with a quarter-over-quarter cost, and the data reinventors already sitting at a 4.5-point EBIT margin advantage are proof that the cost is real and compounding. The path forward doesn’t require betting the company on a single transformation. It requires being honest about which of the three profiles above actually describes you today, picking the one or two moves that matter most for that stage, and executing them with enough discipline to reuse the win rather than rebuild it from scratch next quarter. The 7% who’ve already done this didn’t get there by chasing the newest model. They got there by treating data readiness as infrastructure, not initiative and building it once, so it compounds. If you’re evaluating where to start: whether that’s a Well-Architected review of your existing data environment, a scoped GenAI pilot in a specific business process, a data platform modernization project, or building the agentic workflows this article describes, that’s exactly the kind of conversation our team works through with clients regularly. [As an AWS Premier Tier Services Partner](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/Dedicatted) and a top 2% global AWS partner with GenAI Competency and MSP designation, we combine deep cloud expertise with hands-on engineering delivery. Our teams bring AWS data and analytics expertise and real implementation experience, from the retail, logistics, and e-commerce examples above, to help you plan and execute a data-driven AI strategy. We’re happy to compare notes on where your organization sits against this research and talk through where your environment stands today. [Get in touch with our team at Dedicatted ](https://dedicatted.com/contact)to talk through where your environment stands today. **Categories:** Blog **Services:** Cloud Data Migration & Modernization, Data Architecture, Generative AI **Industry:** Insurance, Retail --- ### [Strengthening Platform Reliability and Operational Excellence through Managed Support Services](https://dedicatted.com/insights/strengthening-platform-reliability-and-operational-excellence-through-managed-support-services) **Published:** March 14, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2023-ongoing #### Industry: High Software Tech #### The service: Managed Services #### Overview Adomik is a data-driven technology company in the digital advertising and publishing space. Their platform aggregates monetization data across multiple sources, providing publishers with actionable revenue performance insights. Built on a cloud-native architecture, the platform supports large-scale data ingestion and analytics critical to their customers’ success. As the business scaled, the internal team faced growing challenges around operational maturity, platform reliability, and continuous compliance. With no structured support model in place, maintaining system health, managing incidents, and fulfilling security requirements began to strain internal resources. To address these gaps, Adomik engaged our **Managed Support Services (MSP)** team to establish a stable, standardized, and scalable operational foundation. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## The Challenge ### ****Lack of Platform Ownership and Support Framework**** The internal team lacked the capacity and structure to manage the platform consistently. Critical responsibilities such as patching, upgrades, and process maintenance were fragmented, leading to gaps in operational continuity. ### ****Growing Compliance Requirements**** As platform adoption expanded, so did expectations for adherence to security and compliance standards such as SOC 2. The internal team lacked the bandwidth and structure to maintain continuous alignment with these frameworks. ### ******No 24/7 Operational Support****** The absence of round-the-clock coverage created delays in incident resolution during off-hours, impacting availability and service reliability. ### ******Insufficient Disaster Recovery Preparedness****** The platform had not undergone regular, structured disaster recovery testing, creating uncertainty around its resilience in the face of service disruptions or infrastructure failures. Without simulation exercises and validation procedures, the organization risked extended downtime and unverified recovery processes in critical scenarios. ### ****Lack of Structured Support Processes and Permissions Management**** Operational workflows were inconsistent, and permissions were managed in an ad hoc manner – creating accountability risks and making access management difficult to control at scale. ### ****Limited Monitoring and Reporting Visibility**** Without structured monitoring and incident tracking, Adomik lacked operational insight into system health, making proactive issue resolution difficult. ## **Our MSP-Based Approach** Dedicatted implemented a structured **Managed Support Services (MSP)** engagement aligned with the Adomik’s current stage and future growth trajectory. The solution was delivered through a scalable support framework with defined responsibilities, proactive controls, and continuous service improvement. #### **24/7 On-Call Support & Major Incident Management (MIM)** We provided full L2/L3 operational support coverage with formal escalation pathways for critical incidents. This ensured consistent service availability and faster resolution times during off-hours and peak periods. #### **Service Desk and Operational Ownership** [![Diagram illustrating the Databricks job workflow, where an EC2 instance is configured as a NAT gateway, a Databricks job is triggered, and an EC2 worker instance is created and later removed after job completion](https://dedicatted.com/wp-content/uploads/2025/03/image.png)](https://dedicatted.com/wp-content/uploads/2025/03/image.png)Our team took over day-to-day support responsibilities, including on/offboarding personnel, handling access permissions, reviewing merge/deploy approvals, and executing patch management activities. #### **Compliance-Ready Operational Standards** The MSP engagement included routine patching of infrastructure components, regular EKS/OS/add-on upgrades, and structured alignment with security certification. #### **Disaster Recovery Preparedness** As part of the annual service cadence, we conducted a **disaster recovery simulation** to validate platform resilience and readiness in the event of service disruptions or infrastructure failures. #### **Structured Monitoring, Reporting & Continuous Improvement** [![Diagram showing the Adomik services job workflow, including MongoDB job presence checks, Kubernetes job creation, third-party ETL dependency handling, and PVC management, with data sources like S3, Databricks, and Snowflake](https://dedicatted.com/wp-content/uploads/2025/03/image-1.png)](https://dedicatted.com/wp-content/uploads/2025/03/image-1.png)We delivered regular performance reports, incident summaries, and proactive recommendations to support ongoing service improvement. In line with the service tier, **Well-Architected Reviews, Vulnerability Scanning**, and **Platform Security State Reports** were conducted as part of the standard service lifecycle. ## **How AWS Was Used** - Patching and upgrades run through AWS Systems Manager Patch Manager on a rolling schedule. All seven production Amazon EKS clusters were upgraded ahead of end of support with zero downtime, with [Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform) and Argo CD upgraded alongside. - AWS CloudTrail was introduced for the API audit record and AWS Systems Manager Session Manager replaced bastion access, so every interactive session is logged. - Compute moved to Amazon EC2 Graviton instances and the data lake to Amazon S3 Intelligent-Tiering as the cost and security workstream, with authentication modernised on Amazon Cognito. - Well-Architected Reviews are run in the AWS Well-Architected Tool as a standard part of the service lifecycle, with vulnerability scanning and platform security state reports alongside. ### **A European data processor that can show where its data lives** Adomik is a French company processing publisher revenue and audience data from more than twenty markets, so the General Data Protection Regulation applies to it as a processor as a matter of law, and its enterprise publisher customers review it against SOC 2-aligned security expectations before they sign. The platform runs in the AWS Europe (Ireland) Region, so storage and processing of publisher data stays inside the European Union, and the controls Dedicatted put behind that position are in the architecture rather than in a policy document: encryption at rest under a customer-managed AWS KMS key with rotation, a data classification tag that drives the treatment of each data store, federated access with no long-lived credentials, AWS Systems Manager Session Manager as the only interactive path with every session logged, and an AWS CloudTrail record introduced on a platform that previously had none. [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) became a by-product of operations rather than a project. Onboarding, offboarding and permission changes run through the service desk with a record, patching and vulnerability scanning run on a defined cadence, drift detection raises a ticket on any change outside the pipeline, and the monthly report is built around control state and trend so that an enterprise publisher’s security questionnaire is answered from evidence. Disaster recovery is exercised as part of the annual service cadence. #### If you find this case interesting, we recommend taking a closer look at [ Managed services ](https://dedicatted.com/what-we-do/devops/devops-as-a-service) [ DevOps Services ](https://dedicatted.com/what-we-do/consulting/devops-consulting) ## **Results & Impact** - **99.95% SLA compliance** maintained through proactive monitoring and 24/7 operational coverage. - **Operational maturity increased** through a structured service desk, streamlined onboarding/offboarding, and permissions handling. - **Reduced incident response time and resolution friction**, with a formal MIM framework in place. - **Improved compliance readiness**, supporting ongoing adherence to SOC 2 related standards. - **Clear visibility into platform performance**, with regular reporting, issue tracking, and service review cycles. - **All seven production EKS clusters upgraded to a supported version with zero downtime**, and control plane currency now tracked monthly against the AWS EKS support calendar. ## **Summary** By integrating our **Managed Support Services (MSP)** model, the Adomik established a structured, reliable, and scalable operational foundation without the need to expand internal resources. The MSP engagement enabled them to achieve high system availability, improve process maturity, and maintain security and compliance readiness – allowing their internal team to focus on product development while we ensured platform stability and operational excellence. **Categories:** Case study **Services:** Cloud and Infrastructure Security, Cloud Operations, Managed Services, Security Compliance **Industry:** High Tech Software --- ### [Scaling Platform Stability and Operational Maturity with Managed Support Services](https://dedicatted.com/insights/scaling-platform-stability-and-operational-maturity-with-managed-support-services) **Published:** March 13, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2022 – ongoing #### Industry: Fin Tech #### The service: Managed Services, DevOps #### Overview A global financial technology company provides a cloud-native platform supporting real-time post-trade processing and reporting. The platform operates across multiple regions and handles high-volume, business-critical workloads, with strict requirements for reliability, security, and performance. As usage grew and operational complexity increased, the client turned to us for full-service **Managed Support Services (MSP)** to ensure platform stability, continuous improvement, and 24/7 operational readiness. ## The Challenge While the client had built a technically sound and scalable platform, they faced growing challenges in day-to-day operations as the business expanded. Platform stability, user experience, and compliance were at risk without a reliable operational backbone. Specific pain points included: ### **No 24/7 coverage or response capability** The client had no internal team available outside standard business hours. Any incidents occurring at night, on weekends, or during holidays were left unresolved until someone became available – a critical risk for a globally operating platform. As customer usage increased, this gap posed a serious threat to reliability and trust. ### **Unstructured platform operations** Basic tasks like user onboarding, access permissioning, and Git deployment approvals were managed manually and inconsistently. As more teams and users were added, this created bottlenecks, security gaps, and audit complexity. ### ****Lack of visibility and accountability**** There was no regular reporting in place to track SLA compliance, incident resolution trends, or platform health metrics. Leadership lacked insights into operational performance, making planning and resource alignment difficult. ### **Fragmented and reactive incident response** Without a structured incident management framework, escalations were informal, responses varied depending on who was available, and root causes often went untracked. This made incident resolution inconsistent and left no clear ownership for follow-up or post-mortem improvement. ### **Patch and upgrade debt** Over time, key platform components like EKS clusters, operating systems, and third-party add-ons began falling behind on versioning. Without a formal patch management cycle, the risk of downtime, vulnerabilities, and compatibility issues continued to grow. ### **No disaster recovery validation** While infrastructure-level failover capabilities existed, no full disaster recovery tests had ever been run. This left open questions about how the platform would behave in a real-world failure scenario – and whether recovery times would meet business expectations. With a lean internal operations team and no bandwidth to scale support processes, the client needed a trusted partner to take over platform management responsibilities in a structured, measurable, and proactive way. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## ****Our MSP Solution**** We onboarded the client under our Managed Support Services (MSP) model, taking full ownership of operational management and delivering a structured, reliable, and secure support framework. ### ****24/7 On-Call Support & Major Incident Management**** We provided continuous L2/L3 support coverage with clearly defined escalation paths. All incidents were triaged through a centralized service desk with SLA-backed response times of 15 minutes for critical incidents (P1) and 30 minutes for high-priority issues (P2). This eliminated downtime gaps and enabled confident service availability across regions and time zones. ### **Service Desk Ownership & Operational Processes** We fully took over routine platform operations, including onboarding and offboarding personnel, managing Git merge and deployment permissions, and ensuring consistent access control workflows. This eliminated ad hoc task handling and brought structure and accountability to day-to-day operations. ### **Patch Management & Upgrade Strategy** All critical infrastructure components – including EKS clusters, OS versions, and add-ons – are now kept continuously up to date on a rolling maintenance schedule. We introduced automated patching workflows and coordinated upgrade windows to ensure zero disruption and reduce operational risk. ### **Disaster Recovery Simulation & Preparedness** To validate system resilience, we conducted a full disaster recovery simulation, testing cross-region failover, backup integrity, and recovery time objectives. The platform achieved its target **maximum downtime of under 1 minute**, ensuring readiness even under worst-case scenarios. ### **Structured Reporting and Continuous Improvement** We deliver monthly and quarterly reports covering SLA performance, incident trends, response metrics, and proactive improvement areas. Based on this visibility, we introduced fine-tuning measures including autoscaler recalibration, alert fatigue cleanup, and access control reviews – all part of our commitment to continuous platform improvement. ### **Security and Compliance Operations** To support the client’s adherence to industry standards, we maintain operational practices aligned with **SOC 2, ISO 27001, and GDPR** requirements. Our MSP team actively manages compliance-related tasks, audit evidence preparation, and platform security posture reviews. ### **Regulated data, isolated by jurisdiction** The client operates a licensed post-trade reporting platform that is supervised by financial regulators in three countries, and each regulator is entitled to see that the data reported under its regime is isolated from every other regime, and that one institutional client’s data is isolated from another’s. Dedicatted built the AWS estate around that requirement rather than around the application: AWS Organizations with organizational units separated first by jurisdiction and then by environment, one AWS account per environment, and service control policies attached at the organizational-unit level so that every new jurisdiction inherits the same preventive boundary on its first day. Twenty environments, twelve production and eight non-production, run under that structure today. The audit record was made tamper-evident and separate from the systems it records. An organization-wide AWS CloudTrail trail delivers to a dedicated log archive account whose storage is protected by S3 Object Lock, so the identities that write the record cannot alter it. AWS Config records configuration history across every account, which is what answers a supervisor’s question about the state of a resource on a past date, and AWS Security Hub tracks a compliance score per standard that is reported to the client monthly as a control position rather than as a list of activities. Human access is federated through AWS IAM Identity Center with no long-lived credentials, and every interactive session runs through AWS Systems Manager Session Manager and is logged. ## ****Results & Impact**** ![Flow diagram showing financial regulatory bodies (CFTC, ESMA, SEC, FSRA, LSEG, ASIC, DTCC, ICE, CME Group) connected to client services including execution, settlement, valuation, margin and clearing processes in purple outline style](https://dedicatted.com/wp-content/uploads/2025/03/KOR-diagram.png) - **SLA uptime consistently above 99.97%**, with 15-minute P1 response time and structured escalation protocols. - **Critical incidents now resolved up to 4x faster**, with clear accountability and full audit trail. - **Infrastructure kept continuously current**, reducing exposure and technical debt. - **Disaster recovery plan fully validated**, with demonstrated recovery in under 1 minute. - **Cross-region disaster recovery exercised annually as a full simulation**, with the most recent exercise measuring maximum downtime under one minute. - **Operational load fully absorbed by MSP team**, allowing the client’s internal team to focus entirely on core business development. - **Security controls and compliance posture strengthened**, with full support across SOC 2, ISO 27001, and GDPR audits. ## **Summary** By partnering with us for **Managed Support Services**, the client transitioned from fragmented operations to a highly structured, responsive, and mature platform management model. With 24/7 support, clear SLAs, proactive maintenance, and continuous fine-tuning, their platform now runs at full velocity – resilient, compliant, and ready to scale. **Categories:** Case study **Services:** Cloud and Infrastructure Security, DevOps Consulting, Managed Services, Security Compliance **Industry:** Financial Services --- ### [Platform & Data Migration with Security Enhancement](https://dedicatted.com/insights/platform-data-migration-with-security-enhancement) **Published:** March 17, 2025 **Author:** Dedicatted **Content:** #### Working time: 2025 – ongoing #### Industry: Driving school #### The service: DevOps Consulting, Data Migration & Modernization #### Overview Quebec’s leading driving school, **Tecnic**, found itself at a digital crossroads. With their website serving as the primary customer acquisition channel, the company faced a critical business challenge: recurring DDoS attacks and security vulnerabilities were threatening the very foundation of their online presence. The situation was compounded by an outdated OpenShift Kubernetes environment that created significant operational inefficiencies. Their legacy infrastructure lacked proper CI/CD pipelines and Infrastructure as Code practices, while unpredictable resource demands constantly strained their database management systems. Each instance of platform downtime directly impacted both their business operations and bottom line. Through their partnership with Dedicatted, Tecnic transformed these challenges into opportunities. Today, their website and cloud environment stand as secure, resilient, and cost-effective assets that strengthen rather than hinder their position as Quebec’s premier driving instruction provider. ## **The client & their challenge** Tecnic was battling a two-front technical crisis that threatened their digital operations: **Security and resource management issues** - Frequent DDoS attacks and security breaches compromised their online platform. - Inadequate database management systems struggled to handle traffic fluctuations. - Unpredictable resource capacity planning left them vulnerable during attacks. - Unsecured network architecture created multiple vulnerability points. **Infrastructure and operational inefficiencies** - An outdated OpenShift Kubernetes environment that fell below industry standards. - Absence of proper CI/CD pipelines slowed development and deployment cycles. - Lack of Infrastructure as Code (IaC) practices made system management manual and error-prone. - Inefficient cloud configuration resulted in poor cost optimization. The combined effect threatened Tecnic’s market leadership position and created unsustainable operational costs at a time when digital excellence had become non-negotiable in the driver education industry. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## The Solution Dedicatted’s solution leveraged a modern [GitOps](https://dedicatted.com/services/glossary-of-terms#term-gitops) and Infrastructure as Code (IaC) approach to transform Tecnic’s digital environment. This operational framework applied DevOps best practices typically used for application development – version control, collaboration, compliance, and CI/CD – to infrastructure automation, creating a cohesive and streamlined technical ecosystem. After careful assessment, the Dedicatted team recommended migrating from the problematic OpenShift environment to AWS EKS for Kubernetes management and AWS RDS for database services. The implementation followed a comprehensive infrastructure modernization strategy: - **Infrastructure Management** All infrastructure was defined and managed through [Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform) using the IaC approach, with code stored in GitHub and state managed by Terraform Enterprise. - **Secret Management Sensitive information was migrated to AWS Parameter Store and retrieved using External Secret Operator, enhancing security practices. - **Domain Management External DNS was implemented to automate domain provisioning into Route 53, simplifying DNS management. - **High Availability A dual-layer scaling solution was deployed combining Karpenter for cluster-level scaling and Horizontal Pod Autoscaler (HPA) for application-level scaling. - **CI/CD [Pipeline](https://dedicatted.com/services/glossary-of-terms#term-pipeline)** A modern delivery pipeline was established using GitHub Actions for continuous integration and ArgoCD for continuous deployment, creating a true GitOps workflow. - **Governance and Audit** AWS CloudTrail was introduced for the API audit record and AWS Config for configuration history and conformance rules, on an estate that previously had neither. Production, non-production and log archive sit in separate accounts under AWS Organizations, so audit logs and configuration records cannot be altered from a workload account. Interactive access runs only through AWS Systems Manager Session Manager, with no bastion hosts and no SSH keys, and user access is federated through IAM Identity Center. The estate was then assessed under a five-pillar AWS Well-Architected Framework Review, delivered as a finding set with a remediation plan and exported with zero high-risk issues in the Security, Operational Excellence and Reliability pillars. [![Infrastructure architecture diagram illustrating Tecnic's AWS-based environment, including EKS cluster, RDS databases, ArgoCD GitOps deployment, Terraform IaC, Kubernetes HPA and Karpenter auto-scaling, Lambda functions for scaling, AWS security tools, and CI/CD flow integrated with Route 53, CloudFront, and Application Load Balancer](https://dedicatted.com/wp-content/uploads/2025/03/Tecnic-New-state.drawio-General.drawio.png)](https://dedicatted.com/wp-content/uploads/2025/03/Tecnic-New-state.drawio-General.drawio.png)### **Student records under a regulated programme** Groupe Tecnic is the largest network of driving schools recognised by the Société de l’assurance automobile du Québec, and the migrated platform is the enrolment channel and the record system for a government-mandated programme that the SAAQ inspects. That changes what “secure” has to mean: the platform holds student personal data, and an inspector’s question about who changed what, and when, has to be answered with a record rather than a description. Dedicatted introduced AWS CloudTrail and AWS Config as part of the migration itself, so the API record and the configuration history of the platform have been produced continuously from the cutover onward. Student data is encrypted under a customer-managed AWS KMS key with rotation, credentials were moved out of configuration into AWS Systems Manager Parameter Store under the same key, and access is federated with no long-lived credentials. The entire estate is Terraform, with scheduled drift detection that raises a ticket on any change made outside the pipeline, and the record store moved to Amazon RDS Multi-AZ with automatic failover, replacing self-managed database instances that had no failover at all. A formal AWS Well-Architected review was delivered after the migration, with findings quantified and remediation evidenced. ## **The results** The migration to AWS EKS and implementation of the GitOps approach delivered meaningful, quantifiable improvements for Tecnic: ****Significant Cost Reduction**** Migration from the outdated OpenShift environment to a properly configured AWS infrastructure resulted in substantial monthly cloud expenditure savings. ****Decreased Operational Overhead**** Manual operations required to manage and maintain infrastructure and applications were reduced by approximately 70%, freeing IT staff to focus on strategic initiatives rather than routine maintenance. ### **Business Impact** The solution transformed Tecnic’s digital operations in several critical areas: - **Increased [Automation](https://dedicatted.com/services/glossary-of-terms#term-automation)** The adoption of GitOps principles eliminated numerous manual interventions, creating a streamlined deployment process that reduced human error and accelerated delivery timelines. - **Improved Security & [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance)** Centralized secret management through AWS Parameter Store and version-controlled infrastructure significantly enhanced Tecnic’s security posture, effectively addressing their vulnerability to DDoS attacks. - **Attributable Change** Every change to the platform holding student and regulatory records is now committed, reviewed, applied by pipeline and recorded in CloudTrail. Before, a change could not be attributed to anyone. - **Optimized Resource Utilization** The combination of Karpenter and Horizontal Pod Autoscaler ensured efficient scaling that automatically adjusted to traffic patterns, reducing costs while maintaining optimal performance during peak registration periods. - **Faster Deployments** The CI/CD pipeline built with GitHub Actions and ArgoCD enabled rapid and reliable deployments, minimizing errors and reducing deployment time from days to hours. - **Enhanced Reliability** Automated infrastructure and application scaling dramatically improved uptime and system resilience, ensuring Tecnic’s website remained available even during periods of unexpected traffic surges. ### **Long-term Benefits** Beyond the immediate improvements, the solution continues to deliver ongoing value: - **Increased Platform Availability The new architecture maintains 99.9% uptime, ensuring prospective students can access registration and information services reliably. - **Sustainable Cost Savings** The optimized infrastructure continues to provide predictable, reduced monthly cloud expenditures without compromising performance. - **Reduced Operational Burden** With minimal human involvement required for infrastructure management, Tecnic’s IT team can focus on business innovation rather than infrastructure maintenance. - **Future-Proof Architecture** The modular, GitOps-based approach enables Tecnic to easily adopt new technologies and scale their digital presence as their business continues to grow across Quebec. The transformation has positioned Tecnic to maintain their market leadership through technological excellence, turning what was once a vulnerability into a competitive advantage. #### If you find this case interesting, we recommend taking a closer look at [ DevOps Services ](https://dedicatted.com/what-we-do/consulting/devops-consulting) [ Managed Services ](https://dedicatted.com/what-we-do/devops/devops-as-a-service) ## **Conclusion**s **Dedicatted transformed Tecnic’s vulnerable digital infrastructure** into a secure, efficient platform that supports Quebec’s leading driving school. Through strategic migration to AWS and implementation of GitOps practices, **Tecnic now enjoys enhanced security, reduced costs, and simplified operations** while maintaining their market leadership position. Ready to modernize your infrastructure? Whether you’re in education, healthcare, retail or finance, our proven approach can help your organization overcome security challenges, reduce operational overhead, and optimize cloud spending. **[Contact Dedicatted today](https://dedicatted.com/contact)** to explore how our AWS expertise and GitOps implementation can transform your digital foundation. **Categories:** Case study **Services:** Cloud and Infrastructure Security, Cloud Data Migration & Modernization, Cloud Migration, Cloud Modernization, Cloud Operations, DevOps Consulting, Security Compliance **Industry:** Automotive --- ### [From Manual to Automated: AI-Powered Workflow Revolution with Amazon Q](https://dedicatted.com/insights/from-manual-to-automated-ai-powered-workflow-revolution-with-amazon-q-2) **Published:** September 11, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2025- ongoing #### Industry: Civil Engineering #### The service: AI Consulting, AI Assistant #### Overview A leading engineering consulting firm specializing in complex infrastructure projects, including aviation, transportation, and civil engineering. With decades of experience, the company manages thousands of project documents and reports across its divisions. Because many projects follow similar patterns, access to structured historical data is critical for making informed decisions, responding to client requests, and preparing standardized documentation. ## Want to get your copy of case study? Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## The Challenge While repetitive projects offer efficiency opportunities, they also highlight a major operational challenge: **accessing historical knowledge quickly and accurately**. Employees previously had to manually search through multiple repositories, leading to: - Time-consuming workflows - Risk of inconsistencies in reports and project outputs - Difficulty leveraging past project insights for decision-making - Limited scalability in task automation Without a robust AI-driven solution, our customer risked slower project turnaround, higher operational costs, and potential errors in client deliverables. The company recognized that leveraging artificial intelligence could be transformative – but only if implemented securely and aligned with corporate privacy requirements. ## Technology & Approach To achieve the dual goals of robust data security and operational efficiency, we implemented a solution that addressed both knowledge infrastructure and process optimization: ![A graphic with four highlighted sections describing Amazon Q benefits. Seamless Knowledge Integration: Historical project information consolidated into a centralized knowledge base, ensuring uninterrupted access for employees. Security-First Framework: Documents and AI outputs securely stored with granular access controls to protect corporate information. Automated Workflow Enablement: Amazon Q integration generates standardized reports, resumes, and client documents automatically, improving accuracy and reducing manual effort. Ongoing Knowledge Growth: Authorized staff can continuously add new documents, allowing Amazon Q to evolve and scale with business needs.](http://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-76-1-1.png) ## Our Solution Dedicatted implemented a comprehensive AI-powered knowledge management solution using Amazon Q, designed to transform our customer’s access to project information and workflow efficiency. Key components of the solution included. - **Internal Virtual Assistant:** Amazon Q was deployed as a secure, AI-driven assistant that provides instant, contextual answers based on historical project data. - **Centralized Knowledge Base:** A dedicated repository was created to store all project-related documentation in a structured, searchable format. Authorized users can continuously add new documents, ensuring the system evolves over time. - **Automated Document Generation:** Routine outputs, such as project reports, resumes, and client requests, are generated automatically, following predefined templates or replicating the structure of previous projects. - **Granular Access Controls:** Two levels of permissions were implemented – subscription type (Pro vs Lite) and departmental access (Marketing, Divisions, or both) – ensuring information is available only to the right employees. - **Transparency & Trust:** Every response includes active links to source documents, allowing employees to validate AI-generated insights and maintain accountability Adoption has been strong across the business units where the platform is live. Managers commended the role-based controls, which maintained compliance without adding friction to daily work, and the ability to add documents directly to the knowledge base has let the system keep pace with the firm’s changing project mix. [![Amazon Q helps generate reports. It starts with an employee's request to create a Project D report in the same structure as Project A. Amazon Q retrieves relevant documents, processes the query by applying Project A's template to Project D's data, and an LLM produces a generative solution with a formatted report aligned to company standards.](https://dedicatted.com/wp-content/uploads/2025/09/Group-13-1.png)](https://dedicatted.com/wp-content/uploads/2025/09/Group-13-1.png)## Outcome The deployment of Amazon Q delivered significant, measurable improvements across our customer’s operations: - Reliable AI Performance: Amazon Q consistently provides accurate, context-aware responses, enabling employees to make informed decisions quickly and confidently. - Streamlined Workflows**:** Routine tasks, such as generating reports, resumes, and client requests, are now automated, freeing employees to focus on strategic, high-value work - Access to the system is carefully managed through tailored controls, including subscription types (Pro vs. Lite) and departmental permissions, ensuring the right information reaches the right teams while maintaining data security. - Adoption was further supported by two targeted workshops: one for non-technical end-users to maximize daily utility and another for IT staff responsible for managing and maintaining Amazon Q internally. What began as a struggle to manage vast amounts of project data has become a story of operational excellence and AI-powered transformation. Amazon Q has turned manual, repetitive tasks into automated, reliable workflows, freeing teams to focus on strategic thinking and creative problem-solving. Our customer now stands at the forefront of intelligent knowledge management, ready to harness AI as a true competitive advantage for years to come. [![Banner promoting Amazon Q. On the left, there's an image of a guide titled ](https://dedicatted.com/wp-content/uploads/2025/09/Group-158-1.png)](https://dedicatted.com/wp-content/uploads/2025/09/Group-158-1.png) **Categories:** Case study **Services:** Generative AI **Industry:** Manufacturing --- ### [AI That Understands You: Taw9eel’s Bilingual Shopping Assistant Delivers Results](https://dedicatted.com/insights/ai-that-understands-you-taw9eels-bilingual-shopping-assistant-delivers-results) **Published:** October 12, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: March 2025 – July 2025 #### Industry: E-commerce #### The service: Generative AI shopping assistant #### Overview **Tawseel** has established itself as one of the most technologically advanced companies in the Middle East, guided by a Board and CTO who see innovation as the foundation of sustainable growth. To strengthen its competitive edge and deliver a next-generation shopping experience, Tawseel partnered with Dedicatted to build a Rufus-style Generative AI shopping assistant tailored for the regional market. ## The Challenge As Tawseel’s customer base grew rapidly across the MENA region, traditional keyword search was no longer sufficient to meet shoppers’ needs. Many customers struggled to express their intent in a single search term – particularly in Arabic, where linguistic nuances and contextual phrasing often complicated product discovery. Queries like “What do I need for a beach day?” or “شنو أحتاج لرحلة؟” were difficult for conventional search to interpret without a shopping assistant. This limitation **created tangible business challenges**: - **Longer browsing times** and more complex journeys to find relevant products. - **Lower conversion rates** and reduced average order value due to missed opportunities for cross-selling and product bundling. - **High bounce rates** and reduced engagement on search sessions. - **Competitive gaps with global e-commerce leaders like Amazon**, who were already experimenting with AI-driven shopping assistants. ## Want to get your copy of case study? Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## Technology & Approach Dedicatted developed a scalable, AWS-native AgenticAI solution. This solution, built on Amazon Bedrock, features a modular Model-Agent-Search (MAS) architecture. Custom MCP (Model Context Protocol) servers orchestrate the entire system. Key elements included: - **Advanced AI Models**: Using Amazon Bedrock models like Claude, Nova, and Titan, the assistant understands natural language, reasons about customer intent, and generates clear, actionable recommendations. - **Custom [Orchestration](https://dedicatted.com/services/glossary-of-terms#term-orchestration) Layer (MCP Servers)**: Purpose-built to manage conversation agents, handle tool orchestration, and isolate third-party connectors. Our servers specialized in product search, trending analytics, ensuring reliable scaling and detailed observability. - **Multi-Agent Orchestration**: The MCP layer coordinates specialized tools to deliver intelligent product discovery: - **StructuredSearch() →** retrieves SKUs via marketplace APIs. - **FreeTextSearch() →** executes semantic and hybrid searches using Amazon OpenSearch Service. - **WebSearch() →** supplements catalog data with curated internet sources when needed. - **GetAnalytics() →** integrates co-bought, co-searched, and trending signals to enrich recommendations and provide explainability. - **[Observability](https://dedicatted.com/services/glossary-of-terms#term-observability) & Guardrails**: Centralized monitoring of MCP server activity, logging of tool call chains, bilingual prompt evaluation, and fine-tuned rate limits to balance performance and cost. - **Operational Design**: Containerized MCP servers deployed in an auto-scaling AWS environment, enabling resilience, version control of agent logic, and rapid iteration without downtime. This AgenticAI-driven architecture provided Tawseel with a flexible orchestration layer that could evolve to support new AI agents, connectors, and customer experiences, while maintaining enterprise-grade control and reliability. [![System architecture diagram for shopping assistant showing user interaction with a frontend that connects to API search tools (MCP server). The MCP server communicates with both a search module and an AI module, which then interact with a database.](https://dedicatted.com/wp-content/uploads/2025/09/Group-96-4-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/09/Group-96-4-scaled.png)### **Implementation plan:** 1. **MVP Planning & Infra Setup** – IAM, Bedrock quotas, OpenSearch provisioning 2. **Multi-Agent Architecture** – agent flows for product search, analytics, internet lookup 3. **Prompt Engineering & Evaluation** – side-by-side benchmarking 4. **Magento Integration** – structured API access to products, bundles, metadata 5. **Phased Launch** – staged rollout with monitored CVR & AOV uplift ## **User Experience & Adoption** The AI-powered shopping assistant was seamlessly integrated into Tawseel’s marketplace, creating a natural, intuitive experience for shoppers: - **Conversational Discovery:** Customers ask in Arabic or English (“What do I need for a beach picnic?”), and the agent returns curated bundles with clear reasoning. - **Bilingual Support:** Seamless switching between languages without loss of context. - **Speed to Value:** Shoppers complete discovery 20-25% faster, saving on average 2 minutes per session. - **Trust & Grounding:** By combining semantic RAG search with curated Internet sources, the assistant provides not just products, but context for why they are recommended. ## Our Solution The solution became one of the MECA region’s first large-scale AgenticAI shopping assistants, giving Tawseel a measurable competitive edge. [![Infographic with four metrics showing the impact of an AI assistant. Efficiency: 1 in 5 daily active users adopted the assistant in the first months of rollout. Productivity: 22% increase in conversion rate for AI-assisted sessions (2.3% → 2.8%). Revenue: 6% uplift in average order value through smarter bundling and cross-sell. Speed: 2 minutes saved per shopping session with faster product discovery.](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-09-26T095758.766-1.png)](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-09-26T095758.766-1.png)### **Business Impact** - First-year payback with **operating costs** kept to **less than 1%** of incremental revenue demonstrates the financial resilience and scalability of the solution. - The solution establishes **multilingual GenAI** capabilities that differentiate Tawseel from competitors such as Noon and Amazon.ae. - By introducing a **reusable AI architecture** based on the MAS stack, Tawseel gains a flexible platform to support future applications while keeping operations lean. **Categories:** Case study **Services:** Cloud Modernization, Generative AI **Industry:** Retail --- ### [Keyless Guest Entry and AI Concierge for Short-Term Rentals](https://dedicatted.com/insights/smart-access-control-for-multifamily-and-short-term-rentals) **Published:** October 10, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2025 to ongoing #### Industry: Hospitality / Short-Term Rentals #### The service: IoT, Generative AI, AWS services #### Tags: Travel & Hospitality, Generative AI #### Overview **SmartphoneKey** is a technology company redefining how people access and manage physical spaces for short-term rentals, hospitality operators and multifamily properties. Its platform merges digital wallet credentials (Apple Wallet, Google Wallet) with secure NFC key cards, offering residents, guests, and property managers a frictionless, app-free entry experience. Today the platform serves 250 managed properties and around 5,000 monthly active users. The system is designed for apartments, condominiums, and hospitality operators seeking to modernize building access without costly infrastructure upgrades. Through **wireless installation** and **cloud-based management**, SmartphoneKey eliminates the complexity of traditional key systems – reducing operational overhead while enhancing security and user convenience. For property managers and hosts, the solution provides a centralized dashboard to issue, track, and revoke digital credentials in real time. Temporary or guest access can be granted instantly, while encrypted, non-copyable key cards ensure continuous availability even when devices are offline. SmartphoneKey stands at the intersection of IoT innovation and hospitality technology, helping property owners deliver the level of seamless, secure access today’s residents and travelers expect , while maintaining operational efficiency at scale. ## The Challenge SmartphoneKey set out to reinvent property access for short-term rentals, hospitality operators, apartments and condos by replacing physical keys with a cloud-connected, IoT-based solution. But building a **secure, scalable, and user-friendly access system** that could operate seamlessly across thousands of locks introduced a range of challenges: - **Guest Questions at All Hours:** guests arrive late, in different time zones, and ask questions in many languages. Concierge response depended on a property manager being awake. - **High Maintenance Costs of Physical Keys:** Property managers spent significant time and money replacing lost or duplicated keys, managing logistics, and coordinating on-site handovers. A new system had to eliminate physical key dependency while ensuring equal or higher security standards. - **Guest Access Friction:** Short-term rental hosts needed a fast and intuitive way to grant guest access. Requiring users to download and register within an app created entry friction, leading to poor guest experience and lost bookings. - **Temporary Code Security:** Distributing temporary numeric codes to guests was unreliable and insecure. Codes could be shared, copied, or reused, exposing properties to unauthorized access. The client required digitally verifiable, time-bound keys with traceability and full control. - **[Scalability](https://dedicatted.com/services/glossary-of-terms#term-scalability) for Multi-Property Management:** For large residential operators managing thousands of units, legacy systems couldn’t scale efficiently. They needed a centralized IoT management layer that could handle key distribution, firmware updates, and user access across an entire portfolio of buildings. - **Device Connectivity & Authentication:** Each intelligent lock needed to connect securely to the cloud for credential validation and updates. Maintaining encrypted, authenticated device communication at scale was critical for reliability and trust. - **Edge Resilience & Offline Operation:** Properties often experience unstable connectivity. The system needed edge intelligence to allow local decision-making and offline access – ensuring residents could still enter even without an internet connection. - **Fleet Management & Over-the-Air Updates:** Managing thousands of IoT locks required remote patching, monitoring, and OTA firmware updates to maintain consistency, performance, and security across the entire device fleet. - **Real-Time Data & Analytics:** To improve operations and user experience, the client needed continuous visibility into access logs, device telemetry, and usage analytics, all synchronized in real time with cloud databases and dashboards. - **Security & [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance):** Since the platform directly manages physical entry, end-to-end data protection and identity integrity were paramount. The client required AWS-grade security mechanisms to safeguard every device and transaction. [![Diagram illustrating the SmartphoneKey Intelligent Access System, an IoT-driven solution for property access management. On the left, a box describes the system as smart access control for residents, guests, and managers using digital wallet keys, NFC cards, and AWS-native authorization. The central workflow includes: Defining property, unit, and lock entities. Defining credential types (Wallet Pass, NFC, Guest Code). Defining access actions (grant, revoke, update). Property managers issuing or revoking keys. Guests receiving temporary credentials. Audit logs tracking and reviewing access events. On the right, AWS components supporting the system are listed: Amazon API Gateway, AWS Lambda, AWS IoT Core, and Amazon DynamoDB. A small note at the top right indicates the step ](https://dedicatted.com/wp-content/uploads/2025/10/Group-162-2.png)](https://dedicatted.com/wp-content/uploads/2025/10/Group-162-2.png)## Technology & Approach Dedicatted architected and deployed a cloud-native, IoT-powered access management platform for SmartphoneKey – designed to securely connect thousands of smart locks, streamline guest access, and empower property managers with complete visibility and control. The solution leveraged **AWS IoT Core** and **AWS IoT Greengrass** as its foundation, combining scalability, automation, and real-time intelligence across distributed edge devices. Key components of the solution: ### Serverless Core & API Management To eliminate backend overhead and ensure infinite scalability, the core application layer was built using: - Amazon [API Gateway](https://dedicatted.com/services/glossary-of-terms#term-api-gateway) + AWS Lambda: serverless APIs managing access credentials, user permissions, and integrations with third-party booking systems (Airbnb, property management tools). - Amazon DynamoDB: served as a high-performance NoSQL database for storing access logs, property metadata, and device configurations, ensuring sub-millisecond response times even under heavy load. This design enabled near-instant key provisioning and credential validation for both tenants and guests – critical for short-term rentals where access windows change frequently. ### IoT Device Connectivity and Management At the heart of the system lies **AWS IoT Core**, responsible for secure communication between intelligent locks, property gateways, and the cloud backend. - Each device was provisioned with mutual authentication certificates, ensuring tamper-proof data exchange. - AWS IoT rules engine was used to route telemetry and lock events (open, denied, tamper alerts) directly into DynamoDB and CloudWatch metrics for monitoring and analytics. To extend reliability and reduce latency at the edge, **AWS IoT Greengrass** was deployed on local gateways within multi-unit buildings. - Greengrass handled offline operations, allowing locks to function and synchronize even when the internet was temporarily unavailable. - It also enabled over-the-air (OTA) firmware updates, Lambda execution at the edge, and local event filtering to minimize cloud traffic and ensure real-time door response. This edge-cloud hybrid ensured that property access worked seamlessly – whether in urban apartments or remote short-term rentals. ### Containerized Microservices For administrative tools and analytics dashboards, Dedicatted implemented: - Amazon ECS on AWS Fargate: hosting microservices responsible for property management, authentication, and access analytics. - Application [Load Balancer](https://dedicatted.com/services/glossary-of-terms#term-load-balancer) (ALB) : routing traffic securely between microservices and public endpoints. This containerized setup allowed quick updates and auto-scaling based on user demand. ### User Experience & Content Delivery To provide frictionless entry without requiring users to download apps: - Apple Wallet & Google Wallet Integration – digital keys are pushed directly into mobile wallets for instant, secure entry. - Amazon S3 + CloudFront – ensured fast, global delivery of static assets, admin dashboards, and wallet pass distribution. - NFC fallback – physical key cards acted as a backup when mobile devices were unavailable or uncharged. ### AI Concierge Guests ask questions at all hours, in many languages, and most of them do not need a person. Dedicatted added a concierge layer built on **Amazon Bedrock** and **Amazon Bedrock Agents**. It answers guest questions in the guest’s own language, provides directions and property information, and can issue an access code or raise the request to the property manager only when a person is needed. Amazon Cognito handles guest and manager identity. ### Automation & Infrastructure Management The entire environment was deployed and maintained through modern automation practices: - GitLab CI/CD + [Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform) – automated Lambda and API Gateway creation whenever new repositories were initialized. This templated pipeline allowed new services to be deployed with zero manual operations. - AWS CloudWatch – centralized monitoring, alerting, and performance tracking across Lambda, IoT Core, and ECS components. - VPC segmentation – private subnets hosted sensitive workloads (ECS, Lambdas), while public subnets exposed only essential endpoints (API Gateway, ALB) for enhanced security and compliance. ### Security by Design Each device was onboarded using unique X.509 certificates and authenticated via **AWS IoT Core’s secure tunneling**, ensuring end-to-end encryption and protection against unauthorized access. The entire environment was isolated through a carefully designed **VPC structure**, separating public interfaces (API Gateway, Load Balancer) from private workloads (ECS services, Lambda functions). This layered, automation-first architecture created a robust foundation for a multi-tenant IoT platform that could adapt dynamically to client needs: from individual Airbnb hosts to enterprise-scale property operators. ## Our Solution Dedicatted approached the SmartphoneKey project as an opportunity to redefine property access – transforming traditional key management into a secure, app-free, and cloud-connected experience. The goal was to deliver a scalable, AWS-powered IoT platform that made access management effortless for property operators while ensuring end-users enjoyed seamless, reliable entry. - **Frictionless Access, No App Required:** SmartphoneKey introduced true convenience by letting users open doors via Apple Wallet or Google Wallet passes, eliminating the friction of app downloads or logins. As a fallback, encrypted NFC key cards offered instant, secure entry even if a phone was offline or out of battery. - **Unified Management for Property Operators:** Through an intuitive admin console, managers could assign, revoke, or monitor access across hundreds of units. The system consolidated building metadata, access logs, and guest permissions into a **single, cloud-driven interface**, reducing manual tracking and operational overhead. - **Edge Resilience & Continuous Connectivity:** Smart locks operated intelligently at the edge using **AWS IoT Greengrass**, maintaining functionality even when cloud connectivity was lost. Once back online, all access data automatically synchronized with the central system via AWS IoT Core, ensuring no gaps in audit trails or analytics. - **Automated Guest Experience for Rentals:** Short-term rental hosts could issue temporary, expiring keys directly from integrated booking platforms , ensuring that guests could access properties securely and effortlessly without any human coordination. - **Answers Without a Person in the Loop:** the Amazon Bedrock concierge handles guest questions in the guest’s language at any hour and escalates to the property manager only when a human is needed. - **Operational Efficiency & Scalability:** From firmware patching to new feature rollouts, **over-the-air updates** were seamlessly pushed to all connected devices, eliminating on-site technician work. The solution scaled effortlessly, supporting thousands of devices and users without added complexity. - **Security & Compliance at Every Layer:** End-to-end encryption, device authentication, and secure key lifecycle management ensured that every access event met enterprise-grade security standards: a core requirement for multi-family housing operators and hospitality providers alike. By combining AWS IoT Core’s secure connectivity, Greengrass’s edge intelligence, Amazon Bedrock’s concierge layer, and Dedicatted’s automation-driven approach, SmartphoneKey now operates as a intelligent , self-managing ecosystem – one that simplifies life for tenants, increases efficiency for operators, and sets a new benchmark for IoT-enabled property technology ## Results - On-site service visits down 45%: engineer dispatches fell from 62 to 34 per hundred doors per year through remote diagnostics and over-the-air firmware delivery, measured over equivalent twelve-month periods from field service dispatch records. - [Deployment](https://dedicatted.com/services/glossary-of-terms#term-deployment) cost per unit down 50% through wireless installation. - Release cycle time down from days to hours: median time from merge to production, measured across all releases in GitLab CI. - DevOps effort down 70% through CI/CD automation. - Physical key replacement eliminated. - Guest entry requires no app install, removing the largest drop-off point in the arrival journey. - Modelled three-year total cost of ownership: approximately USD 1.8 million in savings against an on-premises key system, return on investment at 18 months. [![Infographic highlighting four key intelligent results of the SmartphoneKey smart access solution. Operational Efficiency: Delivered frictionless entry for apartments and rentals using digital wallet keys (Apple & Google) and NFC backups. Eliminated physical key replacements (~$50K/year saved) and reduced technician visits by 45% (~$90K/year). Cost Optimization & Deployment Savings: Wireless installs reduced deployment time and cost by 50%, saving $150K per 1,000 units. Automation via CI/CD and Terraform cut DevOps labor by 70% (~$100K/year). New API services launched in minutes, accelerating time-to-market. Reliability & Scalability: CloudWatch enabled real-time monitoring and issue prevention. IoT Greengrass ensured device functionality offline. Predictable AWS costs: IoT Core ($40K/year), IoT Greengrass ($25K/year), ECS ($10K/year), Bedrock GenAI ($60K/year). Business Impact & ROI: ROI achieved in 18 months. Transition to AWS reduced capital expenses by $500K+. Combined operational savings reached $1.8M over three years.](https://dedicatted.com/wp-content/uploads/2025/10/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-07T194009.980.png)](https://dedicatted.com/wp-content/uploads/2025/10/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-07T194009.980.png) **Categories:** Case study **Services:** Cloud Modernization, IoT Platform, Managed Services **Industry:** High Tech Software --- ### [Pharma & Life Sciences: R&D Acceleration with Cloud & AI](https://dedicatted.com/insights/pharma-life-sciences-rd-acceleration-with-cloud-ai) **Published:** August 26, 2026 **Author:** Serhii Semenchenko **Content:** For a decade, drug R&D productivity barely moved while the cost of getting a single medicine to market climbed past $2 billion, and in some cases past $6 billion once you count the cost of failure and the cost of capital. Every major research house covering the industry has published a version of the same finding in the last two years: the science is ready, the models are ready, but most organizations are not capturing the value at scale. This piece pulls together the sharpest findings from that research, adds the technical detail from AWS’s own Life Sciences guidance, and lays it out as something closer to a working playbook: what the opportunity actually is, what a modern R&D technology stack looks like, which use cases to start with, how to sequence the rollout, and where teams typically get stuck. We’ve added our own take at each stage, based on what we see in client engagements. ## The size of the prize: Life Sciences in numbers [Mckinsey estimates](https://www.youtube.com/watch?v=pNz4E6IX5K8) that a top 10 biopharma company, with $65-75 billion in revenue, could capture $5-7 billion in peak value over five years by scaling AI across the business. Nearly 90% of that value sits in three places: R&D, manufacturing and supply chain, and commercial. R&D alone accounts for 30-45% of it, more than any other function, because AI touches both sides of the ledger there: it cuts the cost of failed programs and it can pull forward the revenue that comes from reaching the market sooner. AWS puts a number on that speed. If companies use intelligent technologies at scale and rebuild workflows around them, they can bring a new medicine to market four years faster and earn an extra $2 billion per successful drug. Even a 1% improvement in clinical success rates is worth hundreds of millions in additional revenue. Latest analysis of AI-discovered molecules found a Phase 1 success rate of 80-90%, well above the historical industry average of 40-65%, though the sample size is still small enough that this needs watching rather than betting the portfolio on. These numbers get thrown around a lot, and it’s easy to treat them as marketing. What we’d flag for any leadership team is that timing detail: if your board expects an immediate top-line lift from a GenAI pilot, reset that expectation early. The cost side moves first, and that’s actually useful, because it means your first pilot can pay for your second one. #### Dedicatted’s take Value doesn’t land evenly across time, either. Our latest five-year value accretion modeling shows that enabling functions like IT, HR, and finance see savings fastest, while R&D and supply chain take longer to reach peak value because of long capital cycles. Within a single initiative, cost reductions typically show up in one to two quarters, while revenue gains take three to four quarters longer to materialize. ## Why the value isn’t showing up yet: Dedicatted take on AI Acceleration Recent analysis is the most direct about the root cause: most pharma companies run on patchwork infrastructure, decades of disconnected systems and workarounds layered on top of each other. This creates three specific failures. ***First***, AI can’t reason reliably without semantic consistency; a system has no way of knowing that “PD marker” in discovery and “biomarker” in translational research mean the same thing unless someone tells it. ***Second***, feedback loops break down, so what one program learns rarely reaches another. ***Third***, scientists end up spending their time manually reconciling data between tools instead of doing science. [![Workflow diagram of governance, data discovery, and bioinformatics pipelines in AWS cloud.](https://dedicatted.com/wp-content/uploads/2026/08/6d6yt0nwIcSyg0nIABySgtQ0swpclN556pd4ny1H2pI-ITpPsJLEWfq3RCG4c1BNcLvNkLFGi1KxdFHsU95g9ZRTlnMlmRZgT_cubYnJlYX_oVu51cgKVziYycCjuveROnQAUS1ndVha0wXFzTEVgju4Ssh_D4cuYiUE4kTSsUCQHeI7GZsjge1rAU7hr5Fi.jpeg)](https://dedicatted.com/wp-content/uploads/2026/08/6d6yt0nwIcSyg0nIABySgtQ0swpclN556pd4ny1H2pI-ITpPsJLEWfq3RCG4c1BNcLvNkLFGi1KxdFHsU95g9ZRTlnMlmRZgT_cubYnJlYX_oVu51cgKVziYycCjuveROnQAUS1ndVha0wXFzTEVgju4Ssh_D4cuYiUE4kTSsUCQHeI7GZsjge1rAU7hr5Fi.jpeg)The pattern will sound familiar to anyone who has sat in a discovery or clinical ops meeting: a molecule generation tool produces strong candidates, but a chemist has to export the structures, reformat them, load them into a separate ADME tool, then copy results into a spreadsheet to cross-reference against synthetic accessibility scores, while historical data on why a similar scaffold failed three years ago sits in a slide deck that only one senior scientist remembers exists. Adding more point-solution AI tools on top of this doesn’t fix it. It adds another system to reconcile. Our answer is architecture-first, built on five pillars: 1. **Platform-centric architecture** – a shared, semantically consistent model of the R&D domain (schema, taxonomy, ontology, workflow logic), not a pile of vendor tools. 2. **Data enablement backbone** – breaking down data silos with data lakes, knowledge graphs, automated ingestion, and active data quality management. 3. **AI-native workflows and talent** – redesigning work so AI sits inside the decision path, not bolted on the side, paired with a genuinely hybrid workforce. 4. **Governance, risk, compliance and security** – making AI decisions auditable and building trust as a system property, not an afterthought. 5. **Programmatic value capture** – treating this as a portfolio of funded initiatives with real KPIs, not a collection of proofs of concept. This is the part of the research we’d want every life sciences client to sit with before signing off on another point tool. As an AWS Advanced Consulting Partner with a GenAI specialization, most of our engagements start with exactly this question: is your data platform actually ready to support the model, or are we about to build a clever tool on top of a shaky foundation: A well-designed data lake or lakehouse, with proper ontologies and access controls, is unglamorous work. It’s also the difference between a GenAI pilot that stays a pilot and one that scales across programs. #### Dedicatted’s take Organizations that commit to this for 12-24 months before expecting acceleration see 2-3x better AI adoption and model performance than those who layer AI onto the fragmentation they already have. ## The reference architecture: what a modern R&D data and AI stack actually looks like This is the technical layer most strategy reports skip, and it’s where projects actually succeed or stall. [AWS’s Well-Architected Life Sciences Lens](https://docs.aws.amazon.com/wellarchitected/latest/life-sciences-lens/life-sciences-lens.html) frames R&D workloads across drug discovery, bioinformatics, genomics, and connected labs, each with its own data volume, compute, and compliance profile. Below is how that maps to a practical stack. ### Data layer: ingestion, storage, and governance - **Object storage as the foundation.** Raw instrument output, sequencing files, imaging data, and lab notebook exports land in tiered object storage (hot for active analysis, cold/archive for long-term retention), because genomics data alone runs 100-200 GB per patient and organizations working across tens of thousands of patients cannot default to “keep everything hot.” - **A catalog and lakehouse layer on top**, so structured trial data, unstructured lab notes, and omics data can be queried together instead of living in separate silos. This is where the “semantic stability” problem gets solved: a shared data catalog with consistent entity definitions (the same “PD marker” means the same thing in discovery and translational science) lets AI reason across the pipeline instead of within one tool at a time. - **Purpose-built genomics and omics tooling.** Managed bioinformatics services exist specifically to run standard workflows (variant calling, alignment, annotation) at scale without every team building its own pipeline infrastructure from scratch, which is the direct answer to the “bioinformaticians spend too much time on infrastructure, not science” problem AWS’s own use case guidance calls out. - **FAIR by design.** Findable, Accessible, Interoperable, Reusable data principles need to be enforced through metadata standards and access policies, not left as an aspiration in a data strategy slide. [![tbC ntMJ ojFi emda Th8N9igbgI YFtpB 3Z0oCCAj H2mxnUtoqycglcF17mQyUzA8smukFf7kQsS1mDznVMaeXo6fxfXdma8p7fTSj7rcLTOXPhaulJAo10hhfSlPBI9pan2NfYFVDFcVaAx3qlpSM6WNWrjQXMBtdz8erA1S byvqpxDM2tcMjc8lqZ](https://dedicatted.com/wp-content/uploads/2026/08/tbC_ntMJ-ojFi_emda_Th8N9igbgI_YFtpB-3Z0oCCAj_H2mxnUtoqycglcF17mQyUzA8smukFf7kQsS1mDznVMaeXo6fxfXdma8p7fTSj7rcLTOXPhaulJAo10hhfSlPBI9pan2NfYFVDFcVaAx3qlpSM6WNWrjQXMBtdz8erA1S_byvqpxDM2tcMjc8lqZ-1024x607.jpeg)](https://dedicatted.com/wp-content/uploads/2026/08/tbC_ntMJ-ojFi_emda_Th8N9igbgI_YFtpB-3Z0oCCAj_H2mxnUtoqycglcF17mQyUzA8smukFf7kQsS1mDznVMaeXo6fxfXdma8p7fTSj7rcLTOXPhaulJAo10hhfSlPBI9pan2NfYFVDFcVaAx3qlpSM6WNWrjQXMBtdz8erA1S_byvqpxDM2tcMjc8lqZ-1024x607.jpeg)### Compute layer: matching workload to infrastructure - **High-performance computing (HPC) for molecular simulation and structure prediction**, provisioned on demand rather than sized for peak load year-round. - **GPU-backed environments for generative chemistry and protein design models**, separate from the general-purpose compute used for clinical data processing, since these workloads have very different cost and performance profiles. - **Managed ML environments for training, fine-tuning, and hosting models**, rather than scientists standing up their own inference infrastructure ad hoc, which is one of the fastest ways to end up with the “shadow IT” version of the fragmentation problem we described before. ### AI/ML and GenAI layer - **A model layer that supports both foundation models and specialized scientific models side by side.** Literature summarization and document drafting are well served by general-purpose large language models; target identification and molecule generation need domain-specific models trained on biological and chemical data. Treating these as one undifferentiated “AI platform” is a common design mistake. - **Retrieval-augmented generation (RAG) over a governed knowledge base**, so GenAI answers about targets, mechanisms, or prior program history are grounded in the company’s actual data rather than the model’s general training, which is also what keeps outputs auditable for regulators. - **An orchestration layer** that chains these steps together (for example: literature mining, target scoring, and candidate shortlisting as one workflow) rather than leaving each capability as a standalone tool a scientist has to open separately. [![piF6tioRFqIuV7lrLeFE4VeptAxJjPg8NV9vBXRbdgqGatdDZtxu11AxxxJJYAZkC4i SinunjLPPJwaA A2gOWhWaOBxi47zX2y8WHoDQSaLDboFsOie7P0o4f2Gw noAPQ2ur jQ2Kzj LcY0wW4trRzKWFpq6A4F1u8XzLuhso uWHUQFMu Aw4nsWRRl](https://dedicatted.com/wp-content/uploads/2026/08/piF6tioRFqIuV7lrLeFE4VeptAxJjPg8NV9vBXRbdgqGatdDZtxu11AxxxJJYAZkC4i_SinunjLPPJwaA-A2gOWhWaOBxi47zX2y8WHoDQSaLDboFsOie7P0o4f2Gw-noAPQ2ur_jQ2Kzj-LcY0wW4trRzKWFpq6A4F1u8XzLuhso_uWHUQFMu_Aw4nsWRRl-1024x762.jpeg)](https://dedicatted.com/wp-content/uploads/2026/08/piF6tioRFqIuV7lrLeFE4VeptAxJjPg8NV9vBXRbdgqGatdDZtxu11AxxxJJYAZkC4i_SinunjLPPJwaA-A2gOWhWaOBxi47zX2y8WHoDQSaLDboFsOie7P0o4f2Gw-noAPQ2ur_jQ2Kzj-LcY0wW4trRzKWFpq6A4F1u8XzLuhso_uWHUQFMu_Aw4nsWRRl-1024x762.jpeg)### Governance and compliance layer - **Identity, access, and encryption controls mapped to GxP context.** Good Laboratory Practices govern the research and discovery stage, Good Clinical Practices govern trials, and Good Manufacturing Practices take over after approval. Access policies, audit logging, and data residency requirements differ across these three contexts, and a single undifferentiated security model tends to be either too loose for GMP data or too heavy-handed for early discovery work. - **Full audit trail and model lineage**, covering what data trained or grounded a model, what version produced a given output, and who reviewed it, because this is exactly the evidence regulators are starting to ask for directly - **A responsible AI review step built into the workflow**, not a separate committee that reviews things after the fact, to catch data bias and unintended consequences before a model output reaches a decision point. [![mF7dzKSv0cOD47xgDxzHCQ2FEMU3YmrzBj smYUXPGWjkF8iZ5 8Xf0AVlGX7mgfmJyd4qVuCvB43zwljQ8kclKzObMuJGWzHsOKvNweRwkiWa3zMRHIV5KZc8MpuBOT0XOCD k21F2Wdg9B f6 15bt1PEtxeac l TK7eTybyCxnm JUwozYqWcXOV8b0](https://dedicatted.com/wp-content/uploads/2026/08/mF7dzKSv0cOD47xgDxzHCQ2FEMU3YmrzBj-smYUXPGWjkF8iZ5_8Xf0AVlGX7mgfmJyd4qVuCvB43zwljQ8kclKzObMuJGWzHsOKvNweRwkiWa3zMRHIV5KZc8MpuBOT0XOCD-k21F2Wdg9B-f6-15bt1PEtxeac_l-TK7eTybyCxnm_JUwozYqWcXOV8b0-1024x500.jpeg)](https://dedicatted.com/wp-content/uploads/2026/08/mF7dzKSv0cOD47xgDxzHCQ2FEMU3YmrzBj-smYUXPGWjkF8iZ5_8Xf0AVlGX7mgfmJyd4qVuCvB43zwljQ8kclKzObMuJGWzHsOKvNweRwkiWa3zMRHIV5KZc8MpuBOT0XOCD-k21F2Wdg9B-f6-15bt1PEtxeac_l-TK7eTybyCxnm_JUwozYqWcXOV8b0-1024x500.jpeg)## The use case playbook, by R&D stage Not every use case needs a five-year platform rebuild first. Here’s a working catalog organized by where value shows up across the R&D lifecycle, with the technical approach and expected value for each. ### 1.Bioinformatics and genomics at scale Processing raw sequencing and omics data into usable insight, at a volume and cost that don’t scale linearly with headcount. **Technical approach:** Managed, reproducible bioinformatics pipelines (variant calling, alignment, annotation) running on elastic compute, with cost tiering between hot storage for active projects and archival storage for long-term retention, plus FAIR-compliant metadata so datasets stay usable across teams and over time. **Expected value:** Removing infrastructure management from bioinformaticians’ day-to-day work lets them focus on the science rather than pipeline maintenance, and standardized, reusable workflows cut the time from raw data to interpretable insight. Our specialists have reworked this client’s drug discovery process on cloud infrastructure with the explicit goal of developing medicines faster, more cheaply, and with a higher chance of success, using machine learning across their research pipeline. ### 2.Connected labs Bridges data silos between lab instruments, Laboratory Information Management Systems (LIMS), and Electronic Lab Notebooks (ELNs), so scientists see integrated, current data instead of hunting across systems. **Technical approach:** Instrument data capture piped from on-premises lab equipment into cloud analytics, governed under FAIR principles, with unstructured data (images, free-text notes) brought under the same governance model as structured results so AI tools can actually use it. **Expected value:** Faster, more comprehensive access to research data across a lab network, and a foundation that makes AI-assisted analysis genuinely usable instead of blocked by missing or unreachable data. This technology has been used across roughly 4,000 research organizations and functions as a central data-sharing hub across decentralized labs, illustrating the direction most R&D organizations are heading even where they haven’t formally adopted a single platform yet. We built a similar multi-agent platform for [Cassen Testing Laboratories](https://dedicatted.com/insights/smarter-science-how-dedicatted-brought-agentic-ai-to-chemical-reporting-at-cassen-laboratories), an ISO 17025-accredited chemical and environmental lab in Toronto, whose reporting workflow was capped at ~50 markers per run, relied on manual regulatory checks, and took days per report. Using Amazon Bedrock with Nova models and AWS AgentCore, we deployed a supervisor agent coordinating specialized sub-agents for pattern detection, RAG-based retrieval, and traceable report assembly against a compound-to-pattern-to-source knowledge base. Throughput more than tripled to 150+ markers per run, new capabilities like VOC signature analysis became possible, and the same dataset now feeds both compliant technical reports and consumer-ready summaries. [![cassen agentic ai 5 scaled 1](https://dedicatted.com/wp-content/uploads/2026/08/cassen-agentic-ai-5-scaled-1-1024x686.png)](https://dedicatted.com/wp-content/uploads/2026/08/cassen-agentic-ai-5-scaled-1-1024x686.png)### 3. Clinical trial design, recruitment, and monitoring Uses digital twins, predictive models, and natural language processing to design smaller, faster, more representative trials, and to find and retain the right patients. **Technical approach:** Digital Twin Generator models trained on historical longitudinal patient data forecast disease progression for control-arm patients, reducing the number of control patients a trial actually needs. Separately, natural language processing over structured and unstructured patient records (EHRs, clinical notes, radiology reports) matches patients to trial protocols in real time. **Expected value:** Digital twins can reduce the need for clinical trial enrollment by 25-50%, cutting both time and cost. In a retrospective analysis of three pivotal Phase III trials in mild-to-moderate Alzheimer’s disease, digital twins of trial participants enabled control arm size reductions of up to 33%, or a 13% boost in statistical power at the same sample size. Precision recruitment platforms let sites recruit patients three times faster than average, finding 25% more eligible patients while spending half the time searching. AI’s precision research platform matches patients and sites to trial protocols using natural language processing over clinical notes and radiology reports within a[ HIPAA-compliant environment.](https://dedicatted.com/services/security) ### 4.Regulatory and quality documentation Uses GenAI to draft, tag, and retrieve the enormous volume of documentation required for regulatory submissions and quality management. **Technical approach:** Semantic (meaning-based, not just keyword) search over a company’s own historical protocols, site contracting agreements, and clinical report forms, paired with GenAI drafting assistance for first drafts of clinical study reports and standard operating procedures, always with a human review step before anything is finalized. **Expected value:** Faster identification of relevant materials during submission assembly, faster first-draft turnaround on recurring document types, and fewer costly misclassifications in quality event management, since undetected quality issues can carry significant regulatory penalties. We designed platform specifically to help biopharma clients speed up research and bring drugs to market faster using this kind of document- and knowledge-layer automation, governed by an internal responsible-AI framework. ## Where we’d recommend starting Pulling this together into a short list: 1. **Run a structured architecture and data readiness assessment first.** A Well-Architected style review against your research, clinical, and manufacturing data environments will tell you more about where you actually stand than any AI vendor demo. 2. **Pick one no-regret use case**, that touches data you already have in reasonably good shape, most often clinical trial recruitment or regulatory document drafting, and scope a pilot with a defined success metric before you build anything. 3. **Build the data and governance foundation in parallel**, not after the pilot succeeds. This is the work that determines whether use case number two is fast or painful. 4. **Fund reskilling in the same budget cycle as the platform.** The tools don’t create value on their own. 5. **Bake in the audit trail and model validation evidence from day one**, so your first regulatory submission that touches an AI-assisted output isn’t also your first attempt at documenting how that AI was validated. None of this is a quick fix, and none of the research pretends otherwise. But the organizations already ahead on relative cycle times and technical success rates have also seen the largest growth in enterprise value over the same period, according to Accenture’s index. The two are connected. If you’re evaluating where to start, whether that’s a Well-Architected review of your existing research data environment, a scoped GenAI pilot in clinical operations, a connected lab integration project, or a genomics pipeline modernization, that’s exactly the kind of conversation our team works through with clients regularly. [**As an AWS Premier Tier Services Partner**](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/Dedicatted) and top 2% global AWS partner with GenAI Competency and MSP designation, we combine deep cloud expertise with hands-on engineering we are happy to compare notes on where your organization sits against this research. Our teams bring the experience, AWS data and analytics expertise, and research initiatives to help you plan and execute your data-driven strategy. [Get in touch with our team at Dedicatted ](https://dedicatted.com/contact)to talk through where your environment stands today. **Categories:** Blog **Services:** Agentic AI, Cloud Modernization, Data Architecture, Generative AI **Industry:** Healthcare --- ### [Your Product Can Explain Itself: An AI Product Expert Inside Any Software You Build](https://dedicatted.com/insights/uniguide-ai-product-expert-inside-any-software) **Published:** August 12, 2026 **Author:** Dedicatted **Excerpt:** UniGuide is an AI product expert that embeds in any software you build. It answers from your own documentation, shows users where to click, and completes the task when they ask. Try the working demo on AWS Marketplace. **Content:** Every software company has a version of the same problem. You built something capable. People sign up, poke at it for ten minutes, and never come back. Or they stay, but use a fraction of what you built, while your support inbox fills with the same handful of questions week after week. The usual answers are documentation, onboarding tours, and a support team. All three share a flaw. They sit outside the moment where the user actually gets stuck. UniGuide takes a different approach. It is an AI product expert that lives inside your product, understands what the user is trying to do right now, and either answers or does it for them. ## Three places software quietly loses money Before the technology, the business case. Products lose value in three predictable places. - **Users never reach first value.** They sign up, cannot work out the first real task, and churn before they see what the product does well. - **Support carries the education load.** A large share of first line tickets are not bugs. They are “how do I” questions that a well trained colleague could answer in fifteen seconds. - **The product team is flying blind.** Users tell you every day what they are struggling with and what they wish existed. That signal arrives as scattered tickets, or not at all. [![Three places software quietly loses money](https://dedicatted.com/wp-content/uploads/2026/08/a1-01-gaps-v2.png)Three places software quietly loses money](https://dedicatted.com/wp-content/uploads/2026/08/a1-01-gaps-v2.png)These are usually treated as three separate problems owned by three separate teams. They are the same problem seen from three angles: the product cannot explain itself at the moment it is being used. ## What UniGuide is, and what it is not UniGuide is not a support chatbot, and it is not a scripted product tour. A chatbot answers questions. A product tour walks a new user through a fixed sequence and breaks the moment somebody asks something off-script, which happens constantly. UniGuide handles the unscripted moment, because it works from what the user is trying to accomplish rather than from the exact words they typed. Here is what happens when a user asks something. 1. **It reads the context.** Which screen they are on, what role they hold, what permissions they have, what they were doing a minute ago. The same question from a new analyst and from an administrator gets a different answer, because the right answer *is* different. 2. **It answers from your knowledge, not from the internet.** Responses are grounded in your curated knowledge base through vector search and reranking. If the answer is not in your material, it says it does not know rather than inventing something plausible. 3. **It shows rather than tells.** Instead of “click the button in the top right”, it walks the user through the interface step by step, highlighting exactly where to go. 4. **It offers to do it.** “Do you want me to do this for you?” If the user says yes, UniGuide completes the task. If they say no, nothing happens. It is a co-pilot, not an autopilot, and the permission gate is the point. [![What happens when a user asks a question](https://dedicatted.com/wp-content/uploads/2026/08/a1-02-pipeline-v2.png)What happens when a user asks a question](https://dedicatted.com/wp-content/uploads/2026/08/a1-02-pipeline-v2.png)## Three engines under the hood Technically, UniGuide is three components working together. ### UI Guidance Engine The AI interacts with your interface directly. It navigates, highlights, and clicks, showing actions step by step rather than describing them in prose. ### Data Analyst Engine UniGuide can run queries against your database in real time during a conversation. A user can ask about their own data, not only about how the product works, and get an answer in the same breath. ### Conversational Intelligence A policy layer governs how the assistant behaves, keeps it inside your verified knowledge, and watches conversations for the signals that matter commercially: a user drifting toward churn, a user ready to upgrade, a feature people keep asking for that does not exist yet. [![Three engines, one install](https://dedicatted.com/wp-content/uploads/2026/08/a1-03-engines-v2.png)Three engines, one install](https://dedicatted.com/wp-content/uploads/2026/08/a1-03-engines-v2.png)## Why it works for any product This is the part people underestimate. UniGuide has no opinion about what your software does. It does not need to be a project management tool, or a CRM, or a finance platform. It reads your interface, learns from your documentation, and follows the scenarios you configure. We have run it inside financial platforms and inside internal line of business tools that will never have a public sign up page. The pattern is the same wherever a product is powerful enough that people need help using it. That includes the cases teams usually write off as too niche for a help system: the internal tool with forty users and no documentation budget, the compliance platform where a mistake is expensive, the analytics product where the hard part is not the interface but knowing which question to ask. Integration is an SDK, roughly one line of code and about an hour of engineering time on your side. Closer to adding an analytics tag than to a platform migration. ## Try it on AWS Marketplace Reading about a guided experience is a poor substitute for using one, so we published a working demo rather than a slide deck. [UniGuide by Dedicatted on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-dkimmxypuku6c) deploys into your own AWS account through a CloudFormation template. It provisions a sample project management application, the kind of tool everyone recognises, with a live UniGuide instance embedded in it. We chose a Jira-like application deliberately. Almost everyone in software has felt the specific frustration of knowing what they want to accomplish and not knowing which of forty menu items gets them there. It is a fair test. Five scenarios come preloaded, the kind of thing a brand new user tries on day one. Ask “how do I create a sprint” or “how do I add members” and watch what happens: UniGuide answers from the application own knowledge base, highlights where to click, and offers to complete the task for you. Say yes and it creates the issue, assigns the role, or walks the audit log itself. The demo runs on Amazon ECS with AWS Fargate from a verified container image, deploys into an existing VPC or a new isolated one, terminates HTTPS at an Application [Load Balancer](https://dedicatted.com/services/glossary-of-terms#term-load-balancer) with your own ACM certificate, and ships with Amazon CloudWatch enabled. It is stateless and uses least privilege IAM roles. No sales call required to get hands on. ## Where the data lives A fair question about any AI assistant embedded in a product: where does the conversation go? UniGuide deploys inside your own perimeter, on AWS, Azure, or Google Cloud. Chat history and customer data stay in your account. The underlying model is not fixed, so the LLM can be chosen or swapped to match your cloud and compliance requirements. The widget is white label and matches your brand. And the code is yours: full IP ownership transfers to you. ## What a pilot looks like A typical pilot runs 4 weeks. It covers a joint workshop to identify the scenarios that matter, SDK integration, and configuration of twenty to thirty guided flows. Our team handles the AI training and the heavier integration work. Your team provides access and validates that the answers are right. Across deployments we see activation lift of 5 to 15%, early churn drop by 10 to 25%, and support ticket volume fall by 30 to 50%. The range is wide because the starting point matters: products with steeper learning curves have more to gain. ## Start with the demo If any of the three problems at the top of this article sounded familiar, the fastest way to judge whether UniGuide helps is to use it. Deploy the demo, ask it something awkward, and see whether it handles the unscripted moment. Then [talk to us](https://dedicatted.com/contact) about your own product. **Categories:** Blog **Services:** Agentic AI, Generative AI **Industry:** High Tech Software --- ### [Custom Computer Vision on AWS: From 5 to 0.73-Second Recognition](https://dedicatted.com/insights/custom-computer-vision-aws-case-study) **Published:** August 15, 2026 **Author:** Dedicatted **Content:** ## About project #### Working time: 2025 – 2026 #### Industry: Consumer Technology, Collectibles #### The service: Computer Vision, Generative AI, Managed Services #### Overview The client’s most important feature was the one thing they didn’t own. Here is how we fixed that: 61 percent lower cost per scan, sub-second recognition, and full ownership of the stack. A North American consumer technology company running one of the largest digital platforms for collectibles. More than 10 million users. Over 800 million tracked products. More than 13 million daily active sessions. A team that grew from under ten people to around fifty in a single year. The product’s defining feature is the camera. A user points a phone at a card. The app identifies it, reads its grade, and prices it into a live portfolio. ## The problem That feature ran on a specialist third-party recognition vendor. Four pressures made that untenable. **Cost.** Pricing scaled with every scan, on a platform processing 2 to 3 million scans a day. A vendor price increase was already scheduled for the following year. Growth was making the product more expensive instead of more profitable. **Speed.** Recognition averaged roughly five seconds. The vendor hosted in Europe. Every scan from a North American user crossed the Atlantic twice. That latency was designed in by someone else’s data center placement. **Reliability.** The client’s own monitoring logged 24 vendor outage incidents in a single 20-day window. Outages they could only report and wait on. **The bar.** The incumbent was no generalist. Collectibles recognition was its entire business, running at 97 percent accuracy. Any replacement had to match that on a catalog of more than 500,000 products, from real phone photos shot through plastic sleeves under kitchen lighting. Not clean reference images. ## What we built **Prove it first.** We ran an AWS-funded proof of concept and validated parity with the incumbent on a representative slice of the catalog before touching production. The client committed with evidence, not hope. **The architecture.** Custom computer vision models locate the card in the frame, separate it from the background, and classify the finish type: holofoil, reverse holofoil, or flat print. That distinction matters because valuation turns on it. The isolated image becomes a vector embedding, searched against a Qdrant vector database on Amazon EC2, which returns the closest match from the full catalog. Structured data lives in Amazon RDS, the image archive in Amazon S3. For graded cards, a GPU-accelerated OCR stage reads the slab label and extracts the grading company, grade score, and certificate serial number across PSA, BGS, CGC, TAG, and ACE. Everything except the load balancer sits in a private subnet, with Amazon GuardDuty, AWS KMS, and AWS IAM handling threat detection, encryption, and access. **Train on the task, not the catalog.** Models were trained on Amazon SageMaker and deployed for inference on Amazon EC2 GPU instances via NVIDIA Triton Inference Server, tuned so a single GPU serves multiple models without idle spend. The decisive choice: we trained the models on the general task of locating, isolating, and classifying cards, not on any fixed card set. A new release never touches the model. The client’s own team loads new images into the vector database and the cards are searchable in minutes. **Ship it like infrastructure.** The inference tier runs as an Auto Scaling Group launched from an immutable, Packer-built golden AMI, scaled on Amazon CloudWatch application metrics and NVIDIA DCGM GPU telemetry, and shipped through CI/CD. Rollout was staged: a user cohort first, then a rising share of live traffic validated head-to-head against the incumbent. Full production cutover came roughly four months after first assessment. **Run it.** The service now operates inside our [24/7 managed operations practice](https://dedicatted.com/services/cloud-devops), monitored by the same team that runs the rest of the client’s platform. ## The results - 61 percent lower cost per scan than the previous vendor, measured in a like-for-like production comparison at live traffic volume - 0.73-second average end-to-end recognition, down from roughly five seconds - 2 to 3 million scans processed per day - Full catalog of more than 500,000 products, across English, Japanese, Spanish, and Chinese - New card sets live in 5 to 15 minutes with zero model retraining, replacing a multi-week vendor cycle - Full intellectual property assigned to the client The last line is the one we care about most. We build the system, prove it in production, and leave the customer owning it outright. The feature that defines their product is no longer rented. ## Why this pattern matters Plenty of platforms outgrow the vendor that got them to market. A black-box AI service was the right first move. Then the numbers turn: costs scale the wrong way, the roadmap belongs to someone else, and the product’s core capability sits on infrastructure you can’t see into. Closing that gap is what we do. As an [AWS Premier Tier Services Partner](https://dedicatted.com/our-partners/aws) holding both the Generative AI and Agentic AI Competencies, we pair the deepest tier of AWS engineering credentials with [AI delivery](https://dedicatted.com/services/artificial-intelligence) that holds up under real production load. If your most important feature runs on someone else’s platform, [let’s talk](https://dedicatted.com/contact). **Categories:** Case study **Services:** Generative AI, Managed Services **Industry:** High Tech Software --- ### [When the Product Is Powerful and Nobody Can Use It: Embedding an AI Product Expert](https://dedicatted.com/insights/ai-product-expert-onboarding-complex-platform) **Published:** August 19, 2026 **Author:** Dedicatted **Excerpt:** A comprehensive platform with deep functionality and users who had no domain training. Instead of rewriting the documentation, we embedded an AI product expert that guides users inside the interface. **Content:** ## About project #### Working time: 2025-2026 #### Industry: Manufacturing #### The service: Agentic AI, Generative AI #### Overview A manufacturer came to us about software that almost nobody in the building could use properly. The system was not bad. It was old, and it was theirs: years of accumulated operational logic covering scheduling, inventory, quality records and reporting, built up over a long life and still running the plant every day. Most of the people who designed it had moved on. The people using it had been handed it and told to get on with it. Documentation existed, and it was reasonably good. Almost nobody opened it, because nobody abandons a half-finished task to go and read. Rather than rewrite the documentation or rebuild the interface, we embedded UniGuide, an AI product expert, directly into the software. It answers strictly from the company’s own verified material, walks the interface on the user’s own screen to show where to click, and asks permission before doing anything on their behalf. ## The Challenge Long-lived operational software fails in a particular way. The problem is rarely that it works badly. It is that most of what it knows lives in the heads of a few long-serving people, and everybody else navigates by memory and guesswork. [![The profile that makes onboarding painful](https://media.dedicatted.com/wp-content/uploads/2026/08/a2-01-profile-v3.png)The profile that makes onboarding painful.](https://media.dedicatted.com/wp-content/uploads/2026/08/a2-01-profile-v3.png)- **Wide surface area.** Many modules, many settings, several valid routes to the same outcome. Power for someone who has used it for a decade, a maze for anyone who has not. - **Users with no training in the system.** The people logging in were not specialists in the software. They often did not know the right question to ask, let alone where to click. - **Documentation nobody read.** It existed and it was decent. It also sat outside the software, which is the one place people will not go while they are mid-task. - **Knowledge concentrated in a handful of people.** Every non-obvious answer routed to the same few long-tenured staff. That is a continuity risk as much as a support cost. - **Records that have to be right.** The system holds operational and quality records where a confident wrong answer costs real time, and occasionally more than time. - **No view of where people got stuck.** The team knew onboarding was hard in a vague and unsatisfying way, without being able to say precisely where. Every proposed fix ran into the same wall. Rewriting the documentation leaves it outside the product. A guided tour works for the first five minutes, then breaks the moment somebody asks what the script did not anticipate. Hiring more support pays for the problem rather than solving it. ## Technology & Approach We embedded UniGuide, an AI product expert, directly into the interface of the existing system. No rewrite, no replatform, and no change to how the software works. [![What we embedded, in order](https://media.dedicatted.com/wp-content/uploads/2026/08/a2-02-steps-v2.png)What we embedded, in order.](https://media.dedicatted.com/wp-content/uploads/2026/08/a2-02-steps-v2.png)### **1. We taught it the product** The existing documentation was not useless. It was simply in the wrong place. We ingested it as a knowledge base together with PDFs and screen recordings the team had made for internal training. Auto-indexing meant that when documents were updated afterwards, the assistant picked up the changes without anyone maintaining a second copy. The important constraint: the assistant answers strictly from that verified material. Asked something outside it, UniGuide says it does not know. On a system where a confident wrong answer can cost an operator real time, that boundary mattered more than breadth. ### **2. We designed the scenarios that actually matter** We ran a workshop with the client’s support and operations people and asked a blunt question: what do people ask you, over and over, that you are tired of answering? That list became the scenario set. Not the flows anyone found most interesting, but the ones consuming the most human hours. It is a better prioritisation method than it sounds, because a support queue is an honest record of where software fails to explain itself. ### **3. We put the guidance inside the interface** This is the part that separated UniGuide from anything the client had tried. Rather than describing a path in prose, the assistant walks the interface: it navigates, highlights the element in question, and moves through the steps in order. The user watches it happen on their own screen, in their own account, with their own data. When the action is one UniGuide can perform, it asks first. Users keep control, which mattered a great deal on a system holding records the business cannot afford to get wrong. ### **4. We connected it to the user’s own data** Some of the most common questions were not about the software at all. They were about the user’s own information, the kind of thing that normally requires knowing which report to run and how to filter it. UniGuide can run queries against the database during a conversation, so somebody asks in plain language and gets the answer without learning the reporting module first. ### **5. We closed the loop back to the product team** Every conversation became structured data: questions clustered by topic, points where users stopped making progress, requests for things that did not exist. The team went from a vague sense that the software was hard to a ranked account of exactly where people got stuck and what they asked for next. ## Business impact This engagement was not set up as an instrumented before-and-after study, so what follows is what the client’s team and ours observed rather than a controlled measurement. - **New users got somewhere faster.** Instead of reading, asking a colleague, or filing a ticket and waiting, people asked and kept working. The gap between not knowing and knowing collapsed to a single question. - **The repetitive tickets stopped arriving.** The educational requests that were never really support work were answered before anyone thought to open a ticket, which freed people who understood the system deeply to work on problems that needed them. - **The knowledge stopped living in a few heads.** The answers those long-tenured staff had been giving verbally now sat in the software, available to everyone, at the moment of use. - **The team finally got visibility.** Not page views, but a ranked account of what people tried to do and where the software failed to make it obvious. Several roadmap decisions came directly out of that list. [![Informational infographic titled 'What changes, and by how much' showing a table of measures with before and after baselines and change ranges (e.g., +5 to 15%).](https://media.dedicatted.com/wp-content/uploads/2026/08/a2-03-impact-v2.png)Ranges observed across UniGuide deployments, not measurements from this engagement.](https://media.dedicatted.com/wp-content/uploads/2026/08/a2-03-impact-v2.png)**For context, across UniGuide deployments generally**, we see activation improve by 5 to 15%, early churn fall by 10 to 25%, and support tickets drop by 30 to 50%. Systems with the profile described here, wide functionality and non-specialist users, sit at the stronger end of those ranges because they have the most ground to make up. Those figures come from our deployment base as a whole; they are not measurements from this client. ## What transfers A few lessons from this engagement apply well beyond it. - **Your support queue is your onboarding roadmap.** The questions your team is tired of answering are the scenarios worth automating first. Start there rather than with the features you most want to show off. - **Documentation is rarely the problem. Location is.** This client’s docs were fine. They were simply somewhere nobody would go mid-task. Moving that knowledge into the moment of use changed the outcome without rewriting a word. - **“I do not know” is a feature.** Where users lack the expertise to spot a wrong answer, an assistant that refuses to guess earns trust faster than one that always has something to say. - **Ask before acting.** An assistant that can complete tasks is far more useful than one that only explains. An assistant that completes tasks without asking is a liability. The permission gate is what makes the capability usable. - **Age is not the obstacle people expect.** The software was old, and integration was still an SDK and about an hour of engineering time. The real work was deciding which scenarios mattered and validating that the answers were right, and that work belongs to the people who know the system. ## What comes next If your software has the same shape, capable, wide, and harder to learn than you would like, the fastest way to judge whether this approach helps is to use it. We publish a working demo on AWS Marketplace: UniGuide by Dedicatted deploys into your own AWS account through a CloudFormation template, with a live assistant embedded in a sample application. A typical pilot runs 4 weeks: a joint workshop, integration, and 20 to 30 guided scenarios. It runs inside your own perimeter, so your data stays in your account and you own the resulting code. **Categories:** Case study **Services:** Agentic AI, Generative AI **Industry:** Manufacturing --- ### [How an Aviation Tech Company Cut POC Time from a Month to Days](https://dedicatted.com/insights/how-an-aviation-tech-company-cut-poc-time-from-a-month-to-days) **Published:** August 19, 2026 **Author:** Andriy Kopachevskyy **Content:** ## About project #### Working time: 2025 – 2026 #### Industry: Airlines & Travel #### The service: Internal POC Factory & AI-DLC Enablement #### Overview Our client builds the digital systems that airlines run their operations on: booking, crew scheduling, disruption management, and day-of-operations tooling. The estate is integration-heavy by nature, spanning around 60 engineers across Canada and the United States, with every system wired into airline-specific data sources and regulatory constraints. That complexity was also the client’s biggest bottleneck. Every new idea needed its own environment, its own architecture research, and its own path through DevOps before anyone could tell if it was worth building. Validating a single idea took about a month, most of it spent on setup rather than engineering. Dedicatted built an internal POC factory: a self-service environment platform paired with an AI-DLC delivery pipeline, so a team can move from a stated intent to a working, standards-compliant prototype inside their own AWS account in days. Because the platform is grounded in the client’s actual architecture and data contracts, viable prototypes don’t get discarded. They become the first commit of the production feature. ## The Challenge **[Environment](https://dedicatted.com/services/glossary-of-terms#term-environment) provisioning was a bottleneck by itself**. Every POC needed its own compute, storage, and data before any code got written. [Provisioning](https://dedicatted.com/services/glossary-of-terms#term-provisioning) ran through DevOps tickets, took days, and typically produced a sandbox that didn’t match production topology closely enough to validate anything meaningful. **Architectural context lived outside the build process**. Reference architectures, approved data sources, and platform standards were split across Confluence spaces, service repos, and institutional knowledge held by a handful of senior engineers. Builders had to manually reconstruct this context for every prototype, and gaps in that reconstruction meant POCs regularly diverged from how the company’s systems were actually built. **POCs carried no forward value**. Because prototypes were built in throwaway sandboxes without the client’s standards or hooks enforced, a POC proving product value still had to be rebuilt from scratch to ship. Validation and delivery were two separate projects instead of one continuous path. **Every stage depended on a different team**. DevOps for environments, platform architects for design guidance, data teams for source access. None of these dependencies were unreasonable on their own, but stacked together they made experimentation slow enough that fewer ideas got tested at all. ## Want to get your copy of the case study? Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## Technology & Approach ### **Step 1: Build a governed context layer** Dedicatted extracted the client’s architectural rules, reference patterns, and approved data sources from Confluence and service repositories into a structured context layer, and exposed it to the AI builder through MCP (Model Context Protocol) tool integrations. This gave the AI-DLC pipeline direct, machine-readable access to the client’s actual standards instead of general-purpose assumptions. ### **Step 2: Provision production-parity environments on demand** A Backstage developer portal serves as the entry point. Golden-path templates provision EKS for compute, RDS for relational data, and S3 for storage, each shaped to mirror the client’s production topology rather than a simplified sandbox. Environments are seeded with representative data drawn from the client’s approved sources, and lifecycle policies automatically decommission environments once a team’s work is done, keeping infrastructure spend contained. ### **Step 3: Ground the AI-DLC builder in the client’s own standards** With an environment live, an AI-DLC builder running on Amazon Bedrock takes an engineer’s stated intent (for example, prototyping a disruption-rebooking flow), plans an implementation, surfaces clarifying questions, and proceeds to build only after explicit human sign-off. Model routing runs behind a governed endpoint inside the client’s own AWS account, so no prototype logic or data leaves their environment boundary. Custom hooks and validation rules enforce the client’s architectural standards throughout the build, rather than checking for compliance after the fact. ### **Step 4: Engineer a direct path from POC to production** Because every prototype is built against the client’s real architecture inside a production-shaped environment, a POC that clears validation carries its codebase forward into the actual product rather than serving as a disposable reference implementation. Validation and delivery collapse into a single pipeline. ### **Step 5: Transfer AI-DLC as a way of working, not just a tool** The platform itself was built using the AI-DLC methodology it now offers to other teams: plan, clarify, build, human sign-off. Dedicatted ran enablement as hands-on mob-programming sessions against live backlog items, so engineering teams learned the workflow by shipping real prototypes with it rather than sitting through a training deck. [![Group 476](https://dedicatted.com/wp-content/uploads/2026/08/Group-476-968x1024.png)](https://dedicatted.com/wp-content/uploads/2026/08/Group-476-968x1024.png)## Business impact - **Idea validation compressed from roughly a month to days.** The gate between “we have an idea” and “we have a working prototype” moved from a multi-week environment-and-context-gathering exercise to a same-week build cycle. - **Environment provisioning collapsed from a multi-day DevOps queue to a two-click self-service action.** Engineers no longer wait on ticket queues to get a production-representative place to build. - **POCs stopped being throwaway work.** Because every prototype is built to production architecture and standards from its first commit, validated ideas move directly into the delivery pipeline instead of requiring a second, separate build effort. - **Cross-team dependency dropped across the validation phase.** Environment provisioning, architectural guidance, and data access are now served by the platform itself, reducing the DevOps, architecture, and data-team involvement previously required for every single POC. - **Platform ownership transferred in-house.** An internal platform team now owns the Backstage templates, the context layer, and the governance rules, so the factory continues to improve and extend after Dedicatted’s direct engagement ends. **Categories:** Case study **Services:** DevOps Consulting, Generative AI, Managed Services **Industry:** High Tech Software, Manufacturing --- ### [From Disaster Recovery to Production: VVN Group's Zero-Downtime SAP Migration to AWS](https://dedicatted.com/insights/vvn-group-zero-downtime-sap-migration-aws) **Published:** February 16, 2026 **Author:** Dedicatted **Excerpt:** VVN Group moved a 20-year-old SAP ECC and Oracle landscape to AWS with no business downtime, starting with a disaster-recovery site that later became production. **Content:** ## About project #### Working time: 2025-2026 #### Industry: Retail, Distribution, Manufacturing #### The service: [Cloud Migration](https://dedicatted.com/services/glossary-of-terms#term-cloud-migration), Disaster Recovery, Managed Services #### Overview VVN Group is an exclusive distributor of household chemicals, cosmetics and personal-care products across Eastern Europe. A group of companies trading as one commercial organisation, carrying more than 10,000 SKUs and over 50 brands held under exclusive agreement with European and regional manufacturers, and supply into more than 60 retail chains, including the largest grocery, drugstore and online marketplace names in its markets, reaching over 3,000 individual outlets. Since 2017 the group has also manufactured private-label lines for those same chains, and it runs its own warehousing and transport against a 24-hour delivery commitment. One SAP ECC landscape covers all of it, on an Oracle database, and it has done since the group started trading. Roughly twenty years of history. The system worked. What had stopped working was the economics of keeping it on hardware the group owned. Every capacity decision was a purchase made a year ahead of the demand it served. Peak was specified once and paid for permanently. Development and QA were sized like production and used during office hours at best. And a genuine disaster-recovery position meant a second landscape in a second building, which is why the group had backups but had never run a recovery. Dedicatted moved the estate to AWS with no business downtime, no ABAP changes and no change to how anyone works. Infrastructure now costs **31% less** than the on-premises run-rate it replaced. The project started as a disaster-recovery build. That turned out to be the cheapest way to construct the migration target. [![The migration in five numbers: zero business downtime, six-minute quiesce at the switch, 31% lower infrastructure run-rate, six weeks to a tested disaster recovery position, 6 TB Oracle database](https://media.dedicatted.com/wp-content/uploads/2026/08/01-numbers-final.png)](https://media.dedicatted.com/wp-content/uploads/2026/08/01-numbers-final.png)## The Challenge The SAP system was healthy. The infrastructure underneath it was expensive, and every year it got harder to justify. - **Capacity had to be bought a year early.** Adding a brand, a category or a country meant sizing hardware for where the business would be in twelve months, then paying for that headroom from the day it arrived. The group had been growing faster than that cycle allows. - **Peak was permanent.** The landscape was specified for the busiest weeks of the year. For the rest of it a large share sat idle, and there was no mechanism to hand any of it back. - **Non-production cost roughly what production cost.** Development and QA ran on their own servers, sized to match, and were in use during office hours at best. Switching them off was not an option, because nothing guaranteed they would come back. - **A refresh was coming.** Servers bought together age together, and the group was looking at one large capital decision covering the entire ERP estate at once. - **Real disaster recovery meant buying the landscape twice.** A recovery site is a second set of hardware in a second location. The group had backups instead. It had never executed a recovery, and the recovery copy sat next to the primary. - **Basis time went on infrastructure.** Storage, firmware, capacity planning, refresh scheduling. Effort that could have gone into the business went into keeping the platform alive. - **There was no window to fix any of it.** Distribution runs seven days a week against a 24-hour delivery promise, and since 2017 a production line has run on the same system. Whatever the answer was, it could not involve stopping. ## Technology & Approach ### **Disaster recovery came first** The group did not open with a request to migrate. It asked what it would cost to have somewhere to fail over to. On AWS that is a small project rather than a capital one. We replicated the landscape into a target environment in eu-central-1, wrote the failover procedure, then ran the failover for real and timed it. Six weeks after kickoff the group had a recovery position it had actually tested, for a fraction of what a second data centre would have cost. It stayed that way for seven months, and during those seven months the target was doing a second job: proving itself. By the time anyone asked whether production could move onto it, the environment had been holding real data and surviving real drills for over half a year. When the migration came it was not a jump onto new infrastructure. It was a promotion of infrastructure that had already been running. [![Two phases: a tested disaster recovery position live in week six, then the migration reused the same environment](https://media.dedicatted.com/wp-content/uploads/2026/08/02-timeline-final.png)](https://media.dedicatted.com/wp-content/uploads/2026/08/02-timeline-final.png)### **Sizing it on what SAP was actually doing** The usual way a project like this loses its business case is by copying the on-premises specification into EC2. Five years of accumulated over-provisioning comes across with it, and the group starts paying monthly for headroom it had already paid for once. We sized from workload instead. EarlyWatch Alert reports and twelve months of ST03N statistics gave the real CPU, memory and response profile for each instance. Every instance family was checked against SAP Note 1656099, which is the binding statement of what SAP supports on AWS; nothing went onto an uncertified type, non-production included. Production and non-production were then specified separately, which is the part that only becomes possible once the hardware is not yours. Development and QA now run to a schedule and stop outside working hours. ### **The landing zone** - Multi-AZ VPC, with the SAP database and application tiers in separate Availability Zones. - The SAP Oracle database self-managed on **Amazon EC2**. SAP supports Business Suite databases on EC2 rather than on RDS, so that is where it went. **Amazon RDS for Oracle** runs the group’s adjacent, non-SAP databases, where a managed service is permitted and takes administration off the team. - Amazon EBS split by role, with IOPS and throughput provisioned separately from capacity. This is usually why SAP runs faster after a migration than it did on the array it left. - Backups to Amazon S3 with versioning and lifecycle rules, held in a different region from the running landscape. - Every system registered with AWS Systems Manager for SAP, so start, stop and patching are application-aware. [![Before and after: SAP ECC and Oracle on owned hardware, moved to a multi-AZ AWS landing zone with Oracle self-managed on EC2 and RDS for the adjacent databases](https://media.dedicatted.com/wp-content/uploads/2026/08/03-architecture-final.png)](https://media.dedicatted.com/wp-content/uploads/2026/08/03-architecture-final.png)### **Moving six terabytes without a maintenance window** The database was the constraint. Six terabytes carrying twenty years of history will not move inside any window a distribution business can offer. So it was not moved inside one. The target was seeded from a backup and restored without affecting production. From that point change data capture carried every new transaction across, continuously. The distance between the two systems stopped being a quantity of data waiting to be copied and became a number on a dashboard: replication lag, measured in seconds. The two ran alongside each other while that number was watched, the interfaces were exercised against the target, and the finance and merchandising teams checked their own data. That went on for weeks rather than hours. Nothing about it needed to be rushed. ### **Choosing the moment to switch** The cutover was planned, not improvised. By the time it was scheduled, replication lag had been sitting at zero for days, so the remaining question was not whether the data was ready but when the switch would cost the business least. We took the window from the group’s own activity data: the quietest point in the week for concurrent users, clear of dispatch cut-offs and outside the production shift. Users were locked out, the final transactions confirmed applied, the application repointed and restarted. Six minutes, start to finish. There was nothing to restore, nothing to import and no reconciliation backlog waiting on the other side, because the data was already there. The runbook had been rehearsed against copies of production and it behaved the way the rehearsals had. **Business downtime was none.** No trading hour was lost, no retail chain missed a file, and the production schedule was not moved. The source system stayed up and untouched through the agreed rollback window, and was decommissioned once nobody wanted it any more. [![The cutover was scheduled for the quietest hour of the week, with replication lag already at zero for days beforehand](https://media.dedicatted.com/wp-content/uploads/2026/08/04-lag-final.png)](https://media.dedicatted.com/wp-content/uploads/2026/08/04-lag-final.png)### **Sixty chains, seventy interfaces** Interfaces break distribution migrations more often than databases do. Every retail chain has its own file format, its own schedule and its own tolerance for something arriving late, and there were around seventy of these flows. Each one was inventoried, repointed and tested against the target using real message traffic rather than a connectivity check. Because the target had been live as a DR site for months, that work spread across weeks of normal working hours instead of being crammed into a cutover. ### **Recovery, tested rather than assumed** Backups existed before this project. Evidence that a recovery would work did not, and the recovery copy lived in the same place as the primary. Both changed. [Backup](https://dedicatted.com/services/glossary-of-terms#term-backup) is now cross-region; the RTO and RPO were agreed with the business rather than inherited from a contract; and a full restore was run end to end and timed at four hours against a 15-minute RPO. The previous position was three days, and it was a document rather than a measurement. ### **Running it, and handing compute back** After go-live the landscape moved onto managed support: monitoring at the SAP layer rather than only at the instance layer, and patching windows agreed against both the distribution and production calendars. Thirty days in we re-sized against measured production behaviour instead of the pre-migration estimate, and handed back around 20% of the compute specified at go-live. Non-production stops outside working hours. Between them, those two habits account for most of the gap between a migration that reduces cost and one that simply relocates it. ## Business impact [![Results after go-live: 31% lower infrastructure run-rate, 58% lower non-production cost, zero unplanned outages, DR RTO from three days to four hours, capacity changes in under an hour, 38% shorter batch window](https://media.dedicatted.com/wp-content/uploads/2026/08/05-results-final.png)](https://media.dedicatted.com/wp-content/uploads/2026/08/05-results-final.png)- **31% lower infrastructure run-rate** than the on-premises equivalent, counting hardware amortisation, hosting, power and maintenance, and measured after the thirty-day right-sizing pass. - **58% lower non-production cost**, from stopping development and QA outside working hours. That saving is not available on hardware the group has already bought. - **The refresh decision is gone.** A single large capital commitment covering the whole ERP estate is no longer on the table. - **Capacity changes take under an hour** instead of a procurement cycle. Adding a brand, a category or a country is no longer a twelve-month forecasting exercise. - **Zero business downtime at cutover**, with the application quiesced for six minutes at the quietest point in the week. - **Zero unplanned outages since go-live**, at 99.95% measured availability across every market the group serves. - **Disaster recovery from three days on paper to four hours executed**, cross-region, with a 15-minute RPO, and without buying a second landscape to get it. - **38% shorter nightly batch window**, from provisioning EBS IOPS and throughput independently of capacity. - **No ABAP changes and no retraining.** Same system, same processes, same screens, which is why adoption never became a workstream. ## What comes next With the estate on AWS, the decisions in front of the group are commercial rather than structural: S/4HANA on a timetable that suits the business, analytics across twenty years of sell-in data and 10,000 SKUs, closer integration with the chains it supplies. None of them now begins with a conversation about whether the infrastructure can take it. **Categories:** Case study **Services:** Cloud Migration, Managed Services **Industry:** Retail --- ### [Dedicatted Signs Strategic Collaboration Agreement with AWS to help organizations take Generative AI from Pilot to Production](https://dedicatted.com/insights/dedicatted-signs-strategic-collaboration-agreement-with-aws-to-help-organizations-take-generative-ai-from-pilot-to-production) **Published:** August 18, 2026 **Author:** Denys Yermakov **Content:** **July 23 2026** – Dedicatted, [AWS Premier Tier Services Partner](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/Dedicatted), announced today that it has signed a strategic collaboration agreement (SCA) with Amazon Web Services, Inc. (AWS), with plans to help organizations design, build, and scale generative AI solutions on AWS. Most organizations have run an AI pilot. Far fewer have an AI system running in production, with real users and measurable results. Under the SCA, Dedicatted will expand its agentic AI delivery practice, grow its team of AWS-certified generative AI practitioners, and develop industry-specific solutions built on AWS services such as Amazon Bedrock and Amazon Q. Dedicatted will also work closely with AWS teams to help customers move generative AI projects from proof of concept into production, with the operational support to keep them there. “Our customers don’t need another AI experiment. They need AI that ships, works, and keeps working, This agreement gives them deeper access to AWS technology and expertise through us. We take an organization from idea to a production AI system faster, and with less risk.” #### Denys Yermakov, CEO and Co-Founder of Dedicatted. Dedicatted holds the AWS AI Competency and is an AWS Managed Service Provider (MSP) Partner, reflecting its track record of delivering and operating AI workloads on AWS for customers across financial services, pharmaceutical, retail, and other industries. This collaboration underscores the commitment of Dedicatted to helping organizations across industries unlock business value from generative AI. For more information, visit [www.dedicatted.com](https://dedicatted.com/) ## About us Dedicatted is a Toronto-based AWS Premier Tier Services Partner that helps organizations build, run, and scale on AWS. Dedicatted holds the AWS AI Competency and is an AWS Managed Service Provider (MSP) Partner, delivering cloud infrastructure, DevOps, and generative AI solutions to customers across North America. **Categories:** Blog **Services:** Cloud Data Migration & Modernization, Cloud Modernization --- ### [Dedicatted Achieves Canadian AWS Premier Partner Status: What it Means](https://dedicatted.com/insights/dedicatted-achieves-canadian-aws-premier-partner-status-what-it-means) **Published:** July 9, 2026 **Author:** Dedicatted **Content:** **Dedicatted **recognized as Canada’s leader in AWS cloud solutions**** **with [AWS Premier Tier Services Partner status](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/Dedicatted)**, the highest tier in the Amazon Web Services Partner Network, awarded to a select group of partners who demonstrate deep technical expertise and proven delivery at scale. Before we say anything else about what that means for us, it’s worth explaining what it means, period, because the term gets thrown around more loosely than it should. In short: Dedicatted is now a **Canadian AWS Premier Tier Services Partner** — the highest tier AWS awards its services partners. Below is what that actually takes, and, more to the point, what it means for you. ![AWS Premier Tier Services Partner badge](https://media.dedicatted.com/wp-content/uploads/2026/07/aws-premier-tier-services-partner-badge.png) ## **What an AWS premier partner actually entails** and how we got there To reach Premier, a partner needs a large bench of accredited and certified staff, at least three AWS Competencies with one of them being MSP or DevOps, and 50 launched customer opportunities that carry meaningful recurring revenue behind them. None of that is a one-time bar to clear, either. A partner has to sustain those numbers for six concurrent months, and only then does AWS schedule an Executive Business Review to decide whether the tier is actually granted. The takeaway is simple: this isn’t a form you fill out. It’s a standard you have to hold steady for half a year before AWS will even sit down and evaluate you. The bar is three Competencies. Dedicatted holds **eight** — Migration & Modernization, DevOps, Data & Analytics, AI Services, Resilience, Financial Services, Retail, and Consumer Goods — plus the AWS Managed Service Provider (MSP) designation, [Authorized Commercial Reseller](https://dedicatted.com/our-partners/aws/aws-reseller) status, and the AWS Well-Architected Partner Program. (Full list below.) “With over 50 certified AWS professionals and a strong track record of cloud success, Dedicatted is committed to enabling organizations to leverage the full potential of AWS. Achieving Premier Tier status is a testament to our team’s dedication, technical excellence, and the trust our clients place in us to deliver innovative cloud solutions.” #### Denys Yermakov, CEO at Dedicatted ## The part that matters a lot: you can’t buy your way in Here’s the detail that should reassure anyone skeptical of vendor tiering programs. All three tiers, Select, Advanced, and Premier, cost the same annual program fee. Money doesn’t move a partner up a level. What separates the tiers is documented, audited evidence: certified staff headcount, completed engagements, and customer satisfaction data that AWS reviews directly. The tier can’t be purchased. It can only be earned, and the paper trail behind it is the whole point. ## Premier vs. Advanced: in practical terms If you’ve worked with an AWS Advanced partner before and are wondering what actually changes at Premier, the short version is this: *Advanced* proves a partner is competent. *Premier* proves breadth, scale, and organizational maturity. *An Advanced partner* has cleared a real bar. *A Premier partner* has cleared that same bar and then sustained it across a much larger base of certified people, delivered engagements, and audited outcomes, long enough for AWS to independently confirm it holds up. Strip away the badge and t**he practical point for a customer is this**: you’re working with a team AWS has independently vetted at its highest level, which lowers your delivery risk. That matters most in exactly the situations where the cost of getting it wrong is highest: [regulated industries and financial services](https://dedicatted.com/industries/financial), [complex migrations](https://dedicatted.com/services/cloud-devops/cloud-migration), and [AI builds](https://dedicatted.com/services/artificial-intelligence). Those are the engagements where a partner’s track record isn’t a nice-to-have, it’s the thing standing between a smooth rollout and a very expensive mistake. ## What does AWS Premier status mean for you? Strip away the badge, and Premier status changes five practical things for the organizations we work with: - **Lower delivery risk.** You are working with a team AWS has independently audited at its highest tier — certified bench, delivered engagements, and customer-satisfaction data all reviewed directly by AWS. That matters most where getting it wrong is expensive: [regulated industries and financial services](https://dedicatted.com/industries/financial), [complex migrations](https://dedicatted.com/services/cloud-devops/cloud-migration), and [AI builds](https://dedicatted.com/services/artificial-intelligence). - **AWS co-investment on the table.** As a Premier partner we can bring AWS funding programs — including the Migration Acceleration Program (MAP) and proof-of-concept credits — to help offset the cost of migrations, modernization, and pilots. - **A direct line to AWS.** Priority escalation and joint architecture support from AWS solutions architects on the hard problems, instead of a support-ticket queue. - **Emerging AWS capabilities first.** Our seat in the AWS Agentic AI pilot and our Generative AI Competency put new AWS AI capabilities on your roadmap early — not months after they go mainstream. - **One accountable team, end to end.** Architecture, migration, managed operations, security, and sustained AI adoption under one roof, with no handoffs between vendors. ## What it means for Canadian organizations Premier status is global; the way we apply it is local. For Canadian organizations, that means **data stays in Canada** — we architect on the AWS Canada (Central) region so regulated workloads and customer data meet residency requirements — **compliance-first delivery** aligned to PIPEDA, provincial requirements, and financial-services and public-sector controls, and **a team in your timezone**, with local presence in Toronto alongside our delivery centers. And it is not theoretical. We recently moved a Canadian retailer off a legacy IBM platform onto AWS: **82% automated code conversion**, roughly **810,000 lines of dead code removed** before migration, a **50% improvement in point-of-sale performance**, and a new e-commerce experience **live within 60 days of go-live**. [Read the case study](https://dedicatted.com/insights/migration-of-canadian-retail-operations-platform-from-ibm-to-cloud-architecture-on-aws). For regulated data specifically, our guide on [AI-powered fraud detection for Canadian banks](https://dedicatted.com/insights/ai-powered-fraud-detection-for-canadian-banks-a-complete-guide) shows the same standard applied to financial services. ## Our AWS credentials at a glance **AWS Competencies** - Migration & Modernization Consulting - DevOps Consulting - Data & Analytics Consulting - AI Services - Resilience Services - Financial Services Consulting - Retail Consulting - Consumer Goods Services **Partner programs** - Premier Tier Services - AWS Managed Service Provider (MSP) - Authorized Commercial Reseller - AWS Well-Architected Partner Program Premier Tier is one credential within a broader portfolio, not the whole narrative. Set alongside the Generative AI Competency, the MSP designation, a top 2% global ranking among AWS partners, and a seat in the Agentic AI pilot, a more complete picture takes shape. The distinction isn’t a single badge displayed on a homepage; it is consistent, independently audited evidence spanning AI, managed services, and delivery execution, each credential reinforcing the same conclusion from a different angle. Taken together, this portfolio is what enables Dedicatted to operate as a single, accountable partner across the full engagement lifecycle: from initial architecture and migration through managed operations, security, and sustained AI adoption, with no handoffs between vendors along the way. Ready to put a Premier-tier team on your next AWS project? [Book a free AWS Well-Architected review](https://dedicatted.com/contact) — and we will flag any AWS migration funding, like MAP, you may qualify for. It is the fastest way to see how Dedicatted can support your organization’s cloud and AI transformation. **Categories:** Blog **Services:** Cloud Data Migration & Modernization, Cloud Migration, Cloud Modernization --- ### [Generative AI for the Rest of Us: A Practical Guide for Companies Without a Data Science Team](https://dedicatted.com/insights/generative-ai-for-the-rest-of-us-a-practical-guide-for-companies-without-a-data-science-team) **Published:** July 16, 2026 **Author:** Andriy Kopachevskyy **Content:** If you run a small or mid-size business, you’ve probably noticed a strange gap in most AI advice: it’s written for people who already have what you don’t. Data scientists. An engineering team. A budget line called “AI initiatives.” The case studies feature companies with thousands of employees and dedicated innovation labs, and the takeaway you’re left with is usually some version of “hire the right people and build a custom model.” That advice isn’t wrong, it’s just not for you, and it’s led a lot of smaller businesses to conclude AI is either irrelevant to them or years away. Companies under $1 billion in revenue employ dedicated AI data scientists at a rate of just 14%, compared to 30% at larger firms, according to [McKinsey’s 2025 State of AI survey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai/?utm_source=chatgpt.com). And yet a [2026 AWS/TechAisle study](https://d1.awsstatic.com/onedam/marketing-channels/website/aws/en_US/smb/approved/images/SMB-Global-Study-10-Key-Takeaways-Global-SMB-Leaders.pdf?utm_source=chatgpt.com) of thousands of SMBs found 96% are already actively on their AI journey, with generative AI spending growing faster than traditional IT spending. The businesses succeeding with AI right now mostly are the ones using pre-built tools well, in ways specific to their industry, often with the right partner filling the gap where in-house expertise runs out. That’s what this guide walks through: where the value is, what it looks like industry by industry, and how to get there without needing to build a technical department from scratch. ## The AI opportunity for mid-market (without the hype) This guide is aimed at small businesses, but the same gap shows up, in a slightly different shape, at mid-market companies too, roughly 50 to 1,000 employees. That size is an uncomfortable middle: too large to run on ad hoc tools and tribal knowledge, too small to justify a dedicated AI or data science function. Most content aimed at this segment is either repackaged enterprise advice assuming a seven-figure innovation budget, or SMB advice assuming a five-person team running everything off one shared inbox. Neither fits, and that gap is exactly where the real opportunity sits. Five use cases consistently deliver real value at this size, using pre-built or lightly configured tools rather than anything custom-built: document processing, extracting structured data from contracts, invoices, and forms instead of manual review; process automation, replacing multi-step email chains and approval loops with a structured, AI-assisted workflow; customer support triage, so your team focuses on the inquiries that need judgment rather than the routine ones; proposal and RFP drafting, generating a strong first draft from your own past work instead of a blank page each time; and internal knowledge search, letting any employee ask a question of your company’s own data instead of hunting through folders. None of these require a model built from scratch. The technology gap mid-market companies often worry about closing usually doesn’t exist anymore; the organizational gap, deciding which one to start with and doing it properly, is the real work, and it’s the same one the industry sections below walk through. Two of these are worth calling out specifically, because they tend to be the easiest entry points regardless of industry. **Document processing is your easiest AI quick win.** Every mid-market company has some version of the same problem: contracts, invoices, applications, or forms someone currently has to read, extract information from, and re-enter elsewhere by hand. It’s repetitive, low-risk to automate since the source documents already exist, and the time savings are immediate and easy to measure. Tools like Document AI or AWS’s enhanced document-understanding solutions pull structured data out of PDFs and scanned forms automatically, turning a manual reading task into a searchable, structured dataset. A U.S. infrastructure and construction firm faced exactly this problem at scale, engineering drawings, regulatory filings, and project reports scattered across legacy systems and departmental silos, slowing teams down and causing duplicated work. Dedicatted implemented Amazon Q Business to turn that fragmented documentation into a single knowledge base employees could query in plain language. **[See the full case study for the details..](https://dedicatted.com/insights/intelligent-ai-assistant-for-centralized-corporate-knowledge-with-amazon-q)** **AI-powered proposals help you win more bids, faster.** For any business that competes for contracts, consulting engagements, construction bids, enterprise sales, the proposal process is often the biggest bottleneck between spotting an opportunity and winning it, and teams frequently start close to from scratch each time. A drafting tool connected to your own past proposals, case studies, and pricing, rather than a generic assistant with no knowledge of your business, changes that. Tools like Amazon Q Business can connect directly to your existing document repositories so that a new proposal starts from a strong draft grounded in your own prior work. For any business responding to multiple bids a month, faster turnaround means more opportunities pursued with the same team, and your best people spend their time refining rather than assembling boilerplate. What you need to get started is less than most companies assume: one clearly defined process worth improving, not a company-wide strategy; the data behind that process reasonably centralized, not perfect; one person or small team willing to own the pilot and use it consistently; and a specific metric to check from week one. What you don’t need, at least to start, is a data science hire or a custom-built model. Those may matter later, once a use case is proven and you want to go further, but they’re rarely the right place to begin, at mid-market size or smaller. ## Where the value shows up, function by function McKinsey’s economic analysis of generative AI, which estimates the technology could add $2.6 to $4.4 trillion in value annually across the economy, found roughly 75% of that value concentrated in customer operations, marketing and sales, software engineering, and R&D. Unless you’re a software company, that means customer service, marketing, and internal admin work are your highest-leverage starting points, regardless of your industry. What changes by industry is what those three categories actually look like day to day, so that’s where this guide gets specific. ### Professional and agency services (consulting, accounting, legal, marketing agencies) This is, by the data, the single largest group of businesses already using AI without a technical team. Latest analysis of AI usage found professional and agency services represent the largest share of active business use, 22%, ahead of every other category. That tracks with what these businesses do all day: draft proposals, write client communications, research regulations, produce reports. A consulting firm can use a tool like Amazon Q Business or Gemini in Workspace to draft first-pass client deliverables and meeting summaries, cutting the time a partner spends writing rather than advising. An accounting or legal practice can use Document AI or AWS’s enhanced document-understanding tools to pull structured data out of contracts, invoices, and filings automatically, work that used to mean a junior associate reading through hundreds of pages by hand. A marketing agency is often already the furthest along here, since content generation is close to its core product; the shift tends to be less “should we use AI” and more “how do we productize what we’re already doing informally into a repeatable service offering.” The risk we see most often in this vertical is confidentiality. Client contracts, financials, and case files are sensitive, so before rolling out a drafting or document tool firm-wide, confirm it runs inside your own AWS environment rather than a public consumer tool, so client data never leaves your control. That one architectural decision tends to matter more to partners’ comfort level than the AI feature set itself. #### Dedicatted`s take ### Retail and E-commerce Retail and e-commerce make up the second-largest share of active AI use, at 21%, and McKinsey separately estimates retail and CPG use cases at $200-340 billion in annual value. The clearest wins cluster around product content, customer support, and personalization. Writing product descriptions at scale is a natural fit for content tools like Bedrock-powered generation in Workspace, and it compounds fast if you carry hundreds or thousands of SKUs. Chatbots or Conversational Agents suite can handle order status, returns, and sizing questions around the clock, the exact kind of repetitive volume that ties up a small support team during sales spikes. Image tools like Amazon Rekognition matter more here too, automating product photo tagging and content moderation on customer reviews. Personalized email and on-site recommendations usually come second, once customer data is clean enough to trust. Start with product content, not the chatbot. It’s the lower-risk win, and it forces you to get your product data structured, which is exactly the prerequisite you’ll need before a support chatbot or personalization engine can give reliable answers. Businesses that reverse this order usually end up relaunching their chatbot a few months in, once they realize their product catalog wasn’t ready. #### Dedicatted`s take ### Software, fintech, and other technical businesses Even technical companies benefit here, often in the development process itself. Oone of our clients, a payments technology company, used AWS`s API for AI-assisted code review and found 54-57% of the AI’s feedback led to meaningful code improvements once the team refined their prompting, a reminder that even in a technical use case, how you prompt matters as much as the tool. Another one of our clients, an education-technology company, used Amazon Bedrock to cut information-processing time by 40% while increasing content output and reliability, letting a small team compete with far larger, better-funded platforms on speed and quality. Financial client’s result is a good reminder that “we tried AI code review and it wasn’t great” is often a prompting problem, not a tooling problem. Before concluding a technical AI tool underperforms, it’s worth spending a week refining how your team prompts it and reviewing the output together, the jump from mediocre to genuinely useful results tends to happen there, not from switching tools. #### Dedicatted’s take ## Matching the tool to your technical capacity The right starting tool depends less on your sector and more on how much technical support you actually have. If you have no developer at all, stay in the no-code tier: PartyRock, Amazon Q Business, Gemini in Workspace, Document AI, or AutoML within Vertex AI, all built for people who will never write a line of code. If you have someone reasonably comfortable with software, tools like Amazon Connect, QuickSight, Dialogflow, or Vertex AI Agent Builder open up, needing configuration but not custom development. If your use case calls for something more tailored, fine-tuning Amazon Bedrock, building on SageMaker, or connecting multiple data sources through a proper AWS architecture, that’s the point at which most small businesses without in-house engineers bring in a partner rather than trying to staff up internally. This is the gap we exist to close. **[As an AWS Premier Tier Partner](https://dedicatted.com/our-partners/aws)**, we work with businesses that recognize the opportunity in the sections above but don’t have, and don’t want to hire, a full-time AI or data engineering team. In practice that means a short assessment of your current data and systems, a recommendation on which AWS tools actually fit your use case and budget rather than the most expensive option available, and hands-on implementation and support so the rollout doesn’t stall the way so many well-intentioned pilots do. ## Proof it works at scale: Kwiksave Logistics success story Kwiksave, one of Canada’s largest courier and fulfillment operators, sits on a steady stream of commercial signals inside its delivery data, but manually qualifying each lead, verifying the recipient, validating the company, finding the right contact, took up to 45 minutes per lead. Early attempts at fully autonomous AI agents made things worse, sometimes fabricating data unfit for business decisions, with unpredictable costs on top. Dedicatted built a hybrid system instead: deterministic validation first, GenAI applied only where it adds real value, and a human review step before anything reached the CRM. The 45-minute research process became a short, structured review, freeing the sales team to focus on deciding rather than digging, with AI costs kept proportional to the value delivered. It’s the same principle this guide has repeated throughout: the win isn’t more AI, it’s AI applied narrowly, after the data-hygiene work, with a human still in the loop.ment velocity. **[Read more to learn all the nuances]()** ## What can you do this quarter for your AI strategy **Weeks 1: Choose your one function.** Don’t try to fix everything at once, that’s the single most common reason AI pilots at small businesses stall before they start. Look at where your specific industry’s version of the problem costs you the most time right now: quoting and invoicing for a contractor, product descriptions and customer chat for a retailer, drafting client deliverables and meeting summaries for a consulting firm, reservation and review handling for a restaurant, appointment reminders for a salon or clinic. Write the function down explicitly, “customer service email replies” rather than “customer service,” and resist the urge to pick two. The businesses that succeed with this framework almost always started narrower than felt comfortable. **Week 2: Audit whether the data behind that function is actually trustworthy.** This is the step most businesses skip, and it’s the reason so many pilots technically launch but never deliver real value. Ask concretely: is the information this function depends on in one place, or scattered across spreadsheets, inboxes, and paper? Is it current, or is half of it six months out of date? If you picked product descriptions, that means checking whether your product catalog has consistent, accurate specs. If you picked customer service, it means checking whether your FAQ, return policy, and past ticket history are documented anywhere a tool could actually read them. AWS’s own research found 47% of SMBs cite poor data quality as their top barrier to AI adoption, and 84% agree an integrated tech stack is critical while only 29% have actually built one, that gap is exactly what this week is meant to close. If the data isn’t ready, that becomes your real starting project this quarter, not the AI tool itself, and it’s worth being honest about that rather than launching on top of a shaky foundation. **Week 3: Choose a tool matched to your actual technical capacity, not the most impressive option available.** If nobody on your team writes code, stay in the no-code tier, tools like PartyRock, Amazon Q Business, Gemini in Workspace, or Document AI are built assuming exactly that. If you have someone comfortable configuring software without being a formal engineer, a slightly wider set opens up, Amazon Connect, QuickSight, or Dialogflow, for example. If your use case genuinely calls for something custom, and you don’t have in-house engineers to build it, this is the point to bring in a partner rather than trying to staff up. A quick assessment from a partner like Dedicatted can tell you within a short conversation whether your use case needs a lightweight, off-the-shelf tool or genuinely warrants custom development, which saves you from either overbuying capability you won’t use or underbuilding something that can’t actually do the job. **Weeks 4 through 12: Run the pilot with one team, and measure something concrete from day one.** Don’t wait until the end of the quarter to decide what success looks like, decide before you start. If you picked quoting, track hours spent per quote or turnaround time from request to sent quote. If you picked customer service, track response time or the percentage of inquiries the tool resolves without a human. If you picked content, track how much time your marketing person gets back each week. Check in weekly, not just at the end, since small adjustments to how the tool is used, better prompts, tighter guardrails, a clearer handoff to a human when needed, tend to matter more than switching tools entirely. Give the team using it real ownership of the pilot rather than treating it as something imposed on them; 64% of CEOs report that success with generative AI depends more on whether people actually adopt the tool than on how advanced it is, and that adoption happens at the team level or not at all. **After the quarter: expand only once the first use case is genuinely routine, not just technically working.** There’s a real difference between a tool that’s live and a tool that’s actually part of how your team works day to day, and it’s worth being honest with yourself about which one you have before moving on. If the metric you picked in week 4 has clearly moved and the team is using the tool without being reminded to, you’ve earned the right to look at a second use case from your industry. If it hasn’t, spend another few weeks understanding why before adding anything new, a second half-adopted tool won’t fix a first one that never took hold. This is a deliberately slower path than “roll out AI everywhere this quarter,” and that’s the point: it’s also the path that actually compounds, one working use case at a time, instead of several abandoned ones. ## Conclusion Everything in this guide points to the same conclusion: the technology is ready, and it has been for a while. What separates the small businesses getting real value from generative AI from the ones still circling it isn’t budget, headcount, or a data science team they don’t have. It’s picking one function, getting the data behind it in order, choosing a tool that actually fits, and giving one team the time and ownership to make it stick before moving on to the next thing. That’s a sequence any business can run, whatever industry you’re in. Where it usually helps to have a partner is exactly at the points this guide has flagged: knowing whether your data is actually ready, knowing which tool tier fits your situation instead of the one a vendor is pushing, and building the thing correctly the first time so it doesn’t stall out in month two. That’s the work Dedicatted does daily as an AWS Premier Tier Partner, from a short data and systems assessment through to hands-on implementation and support, on exactly the kind of use case outlined in your industry section above. If you’re not sure which function to start with, or whether your data is ready, or which AWS tool actually fits your budget and use case, that’s a conversation worth having before you spend anything. And if any of the terminology here is unfamiliar, our [glossary of cloud and AI terms](https://dedicatted.com/services/glossary-of-terms) explains it in plain language. [**Reach out to Dedicatted, and let’s figure out your quarter-one use case together.**](https://dedicatted.com/contact) **Categories:** Blog **Services:** Data Architecture, Generative AI **Industry:** Financial Services, Healthcare, High Tech Software --- ### [The agent that can't act alone: Agentic AI inside a regulated Financial Institution](https://dedicatted.com/insights/the-agent-that-cant-act-alone-agentic-ai-inside-a-regulated-financial-institution) **Published:** August 10, 2026 **Author:** Dedicatted **Content:** If you own, run, or sit on the board of a bank, insurer, pension fund, or payments company, you’ve probably already had the pitch: agentic AI can cut operational costs, compress cycle times from days to minutes, and free your best people from repetitive work so they can focus on judgment calls only humans can make. All of that is true. It’s also incomplete, because the same capability that makes agentic AI powerful: the ability to plan, act, and adapt with little or no human involvement is exactly what makes it a new category of risk that your existing controls were never built to catch. Agentic AI is already inside production systems at the largest institutions in the world. BNY has agents validating payment instructions and writing code. JPMorgan Chase built an agentic legal-workflow system, LAW, that processes complex custody and fund-services contracts with 92.9% accuracy across various query types. Wells Fargo is building custom agents on Google’s Agentspace. According to [recent industry research](https://www.bankofcanada.ca/2026/05/financial-system-survey-highlights-2026/), mortgage processing – a workflow that can run 45 distinct compliance, document-review, and underwriting steps behind a single customer meeting is now considered one of the most ripe targets for agentic reinvention in the entire sector. One in three financial institutions is setting aside dedicated budget for agentic AI right now, and new roles: AI risk officer, behavior auditor, agent steward are appearing on org charts that didn’t exist two years ago. This piece is about what governance infrastructure actually looks like, what it’s worth in hard numbers, and specific ideas your team can act on this quarter , including where a partner like Dedicatted fits into making that infrastructure real rather than theoretical. [![The agent that can’t act alone: Agentic AI inside a regulated Financial Institution](https://dedicatted.com/wp-content/uploads/2026/08/Screenshot-2026-08-10-at-13.23.25-1024x769.png)](https://dedicatted.com/wp-content/uploads/2026/08/Screenshot-2026-08-10-at-13.23.25-1024x769.png)## ****Three Agentic AI failure patterns you will likely recognize**** Governance failures are structural and repeat in three specific, nameable patterns. None of them require malice or incompetence. All of them are preventable with the right infrastructure, and all of them get more expensive to fix the longer they run undetected. **Scope creep by a thousand reasonable decisions.** A transaction or payments agent starts as decision support: it flags anomalies, a human acts. Confirmation requirements get relaxed because the system proved reliable. Autonomy thresholds get raised because the business case for speed is compelling. New APIs get connected because a new use case was approved. Each change is individually defensible. Eighteen months later, the system holds the action authority of a high-risk deployment while still being governed like the cautious pilot it started as and no single person or committee ever made the decision to reclassify it. The fix isn’t slowing down approvals; it’s building automatic re-classification triggers so that a material change in tool access, action authority, or data reach forces a fresh risk review by design, not by memory. **Accountability that was never assigned.** In credit and risk-decisioning systems, product owns the model, engineering owns the infrastructure, compliance owns the policy and when a decision is challenged by a customer or regulator, every function can credibly point elsewhere. This is exactly why we recommend naming an agent owner, validator, and steward for every production system, with ownership that survives the original deployment team. The cost of skipping this step isn’t theoretical: 56% of financial-sector respondents in the Bank of Canada survey cite talent-related constraints as a barrier to AI expansion, and unclear ownership is one of the fastest ways to burn through the AI talent you already have, because nobody wants to inherit accountability for a system they didn’t design and can’t fully explain. **The vendor update nobody flagged.** Once an agent routes through external APIs or counterparty systems, your institution’s risk perimeter extends into infrastructure you don’t control and often can’t observe. Consider [the CrowdStrike outage of July 2024](https://www.reuters.com/technology/fortune-500-firms-see-54-bln-crowdstrike-losses-says-insurer-parametrix-2024-07-24/): a single third-party technology failure cost the Fortune 500, excluding Microsoft, an estimated $5.4 billion. That was a conventional software update gone wrong at one vendor, rippling through institutions that depended on it. This is the cautionary benchmark every procurement and vendor-risk team should have pinned above their desk before signing the next agentic AI contract. [![The agent that can’t act alone: Agentic AI inside a regulated Financial Institution](https://dedicatted.com/wp-content/uploads/2026/08/Screenshot-2026-08-10-at-13.38.09-1024x501.png)](https://dedicatted.com/wp-content/uploads/2026/08/Screenshot-2026-08-10-at-13.38.09-1024x501.png)## **What good Agentic AI governance actually costs** Here’s the reframe worth bringing into your next budget conversation: strong agentic AI governance is not a tax on innovation. It’s the infrastructure that lets you deploy more autonomy, in more workflows, faster than competitors governing by instinct. An institution with a live agent registry, automatic re-classification triggers, and clean audit trails can approve the next expansion of an agent’s authority in days, because the review process is fast and the paper trail already exists. An institution without that infrastructure either says no to reasonable expansions out of caution, leaving real efficiency gains on the table or says yes without adequate review and quietly accumulates the exact risk described above. Neither path beats a competitor who’s built the infrastructure to say yes safely and quickly. Six commitments show up consistently across the regulatory guidance, the engineering frameworks, and the institutions that have avoided the failure patterns above. Each one is an idea your team can turn into a work item this quarter. 1. **Give every agent a corporate identity.** A distinct digital identity, scoped permissions, and full action logging – the same standard you’d apply to a new employee with system access. This is the control that makes every other control possible, because you cannot govern something you cannot uniquely identify and trace. 2. **Build narrow, modular agents instead of one broad one.** Specialized sub-agents with tightly scoped permissions : the pattern already running in multi-agent AML and KYC workflows at several banks are inherently easier to explain, audit, and contain than a single generalist agent with sweeping access. 3. **Put circuit breakers inside the workflow, not just approval gates in front of it.** For high-stakes use cases: large or unusual payments, credit decisions above a threshold, trading build explicit kill switches, dynamic limits, and mandatory human checkpoints triggered by the specific action, not only by the initial deployment approval. 4. **Make risk classification self-triggering.** Canada’s OSFI has already written this into its updated Model Risk Management guideline (E-23), requiring risk ratings to be reassessed whenever a model’s use, data, or infrastructure changes materially. 5. **Name an owner who survives the deployment.** Agent owner, validator, and steward roles, backed by cross-functional governance spanning risk, compliance, and cybersecurity. 6. **Extend your existing model risk function, don’t build a parallel one.** Add level of autonomy as a formal rating factor to the model inventory and review cadence you already run. This is measurably cheaper than standing up a separate “AI governance” silo, and it keeps Agentic AI inside the accountability structure your board already understands. The AGILE framework: Awareness, Guardrails, Innovation, Learning, Ecosystem Resiliency developed through a forum of more than 170 participants spanning banks, insurers, regulators, and consumer advocates, distills this into one line worth repeating in your next strategy session: *“the biggest risk is not doing enough.”* Inertia is not the safe choice. Ungoverned speed is not the safe choice either. The safe choice is speed with infrastructure underneath it. ## **A practical guide: what to do, why it matters, and how it plays out** Knowing the principles is one thing. Turning them into a sequence of actions your institution can actually execute with the right people doing the right things in the right order is another. Below is a phased roadmap, followed by what changes for each stakeholder group, followed by worked examples of how this looks in a real workflow. ### **Phase 1 : See what you actually have** Commission a full inventory of every AI system currently in production, pilot, or shadow use across the institution, including tools business units may have adopted without a formal review (a phenomenon sometimes called “shadow AI,” the agentic-era version of shadow IT). For each system, capture: what it’s authorized to do, what it can actually reach (data, APIs, systems), who owns it, and when it was last reviewed. You cannot govern, price, or defend what you cannot list. This is also almost always the moment institutions discover the gap between “what we approved” and “what’s running” described earlier in this piece and it’s far cheaper to find that gap yourself than to have a regulator or auditor find it for you. OSFI’s E-23 guideline effectively makes this mandatory for federally regulated institutions: an “accurate, evergreen” model inventory subject to robust controls is now a stated expectation #### Dedicatted Top-Tier AI Specialisy Expect the inventory to surface more systems than leadership expects:often two to three times the number the CTO’s office originally estimated, once business-unit pilots and vendor-embedded AI features are counted. That’s normal, not alarming, provided it triggers phase 2 rather than getting filed away. CRO or Chief [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) Officer sponsors it; CTO/Head of Engineering executes it; a small cross-functional team (risk, compliance, engineering, one business-unit representative) does the actual cataloguing. This is exactly the scoped, bounded engagement a partner like **[Dedicatted](https://dedicatted.com/services/artificial-intelligence/agentic-ai)** can accelerate, bringing AWS-certified engineering capacity to do the technical discovery quickly, without pulling your internal team off their current roadmap for a quarter. ### **Phase 2 : Assign ownership and build the re-classification trigger** For every system in the inventory, assign a named agent owner, validator, and steward, not a committee. Build (or extend your existing model risk platform to include) automatic triggers that force a fresh risk review whenever a system’s tool access, data reach, or action authority materially changes. Score every system on autonomy level, using a framework like the industry’s five-level scale: Tool, Assistant, Operator, Actor, Agent ,so risk and business leadership share a common vocabulary for “how much is this system allowed to do without a human.” This is the single control that would have prevented every scope-creep failure described earlier in this piece. Institutions that build the trigger once spend far less on emergency remediation later: the RAI Institute’s review found that in every scope-creep case examined, no one had ever made the decision to expand the system’s authority; it happened by accumulation. A trigger converts accumulation into a decision, every time. Expect friction here, because this phase asks business units to slow down slightly on the systems that are working well, in exchange for being able to move faster and with more confidence on the next one. Frame it that way internally: it’s the reason you’ll be able to approve the next agent expansion in days instead of months once the infrastructure exists. #### Dedicatted Top-Tier AI Specialist Joint ownership across risk, compliance, and engineering, with board-level reporting on coverage (what % of the inventory now has a named owner and an active trigger policy) as a standing metric: the same way you’d report on any other risk-remediation program. ### **Phase 3 (ongoing): Pilot, prove, then scale autonomy deliberately** Choose one narrow, high-value, lower-risk workflow, build it with guardrails and monitoring designed in from day one, run it in a constrained pilot, and use the results to justify the next expansion, rather than approving a broad rollout up front. Report results against both value delivered (cycle time, cost per case, error rate) and governance health (time-to-detect an anomaly, time-to-human-escalation, audit-trail completeness). This is how every credible deployment cited throughout this piece actually scaled , not with a single “go live” moment, but with a proof point that earned the next increment of trust and authority. It’s also how you build the internal muscle memory and the evidence base your board, and eventually your regulator, will want to see. **How it affects your organization.** Done well, this phase compounds. The governance infrastructure built for the first use case: agent identity, logging, re-classification triggers is largely reusable for the second, third, and tenth. The marginal cost of governing each additional agent drops sharply once the foundation exists, which is exactly why institutions that invest early tend to pull ahead on both speed and safety simultaneously. ## **Worked examples: how this looks in practice** from Dedicatted **Anti-money-laundering investigation.** A three-agent workflow : one agent reviews the triggering alert, one reviews current and historical transaction data, one drafts findings and a recommendation, runs end-to-end without human intervention until a human validates the final report before filing. Applying the framework above: each agent gets a distinct identity and scoped access (the alert-review agent doesn’t need write access to case files; the drafting agent doesn’t need direct database access at all), the system is scored at “Operator” level rather than “Agent” level because filing still requires human sign-off, and any request to expand it toward autonomous filing triggers a fresh review rather than a quiet policy change. The measurable payoff mirrors what the sector is already reporting: faster investigation cycle times and the ability to surface illicit patterns a purely manual review might miss, without moving the actual regulatory filing decision out of human hands. **Treasury cash-sweep optimization.** An existing RPA process that moves routine cash sweeps gets elevated by an AI agent that also makes pricing and hedging recommendations: a concrete example already cited by industry research as a realistic evolution of legacy automation. Framework applied: the agent’s action authority is explicitly capped (it can recommend and execute within a pre-set limit; anything above the limit routes to a human), and that cap is exactly the kind of dynamic risk limit and circuit breaker described earlier. The business case is direct, more strategic liquidity management without adding headcount — and the governance case is equally direct: a single, clearly bounded action authority that’s easy to audit. **Credit decisioning.** This is the workflow where the accountability gap is most dangerous, precisely because product, engineering, and compliance all have a legitimate claim to partial ownership. Applying the framework: a named agent owner sits above all three functions specifically for the credit-decisioning agent, explainability requirements are set before deployment (not retrofitted after a declined applicant complains), and the model’s risk rating explicitly accounts for its level of autonomy: consistent with OSFI’s E-23 requirement that qualitative factors like “model complexity or level of autonomy” feed directly into the risk rating. The payoff: faster, more consistent underwriting decisions that the institution can actually explain and defend, which is the difference between a fair-lending inquiry that resolves in an afternoon and one that becomes a multi-month investigation. **Where Dedicatted fits across all three examples.** In each case above, the governance requirements (agent identity, scoped access, action-authority caps, audit trails) are not policy documents, they’re engineering decisions made at build time. That’s the layer where Dedicatted’s multi-agent AWS architectures and built-in guardrails and monitoring do the actual work, so that when your compliance team asks “can we prove this agent never had access to X,” the honest answer is yes, because the system was built that way from the start rather than patched to demonstrate it after the fact. Contact us to talk through where your environment stands today. ## **Ideas for your AI strategy you can act on this quarter** For an executive team looking for where to start, five moves consistently separate the institutions ahead of this curve from the ones playing catch-up: - **Commission a full agent inventory now**, before your next audit cycle forces the question. If you can’t currently list every agentic system in production with its current autonomy level and last review date, that’s the single highest-leverage gap to close first. - **Set a re-classification trigger policy** so that any material change to an agent’s tool access, data reach, or action authority automatically forces a review , mirroring what OSFI has already written into E-23 for federally regulated institutions. - **Pilot one narrow, high-value, lower-risk use case with guardrails built in from day one**: KYC maintenance, AML investigation support, or treasury cash-sweep optimization are the workflows already proven out at BNY, Intesa Sanpaolo, and others and use it to prove the governance model works before scaling autonomy further. - **Bring in engineering capability that’s built for regulated environments from the start.** Whether that’s Dedicatted or another partner with equivalent compliance-native experience, the point is the same: don’t ask your internal team to become agentic AI architecture experts and AI governance experts simultaneously under deadline pressure. Buy the expertise you don’t have time to build from scratch, and put your internal effort into ownership, oversight, and the decisions only your institution can make. ## **Where a partner like Dedicatted fits into that infrastructure** Everything above is a governance and architecture problem before it’s a compliance problem, which means the fastest, cheapest way to close the gap is usually not to build a parallel AI governance function from scratch internally, but to bring in engineering capability that already knows how to build agentic systems with the controls baked in from day one, on infrastructure your risk and compliance teams can actually audit. This is precisely the space Dedicatted operates in. [**As a Canadian AWS Premier Tier Services Partner**](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/Dedicatted) and top 2% global AWS partner with GenAI Competency and MSP designationwith more than 100 AWS-certified engineers and 20-plus AWS competencies across cloud, security, and DevOps, Dedicatted builds custom, multi-agent AI systems specifically for regulated, highly compliant environments: banking, capital markets, insurance, and payments among them. A few ways that capability maps directly onto the six commitments above: - **Guardrails and monitoring built in at the architecture stage, not bolted on afterward.** Dedicatted’s agentic AI engagements are explicitly designed with guardrails, monitoring mechanisms, and system controls so that multi-agent systems operate safely and consistently in production, which is the technical backbone that makes agent identity, action logging, and audit trails achievable in practice rather than aspirational in a policy document. - **Multi-agent architectures on AWS that map naturally to modular, scoped-permission design.** Rather than one broad system, Dedicatted’s approach coordinates specialized agents across tool-enabled workflows the same narrow, auditable structure that regulators and risk frameworks are converging on as best practice, built on infrastructure your team already trusts for other core systems. - **Deep familiarity with your existing compliance surface.** Dedicatted’s financial-sector work already integrates AI assistants into loan origination, fraud detection, and real-time reporting inside highly regulated payment, trading, and insurance environments, meaning the conversation with your compliance and risk teams starts from a system built for your regulatory context, not adapted from a generic template after the fact. - **DevOps and cloud-native delivery that lets you move at the speed your competitors are moving, without trading away control.** Cloud-native pipelines, automated security gates, and continuous testing mean new agentic capabilities can ship without forcing a choice between speed and governancen – the two are engineered together. The practical starting point for most institutions isn’t a full agentic rollout. It’s an audit: map every AI system currently in production or pilot, score its level of autonomy and action authority, identify where ownership is unclear, and build the registry and re-classification triggers described above , then use that foundation to decide, deliberately, where the next expansion of agent authority should happen and where it shouldn’t yet. That’s a scoped, bounded engagement, not a multi-year transformation program, and it’s exactly the kind of work that turns “we think we’re compliant” into “we can produce the report that proves it” the day a regulator, auditor, or board member asks. [![Banner with Dedicatted logo on a dark, abstract network background. Large headline reads: “Helping you harness the power of Agentic AI to unlock your business innovation,” with “unlock your business innovation” highlighted in teal. Below the text is a row of AWS Partner badges indicating multiple competencies and certifications.](https://dedicatted.com/wp-content/uploads/2026/05/Group-2751-1024x307.png)](https://dedicatted.com/wp-content/uploads/2026/05/Group-2751-1024x307.png) **Categories:** Blog **Services:** Agentic AI **Industry:** Financial Services, High Tech Software --- ### [Road map of DevOps Transformation](https://dedicatted.com/insights/road-map-of-devops-transformation) **Published:** April 13, 2023 **Author:** Dedicatted **Content:** A DevOps roadmap is a comprehensive software development strategy that encompasses the entire DevOps lifecycle. In essence, it outlines the strategies and techniques necessary to achieve an organization’s DevOps objectives and targets. A DevOps transformation plan is a valuable instrument because it serves as a thorough guide to organizing the transformation process. By utilizing a transformation roadmap, the entire DevOps team can stay aligned in managing priorities and timelines. Since 2016, Dedicatted has collaborated with numerous businesses to enhance their DevOps procedures and systems. We provide a variety of outsourcing options that can be tailored to suit your specific requirements. Through our DevOps Consulting Package, we can work with you to develop a personalized DevOps transformation roadmap for your company. [Revolutionize your business through DevOps with our experts ](https://dedicatted.com/contact) ## Steps for DevOps Transformation There are several essential steps that should be followed to create a roadmap for a successful DevOps transformation. If you choose to work with Dedicatted and outsource the creation of your transformation plan, our team will manage these steps with your input and provide guidance to help you develop the most suitable DevOps solution for your business ### Specify your requirements Before designing a roadmap for your DevOps adoption or transformation, it’s crucial to assess your current operations and determine your objectives and requirements. Gaining a comprehensive understanding of your existing workflows and their impact on your business is necessary to establish realistic and attainable goals. As many organizations may not have a clear understanding of their needs, our approach starts with a consultation about your business objectives and evaluating your current systems. ### Develop a Plan When planning your DevOps transformation, it’s crucial to align your goals with your business objectives. You must also consider the factors that may affect your entire system and how they’ll influence your desired outcomes. When setting goals for your plan, it’s recommended to focus on short- and medium-term business objectives. These goals are more attainable and can serve as stepping stones for implementing the transformation roadmap. You can utilize these business goals to plan the different phases of progressive improvements. ### Build a Team To execute your DevOps transformation roadmap, you’ll need a skilled and experienced team. However, assembling a team can be a daunting task, especially if you’re new to DevOps and aren’t sure which professionals you require. Assembling a team on your own can be a challenging task, particularly if you’re new to DevOps and unsure about the specific expertise you need. By outsourcing your DevOps transformation roadmap to a reputable and dependable company like Dedicatted, you can avoid the complexities of building a DevOps team from scratch, and rest assured that you’ll be working with knowledgeable professionals who can handle the job. ### Select the Right Tools Selecting the appropriate tools for your DevOps transformation is a vital aspect of the process. It’s crucial to choose tools that align with your requirements and are compatible with your current systems and workflows. Additionally, it’s important to assess your existing tooling to determine if they’re the best fit for the job, instead of solely relying on familiarity. Failure to do so could result in technical issues, reduced productivity, and scalability, or suboptimal performance that doesn’t meet your needs ### Rank Short-Term Goals Giving priority to short-term goals can facilitate the speedy implementation of important projects. By focusing on short-term goals, you can better manage multiple tasks simultaneously and increase flexibility in executing and implementing your overall plan. Adopting this approach can be an effective way to initiate your transformation and see tangible results sooner. ### Mechanize your Infrastructure Continuous Integration (CI) and Continuous [Deployment](https://dedicatted.com/services/glossary-of-terms#term-deployment) (CD) can ensure that your business maintains its operations while meeting the evolving market needs. By leveraging these automation tools, you can conserve the quality of your services and products while addressing issues related to deployment. Additionally, integrating infrastructure automation with CI/CD approaches can provide you with a more robust solution to address potential problems in your deployment pipeline. [ ![Enabling Security Compliance and DevOps Agility in a Regulated Healthcare Environment](https://media.dedicatted.com/wp-content/uploads/2025/04/Zivian-Cover-2-300x157.webp) Case studyDedicatted Petlichenko 5 min to read Also read ### Enabling Security Compliance and DevOps Agility in a Regulated Healthcare Environment Healthcare Cloud and Infrastructure Security Cloud Migration DevOps Consulting Security Compliance ](https://dedicatted.com/insights/enabling-security-compliance-and-devops-agility-in-a-regulated-healthcare-environment)## Popular Problems & How We Bypass Them Creation and implementation of a plan for a DevOps model can be a challenging task, as there are many common problems that may arise. However, with our years of experience, we have fine-tuned our process to avoid these common pitfalls. ### Failure of communication Effective communication is essential when creating a roadmap, but it’s not uncommon for key stakeholders to lack proper coordination. This can lead to confusion and misunderstandings among team members, resulting in misaligned priorities and missed action items. Dedicatted endeavors to mitigate these concerns by developing a comprehensive strategy that facilitates ongoing communication throughout the project, enabling us to promptly address any issues or modifications as they arise. A crucial element of our methodology involves discussing business objectives and requirements beforehand, to circumvent any potential misunderstandings preemptively. Prior to initiating the development of your roadmap, we establish a collaborative Slack channel to enable seamless coordination between our engineers and your team in real time. Utilizing a Slack channel, you can stay abreast of the latest updates on your project lets you stay abreast of your project’s latest updates as they unfold. Moreover, this facilitates direct communication with our engineers, enabling you to promptly convey any changes or adjustments to your requirements or goals as they arise. ### Deficit of clearness in selecting aims One frequent problem observed in DevOps teams is that members may hold varying views on the relative importance of project objectives. This lack of consensus can lead to misunderstandings and hinder the team’s ability to execute the roadmap seamlessly. In terms of defining goals for your projects, we entrust that responsibility entirely to you. We believe that you know what’s best for your business and should set goals and priorities accordingly. However, we’re here to assist you in comprehending the ramifications of various choices and furnish you with the specialized knowledge necessary to make informed decisions that align with your goals. ## Initiate Your DevOps Transformation Journey with a Roadmap Developing a roadmap is a wise decision for any business as it provides a clear delineation of your objectives and strategies with actionable measures to accomplish them. It’s also an excellent approach to ensuring that your entire team is well-informed of your present goals and your progress toward achieving them. Opting to outsource the formulation and implementation of your DevOps transformation roadmap can be an effective approach to saving both time and money. By doing so, you circumvent the hassle of individually hiring an entire team and instead collaborate with a reliable team of DevOps engineers with prior experience working together to design and execute roadmaps. ## Conclusion [Partnering with Dedicatted](https://dedicatted.com/contact) affords you the expertise and individualized attention of working with a full-time engineer, without the headache of recruiting an entire team. With a decade of experience, we are well-equipped to undertake DevOps projects of any magnitude or complexity, utilizing any technology stack or hosting preference. Get in touch with us today to commence your DevOps transformation roadmap journey. **Categories:** Blog **Services:** DevOps Consulting --- ### [Managed services vs professional services: Differences and applications](https://dedicatted.com/insights/managed-services-vs-professional-services-differences-and-applications) **Published:** March 13, 2025 **Author:** Dedicatted **Content:** Every business reaches a point where strategic IT decisions shape its future. Do you engage a professional services firm for specialized expertise and project execution, or invest in a managed services provider (MSP) for continuous optimization and long-term stability? The answer isn’t always straightforward. Both services offer unique benefits that can help you – but which one is right for your organization? By the end of this article, you should have a better understanding of what each service entails, along with how businesses might decide between the two. ## **Managed Services vs. Professional Services: A Strategic Comparison** ### **Managed Services: Ongoing Operational Excellence** Managed services involve continuous, proactive oversight of a company’s IT infrastructure by an external provider. This approach ensures systems remain secure, optimized, and up-to-date while preventing potential issues before they disrupt operations. Businesses benefit from round-the-clock monitoring, maintenance, and support. Common managed services include: 1. **Infrastructure management:** MSPs oversee servers, networks, and storage, ensuring seamless performance and quick issue resolution. 2. **Network security services:** Tasks like firewall management, intrusion detection, and vulnerability assessments protect businesses from cyber threats. 3. **Help desk support:** Employees receive assistance with technical issues, such as troubleshooting software or resetting passwords. 4. **Cloud management:** An MSP handles cloud applications like Microsoft 365 or Salesforce, taking care of provisioning, configuration, and maintenance. 5. **Data backup and disaster recovery:** These services protect critical data and ensure rapid system restoration in case of outages or breaches. 6. **SaaS management:** MSPs optimize and secure SaaS applications through ongoing updates and configuration. Imagine investing in a cloud platform, only to find that as traffic surges and customer expectations rise, your system struggles to keep up. That’s exactly the challenge one of our clients faced – until they partnered with Dedicatted. As a direct result of the platform maturity and support provided through our MSP engagement, the client successfully navigated their most critical commercial period – Black Friday – without a single service disruption. [ ![Strengthening Platform Reliability and Service Continuity Through Managed Support Services](https://media.dedicatted.com/wp-content/uploads/2025/03/Tawseel.-Cover-300x157.webp) Case studyDedicatted Petlichenko 5 min to read Also read ### Strengthening Platform Reliability and Service Continuity Through Managed Support Services Retail Cloud and Infrastructure Security Managed Services Security Compliance ](https://dedicatted.com/insights/strengthening-platform-reliability-and-service-continuity-through-managed-support-services)### **Benefits of managed services** Many businesses turn to managed services to ensure their critical infrastructure is always maintained, allowing them to stay focused on their core operations without IT distractions. This approach is also more cost-effective than maintaining an in-house team, eliminating the need for upfront investments in hiring, salaries, and ongoing training. Knowing ***why*** managed services matter is just as important as knowing ***what*** they are.Here’s why managed services stand out: - Proactive problem-solving: MSPs monitor and manage IT systems continuously, identifying and resolving potential issues before they disrupt operations. - Predictable costs: With a subscription-based model, businesses can plan their budgets more effectively and avoid unexpected IT expenses. Compared to an in-house IT team, MSPs eliminate hiring, training, and salary expenses, often cutting IT costs by 25-45% - Expert support: MSPs provide specialized expertise and resources that might not be available internally, improving efficiency and security. - Business focus: By outsourcing IT management, companies can dedicate more time and resources to strategic priorities and growth. - [Scalability](https://dedicatted.com/services/glossary-of-terms#term-scalability): As businesses expand or adopt new technologies, MSPs can adapt their services to meet evolving needs. ### **Professional Services: Expertise for Specific Goals** Professional services focus on specialized, project-based tasks that require deep expertise. Typically engaged for specific initiatives with a clear scope and timeline, these services help businesses solve complex challenges, implement new technologies, and develop tailored solutions. Examples include: 1. **Technology consulting:** Strategic planning and advice to enhance IT infrastructure and align technology with business objectives. 2. **Software development:** Custom application design, development, and deployment to meet unique business needs. 3. **System integration:** Connecting disparate technologies to streamline workflows and enhance efficiency. ### **Benefits of professional services** When tackling complex or large-scale projects, businesses often turn to professional services for specialized expertise and efficient execution. Whether it’s deploying new software, migrating to the cloud, setting up IT infrastructure, or implementing a data recovery plan, these services provide the skills and resources needed to get the job done right. Here’s what makes professional services invaluable: - Specialized expertise – Professional service providers bring deep technical and industry knowledge, ensuring tailored solutions that align with business objectives. - Flexibility – Engaging experts on a project-by-project basis gives companies access to the right skills when needed – without long-term commitments. - Custom solutions – Off-the-shelf software may work for some, but tailored solutions are what drive efficiency, scalability, and competitive advantage. - Speed & efficiency – With defined scopes and dedicated specialists, projects are completed faster, allowing businesses to implement new technologies and strategies without delays. - Innovation-driven approach – Professional service providers stay ahead of industry trends, offering fresh insights and cutting-edge technologies that help businesses evolve. [![Comparison table of Managed Services vs Professional Services, outlining key differences in business needs, budget, internal resources, flexibility, and strategic goals. Managed services focus on ongoing tech engagement, predictable costs, and long-term management, while professional services offer project-based expertise, variable costs, and short-term flexibility.](https://dedicatted.com/wp-content/uploads/2025/03/managed-services-vs-professional-services-1024x574.png)](https://dedicatted.com/wp-content/uploads/2025/03/managed-services-vs-professional-services-1024x574.png)### **Choosing the right service for your business: Checklist** **1.Define Your Business Needs** Do you need continuous IT support for infrastructure, security, and system maintenance? Managed services ensure your critical systems run smoothly, freeing your internal team to focus on strategic business initiatives. Or are you dealing with a one-time, specialized project like software development, cloud migration, or system integration? Professional services provide the targeted expertise needed to complete complex tasks efficiently. **2. Plan Your Budget** Professional services typically have one-time, upfront costs, whereas managed services require ongoing investment. Customers with short-term budgets may favor PS, while those seeking predictable spending prefer MS. **3. Evaluate your in-house capabilities** Managed services can fill skill gaps, providing ongoing support and reducing the need for additional hires. Small businesses can save costs this way, while larger companies can outsource specific functions like cybersecurity. Or does your team just need temporary expertise for certain projects? Professional services provide high-quality, specialized assistance for short-term needs without long-term commitments . **4. Consider Flexibility & Growth** Is your business growing or experiencing fluctuating IT demands? Managed services offer scalable solutions that adapt as your company evolves. Or do you need expert assistance for a specific, short-term project? Professional services allow businesses to bring in specialists only when required, offering agility without long-term contracts. **Important note:** Managed services can handle complex, multi-faceted environments with integrated solutions, while professional services are better suited for specific workloads. **5. Align with Your Strategic Goals** Are you looking for a long-term IT partner to support your company’s growth and infrastructure improvements? Managed services provide continuous support, strategic guidance, and proactive maintenance. Or do you need immediate results from a project-focused engagement? Professional services offer the expertise required to complete specific initiatives without ongoing commitments. MS is the better fit if the customer’s goal is an ongoing operational outcome, such as reduced downtime or improved performance. Professional services are more appropriate if the result is a completed project, such as migrating to a new platform. Every growing business faces a critical choice: rely on one-off professional services for specialized projects or partner with a Managed Services Provider (MSP) for continuous, proactive support – and for a global fintech company handling high-volume, business-critical workloads, this decision made all the difference. While professional services solve immediate challenges like cloud migrations or system integrations, managed services keep platforms secure, optimized, and available 24/7. By working with Dedicatted, our client transformed fragmented, reactive operations into a resilient, fully supported platform with structured processes, automated patching, validated disaster recovery, and lightning-fast incident resolution. The results speak for themselves: SLA uptime above 99.97%, incidents resolved up to 4x faster, and an internal team freed to focus entirely on growth. Curious how a structured MSP framework can turn operational chaos into a mature, scalable, and compliant platform?[ **Check out the full case study here.**](https://dedicatted.com/insights/scaling-platform-stability-and-operational-maturity-with-managed-support-services) ## **Instead of Choosing Between Managed Services vs. Professional Services, Choose A Provider That Can Offer Both** One unique advantage of partnering with an MSP is that most of them can do the project-based work that professional services handle. There’s no reason for you to outsource professional services if you already have a managed IT contract. By choosing [Dedicatted](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/Dedicatted) as your IT partner, you’ll get the best of both worlds. With fast response times and over 10 years of experience in the industry, we offer quick, effective results: whether you need someone to manage your day-to-day operations, spearhead a new hardware rollout or something in between, Dedicatted is there. If you’re still not sure which approach is best for you or if you want more information about our services, reach out today! **Categories:** Blog **Services:** Cloud and Infrastructure Security, Managed Services, Security Compliance **Industry:** Automotive, Financial Services, Healthcare, High Tech Software, Insurance, Manufacturing, Retail --- ### [RAG Pipelines Explained: Real-Time AI Responses for Enterprises](https://dedicatted.com/insights/rag-pipelines-explained-real-time-ai-responses-for-enterprises) **Published:** May 6, 2026 **Author:** Dedicatted **Content:** Most AI demos are persuasive. That’s the problem. A confident answer to the wrong question is worse than no answer at all, especially when it’s going to a customer or feeding into a compliance review. RAG is the boring, citation-first plumbing that turns generative AI from a confident guesser into something you can actually defend in an audit. Get this wrong at scale, and small retrieval errors don’t stay small – they compound into systemic risk. What looks like a harmless 2% miss rate can quietly cascade across thousands of queries, eroding trust, distorting decisions, and turning your AI from an asset into a liability. This article explains what RAG is, how it works, where it creates real business value, and how to avoid the mistakes that derail most RAG projects. No PhD in machine learning required. ## **What RAG Actually Is** In plain English: RAG is a system that lets an AI look things up before answering your question. Without RAG, when you ask an AI a question, it generates an answer purely from what it learned during training, which is a fixed snapshot of public internet data. It cannot access your internal documents, product database, support tickets, or company wiki. If the answer requires that information, the AI will either refuse to answer or, worse, fabricate something plausible but wrong. With RAG, the process changes fundamentally. When you ask a question, the system first searches your own data to find relevant information, then passes that information to the AI along with your question. The AI generates its response using your actual data as context. The result is an answer that is grounded in your specific business knowledge. e the answer. Think of it this way: without RAG, you are asking someone to answer questions about your business using only their general knowledge. With RAG, you are handing them your company’s filing cabinet and saying “use this to answer the question.” The difference in answer quality is dramatic [![Step-by-step flowchart of a Retrieval-Augmented Generation system: (1) user submits a query, (2) retrieval component searches and ranks results from knowledge bases and document stores, returning relevant context, (3) context augmentation combines the original query with retrieved data into an enhanced prompt, and (4) an LLM generates the final, context-aware response.](https://dedicatted.com/wp-content/uploads/2026/05/unnamed-1.jpg)](https://dedicatted.com/wp-content/uploads/2026/05/unnamed-1.jpg)## **Core components of RAG evaluation** A rigorous RAG evaluation framework must examine both the mechanics of retrieval and the integrity of generation, as well as how both interact under real usage conditions. Measuring output fluency alone is insufficient. The evaluation must determine whether the system retrieves the correct evidence, uses it correctly, and delivers answers aligned with user intent and defensible against the source documents. Three core dimensions underpin such an assessment: context relevance, faithfulness, and answer relevance. Each dimension maps to a specific layer of the RAG architecture. [![Flowchart showing a Retrieval-Augmented Generation (RAG) pipeline: user query → retrieval/search → context augmentation → LLM response → final answer.](https://dedicatted.com/wp-content/uploads/2026/05/unnamed-2-1024x576.jpg)](https://dedicatted.com/wp-content/uploads/2026/05/unnamed-2-1024x576.jpg)## How RAG Works: The Four-Stage Pipeline RAG systems follow a consistent architecture regardless of implementation. Understanding these four stages helps you make better decisions about building, buying, and maintaining a RAG pipeline. - ****Stage 1: Document Ingestion and Chunking**** Before your AI can search your data, that data needs to be processed into a searchable format. This starts with ingestion: pulling documents from wherever they live (SharePoint, Google Drive, Confluence, databases, PDFs, websites) and converting them into clean text. Once you have raw text, it gets split into chunks. Chunking is the process of breaking long documents into smaller, meaningful segments. A 50-page product manual might be split into 200 chunks, each covering a specific topic or section. Chunking sounds simple but it is one of the most consequential decisions in a RAG pipeline. Chunks that are too small lose context – a chunk that says “the warranty period is 24 months” is useless if it does not include which product the warranty applies to. Chunks that are too large dilute relevance – a 3,000-word chunk about an entire product range will match too many queries and provide too little specific information. Most production RAG systems use chunks of 200-500 tokens (roughly 150-375 words) with some overlap between adjacent chunks to preserve context at boundaries. But the optimal size depends entirely on your data and use case. Product specifications need different chunking than customer support transcripts. - ******Stage 2: Embedding Generation****** Here is where the clever part happens. Each chunk of text is converted into a vector embedding: a numerical representation that captures the meaning of the text, not just the words. Traditional search works by matching keywords. If you search for “return policy” it finds documents containing those exact words. Embedding-based search works by matching meaning. A search for “return policy” will also find chunks about “sending items back,” “refund process,” and “exchange procedures” , even if they never use the phrase “return policy.” Embedding models like OpenAI’s text-embedding-3-large or open-source alternatives like BGE and E5 convert each chunk into a vector of 1,000-3,000 numbers. These numbers position the chunk in a high-dimensional space where semantically similar content clusters together. When a query comes in, it gets embedded using the same model, and the system finds the chunks whose vectors are closest to the query vector. This semantic understanding is what makes RAG dramatically better than keyword search for question-answering. Users do not need to know the exact terminology in your documents. They can ask questions in their own words and still get relevant results. - ****Stage 3: Vector Storage and Indexing**** Those embedding vectors need to be stored somewhere that supports fast similarity search. This is the role of vector databases – purpose-built systems designed to store millions of vectors and find the most similar ones in milliseconds. The main options in this space include Pinecone (managed, easy to start), Weaviate (open-source, feature-rich), Qdrant (open-source, performant), ChromaDB (lightweight, good for prototyping), and pgvector (PostgreSQL extension, good if you are already on Postgres). For most implementations, the choice of vector database matters less than the quality of your chunking and embeddings. Start with whatever is easiest to integrate with your stack. You can migrate later if needed: the vector database is the most replaceable component in the pipeline. - ********Stage 4: [Query](https://dedicatted.com/services/glossary-of-terms#term-query), Retrieval, and Generation******** This is the stage that users actually interact with. When someone asks a question: 1. The query is converted into a vector embedding using the same model that embedded your documents 2. The vector database performs a similarity search and returns the most relevant chunks (typically three to ten) 3. Those chunks are inserted into a prompt along with the original question 4. The LLM generates a response using the retrieved chunks as context 5. The response is returned to the user, ideally with citations pointing back to source documents This retrieval step is what grounds the AI’s response in your actual data. Instead of generating an answer from general knowledge, the model is working from specific, relevant excerpts of your business documentation [![unnamed 3](https://dedicatted.com/wp-content/uploads/2026/05/unnamed-3-1024x151.jpg)](https://dedicatted.com/wp-content/uploads/2026/05/unnamed-3-1024x151.jpg)## ****Real Use-Cases**** These are implementations delivering measurable value for businesses right now: ### Internal Knowledge Base Every organization has institutional knowledge scattered across wikis, documents, Slack threads, and people’s heads. A RAG-powered internal assistant lets employees ask questions in natural language and get answers drawn from across all these sources. New employees get up to speed faster. Experienced employees find information without hunting through dozens of documents. A professional services firm with 200 staff and 15 years of accumulated documentation can see dramatic reductions in time spent searching for information. Instead of emailing three colleagues to find the right process document, employees ask the AI and get an answer with a link to the source document in seconds. This approach mirrors what we implemented in our **[AI-powered document categorization](https://dedicatted.com/insights/from-manual-to-automated-ai-powered-workflow-revolution-with-amazon-q-2)** solution for civil engineering firms, where vast amounts of technical documentation were previously siloed and difficult to navigate. By structuring and retrieving knowledge intelligently, teams significantly reduced manual search time and improved operational efficiency across projects ### Customer support RAG transforms customer support by giving agents and AI assistants – instant access to product documentation, policies, and historical interactions. Instead of relying on memory or switching between systems, agents receive context-aware answers in real time. The impact is both operational and financial. In one of our implementations of a **[bilingual AI shopping assistant for Taw9eel](https://dedicatted.com/insights/ai-that-understands-you-taw9eels-bilingual-shopping-assistant-delivers-results),** the system was designed to understand customer intent across languages and retrieve accurate product and order information. This led to faster response times and more consistent customer interactions, while reducing the load on human support teams. Across similar deployments, businesses typically automate **40-60% of inbound queries**, allowing support teams to focus only on complex or high-value cases.ustomer a cleaner, more accurate answer. ### Document Analysis Legal, compliance, and finance teams spend a significant portion of their time searching through large volumes of documents. RAG turns this into a conversational process. Instead of manually reviewing hundreds of files, teams can ask precise questions and receive sourced answers instantly. For example, in our work on AI-driven document processing and classification, organizations dealing with regulatory or technical documentation saw not only faster access to information, but also improved consistency in how documents were interpreted and used. In real-world scenarios, this reduces review cycles by **up to 40%** and minimizes the risk of missing critical clauses or inconsistencies. ### Sales Enablement Sales teams operate in high-pressure environments where access to the right information at the right time directly impacts revenue. A RAG-powered assistant connected to sales materials, product documentation, and case studies allows reps to ask questions like “How do we position against Competitor X?” and receive accurate, up-to-date answers instantly. This is particularly powerful when combined with real case data. For example, in our cloud enablement and platform optimization projects, including our work for a global automotive enterprise , centralizing and structuring knowledge enabled teams to align faster on messaging, reduce internal back-and-forth, and accelerate decision-making. In sales contexts, this typically leads to shorter deal cycles and more consistent positioning across teams. ## Common Pitfalls Having built and reviewed numerous RAG implementations, these are the mistakes that cause the most problems. 1. **Wrong Chunk Size.** This is the most common technical error. Chunks that are too small produce retrieval results that lack context. Chunks that are too large produce results that contain too much irrelevant information. There is no universal correct size – it depends on your content type and query patterns. Start with 300-400 tokens, measure retrieval quality, and adjust. 2. **No Evaluation Framework.** If you cannot measure whether your RAG system is returning good answers, you cannot improve it. Build an evaluation set: a collection of questions with known correct answers drawn from your documentation. Run these regularly against your pipeline and measure retrieval accuracy (did it find the right chunks?) and answer quality (did the LLM use the chunks correctly?). Without this, you are flying blind. 3. **Hallucination Not Managed.** RAG reduces hallucination dramatically compared to using an LLM alone, but it does not eliminate it entirely. The model can still misinterpret retrieved chunks, combine information incorrectly, or fill gaps with fabricated details. Mitigation strategies include: instructing the model to only use provided context, requiring citations for every claim, implementing confidence scoring, and having clear escalation paths when the system is uncertain. 4. **No Feedback Loop.** The best RAG systems improve over time because they capture user feedback. When an answer is wrong or unhelpful, that signal should feed back into the system, flagging chunks that need updating, identifying gaps in documentation, and surfacing queries that the current knowledge base cannot handle. Without this loop, your RAG system’s quality is static while your business keeps changing. 5. **Ignoring Metadata.** Raw text search is only part of the equation. Metadata – document dates, authors, product categories, document types enables filtering that dramatically improves retrieval quality. A query about “current pricing” should prioritise recently updated documents. A query about “Product X” should filter to Product X documentation before searching. Embedding metadata into your pipeline is low effort with high impact. ## **Build vs Managed** The build-versus-buy decision for RAG depends on your technical capability, scale, and customization needs. ### **Managed RAG Solutions** Platforms like Pinecone’s assistant API, LangChain’s hosted offerings, and various vertical-specific RAG products offer quick deployment with minimal engineering. You upload documents, configure basic settings, and get a working RAG system in days. Choose managed when: you want fast time-to-value, your use case is standard (knowledge base Q&A, document search), you lack in-house AI engineering capability, or your document corpus is under 10,000 pages. ### Custom RAG Pipelines Building your own pipeline using components like LangChain or LlamaIndex for orchestration, a self-hosted vector database, and direct LLM API calls gives you full control over every stage. You can implement custom chunking strategies, hybrid search (combining vector and keyword search), re-ranking, query expansion, and domain-specific optimizations. Choose custom when: you need non-standard chunking or retrieval logic, you have compliance requirements that preclude sending data to third-party platforms, your corpus is large or frequently updated, or retrieval quality from managed solutions is not meeting your standards. For most businesses, the pragmatic path is to start with a managed solution, validate the use case, and migrate to custom only if you hit limitations that the managed platform cannot address. The worst outcome is spending three months building a custom pipeline only to discover that the use case does not deliver the expected value. ![Promotional banner for Dedicatted featuring the headline ](http://dedicatted.com/wp-content/uploads/2026/02/unnamed-98.png) ## **Best practices for evaluating RAG systems by Dedicatted** We evaluate RAG systems the same way we build them: systematically, with measurable criteria and full architectural transparency. The goal isn’t just to see *if* something works, but to understand *why* it works or fails under real conditions. ***When something goes wrong in a RAG system, the first question is always: where?*** We isolate failure at the architectural level by evaluating embedding models, retrievers, rerankers, and generators independently before validating the full pipeline end-to-end. This allows us to identify the exact source of degradation instead of guessing. Generic benchmarks rarely reflect real-world complexity, which is why ***we design domain-calibrated evaluation datasets***. These include production, like query-context-answer pairs that capture actual user behavior, domain-specific language, edge cases, and multi-step reasoning patterns your system will face in practice. A RAG system doesn’t fail in just one dimension, so ***we operationalize the RAG Triad in every assessment.*** We measure context relevance, faithfulness, and answer relevance together to determine whether issues stem from poor retrieval, incorrect synthesis, or misaligned responses. [Automation](https://dedicatted.com/services/glossary-of-terms#term-automation) brings scale, but not always accuracy. That’s why we ***combine LLM-based scoring with expert calibration***, continuously validating automated judgments against subject matter experts to eliminate bias and maintain evaluation integrity. Optimization without control leads to noise, not progress. ***We run controlled, iterative experiments where only one variable***, such as chunking strategy, embedding model, reranking logic, or prompt design is changed at a time, ensuring every improvement is measurable and attributable. Security and robustness are not afterthoughts – they are built into the evaluation cycle. We test for prompt injection resistance, sensitive data leakage, adversarial inputs, and the system’s ability to safely refuse unsupported queries before anything reaches production. A RAG system is not “done” at deployment. We ***integrate evaluation into CI/CD and observability workflows***, continuously monitoring retrieval quality, groundedness, latency, token usage, and drift signals to catch performance degradation before it becomes a business issue. ## **RAG Cost Breakdown: What Actually Drives Spend in Production AI** One of the first questions you’ll face when moving a RAG system into production is simple: *what does this cost to run at scale?* The good news is that RAG doesn’t have a mysterious cost structure – it’s actually quite predictable once you break it down. First, **embedding costs are largely one-time**. You pay to process and vectorize your knowledge base: documents, PDFs, tickets, etc. and that cost only repeats when data changes. In practice, even large knowledge bases (hundreds of thousands of documents) are relatively inexpensive to embed compared to ongoing usage. This aligns with what Accenture highlights: the upfront effort is less about cost and more about *data readiness and structuring*. Second, **vector database costs behave like a steady infrastructure layer**. Whether you use managed services or self-hosted solutions, you’re typically paying a **predictable monthly cost** for storage, indexing, and retrieval performance. For most mid-sized use cases, this sits in the range of tens to a few hundred dollars per month, scaling with data size and latency requirements – not with the number of queries. The real cost driver and where your CFO attention should go is **query cost**. Every user question triggers multiple steps: retrieval (sometimes multiple queries), optional reranking, and LLM generation. These are billed per token or per request, which means cost scales linearly with usage. As Deloitte describes through the concept of *tokenomics*, this usage-based pricing becomes the dominant factor at scale, with typical enterprise workloads falling in the range of $0.01 to $0.30+ per query depending on model choice, context size, and response length. What makes this non-trivial is how quickly it compounds. A system handling 1,000 queries per day might cost almost nothing in the context of an IT budget. The same system at 100,000 queries per day becomes a six-figure annual line item if left unoptimized. Strategy& emphasizes that this shift toward consumption-based pricing fundamentally changes how AI systems should be designed: unit economics matter from day one. This is why practical optimization decisions have direct financial impact: - Chunk size and retrieval quality affect how much context you send to the model (and therefore cost per query) - Model selection (e.g., smaller vs. larger LLMs) can change costs by 5-10× for the same use case - Caching frequent queries can reduce repeated generation costs significantly - Reranking strategies can improve accuracy without always increasing token usage if applied selectively - Answer length control (concise vs. verbose outputs) directly reduces generation cost In well-architected systems, these levers can reduce per-query cost by **30-70% without sacrificing quality**.The organizations that scale it successfully are the ones that track cost per query, cost per answer, and cost per user as core metrics alongside accuracy. That’s the level where technical design and business value finally meet. [![Pic](https://dedicatted.com/wp-content/uploads/2026/05/Pic-1024x576.jpg)](https://dedicatted.com/wp-content/uploads/2026/05/Pic-1024x576.jpg)## Where to take it next Anyone can spin up a RAG demo. Making one that holds up in production – accurate at 2 a.m., fast under real traffic, secure under audit, and affordable at the end of the month is where the real engineering begins. That’s the part most teams underestimate. It’s also the part we love. At **[Dedicatted](https://dedicatted.com/)**, we build RAG systems that earn their keep. As one of the **top 2% of AWS partners worldwide** and the **only AWS GenAI and MSP partner in Canada**, we work where the serious AI workloads live: Amazon Bedrock, Kendra, OpenSearch, SageMaker, S3, Lambda, and Amazon Q for Business and we tune every layer to your data, your latency, and your compliance reality. Ten years in, 300+ projects delivered, a 93% client return rate, and SOC 2 + ISO 27001 on the wall. Our Amazon Q for Business deployments have delivered up to **300% ROI**, not because we got lucky, but because we sweat the details across **[Generative AI](https://dedicatted.com/services/generative-ai), [Agentic AI](https://dedicatted.com/services/artificial-intelligence/agentic-ai), and [Data Architecture](https://dedicatted.com/services/data/architecture).** So if your RAG pipeline is ready to graduate from “interesting prototype” to “indispensable business asset,” **[let’s talk](https://dedicatted.com/contact)**. Bring the data – we’ll bring the architecture, the AWS firepower, and the team that’s done it before. [![Group 274ng](https://dedicatted.com/wp-content/uploads/2026/05/Group-274ng-1024x267.png)](https://dedicatted.com/wp-content/uploads/2026/05/Group-274ng-1024x267.png) **Categories:** Blog **Services:** Agentic AI **Industry:** Financial Services, Insurance, Manufacturing, Retail --- ### [Ultimate Guide to AWS vs GCP vs Azure: What Works Best for your business](https://dedicatted.com/insights/ultimate-guide-to-aws-vs-gcp-vs-azure-what-works-best-for-your-business) **Published:** June 24, 2025 **Author:** Dedicatted **Content:** In the world of cloud computing, choosing the right platform is like picking the perfect set of tools for a master craftsman – your business’s success depends on it. With Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) leading the market, the decision is anything but simple. **AWS** is the seasoned craftsman of the cloud world. With the largest global presence and the most extensive range of services, it’s like having every tool imaginable at your fingertips. Need scalability? Done. Looking for robust analytics? No problem. Want to deploy across continents with low latency?[ Dedicatted and AWS](https://dedicatted.com/our-partners/aws) have you covered. ## Let`s drive consistent business value with Dedicatted on AWS [ SPEAK TO OUR EXPERT ](https://bit.ly/4mZnwMs) [ VISIT OUR AWS PAGE ](https://bit.ly/4kSsBEe) Then there’s **Microsoft Azure**, the seamless multitasker. It’s the cloud of choice for enterprises tied to Microsoft’s ecosystem. If your business is already running on Windows Server, Office 365, or Active Directory, Azure feels like an extension of your existing setup – effortless and interconnected. And finally, we have **Google Cloud Platform (GCP)** – the visionary. Known for its prowess in big data and machine learning, GCP isn’t just a cloud provider; it’s a data scientist’s playground. If your business is driven by analytics and innovation, Google’s infrastructure is where your ideas can truly take flight. In this in-depth comparison, we’ll explore how these cloud giants stack up across critical areas like global reach, service offerings, hybrid capabilities, and more. By the end, you’ll have a clear picture of which cloud platform can help you build, scale, and innovate faster than ever before. [![Comparison chart of AWS, Google Cloud Platform (GCP), and Microsoft Azure highlighting key features, services, and differences between the three major cloud providers.](https://dedicatted.com/wp-content/uploads/2025/06/Comparison-Table-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/06/Comparison-Table-scaled.png)## ****Global Reach and Infrastructure**** When it comes to physical infrastructure and global scale, AWS maintains a clear lead. As of now, AWS operates across 30+ regions and 100+ availability zones, with more on the way. This expansive network supports its aggressive push toward low-latency, high-resilience cloud deployments in virtually every corner of the world. AWS’s true brilliance shines when innovation meets speed and when complex systems demand more than just uptime. A striking example is KwikSave, a rapidly growing logistics platform in the travel and hospitality sector.[ **Partnering with Dedicatted**](https://dedicatted.com/insights/smarter-logistics-for-travel-and-hospitality-kwiksave-aws-cloud-success), the company transformed its fragmented logistics into an intelligent, self-orchestrating ecosystem powered by AWS. Using AWS Lambda, Step Functions, and Bedrock, KwikSave cut manual coordination by 35%, reduced operating costs by 30%, and achieved ROI in just 12 months. This is what AWS does best: turning cloud power into business acceleration. [![The following is a list of the largest users of AWS, sorted by size of annual contract:. The Guardian is the largest user with an annual contract of $30 billion. Netflix ranks second with $29.8 billion per year.Pfizer is in third place with $27.7 billion a year. Mcdonalds and Nordstorm close the top five with contracts of $23.2 billion and $14.7 billion respectively. The list also includes Airbnb, Twitch, Kellogs and Twitter](https://dedicatted.com/wp-content/uploads/2025/06/AI-2025-06-10T153705.077.png)](https://dedicatted.com/wp-content/uploads/2025/06/AI-2025-06-10T153705.077.png)One powerful example of the transformative impact can be seen in our AWS and GenAI solution for [Hercules’ financial operations](https://dedicatted.com/insights/automating-remittance-workflows-and-reducing-costs-with-genai-and-aws) with measurable results: 40% cost reduction across financial operations with significantly reduced processing time **Azure**, however, surpasses both AWS and GCP in geographic spread, operating in over 60 regions worldwide with 116+ availability zones – the highest regional coverage among the three. Microsoft’s unique emphasis on global compliance and hybrid cloud readiness has made Azure the go-to choice for governments and multinational enterprises alike. Azure’s true advantage lies in its ability to bring order and trust to complex, compliance-driven industries. A great example is our success story with Zivian Health – a U.S.-based company that simplifies compliance for healthcare professionals across multiple states. When the need for tighter data governance and HIPAA alignment grew, Dedicatted guided Zivian through **[a seamless AWS-to-Azure migration.](https://dedicatted.com/insights/building-a-compliance-ready-cloud-zivian-healths-aws-to-azure-migration)** The project went beyond migration: Infrastructure as Code with [Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform) and a move from Angular to React modernized delivery, while Azure’s governance tools reinforced HIPAA and SOC 2 alignment. Based on the annual contract size, the largest Azure users are: Verizon , MSI Computer, LG Electronics, Wikimedia Foundation, LinkedIn Corp. [![The following is a list of the largest users of Microsoft Azure, sorted by size of annual contract:. Verizon is the largest user with an annual contract of $79.9 million. MSI ranks second with $78 million per year. LG Electronics is in third place with $76.7 million a year. CenturyLink and NTT close the top five with contracts of $61.9 million and $48.7 million respectively. The list also includes Wikimedia Foundation, LinkedIn, News Corp, Adobe and Intel.](https://dedicatted.com/wp-content/uploads/2025/06/AI-2025-06-10T153933.615.png)](https://dedicatted.com/wp-content/uploads/2025/06/AI-2025-06-10T153933.615.png)**Google Cloud**, while smaller in scale, has built a high-performance, developer-optimized backbone with 38 regions and 100+ zones, strategically located to support advanced data workloads and AI-powered applications. GCP focuses less on brute-force expansion and more on high-throughput infrastructure and cutting-edge innovation. Thinking about moving to the cloud? Our article,[ Unlocking the Power of Cloud Migration: Why Enterprises Are Embracing the Future](https://dedicatted.com/insights/unlocking-the-power-of-cloud-migration-why-enterprises-are-embracing-the-future), shows how seamless cloud migration can transform your business. **Make Your Cloud Move with Confidence.** Let Dedicatted – an AWS Premier Tier Services Partner – guide you through every step. [Get in Touch](https://dedicatted.com/contact) ## ****How to make the right choice: AWS vs Azure vs GCP**** From compute services to pricing, here is what sets each platform apart: [![Comparison table of cloud platforms - AWS, Azure, and GCP - across five criteria. Serverless: AWS uses Lambda and FarGate; Azure uses Functions and ACI; GCP uses Cloud Functions, AppEngine, and Cloud Run. Services: AWS and Azure offer 200+ services; GCP offers 100+. Networking: AWS has Virtual Private Cloud, Azure has Virtual Network, and GCP has VPC. Open-source technologies: AWS uses S3 and StackDriver; Azure has none; GCP uses Kubernetes and Istio. Compute: AWS EC2 offers scalable, customizable VMs; Azure supports VMs and scale sets; GCP GCE offers similar VM capabilities.](https://dedicatted.com/wp-content/uploads/2025/06/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-07T112907.316.png)](https://dedicatted.com/wp-content/uploads/2025/06/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-07T112907.316.png)**Services’ Ecosystem**. Even though there are some differences between AWS, Azure, and Google Cloud’s number of services, we should remember that Some GCP services work as part of the “umbrella,” such as the GCP Migration Center. From the ecosystem point of view, Azure and GCP, at the forefront of the ecosystem, showcase LDAP and collaborative apps, slightly outpacing AWS. **Hybrid cloud.** As we know, many cloud customers want to avoid vendor lock-in; part of this is hybrid and multi-cloud environments. From the Hybrid cloud perspective, all three providers have the hybrid part. Azure is known for its strong hybrid cloud capabilities. It seamlessly integrates with on-premises environments, making it suitable for organizations with a mix of cloud and on-premises infrastructure. In cases of using AWS, you should have the specialized hardware. In Google Cloud, a complete software stack can be installed on your existing hardware or your data centers. ## ****Computing. Databases & Analytics**** Compute is a term that describes how computers work. Connecting many nodes is simple for a good cloud provider. Here is a look at each platform’s computational capabilities. AWS key tools **Artificial Intelligence and Machine learning**: AWS released Gluon. This open-source deep-learning library allows developers and non-developers to build neural networks without prior knowledge of AI. DeepLens is an AI-powered camera that may create and implement machine learning algorithms for optical character recognition, image identification, and object recognition. **SageMaker to [Serverless](https://dedicatted.com/services/glossary-of-terms#term-serverless)**: SageMaker is another AWS service used to train and deploy machine learning models. It also includes the Lex conversational interface, enabling Alexa services, Greengrass IoT messaging, and Lambda serverless computing. *Azure key tools* **Cognitive Services**: Bing Web Search API, Face API, Computer Vision API, and Custom Vision Service are among the cognitive services available. Microsoft offers many IoT management and analytics services and functions, and a serverless computing solution. *Google Cloud key tools* **IoT to Serverless**: Google Cloud includes APIs for natural language, speech, translation, and other advanced technologies. It also offers IoT and serverless services. However, these are still beta versions. **Big On AI**: Google Cloud is now the frontrunner in AI advancement. TensorFlow, an open-source software library for creating machine learning applications, deserves credit. Many developers love TensorFlow. [![Comparison table of cloud platforms - AWS, Azure, and GCP - across five criteria. Serverless: AWS uses Lambda and FarGate; Azure uses Functions and ACI; GCP uses Cloud Functions, AppEngine, and Cloud Run. Services: AWS and Azure offer 200+ services; GCP offers 100+. Networking: AWS has Virtual Private Cloud, Azure has Virtual Network, and GCP has VPC. Open-source technologies: AWS uses S3 and StackDriver; Azure has none; GCP uses Kubernetes and Istio. Compute: AWS EC2 offers scalable, customizable VMs; Azure supports VMs and scale sets; GCP GCE offers similar VM capabilities.](https://dedicatted.com/wp-content/uploads/2025/06/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-07T112907.316-1.png)](https://dedicatted.com/wp-content/uploads/2025/06/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-07T112907.316-1.png)## **Cost. Billing. Budget Control** Pricing and Cost Management: 1. AWS: AWS operates on a pay-as-you-go model with pricing tools like AWS Cost Explorer and Trusted Advisor for cost optimization. It also offers Savings Plans and Reserved Instances for long-term commitments. 2. Microsoft Azure: Azure matches AWS with pay-as-you-go options and adds Azure Cost Management for budget tracking. Azure also offers Hybrid Use Benefits for users extending existing licenses to the cloud. 3. Google Cloud Platform: GCP is recognized for its competitive pricing and transparent billing, with sustained use discounts and custom VM configurations that often reduce costs significantly. ## **Why AWS is the Partner of Choice** When technology leaders choose a cloud provider, the priority is performance, scalability, and operational control. AWS delivers on all three. According to IDC, AWS customers typically achieve a 50% reduction in 5-year operational costs and a 2.3× increase in feature delivery – results that translate directly to revenue growth. As an [AWS Premier Tier Partner](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/), Dedicatted helps you optimize your AWS environment for performance and cost-efficiency – aligning every cloud decision with your bottom line. With over 50 cloud experts on board, we are fully equipped to help you develop cloud-native apps or move your existing applications to the cloud. From cloud readiness assessment and strategy development to infrastructure setup, architecture redesign, migration, and maintenance, our specialists will support you at any stage of your cloud journey [![Graphic titled "Simplify, scale with Dedicatted on AWS " with the graphic og the Dedicatted team at the right corner](https://dedicatted.com/wp-content/uploads/2025/06/Group-141-9-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/06/Group-141-9-scaled.png) **Categories:** Blog **Services:** Cloud and Infrastructure Security, Managed Services, Security Compliance **Industry:** High Tech Software, Insurance, Manufacturing, Retail --- ### [CI/CD in 2026: The Pipeline Patterns That Actually Scale](https://dedicatted.com/insights/ci-cd-in-2026-the-pipeline-patterns-that-actually-scale) **Published:** July 10, 2026 **Author:** Dedicatted **Content:** For most of the last decade, “scaling CI/CD” meant adding tools: another linter, another scanner, another dashboard. In 2026, that approach has run out of road. Across the engagements and data we track at Dedicatted, one pattern keeps repeating: organizations are short on coherence between the tools they already have. Our analysis of how engineering organizations structure platform and DevOps work shows that developer friction is no longer concentrated in one obvious bottleneck. Limited visibility into systems, difficulty finding standards or “golden paths,” high cognitive load, manual processes, and slow feedback loops all sit within a few points of each other, in the 40–41% range. That even spread is the tell: the problem isn’t a missing tool. It’s a system that has grown too distributed for any single team to hold in their head. Data from IT, development, operations, and platform engineering professionals backs this up from a different angle. CI/CD pipelines are now table stakes, but their maturity varies wildly. Only **27% of organizations** validate code daily; **51% still test on a weekly cadence,** which is plenty of time for undetected issues to compound before they surface, usually after release. Meanwhile, **84% have already adopted code scanning tools and 85% are using AI for real-time issue detection,** so the raw capability is there. What’s missing is a pattern for putting it all together. *At Dedicatted, this is the conversation we have with almost every engineering leader we talk to right now: not “which tool should we add,” but “why does adding tools keep making things feel more complicated, not less.” Below are the patterns we’re seeing actually move the needle for teams trying to scale CI/CD in 2026 and the ones that look good in a slide deck but don’t survive contact with a real engineering org.* ## Pattern 1: Pick one path forward, not a menu of options The instinct when a pipeline breaks or a new team onboards is to give them options: pick your build tool, pick your test runner, pick your deployment target. In practice, this is exactly what has made toolchains unmanageable. Every team ends up maintaining a slightly different stack, and every reorg or hire means relearning tools that, underneath, don’t do anything meaningfully different from the ones they replace. The pattern that scales instead is the “golden path”: one well-supported, opinionated, largely automated route from commit to production that covers the 80% of cases that don’t need anything exotic. Teams that deviate from it can still do so, but they own that complexity themselves rather than pulling the whole organization into supporting it. This is precisely the argument platform engineering has been built on: reduce the number of decisions a developer has to make before they can ship anything, and let a platform team’s expertise be encoded into the paved road rather than re-explained in every onboarding ticket. At Dedicatted, this is how we approach CI/CD automation engagements. We start by understanding your business drivers and current tech stack, then design a streamlined pipeline that covers your standard deployments without locking out the exceptions. The result is self-healing CI/CD that accelerates time-to-market while eliminating the cognitive overhead of constant tool switching. #### Head of Platform Engineering ## Pattern 2: Policy and security as pipeline logic, not a pre-deploy checklist Security and compliance rank as the single largest priority for platform and DevOps investment we’re seeing across the market (**45%**), narrowly ahead of deployment reliability (**43%**) and developer productivity (**42%**). More tellingly, **52% of teams** say security scanning and policy automation is the toolchain area they most want integrated into a unified platform: the highest of any category, ahead of even CI/CD workflows themselves (42%). That reordering matters. Security is no longer a gate bolted onto the end of a pipeline; it’s becoming pipeline logic: policy-as-code that runs automatically at every stage, the same way a build step or a test suite does. Teams that treat scanning as a separate, late-stage checklist are the ones still fighting fires after release. Teams that write policy directly into the pipeline definition are the ones reporting the benefit organizations most associate with mature platform engineering: stronger security enforcement, cited by 51% of respondents as the top payoff of doing this well. DevSecOps is where this shows up in practice. Embedding SCA (software composition analysis), SAST (static application security testing), secrets scanning, and policy-as-code directly into your CI/CD workflow means vulnerabilities and misconfigurations are caught at commit time, not in production. Dedicatted’s approach to DevSecOps embeds these controls into your pipeline without slowing releases, shifting security left without creating bottlenecks. #### Head of Platform Engineering ## Pattern 3: Continuous validation, not batch testing The gap between the 27% of organizations testing daily and the 51% testing weekly is one of the more actionable numbers we track. Weekly validation isn’t a minor cadence difference – it’s a structurally different risk profile. Issues introduced early in the week sit undetected for days, and by the time they surface, they’re tangled up with every other change that landed in the meantime, making root cause analysis slower and rollbacks messier. Shifting from weekly to daily (or per-commit) validation is less about buying a new testing tool and more about pipeline design: parallelized test suites, environment provisioning that doesn’t bottleneck on a shared staging server, and feedback loops short enough that developers actually wait for the result instead of context-switching away and losing the thread. This is also where slow feedback loops show up as a top complaint (cited by 40% of respondents, and 52% among the most experienced practitioners) – the more experienced the engineer, the more feedback speed dominates their list of frustrations. Infrastructure as Code plays a critical role here. When your test and staging environments are defined as code ([Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform), CloudFormation) rather than hand-provisioned, you can spin up parallel test environments on-demand, eliminate wait times, and ensure every environment matches production. Dedicatted uses IaC and [GitOps](https://dedicatted.com/services/glossary-of-terms#term-gitops) to make environment provisioning part of the pipeline itself, turning a manual bottleneck into an automated, repeatable step. #### Head of Platform Engineering ## Pattern 4: AI in the pipeline, with a human still holding the wheel AI-assisted automation is now the single largest investment priority for platform and DevOps teams looking ahead (46%), ahead of predictive incident response (41%) and pipeline optimization (40%). On the observability side, 85% of organizations are already using AI-powered tools for real-time issue detection. The pattern that scales isn’t “hand the pipeline to AI.” It’s AI doing the narrow, well-bounded jobs: flagging anomalies, suggesting flaky-test culprits, predicting which change is likely to break a downstream service, while humans keep control over what actually merges and deploys. The data shows why this distinction matters: 70% of engineers with under a year of experience want more AI-driven automation, while the most experienced practitioners are notably more cautious, particularly about validation and control. That caution isn’t resistance to change; it’s earned knowledge that systems relying heavily on automated decisions get harder to reason about exactly when something goes wrong. Pipelines that scale build in the audit trail and the override, not just the automation. Dedicatted’s approach to AI-driven DevOps applies GenAI for root-cause analysis, predictive alerts, and intelligent runbooks: cutting MTTR and release risk through data-backed decisions, not blind automation. When you embed AI into observability and incident response, you amplify your team’s ability to respond faster without surrendering visibility or control. #### Head of Platform Engineering ## **Pattern 5: Cost and compliance as pipeline outputs, not quarterly audits** Among more tenured practitioners, cost governance and compliance both jump to 56% as infrastructure priorities: well above where less experienced teams rank them. That’s a signal that as organizations scale, the CI/CD pipeline itself becomes the natural place to answer questions that used to live in a spreadsheet after the fact: what did this environment cost, who approved this change, does this deployment meet the policy baseline. Building that instrumentation into the pipeline, rather than reconstructing it after an audit request, is one of the clearer maturity signals we look for when we assess a client’s DevOps setup. Cost optimization and observability go hand-in-hand. When you instrument your pipeline to track infrastructure spend in real time and correlate it with deployment activity, you can optimize cloud usage before bills surprise you. **[Dedicatted’s cost optimization](https://dedicatted.com/what-we-do/cloud-services/cloud-optimization)** and observability-SRE services build that visibility into your CI/CD workflow and production systems, so compliance and cost governance aren’t overhead – they’re built-in outcomes of how you deploy. ## Pattern 6: Measure before you scale It’s tempting to treat “scale” as a tooling problem you can solve by buying more automation. The teams that actually get there first get honest about where they stand. A proper DevOps strategy engagement starts with an assessment: mapping culture, technical practices, infrastructure, and security against a defined maturity baseline, before committing to a roadmap. Skipping that step is how organizations end up investing in AI-assisted automation or a shiny new orchestrator while the underlying friction: unclear ownership, inconsistent environments, no golden path stays exactly where it was. This is Dedicatted’s “Explore” phase: a deep dive into your business drivers, tech stack, and current state, resulting in a clear roadmap of what to build, in what order, and why it matters to your business. From there, we move to “Implement” – building the actual CI/CD, containerization, and infrastructure-as-code to support your golden path and then “Maintain” to ensure the platform evolves with your needs. [![CI/CD in 2026: The Pipeline Patterns That Actually Scale](https://dedicatted.com/wp-content/uploads/2026/07/Frame-21457-1024x399.png)](https://dedicatted.com/wp-content/uploads/2026/07/Frame-21457-1024x399.png)## Where these patterns come to life: Real scenarios ### **Scenario 1: The high-growth SaaS team – Speed vs. consistency** A fast-growing fintech startup was shipping features but losing speed to deployment delays and environment inconsistencies. Every team had built their own CI/CD workflow; staging deployments took 3+ hours and frequently failed in unpredictable ways. Problem: they’d optimized for feature velocity without optimizing for deployment velocity. Dedicatted conducted a DevOps strategy assessment and discovered the real bottleneck was environment provisioning. We implemented a unified golden path using Infrastructure as Code (Terraform) and a self-service portal for developers to spin up ephemeral test environments. Every commit now spins up a parallel test environment automatically. **Result:** deployment time dropped to 15 minutes, environment parity became automatic, and the team shipped on a daily cadence without engineering as the gating function. ### Scenario 2: The compliance-heavy enterprise – Speed vs. security A healthcare organization had layered security checks onto their pipeline as audit requirements, but the checklist became so burdensome that teams were finding workarounds. Problem: compliance had become the enemy of delivery. Dedicatted re-architected their CI/CD with DevSecOps controls baked directly in: SCA, SAST, secrets scanning, and policy-as-code ran on every commit, with intelligent exception handling and transparent approval workflows. Security didn’t slow releases; it became a routine part of the pipeline. [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) audits went from weeks of manual evidence gathering to an automated report pulled from pipeline logs. They doubled release frequency while improving their audit score. ### Scenario 3: The multi-cloud transition – Lock-in vs. flexibility A retailer was negotiating with AWS but realized their deployment scripts were AWS-specific, eliminating negotiating leverage. Problem: Infrastructure was tightly coupled to a single cloud vendor, making cost optimization and flexibility impossible. Dedicatted designed a cloud-native, platform-agnostic architecture using Kubernetes for orchestration and IaC (Terraform) for infrastructure abstraction. The same [Helm](https://dedicatted.com/services/glossary-of-terms#term-helm) charts and Terraform modules now deploy identically across AWS, Azure, and GCP. **Result:** They could run RFPs across all three clouds, secure better pricing, and had a true multi-cloud strategy. What would have been a six-month cloud migration became a strategic advantage. ### Scenario 4: The observability blindspot – Deploying vs. debugging An operations team had containerized microservices and moved to Kubernetes, but they still couldn’t see inside the system. When incidents happened, MTTR was 2+ hours because debugging required manual context-switching between scattered logs, metrics, and traces that weren’t connected. Problem: they had cloud-native infrastructure but pre-cloud-native visibility. Dedicatted built out observability and SRE practices: unified logging with structured context, distributed tracing that follows requests end-to-end, and AI-assisted alerting that surfaced likely root causes before the customer noticed. Incidents that used to take 2 hours to resolve now took 10 minutes, even for engineers new to the service. They also reduced alert fatigue dramatically by cutting noise with AI-driven anomaly detection*.* ### Scenario 5: The containerization sprawl – Moving fast vs. staying safe A payments processor had containerized their monolith and moved to Kubernetes, but deployment consistency suffered. Some teams used custom [Docker](https://dedicatted.com/services/glossary-of-terms#term-docker) images, others used container registries inconsistently, and there was no standard for how to handle secrets, config, or rolling updates. Problem: containers promised portability and consistency but delivered neither. Dedicatted established platform engineering practices with a golden path for containerization and orchestration. We built a developer portal that abstracts Kubernetes complexity:developers define their app’s resource needs and environment variables, and the platform handles the rest: container builds, registry management, secrets injection, and rolling deployments. It looked like infrastructure magic; it was actually just opinionated defaults. Release velocity increased, operational incidents from configuration drift dropped 80%, and the platform became self-service. [![Flow diagram of Internal Developer Portal showing the Golden Path components: Repository Templates, Documentation, and Software Catalog Entry within a dashed blue boundary (platform perks like Scorecards and Platform Services connect below).](https://dedicatted.com/wp-content/uploads/2026/07/3S1in5BDxiyG3m6KU1snwZH0eOAJEstrgK8W5TgCySuhSPZLFEHwGlFVnansrBalkZTlhzev9u2FTUdXQSwLbxyi8YFpXnhc249zXevtj7C63qEVNAAMOCL_hoWNAcJNIpgJnzTpruKLSq78eL47NVNr0Z0KqN2f48qR0c5cR22XUUcDWmjDraAx0ZLhpoo7-1.jpeg)](https://dedicatted.com/wp-content/uploads/2026/07/3S1in5BDxiyG3m6KU1snwZH0eOAJEstrgK8W5TgCySuhSPZLFEHwGlFVnansrBalkZTlhzev9u2FTUdXQSwLbxyi8YFpXnhc249zXevtj7C63qEVNAAMOCL_hoWNAcJNIpgJnzTpruKLSq78eL47NVNr0Z0KqN2f48qR0c5cR22XUUcDWmjDraAx0ZLhpoo7-1.jpeg)### Scenario 6: The healthcare provider – Compliance vs. speed (the false choice). A fast-growing digital healthcare company needed to migrate from AWS to Azure, serve thousands of active users without downtime, maintain HIPAA compliance, and somehow ship features faster. The trap most regulated orgs fall into: you get security *or* speed, pick one. They thought it was impossible until they realized security and speed aren’t enemies – they just need to be architected together. We migrated them to Azure with Infrastructure as Code, built GitOps-driven ephemeral environments (every development branch spun up its own isolated, fully compliant environment automatically), and embedded DevSecOps controls directly into the pipeline (SCA, SAST, secrets scanning, policy-as-code on every commit). **The result:** zero downtime migration, HIPAA controls built-in not bolted-on, deployment time dropped, and compliance audits went from weeks of manual evidence gathering to an automated report pulled from pipeline logs. They doubled release frequency while strengthening their security posture. That’s what happens when you stop treating compliance as overhead and start treating it as pipeline design. **[Read more ](https://dedicatted.com/insights/building-a-compliance-ready-cloud-aws-to-azure-migration)** ## How Dedicatted Approaches CI/CD in 2026 Modern software delivery is built on coherent systems where competing demands (speed, security, reliability, cost) are reconciled through architecture, not traded off team by team. The organizations scaling in 2026 aren’t the ones with the most tools – they’re the ones making their existing systems work together. But building coherent systems requires more than consultant recommendations. It requires hands-on engineering, architecture that survives reality, and deep expertise in cloud platforms and DevOps practices. That’s where Dedicatted comes in. We’re cloud engineers who’ve built scalable DevOps infrastructure for fintech startups, healthcare providers, retailers, and payments processors. We migrate complex systems without downtime. We embed security into pipelines so compliance becomes a side effect of how code ships. Our approach starts with honest assessment: we audit your business drivers, map your current state, identify real gaps. Then we build: CI/CD automation, Infrastructure as Code, Kubernetes, DevSecOps, observability, cloud-native architecture, cost optimization. All grounded in patterns that actually scale. [**As an AWS Premier Tier Services Partner**](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/Dedicatted) and top 2% global AWS partner with GenAI Competency and MSP designation, we combine deep cloud expertise with hands-on engineering. But credentials matter less than track record: we’ve doubled release frequency while improving security scores, turned cloud migrations into strategic advantages, and built platforms that make the hard way to ship code become the easy way. Here’s the question: where’s your friction? Scattered visibility? Unclear standards? Manual slowdowns? High cognitive load? Or deeper: a lack of coherence across your entire delivery system? We start by getting honest about that, then build the solution that fits your business. **[Let’s talk about your friction and what scaling actually looks like for you.](https://dedicatted.com/contact)** **Categories:** Blog **Services:** Cloud Modernization, DevOps Consulting, Managed Services, Security Compliance **Industry:** Financial Services, Healthcare, High Tech Software --- ### [Data Lake Migration to the Cloud: What Our Experts at Dedicatted Are Telling Clients Right Now](https://dedicatted.com/insights/data-lake-migration-to-the-cloud-what-our-experts-at-dedicatted-are-telling-clients-right-now) **Published:** July 17, 2026 **Author:** Dedicatted **Content:** The way that organizations collect, store, and analyze data has evolved over the years. From databases, to data warehouses, to data lakes, this evolution has gone hand-in-hand with other innovations: like the internet, big data analytics, and now AI. To help understand where we are today, let’s take a look back at how far data lakes have come. It all started with big data. In the early days of big data, traditional data warehouses struggled to keep up with the growing volume and variety of information being produced. Designed for structured data, they left unstructured and semi-structured data largely untapped. Data lakes, which emerged in the early 2010s, set out to close the gap. A data lake is a vast repository that stores raw data in its native format, regardless of its structure or intended use,giving organizations new opportunities to collect and analyze all types of data. Data lake migration has moved to a prerequisite for any organization serious about AI. That’s the view our experts at Dedicatted keep arriving at with clients: the infrastructure decisions made years ago are now the biggest blocker to getting the most out of AI investments. Here’s our take on why that is, where the economics actually land, and how we advise clients to run the migration itself. ## Legacy data lakes came with challenges While offering control and security, on-premises data lakes made it hard for organizations to get the most from their data assets. Challenges included: - **[Scalability](https://dedicatted.com/services/glossary-of-terms#term-scalability) limitations**. Constrained by the physical capacity of hardware infrastructure, it was costly and time-consuming to scale up to accommodate growing data volumes and processing demands. This led to performance bottlenecks and hindered the ability to handle spikes in data or workloads. - **High Upfront and Maintenance costs.** Building and maintaining an on-premises data lake involved substantial upfront investment in hardware, software licenses, and IT infrastructure. Ongoing maintenance, including hardware upgrades, software updates, and security patching, added to the total cost of ownership. - **Management Overheads.** Specialized expertise and dedicated IT resources were needed for tasks like hardware provisioning, software installation, configuration, performance tuning, and security management. This took time away from higher-value activities like data analysis and insight generation. - **Limited access to innovation.** The latest advances in analytics, AI, and cloud-native services: such as serverless computing, AI/ML platforms, and advanced analytics tools were not compatible with on-premises data lakes. This impeded innovation and made it hard to drive business advantage from data. We advise clients to think of a modern cloud data lake as four layers working together: ***storage*** (structured, semi-structured, and unstructured data, increasingly in formats like Apache Iceberg, which adds transactional guarantees, schema evolution, and time travel that legacy Hive tables never offered), ***processing*** (elastic engines like Spark, Flink, or Ray that spin up only when needed), ***tools and interfaces*** (BI tools, notebooks, SQL workbenches, APIs), and ***governance*** (metadata management, access control, lineage, and quality checks). Our view is that this structure is what actually closes the gap between having data and using it and it’s the direct reason cloud migration has become an AI-readiness project as much as an infrastructure one. Legacy lakes simply can’t handle the volume of unstructured data AI workloads demand, and data silos across warehouses, lakes, and clouds only compound the problem. The results we point clients to speak for themselves: one of our clients – a Canadian insurer migrated a legacy on-premises platform to BigQuery in just 10 months, cutting annual infrastructure and **operations costs by more than 30%** while improving infrastructure setup **speed by 10x.** [![Data Lake Migration to the Cloud: What Our Experts at Dedicatted Are Telling Clients Right Now](https://dedicatted.com/wp-content/uploads/2026/07/Screenshot-2026-07-17-at-13.05.13-1024x596.png)](https://dedicatted.com/wp-content/uploads/2026/07/Screenshot-2026-07-17-at-13.05.13-1024x596.png)## The economics of Data Lake Migration, in our experts’ opinion **Cost** is almost always the trigger for a migration conversation, and it should be. On-premises data lakes carry heavy capital and operational expenditure, require costly hardware additions as data grows, and demand expensive specialized staff to keep running. But our experts are just as focused on the opportunity cost side of the ledger: the inflexibility of on-premises systems slows innovation and AI adoption, and that’s a cost that compounds every quarter an organization waits. The cloud replaces that model with pay-as-you-go pricing tied to actual usage, and we recommend clients map their total cost of ownership across six components: *storage* (priced per gigabyte and shaped by access tier), *compute* (charged by instance type and duration for ETL, querying, and ML), [managed services](https://dedicatted.com/what-we-do/devops/devops-as-a-service), [data transfer, orchestration and management](https://dedicatted.com/services/data/architecture), and governance [(security/compliance)](https://dedicatted.com/services/security). Independent research backs up what we see in practice: organizations using a managed cloud data lake instead of a self-managed or on-premises one typically **see 20–62% lower costs**, and moving to a lakehouse architecture that unifies data lakes and warehouses on a managed service can **cut costs by 80–90%.** We’ve watched this play out with real companies. One of our clients saw BigQuery storage deliver cost reductions of up to 18%, plus reduced downtime and faster app deployment. Another, migrating from Hadoop to Dataproc, achieved around 30% cost savings in certain clusters by tuning the balance of on-demand and spot VMs, with one application ending up over 10x faster. #### Dedicatted team That said, we’re always upfront with clients that migration isn’t free, and we build the following into every cost model: cloud infrastructure that has to run in parallel with the legacy system until cutover, professional services (consulting, implementation, training), data transfer and egress fees, data transformation and cleansing work, and new licensing costs. Post-migration, our recommendation is a continuous optimization discipline: right-sizing compute and storage, autoscaling, choosing the correct storage tier, using reserved or spot instances where workloads allow it, compressing data and applying lifecycle policies, and monitoring spend on an ongoing basis rather than treating cost control as a one-time exercise ## Migration risks that must be carefully managed When migrating a data lake to the cloud, there are three broad areas that can pose a risk to your organization. Here’s what to watch out for and how to mitigate the risks **Project Management and Execution Risks.** Unrealistic timelines: Underestimating the complexity and time required for migration can lead to rushed decisions, increased errors, and ultimately project failure. Thorough planning, including realistic timelines with buffers. Skill Gaps: A lack of cloud expertise, including skills in cloud platforms, data migration tools, and security best practices can significantly hamper the project. Training existing staff or bringing in experienced cloud professionals is often necessary for unexpected issues, is key. Poor communication: Migration projects involve various stakeholders. Poor communication can lead to misunderstandings, missed deadlines, and conflicts. Establish clear communication channels and ensure all stakeholders are informed and aligned. Insufficient Testing. Testing is crucial to identify and address potential issues before they impact production. If it’s rushed or inadequate, costly post-migration problems can arise. Comprehensive testing strategies, including performance and security testing, are a must. [![Data risks](https://dedicatted.com/wp-content/uploads/2026/07/Data-risks-1024x368.png)](https://dedicatted.com/wp-content/uploads/2026/07/Data-risks-1024x368.png)## Planning and Executing a Successful Data Lake Migration Our experts structure every engagement around five phases, and we won’t move past the first one until an organization has secured C-level sponsorship and assembled a genuinely cross-functional team spanning IT, data engineering, data science, BI, and the business units that actually consume the data. Communication channels and a plan for upskilling need to exist before a single workload moves. **Phase 1: Discovery.** We build a full inventory of the current environment: data assets (sources, types, volumes, downstream consumers, metadata), workloads (ETL pipelines, queries, ML models, and their dependencies), governance (classification, retention, access rules, compliance), and workflows (DAGs, schedules, resource requirements) and we map business SLAs alongside the technical detail, since those SLAs are what should actually drive migration priority. **Phase 2: Assessment.** This is where we help clients pick a strategy, and our advice differs by situation: *lift-and-shift* (move as-is, fastest, but leaves cloud benefits on the table), lift-and-optimize (migrate first, then incrementally adopt managed services – usually our default recommendation for balancing speed and payoff), or full modernization (re-architect for cloud-native capabilities from day oneL higher upfront cost and timeline, but the strongest long-term return). We also design the target architecture across six elements: storage, compute, networking, security, governance and metadata unification, and migration tooling. **Phase 3: Planning.** We insist on a living project plan: timelines and milestones phased by business criticality, task breakdowns and dependencies, clearly assigned roles and responsibilities, a real budget, and a documented rollback plan, because in our opinion a migration plan without a rollback plan isn’t actually a plan. **Phase 4: Execution.** Data, metadata, governance, workflows, and workloads move in this phase, and we treat data integrity verification, checksums, schema validation, reconciliation, as non-negotiable before, during, and after transfer. Metadata migration has to preserve lineage. Governance policies need to be enforced consistently via IAM throughout, not bolted on afterward. We typically recommend refactoring workflows onto cloud-native orchestration (Cloud Composer, for example) and workloads onto serverless compute rather than a pure lift-and-shift, because that’s where most of the long-term value gets left on the table if skipped. **Phase 5: Optimization.** We tell every client that migration doesn’t end at cutover: ongoing performance tuning, continuous cost optimization, stronger governance tooling, and adoption of new cloud-native features as they ship are what compound the value of the move over the following years. ## How Dedicatted approaches data lake migration When implemented correctly, Data Lakes accelerate how organizations can use data to drive results. Dedicatted optimizes and automates the configuration, processing, and loading of data into an AWS Data Lake. Our approach eliminates time-consuming setup and management efforts while ensuring you can quickly integrate with sophisticated BI tools like Tableau, Power BI, AWS Quicksight, and other modern analytics platforms. We bring our clients hands-on migration expertise, architecture reviews, and the kind of pattern-matching that only comes from running this process repeatedly across industries, helping teams avoid the timeline and cost overruns that undermine so many migrations, and making sure the AI capabilities the business actually wants are reachable on the other side of the move, not just theoretically available. [**As an AWS Premier Tier Services Partner**](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/Dedicatted) and top 2% global AWS partner with GenAI Competency and MSP designation, we combine deep cloud expertise with hands-on engineering and help organizations develop robust [Data Lakes and comprehensive data and analytics strategies](https://dedicatted.com/services/data/cloud-data-migration) that drive results. Our teams have the experience, AWS data, and analytics knowledge, plus our own research initiatives, to help you plan and execute your data-driven strategy. If your organization is weighing a data lake migration, our recommendation is simple: start with discovery before you start comparing vendors. [Get in touch with our team at Dedicatted ](https://dedicatted.com/contact)to talk through where your environment stands today. **Categories:** Blog **Services:** Cloud Data Migration & Modernization, Cloud Modernization, Data Architecture **Industry:** Automotive, Healthcare, Retail --- ### [AI-Powered Fraud Detection for Canadian Banks: A Complete Guide](https://dedicatted.com/insights/ai-powered-fraud-detection-for-canadian-banks-a-complete-guide) **Published:** May 28, 2026 **Author:** Dedicatted **Content:** In 2024, the CFO of a Hong Kong-based multinational joined a video call with colleagues: the CEO, the CFO, several familiar faces. They discussed an urgent wire transfer. He authorized it. The total: USD $25 million. Every person on that call, except him, was a deepfake. This wasn’t a niche attack on an obscure company. It was a proof of concept and it has since been replicated across industries worldwide, including financial services. Canadian banking is not insulated from this reality. According to a recent Canada survey, 81% of Canadian organizations experienced attempted or successful AI-powered fraud in the past twelve months. Sixty percent fell victim to fraudulent communications using AI agents or AI-generated content. Twenty-four percent were hit by voice clone attacks. And 72% of those targeted were hit more than once, because the attacker learned from the first attempt and adapted. “What we’re seeing in our work with Canadian financial institutions is that the threat has moved faster than most organizations’ mental models of it,” says a **Senior Dedicatted consultant** specializing in financial crime prevention. “Leadership teams are still thinking about fraud as something that looks anomalous. The most dangerous attacks today look completely normal, until the damage is done.” The financial industry globally still detects only about 2% of financial crime flows, despite increasing compliance spending year over year. And with FINTRAC, OSFI, and the advancing provisions of Bill C-27 demanding more accountability than ever, the pressure is arriving from every direction at once. The question for Canadian banking leaders is no longer whether AI fraud detection is worth the investment. It is whether your institution is deploying it with the sophistication the threat now demands. [![Top 20](https://dedicatted.com/wp-content/uploads/2026/05/Top-20-1024x576.png)](https://dedicatted.com/wp-content/uploads/2026/05/Top-20-1024x576.png)## The Industrialization of Fraud: How AI Has Changed the Threat Landscape What makes today’s fraud environment categorically different isn’t just the technology – it’s the economics. For most of financial crime’s history, sophisticated fraud was expensive to execute. It required skilled operatives, significant preparation time, and manual effort that naturally limited scale. AI has dissolved all three constraints simultaneously. The same machine learning tools, the same large language models, the same automation capabilities available to your technology team are available to the people targeting your institution, often deployed faster, with fewer governance hurdles, and at a fraction of the cost. The result is fraud that is highly personalized, adaptive, and cross-channel by design. Four threat vectors are hitting Canadian banks hardest right now ### Synthetic Identities They have become AI’s most insidious contribution to financial crime. These aren’t crude fake IDs – they are fully realized digital persons, internally consistent across names, addresses, Social Insurance Numbers, credit histories, transaction behaviours, and social media presence. AI generates them in minutes. They are then carefully nurtured over months: small accounts opened, modest transactions made, credit limits incrementally increased, credibility established. Then, in what the industry calls a “bust-out,” the synthetic identity rapidly exhausts every credit facility it has built and disappears. Canadian institutions often don’t discover the loss until well after the account is closed and by then, the same identity factory has generated hundreds more. We worked with a mid-tier Canadian lender that had been approving a cluster of applications that looked, individually, completely clean. Good credit scores, consistent employment history, reasonable loan amounts. It was only when we mapped the relationship graph: shared devices at application, overlapping address histories, coordinated drawdown timing, that the pattern became visible. Traditional rule-based systems never would have caught it because no single application broke any rule. #### Dedicatted financial crime specialist ### **Deepfake social engineering** It has moved from theoretical concern to documented operational reality. Voice cloning technology is now accessible enough that a few minutes of audio from a public earnings call, an investor presentation, or a media appearance is sufficient to generate a convincing simulation of an executive’s voice. The attack vector is straightforward: call a finance team member, simulate an executive, manufacture urgency, request a wire. The psychological defence that “I’d recognize their voice” is no longer valid and training people to question that instinct, while preserving the efficiency of normal operations, is one of the harder change management challenges Canadian institutions now face. ### **Machine identity exploitation** It is the threat most Canadian executives have yet to fully reckon with. Financial institutions now operate with more non-human identities: APIs, service accounts, automated agents, third-party integrations, than human ones, and most are poorly governed. Long-lived credentials, infrequent rotation, weak monitoring, and unclear ownership create persistent entry points. An attacker who compromises a machine identity doesn’t need to phish a human being. They already have system-level access, and they can operate quietly for months – automating payment attempts, testing account credentials at scale, or exfiltrating transaction data that will power the next generation of synthetic identity attacks. ### Insider Threats They deserve a separate mention, because they are frequently underweighted in Canadian institutions’ threat models. A privileged employee with access to customer data, transaction systems, or approval workflows represents a fraud risk that external controls alone cannot address. The challenge is that insider fraud is designed to look like authorized activity, because it is authorized, by someone who shouldn’t be authorizing it. User and Entity Behaviour Analytics, a specific application of AI that monitors employee behaviour patterns across systems and flags deviations from established norms, is increasingly essential for institutions that take this risk seriously. We had a client where the fraud had been occurring for nearly two years before it was detected. A payments operations employee was making small, authorized-looking adjustments to beneficiary account details on a recurring basis. Each individual transaction was within their authorization limits. It was only when an AI system flagged the cumulative pattern – the same employee, the same type of adjustment, the same timing – that anyone looked. By that point, the losses were significant. The lesson is that insider fraud is a patient crime, and patience is exactly what rule-based systems can’t detect. #### Dedicatted risk specialist These four vectors share a design principle: they are built to look like normal activity. Traditional fraud controls were built to catch anomalies. That fundamental mismatch is why they are failing. ## What AI Fraud Detection Actually Does The shift from rule-based fraud prevention to AI-powered detection is often framed as a technology upgrade. It is better understood as a fundamentally different philosophy of defence: one that moves from asking “does this match a known bad pattern?” to asking “given everything we know about this customer, this device, this moment, and this context, does this make sense?” The difference in detection capability is substantial. AI-based systems have demonstrated the ability to detect two to four times more suspicious activity while reducing alert volumes by more than 60%. For compliance teams drowning in false positives and the review fatigue that generates, that second number matters as much as the first. In practice, modern AI fraud detection operates across four layers that work in concert. [![Figure 2](https://dedicatted.com/wp-content/uploads/2026/05/Figure-2-1024x576.png)](https://dedicatted.com/wp-content/uploads/2026/05/Figure-2-1024x576.png)**Continuous behavioural monitoring** builds a dynamic profile of what normal looks like for each customer – their typical transaction size, their usual locations, their device patterns, their time-of-day behaviour, their navigation cadence within digital banking platforms. When something deviates from that profile in a statistically meaningful way, the system flags it, not because it matches a rule, but because it doesn’t fit the pattern. This is how institutions catch the low-and-slow fraud that deliberately stays beneath static thresholds. Consider a real scenario: a long-standing customer at a major Canadian bank suddenly initiates three international wire transfers over a forty-eight hour period to accounts in jurisdictions the customer has never transacted with before. The amounts are individually below reporting thresholds. A rule-based system passes all three. A behavioural AI system flags the cluster immediately, not because any single transfer broke a rule, but because the combination represents a complete deviation from eighteen months of established customer behaviour. An analyst reviews, contacts the customer, and discovers they have been the victim of a romance scam. The transfers are halted. The customer’s funds are protected. That outcome is only possible with continuous behavioural monitoring. **Network and relationship analysis** addresses the reality that fraud rarely operates in isolation. Organized fraud rings, money mule networks, and synthetic identity operations leave traces in the relationships between accounts, devices, and transactions that no transaction-level analysis would detect. Graph analytics maps these connections in real time, surfacing that five new account applications share a device fingerprint, or that a pattern of payment flows between twelve accounts follows a structure consistent with layering in a money laundering operation, or that a set of loan applications submitted through different branches share underlying data points pointing to a common origination source. The cases that concern us most are the ones that look perfectly clean at the transaction level. Organized fraud groups are sophisticated enough to ensure each individual touchpoint passes scrutiny. Where they can’t hide is in the network – the relationships between accounts, devices, and behavioural patterns that only become visible when you’re looking at the whole graph rather than individual nodes. That’s where AI creates an asymmetric advantage for the defender. #### Dedicatted risk architecture specialist ### **Real-time identity and document verification** It has become a critical battleground as digital onboarding has expanded. AI systems applying computer vision and deepfake detection can identify forged documents, synthetic selfies, and injected media attacks during the onboarding process, flagging anomalies that human reviewers would miss and that rule-based checks cannot evaluate. Liveness detection has evolved beyond simple movement prompts to identify replayed media, synthetic imagery, and AI-generated faces with a sophistication that keeps pace with the tools attackers are using to generate them. A practical example: a Canadian digital lender processing high volumes of online applications began seeing a cluster of approvals that subsequently defaulted within ninety days. Post-incident review revealed that a significant proportion had passed standard document verification: government IDs that looked legitimate, selfies that cleared liveness checks, but had been generated using AI tools that had improved faster than the lender’s verification vendor had updated its detection models. The lesson is not that identity verification is futile. It is that it must be continuously updated against the current generation of forgery tools, not the generation that existed when the contract was signed ### Autonomous case management Agentic AI is where the operating model transformation becomes most significant. Rather than simply flagging a suspicious transaction for human review, an agentic system can detect the event, contact the customer through a personalized, contextually appropriate message to verify whether the transaction is legitimate, evaluate the response, and take appropriate action, all within seconds, and all with a complete, auditable record of every step. A customer initiates an unusual large transfer. The system detects the anomaly, sends a personalized verification message referencing the specific transaction, receives a concern flag from the customer, and pauses the transfer pending analyst review before the funds have moved. [![Fig 3](https://dedicatted.com/wp-content/uploads/2026/05/Fig-3-1024x576.png)](https://dedicatted.com/wp-content/uploads/2026/05/Fig-3-1024x576.png)For Canadian banks managing thousands of alerts daily, this shifts the analyst’s role from processing volume to handling genuine complexity. Institutions deploying agentic AI in financial crime operations have reported productivity improvements ranging from 200% to 2,000% – not because they replaced their people, but because each person is now doing work that actually requires human judgment. ## **The Financial Impact: What’s Actually at Stake** Fraud prevention is sometimes discussed in Canadian boardrooms primarily as a compliance cost: something that must be managed to satisfy regulators, rather than an investment with a measurable return. That framing undersells both the risk and the opportunity. The direct financial exposure is significant and growing. US consumers alone reported over $12.5 billion in fraud losses in 2024: nearly quadruple the $3.5 billion lost four years earlier. Canadian figures follow the same trajectory. A KPMG Canada survey found that 72% of respondents had lost up to 5% of business profits to AI-powered attacks in the previous twelve months alone. For a mid-sized Canadian bank, 5% of annual profit is not a rounding error. It is a material financial event that affects shareholder returns, capital ratios, and the ability to invest in growth. The indirect costs are equally significant and more difficult to quantify. Fraud losses that become public erode customer trust in ways that take years to recover. Regulatory findings: particularly those involving inadequate detection or reporting of suspicious activity, carry both financial penalties and reputational consequences that affect institutional credibility with regulators, counterparties, and enterprise clients. And in a competitive environment where digital banking experience has become a primary differentiator, the friction created by high false positive rates — legitimate transactions declined, customers asked to verify routine activity carries a measurable impact on retention and lifetime value. “The ROI conversation for AI fraud detection is actually quite compelling when you model it correctly,” says a Dedicatted financial advisory specialist. “Most institutions are only counting the direct fraud loss reduction. But when you factor in the compliance cost efficiency, the false positive reduction and its effect on customer experience, and the avoided regulatory exposure, the business case strengthens considerably. The institutions that have done that full analysis are not debating whether to invest. They’re debating how fast to move.” More than 93% of surveyed financial institutions globally are planning to invest in AI in the next two to five years, with fraud detection consistently ranking as the top application. The question for Canadian banking leaders is not whether this investment is coming – it is whether your institution is leading that transition or responding to it. ## The Cybersecurity Dimension AI fraud detection and cybersecurity are not the same function, but the line between them has become meaningfully blurred, and institutions that treat them as entirely separate domains are leaving a significant gap in their defences. The most sophisticated financial fraud attacks today are hybrid operations. They begin with a cybersecurity breach – a compromised credential, an exploited API vulnerability, a machine identity taken over and then use that access to facilitate financial crime. The attacker who compromises a service account doesn’t immediately drain accounts. They watch. They learn the institution’s transaction patterns, identify the approval workflows, understand which controls exist and how they are monitored. Then they act, in a way specifically designed to avoid triggering those controls. Addressing this requires that fraud and cybersecurity functions share intelligence in real time, not through periodic reporting cycles. An anomaly detected by the security operations center: unusual API call volumes, a service account accessing data outside its normal scope, a login from an unexpected geography should immediately inform fraud risk scoring for transactions associated with that account or customer. Currently, most Canadian institutions do not have the integration architecture to make that happen automatically. We’ve done assessments where the security team and the fraud team were both aware of signals that, taken together, would have clearly indicated an active attack. But because those signals lived in separate systems with separate alerting processes and separate escalation paths, neither team had the full picture. The attack succeeded. The integration work to connect those functions isn’t glamorous: it’s plumbing, but it is some of the highest-value risk reduction work we do. #### Dedicatted cybersecurity integration specialist. [Zero Trust](https://dedicatted.com/services/glossary-of-terms#term-zero-trust) architecture is increasingly relevant in this context. Rather than assuming that entities inside the network perimeter are trustworthy, Zero Trust continuously verifies the identity and integrity of every user, device, and application: before, during, and after they’ve accessed resources. For financial institutions where the perimeter has effectively dissolved across cloud environments, third-party integrations, and remote workforces, Zero Trust provides the architectural foundation on which both cybersecurity and fraud controls can operate with shared intelligence. [![zero](https://dedicatted.com/wp-content/uploads/2026/05/zero-1024x576.png)](https://dedicatted.com/wp-content/uploads/2026/05/zero-1024x576.png)AI also introduces its own security considerations that Canadian banking leaders must account for. Agentic AI systems can take autonomous action within financial workflows – represent a new category of attack surface. They can be vulnerable to prompt injection attacks that manipulate their inputs, adversarial examples designed to cause misclassification, and exploitation of the API connections that give them access to core systems. This is not an argument against deploying agentic AI. It is an argument for deploying it with the same rigorous security testing: red teaming, adversarial simulation, privilege boundary enforcement , that any other critical system would require. ## The Canadian Compliance Imperative Technology capability and regulatory compliance are often framed as competing priorities. In the Canadian context, they are increasingly the same requirement and institutions that have not recognized that alignment yet will be forced to by the regulatory environment that is arriving. FINTRAC’s suspicious transaction reporting regime demands not just that institutions detect financial crime, but that they document the reasoning behind every report and demonstrate that the detection process itself is sound. OSFI’s model risk management guidelines require that AI systems used in risk decisions be validated, monitored, and governed with the same rigour applied to any other risk model. And Canada’s Bill C-27, currently advancing through Parliament, will establish explicit legal requirements for AI explainability: high-impact automated decisions must be explicable to the people they affect and defensible to regulators. This creates a specific design requirement that many institutions are not yet meeting. An AI fraud detection system that produces accurate results but cannot explain how it reached them is a compliance liability in Canada, not a compliance solution. When a FINTRAC auditor asks why a suspicious activity report was filed or why it wasn’t “the model flagged it” is not an adequate answer. We talk to compliance officers across the sector who are genuinely excited about what AI can do for their detection capabilities, but they’re nervous about the governance side. And they should take that seriously. Bill C-27 is going to require explainability at a level that a lot of current AI deployments simply cannot provide. The institutions that build that in from the start will have a significant advantage when the legislation comes into force. The ones that retrofit it afterward will have a painful and expensive experience. Most Canadian institutions believe their AI governance is in reasonable shape. The ones that have actually benchmarked it against OSFI Guideline E-23 often discover otherwise: gaps in explainability documentation, model lifecycle processes that don’t meet the standard, third-party vendor risk that hasn’t been formally assessed. We built a free tool specifically for this: 30 questions across six categories, aligned to OSFI E-23, the EDGE principles, and the AMF AI Guidelines, with a personalized scorecard you can put in front of your board or your next OSFI examiner. It takes ten minutes. The findings usually take longer to sit with.**[ Find out where your institution actually stands with Dedicatted free assessment ](https://fsi-readiness.dedicatted.com/)** The institutions getting this right are building explainability in as an architectural requirement from day one. Every alert comes with a plain-language summary of what triggered it, which features drove the risk score, and what the system’s confidence level is. Every human decision is logged with the analyst’s reasoning. Every model is monitored for performance drift and recalibrated on a documented schedule. The audit trail is not an afterthought – it is the product. There is also a fairness dimension that Canadian boards are beginning to take seriously. AI models trained on historical data can encode systemic bias: disproportionately flagging certain customer demographics, or using proxy variables like postal codes that reflect socioeconomic patterns rather than genuine risk signals. The institutions monitoring for this proactively will be well positioned when regulators begin requiring documented bias assessments. Those that haven’t will face an uncomfortable conversation at an inopportune moment. Royal Bank of Canada has publicly discussed its investment in AI-driven compliance infrastructure specifically designed to produce explainable outputs for regulatory review — framing explainability not as a constraint on detection performance but as a core product requirement. That framing is where Canadian banking is heading. The question is whether your institution arrives there by design or by necessity. ## People, Judgment, and Accountability: The Human Side of AI Fraud Detection Every serious analysis of AI fraud detection arrives at the same point: the technology is only as effective as the people and governance structures around it. This isn’t a caveat – it is a design principle that separates institutions realizing value from those that don’t. AI processes scale, identifies patterns, and moves at machine speed. It does not make the judgment calls that define sophisticated financial crime cases. The customer behaving strangely because they are a victim of elder fraud and need protection, not investigation. The transaction pattern that resembles layering but has a legitimate cross-border business explanation that only a relationship manager would know. The suspicious activity report decision that carries reputational and legal consequences and requires a named human being to be accountable for it. Those decisions belong to people and effective AI fraud detection is designed to surface them to the right people with the right context, not to make them autonomously. One thing we push back on with clients who want maximum automation is the question of what happens when the system is wrong. And it will be wrong sometimes. The question is whether your organization has built the human oversight layer to catch those cases or whether you’ve automated past the point where a human being is still in the loop when it matters. Getting that balance right isn’t a technology problem. It’s a governance problem. #### Dedicatted implementation lead. The practical implication is an organizational design question as much as a technology question. Fraud, cyber, identity, and compliance functions operating in silos cannot mount a coordinated defence against threats operating across all those domains simultaneously. A deepfake wire fraud attempt involves social engineering, identity verification failure, payment controls, and potentially compromised internal communications: touching four different functional owners in most Canadian institutions, none of whom has full visibility. The institutions seeing the best outcomes from AI fraud detection have reorganized around cross-functional teams with shared intelligence, common metrics, and joint accountability for outcomes. The change management challenge is consistently underestimated. Building the technology takes months. Changing how analysts, compliance officers, and relationship managers work with that technology takes longer. Recent Mckinsey surveys found that only 26% of Canadian organizations have a tested, formal fraud incident response plan explicitly covering AI-powered attacks. That gap cannot be closed with technology.That gap cannot be closed with technology. It requires deliberate investment in training, role redesign, simulation exercises, and leadership that treats fraud preparedness as an ongoing operational priority rather than a project with a completion date. ## What Good Looks Like: Dedicatted in Practice Dedicatted took over full Managed Support Services for a global financial technology company operating a cloud-native platform across multiple regions – an environment with the same reliability, security, and compliance demands that Canadian banks running AI fraud detection face every day. The operational gaps were significant: no 24/7 coverage meant incidents occurring outside business hours went unresolved until someone became available, a critical exposure for any platform where fraud doesn’t observe business hours. Infrastructure patch debt had accumulated across EKS clusters and operating systems, precisely the unpatched components that machine identity exploitation attacks are designed to find. And a disaster recovery plan existed on paper but had never been tested. Nobody actually knew whether the platform would meet its recovery objectives when it mattered most. Under Dedicatted’s MSP model, the platform reached 99.97% uptime with 15-minute P1 response times, rolling patch management eliminating accumulated infrastructure debt, and a full disaster recovery simulation confirming cross-region recovery in under one minute. Security controls and compliance posture were strengthened across SOC 2, ISO 27001, and GDPR requirements – the same compliance framework Canadian banks must demonstrate to OSFI and FINTRAC examiners. The lesson applies directly: the most sophisticated AI fraud detection capability means nothing if the platform running it goes dark at 2am, or if an unpatched component becomes the entry point for the exact attack the system was built to prevent. Operational resilience isn’t the unglamorous part of fraud prevention. For Canadian banks serious about AI-powered financial crime defence, it is fraud prevention. Want to see exactly how we did it? **[Read the full case study](https://dedicatted.com/insights/scaling-platform-stability-and-operational-maturity-with-managed-support-services)** ## What Canadian Banking Leaders Should Do Now Canadian banking leaders who are still treating AI fraud detection as a technology project are looking at it through the wrong lens. This is a strategic risk question – one that simultaneously touches financial performance, regulatory standing, customer trust, and competitive positioning. The industrialization of fraud means that the gap between institutions with sophisticated AI-powered defences and those relying on legacy controls widens every year, as fraudsters access better tools, accumulate better intelligence on which institutions are easier targets, and refine their approaches based on what has worked before. Here’s a practical starting point: before your next board discussion on AI fraud detection, know your governance score. Our free OSFI AI Governance Assessment benchmarks your institution against Guideline E-23, the EDGE principles, the AMF AI Guidelines, and the NIST AI Risk Management Framework – the exact standards your regulators are using to evaluate you. It covers the six areas where Canadian banks most commonly have gaps: governance and oversight, explainability and transparency, data quality, ethics and fairness, model lifecycle management, and third-party risk. Thirty questions. Ten minutes. A scorecard that tells you not just where you stand, but what closing each gap would actually require.[ **Take the free assessment and walk into your next board meeting prepared**](https://fsi-readiness.dedicatted.com/) “We see institutions that have invested seriously in AI fraud detection using it as a differentiator in enterprise client conversations,” notes a Dedicatted senior advisor. “Large corporate clients, high-net-worth individuals: they are asking harder questions about how their bank protects them. The answer ‘we have rule-based transaction monitoring and an alert team’ is not landing the same way it did three years ago.” The near-term priorities are clear. Audit your actual fraud exposure against today’s threats: synthetic identities, deepfake social engineering, machine identity compromise, insider risk, not against the landscape of five years ago. Invest in data governance before model sophistication, because the most common barrier to effective AI fraud detection is fragmented and inconsistently structured data, not model quality. Design for explainability from the beginning, because Bill C-27’s requirements are coming and retrofitting compliance into AI systems is expensive and often inadequate. Integrate your fraud and cybersecurity functions around shared intelligence, because the most damaging attacks of the next five years will operate across both domains simultaneously. And define the human role explicitly, because AI operating without clear governance boundaries is a liability, not an asset. Most importantly: treat fraud prevention as what it has become. Not a compliance function that surfaces to the board when something goes wrong. A strategic capability: one that protects revenue, protects customers, satisfies regulators, enables growth, and increasingly differentiates institutions that are serious about the future from those managing the present. The institutions that will lead Canadian banking in five years are building that capability now. The signal to act arrived some time ago. The question is what your institution is doing with it. At Dedicatted, financial crime prevention is work we do every day: from AML transformation and AI fraud detection architecture to the managed infrastructure that keeps those systems running at the reliability Canadian banking demands. If you want to understand where your institution’s fraud detection posture stands today and what it would take to get it where it needs to be, we’d be glad to [**start that conversation.**](https://dedicatted.com/contact) We collaborate closely with financial institutions to design, implement, and continuously improve AI-powered fraud detection systems that are built for performance and compliance from the ground up – explainable outputs, auditable decision trails, human-in-the-loop governance, and the operational resilience to run 24/7 without gaps. As an **[AWS Premier Tier Partner](https://aws-marketplace-catalogue.dedicatted.com/)** with the Generative AI Competency, MSP designation, and a place in the AWS Agentic AI Pilot program, we bring a combination no other Canadian partner holds. We have built and operated agentic AI systems across financial services, healthcare, manufacturing, and SaaS and we understand the specific regulatory expectations Canadian banks face under FINTRAC, OSFI, and the advancing provisions of Bill C-27. Our team of over 50 AI, Data, and ML experts is ready to map your fraud detection use case, assess your current posture against today’s threat environment, and scope a path forward that is honest about effort, timeline, and cost. [**Talk to us.**](#contact "Talk to us.") [![Banner with Dedicatted logo on a dark, abstract network background. Large headline reads: “Helping you harness the power of Agentic AI to unlock your business innovation,” with “unlock your business innovation” highlighted in teal. Below the text is a row of AWS Partner badges indicating multiple competencies and certifications.](https://dedicatted.com/wp-content/uploads/2026/05/Group-2751-1024x307.png)](https://dedicatted.com/wp-content/uploads/2026/05/Group-2751-1024x307.png) **Categories:** Blog **Services:** Agentic AI, Generative AI, Security Compliance **Industry:** Financial Services, High Tech Software --- ### [How to Manage AWS SageMaker and Bedrock Costs: A FinOps Playbook for AI Teams](https://dedicatted.com/insights/how-to-manage-aws-sagemaker-and-bedrock-costs-a-finops-playbook-for-ai-teams) **Published:** June 5, 2026 **Author:** Denys Yermakov **Content:** There’s a number every CTO in financial services, insurance, and SaaS has seen at least once: a cloud bill that grew 40% quarter-over-quarter with no corresponding growth in revenue, users, or shipped features. According to recent surveys , 90% of multinational organizations have already identified measurable AI/ML cost optimization opportunities they haven’t acted on. 60% report persistent underutilized AWS cloud resources they can’t explain. The cloud FinOps market is on track to reach USD 26.9 billion by 2030 precisely because this problem is getting harder, not easier – AI workloads are the fastest-growing line item on the cloud bill, and the least understood by the people approving the budget. The root cause is structural. AI/ML workloads don’t behave like the compute and storage costs finance teams learned to forecast. They’re priced through tokens, provisioned throughput units, and GPU-hours – mechanisms that don’t map to how annual budgets are built. A single poorly engineered prompt can cost more than a thousand well-designed ones. A GPU instance left running through a long weekend can erase a month of savings plan optimization. A model deployed on the wrong instance type at launch will overspend every day for the life of that model in production. None of this shows up in a standard cloud cost dashboard without governance infrastructure built specifically for AI/ML workloads. This article is the operational guide for building that infrastructure: phase by phase, mapped to the AWS Well-Architected Machine Learning Lens, with the real-world patterns that show what it looks like when it works. ## **Before we start: five terms you’ll need** **FinOps** – the discipline of applying financial accountability to cloud infrastructure. It bridges engineering decisions and financial outcomes by giving both teams a shared language around cost. **Tokens** – the unit LLMs read and write, and how API usage gets billed. Roughly ¾ of a word each. Input tokens (what you send) and output tokens (what the model returns) are priced separately. A poorly constructed prompt can cost 10× more than a well-engineered one doing the same job. **Provisioned Throughput Units (PTUs)** – reserved LLM inference capacity you commit to and pay for whether you use it or not. Cheaper per-token than on-demand when utilization is high; more expensive when it isn’t. **Showback vs. chargeback** – showback tells teams what they spent; chargeback makes them pay for it out of their own budget. Showback drives awareness. Chargeback drives behaviour change. **Unit economics** – cloud spend expressed per meaningful business output: cost per inference, cost per training run, cost per 1,000 tokens. This is what transforms a billing dashboard into a decision-making tool. ## Why AI/ML Costs Are Different Before getting into specifics, it’s worth understanding why AI/ML cost management is a materially different discipline from general cloud FinOps. **Token-based billing doesn’t behave like compute billing.** A single poorly constructed prompt can consume more tokens than a hundred well-engineered ones. Input and output tokens are often priced differently. Context window size: how much prior conversation the model can “see” directly multiplies your token count. None of this is visible in a standard cost dashboard without purpose-built attribution. **GPU scarcity means provisioning decisions have long lead times.** Unlike CPU instances where scaling is near-instant, GPU capacity, especially for large training runs: may require weeks of lead time to secure at scale. That means cost forecasts need to be built ahead of provisioning decisions, not after. **Experimentation is structurally expensive.** ML development is iterative. Teams run hundreds of small training jobs, compare model variants, retrain on new data, and tune hyperparameters continuously. Each of those operations costs money, and without guardrails, experimentation costs in development environments can rival production inference costs. **The ML lifecycle has six distinct cost phases.** The AWS Well-Architected ML Lens defines these as: business goal identification, ML problem framing, data processing, model development, deployment, and monitoring. Each phase has its own dominant cost drivers, anti-patterns, and optimization levers. Managing total AI/ML spend requires operating across all six simultaneously. ## Phase 1: Business Goal Identification – Build the ROI Case Before You Build the Model The most expensive mistake in ML is building a model for a problem that didn’t need one. The second most expensive is building the right model without defining how you’ll measure its value. AWS Well-Architected best practice makes this explicit: define overall return on investment and opportunity cost before committing resources. This means classifying every ML initiative as either research-oriented (long-horizon, exploratory, uncertain returns) or development-oriented (applying established methods to near-term business value). The classification matters because the financial model is completely different – research projects need budget flexibility and patience; development projects need clear ROI timelines and production cost projections. For CTOs and CFOs in regulated industries, this distinction also has governance implications. A research project that unexpectedly transitions into a production workload without updated cost modeling is a budget control failure waiting to happen. **What good looks like:** A cost-benefit model built before the first GPU spins up. That model should account for data preparation, infrastructure, data scientist time, ongoing maintenance and retraining, and the business cost of model errors – not just raw compute. Use AWS Cost Explorer, AWS Budgets, and AWS Cost Anomaly Detection to set the financial baseline and alerting thresholds from day one. The second foundational practice here is using managed services to reduce total cost of ownership. The TCO of **Amazon SageMaker AI** over a three-year period is substantially lower than equivalent self-managed infrastructure on EC2 or EKS, primarily because you’re not paying for the operational overhead of managing the infrastructure layer. For organizations with constrained ML engineering talent, which is most organizations this isn’t just a cost argument, it’s a capacity argument. ## Phase 2: ML Problem Framing – Validate the Approach Before Committing to the Architecture Not every business problem that sounds like an ML problem is one. Fraud detection with constantly evolving patterns benefits from ML. Inventory reorder logic with stable rules probably doesn’t. Before investing in model development, AWS Specialists recommend a structured comparison against simpler alternatives: rules-based systems, lookup tables, statistical heuristics. The cost implication is significant: a rules-based system that achieves 85% of the accuracy of a custom ML model at 10% of the operational cost is often the right answer for a VP of Engineering trying to deliver value efficiently. When ML is the right answer, they introduce the custom-versus-pre-trained decision. This is where many organizations leave money on the table. **Amazon SageMaker AI JumpStart** provides access to over 150 pre-trained open-source models deployable in minutes. **Amazon Bedrock** offers foundation models from leading providers through a single managed API. For many common use cases: document classification, sentiment analysis, entity extraction, code generation starting with a pre-trained model and adapting it via fine-tuning or RAG is faster, cheaper, and less risky than custom development. The hidden cost in always building custom: data scientist time. Senior ML engineers are one of the scarcest and most expensive resources in technology. Every hour they spend rebuilding functionality available in a managed service is an opportunity cost, not just a direct cost. ## Phase 3: Data Processing – Where 60–80% of ML Time Goes Data preparation consistently consumes the majority of ML project time and cost. The FinOps opportunity here is structural: managed tooling, feature reusability, and automated labeling. Our practice addresses data labeling, which can be surprisingly expensive at scale. **Amazon SageMaker Ground Truth Plus** delivers up to 40% cost reduction versus building custom labeling infrastructure, by combining an expert workforce with active learning that automates labeling for similar items over time. What is active learning in data labeling? A technique where the labeling system learns from human annotations and begins automatically labeling items it’s confident about only flagging uncertain cases for human review. Over time, the proportion requiring human labeling decreases, reducing cost per labeled record. SageMaker Ground Truth has active learning built in. For organizations in financial services or insurance where labeled datasets are often proprietary and expensive to produce, this is material. Our Practice targets the analyst productivity problem: data preparation shouldn’t require custom Python scripts for every task. **Amazon SageMaker AI Data Wrangler** and **SageMaker AI Canvas** provide visual interfaces that dramatically reduce the time from raw data to model-ready features. The integration of Amazon Q for natural language data preparation assistance further compresses this timeline. The highest-leverage cost practice in data processing is enabling feature reusability through **Amazon SageMaker AI Feature Store**. What is a Feature Store? A centralized repository for storing, sharing, and reusing the engineered data inputs (called “features”) that ML models are trained on and scored against. Without a Feature Store, different teams independently recalculate the same features: paying for the same compute multiple times and introducing inconsistency between training and production environments. SageMaker Feature Store has two storage layers: an online store (millisecond-latency retrieval for real-time inference) and an offline store (historical data in S3 for model training and batch scoring). Both stay in sync automatically. ## **Phase 4: Model Development – Where the Largest Cost Variability Lives** This is the phase where cloud bills can move by orders of magnitude based on a handful of engineering decisions. Fourteen distinct cost optimization best practices apply here in the Well-Architected ML Lens. The most impactful for organizations at scale: **Instance selection is not a set-and-forget decision**. The same training job can cost dramatically different amounts depending on whether you’re running on a P3, P4, G4, or Trainium instance and the right choice depends on your model architecture, dataset size, and training approach. Deep learning on image, video, or language data benefits from GPU-accelerated instances. Traditional ML algorithms are often more cost-effectively trained on CPU instances. The anti-pattern here is significant and widespread: teams provision the same instance type for training and inference, or default to the largest available GPU instance “to be safe.” Both behaviours create unnecessary cost. **Manage the experimentation tax (MLCOST04-BP03, BP08, BP09).** Local training for small-scale experiments avoids unnecessary cloud spend during prototyping. Starting training runs with small datasets to validate approach before scaling to full data reduces the cost of failed experiments. Stopping resources when not in use , through automated policies, not manual discipline eliminates idle GPU costs. SageMaker AI supports automatic shutdown of training jobs and notebook instances; these features should be enabled by default in any cost-conscious engineering organization. **Managed Spot Training (MLCOST04-BP06) is the single highest-ROI cost lever for training workloads.** Spot instances can reduce training costs by up to 90% compared to on-demand pricing. SageMaker AI’s managed Spot training handles checkpointing and automatic recovery from interruptions, making it practical for production training pipelines, not just experimental ones. **Quantization changes the economics of inference**. Accuracy impact is typically minimal for production use cases. A Llama 2 13B model quantized to 4-bit can run on a $500/month instance instead of a $2,500/month instance with no measurable degradation in output quality for most text tasks. Quantization is an engineering decision made once at deployment that determines your cost structure permanently. **Budget and tagging discipline from the start**. Every training job, every endpoint, every processing job should be tagged with project, team, environment (dev/staging/prod), and model version. **AWS Cost Categories** and **AWS Cost Explorer** turn these tags into real-time spend attribution. Without tagging, you have a total AI/ML bill. With tagging, you have accountability and the ability to identify which model versions, teams, or experiments are driving cost growth before it becomes a problem. **Tagging is the prerequisite for everything else.** AWS resource tags are key-value pairs attached to every cloud resource. A well-designed tag taxonomy looks like this: `team: fraud-platform`, `environment: production`, `model: credit-risk-v3`, `cost-centre: retail-banking`. These tags flow through to Cost Explorer, AWS Budgets, and Cost Anomaly Detection, enabling cost attribution by team, model, and environment without any custom tooling. Tagging must be established before production deployment. Retrofitting it after the fact against six months of untagged costs is one of the most painful and avoidable FinOps remediation exercises. ## Phase 5: Deployment – Matching Infrastructure to Inference Patterns [Deployment](https://dedicatted.com/services/glossary-of-terms#term-deployment) is where AI/ML costs become recurring. The decisions made here determine the ongoing monthly spend for the life of the model in production. Our practicioners introduce the core deployment decision framework: the right hosting option depends on your inference pattern, not your preference. ### **Four SageMaker inference modes: which one fits your workload?** Right-sizing the inference fleet. SageMaker AI Inference Recommender automates this process: it benchmarks your model against different instance types and provides recommendations based on actual latency and throughput performance, not guesswork. Multi-model endpoints, which host multiple models on a single instance, can dramatically improve utilization for organizations with many smaller models. The provisioned throughput decision requires a different cost model. For API services like Amazon Bedrock and Azure OpenAI, the choice between on-demand per-token billing and provisioned throughput (PTU commitment) isn’t purely about cost – it’s about utilization. ### **On-demand vs. provisioned throughput – the utilization test** On-demand billing charges per token consumed. No commitment, no minimum, full flexibility. The right default for variable or growing workloads. > Provisioned throughput charges a fixed hourly rate for a reserved capacity block, regardless of actual usage. At 70%+ utilization, it’s cheaper per token than on-demand. At 30% utilization: typical of a business application used only during office hours: the effective cost-per-token can exceed on-demand pricing. > > The test before committing: benchmark your peak tokens-per-minute, calculate your average daily utilization rate, and model both scenarios at your actual traffic pattern. Buying PTUs without this calculation is one of the most common and expensive AI/ML FinOps mistakes. For regulated industries: AWS Inferentia2 and Trainium2 instances provide dedicated, single-tenant compute for inference and training workloads, which matters for data residency and compliance requirements in financial services, insurance, and healthcare. ## Phase 6: Monitoring – Protecting ROI After Launch Model monitoring is where AI/ML FinOps closes the loop between technical performance and financial performance. A model that degrades silently costs money two ways: it consumes inference compute while delivering declining business value, and eventually it requires expensive emergency retraining. Our practitioners establish the foundation: monitor usage and cost by ML activity. **Amazon SageMaker AI Model Monitor** continuously checks for data drift, model drift, and prediction quality degradation. **Amazon CloudWatch** surfaces these metrics alongside infrastructure utilization, enabling unified dashboards that correlate model performance with cost per inference. ### **Data drift and model drift: the silent cost multipliers** Data drift occurs when the statistical distribution of incoming data shifts away from what the model was trained on. Example: a fraud detection model trained before a new payment method was introduced starts seeing transaction patterns it was never trained on and its accuracy degrades without any error being thrown. > Model drift occurs when the relationship between inputs and the correct output changes due to real-world shifts : customer behaviour after a market event, seasonal patterns the model didn’t learn. Both types degrade performance silently, consuming inference cost while delivering less business value every day they go undetected. > > SageMaker Model Monitor detects both automatically, comparing production data statistics against a baseline established at deployment and alerting when deviation exceeds your configured thresholds. **[We at Dedicatted](https://dedicatted.com/services/artificial-intelligence)** take this further: monitor return on investment for ML models directly. This means establishing business KPIs tied to model outputs: conversion rates, fraud catch rates, churn prediction accuracy and tracking them alongside cost metrics. A model that costs $15,000/month in inference but prevents $500,000/month in fraud losses has a clear ROI story. A model that costs $8,000/month and hasn’t moved its target business metric in six months is a candidate for replacement or simplification. ## The FinOps Maturity Ladder for AI/ML Organizations in the early stages of AI/ML adoption typically start with reactive cost management: they see the bill, investigate the spike, add a guardrail. Mature organizations operate with proactive financial governance built into the ML development lifecycle from the first commit. The markers of maturity look like this: The distance between crawl and run isn’t primarily a tooling problem. AWS provides most of the instrumentation needed. It’s a process and accountability problem and that’s exactly where a FinOps discipline, applied specifically to AI/ML workloads, delivers its value. ## Where Dedicatted Can Help Regulated industries face a compounding challenge: the AI/ML cost management practices described above need to coexist with compliance requirements around data residency, model explainability, audit trails, and access controls. A chargeback model built on tagging is straightforward in a greenfield environment; it’s considerably more complex in a financial services organization managing dozens of business units across multiple regulatory jurisdictions. Dedicatted’s work as an **[AWS Premier Tier Services Partner](https://aws-marketplace-catalogue.dedicatted.com/)** and responsible AI advisory firm is precisely at this intersection. We help organizations in financial services, insurance, retail, and SaaS build the governance frameworks, tagging architectures, and FinOps practices that make AI/ML cost management both rigorous and compliant without slowing down the teams building the models that matter. The cloud bill is decodable. It just requires the right lens. ## Real-World Use Cases: What FinOps for AI/ML Looks Like in Practice The frameworks in this article aren’t advisory positions – they’re the architecture of work we’ve already done. What follows are three engagements drawn from Dedicatted’s practice in regulated industries. Client names are withheld; the problems, the interventions, and the outcomes are real. ### Financial Services: Building a Compliance-Ready Chargeback Model from Zero **The situation.** A Canadian financial institution with multiple business lines: personal banking, commercial lending, and wealth management had been running AI/ML workloads on AWS for 18 months. Each line of business had its own data science team, its own SageMaker environment, and its own interpretation of what “cloud cost” meant. Finance could see a total monthly AI/ML bill. They could not tell which business line was responsible for any part of it. When the internal audit team flagged the opacity of AI infrastructure spend as a governance gap, the engagement started. **What we built.** Dedicatted designed and implemented a three-layer cost attribution architecture. The first layer was a tagging taxonomy aligned with the institution’s regulatory reporting structure: every SageMaker training job, processing job, endpoint, and storage bucket tagged with business line, model identifier, environment (dev/staging/prod), and cost centre. The second layer was AWS Billing Conductor, configured to distribute shared infrastructure costs: Reserved Instance savings, shared data pipelines, centralized Feature Store across business units according to actual consumption ratios rather than arbitrary splits. The third layer was a set of AWS Cost Explorer dashboards, scoped by business line and surfaced to both the engineering leads and their corresponding finance partners – the first time those two groups had looked at the same number at the same time. **What changed.** Within 90 days of go-live, each business line’s data science team was receiving weekly cost attribution reports tied to their own budget. A credit risk model that had been running an oversized real-time endpoint 24/7 : despite being used only during business hours was identified and moved to a scheduled endpoint pattern, cutting its monthly inference cost by 61%. The fraud detection team, seeing their own cost-per-inference for the first time, voluntarily initiated a model quantization review that reduced their GPU instance requirement by one tier. **The principle behind this engagement:** Cost accountability only changes behaviour when the people making the engineering decisions can see the financial consequences of those decisions in their own reporting line. Chargeback is the mechanism. Tagging architecture is the prerequisite. Here’s a practical starting point: before your next board discussion on AI fraud detection, know your governance score. Our free OSFI AI Governance Assessment benchmarks your institution against Guideline E-23, the EDGE principles, the AMF AI Guidelines, and the NIST AI Risk Management Framework – the exact standards your regulators are using to evaluate you. It covers the six areas where Canadian banks most commonly have gaps: governance and oversight, explainability and transparency, data quality, ethics and fairness, model lifecycle management, and third-party risk. Thirty questions. Ten minutes. A scorecard that tells you not just where you stand, but what closing each gap would actually require.[**Take the free assessment and walk into your next board meeting prepared**](https://fsi-readiness.dedicatted.com/) ### Regulated Enterprise: Embedding FinOps Governance Across a Multi-Team AWS Estate **The situation.** A federally regulated Canadian enterprise with over 40 AWS accounts and seven internal product teams had reached a FinOps maturity ceiling. They had tagging policies inconsistently applied. They had AWS Budgets alerts: mostly ignored because they were set at account level, not workload level. They had a central cloud team trying to govern spend they couldn’t attribute and couldn’t enforce. When AI/ML workloads began scaling across three of the seven teams simultaneously, the governance gap became urgent. **What we built.** Dedicatted ran a four-week FinOps maturity assessment across all 40 accounts, mapping actual tagging compliance rates, identifying the 23 resource types generating 89% of AI/ML spend, and benchmarking utilization against the AWS Compute Optimizer recommendations that had been sitting unreviewed in the console for months. The output was a prioritized remediation roadmap with estimated cost impact per initiative. The first intervention was tagging enforcement: we implemented AWS Config rules that flagged untagged SageMaker resources within 15 minutes of creation and triggered a notification to the owning team’s Slack channel. Non-compliance dropped from 67% to under 8% within six weeks. The second intervention was Budget Actions: Budgets were rebuilt at workload level one per model family, not one per account with automated actions that throttled new training job launches when a workload exceeded 85% of its monthly allocation before the 25th of the month. The third intervention was a quarterly commitment optimization cadence: Dedicatted reviews the client’s SageMaker Savings Plans coverage ratio and Reserved Instance utilization every quarter and adjusts commitments based on the prior 90 days of actual consumption patterns. **What changed.** Total AI/ML spend in the 12 months following the engagement was 34% lower than the preceding 12 months, despite a 40% increase in the number of models in production. The cost reduction came entirely from governance: eliminating idle resources, right-sizing endpoints, enforcing Spot Training for non-critical runs, and capturing Savings Plans discounts on workloads that were running continuously but unprotected by commitments. **The principle behind this engagement:** FinOps maturity isn’t a one-time project. The quarterly commitment cadence is where the ongoing savings live and it requires someone who tracks AWS pricing model changes and knows when your usage patterns have shifted enough to justify restructuring your commitments. ### Retail & CPG: Turning Retraining Cadence into a Governed Budget Line **The situation.** A Canadian grocery retailer was running SageMaker-based demand forecasting across approximately 180,000 SKUs in 340 store locations. The forecasting platform had been built over two years and was genuinely good – forecast accuracy was a competitive advantage in their category management operation. The cost problem was invisible to everyone except the cloud team: retraining was running on a weekly cadence for all SKU categories, including shelf-stable products where a monthly cycle would produce statistically identical forecast accuracy. Each weekly retraining run was on-demand compute, unprotected by Spot pricing, because the original architecture assumed time-sensitivity that the majority of SKUs didn’t have. Finance had no view into retraining cost at all: it appeared as an undifferentiated line in the SageMaker billing. **What we did.** Dedicatted started with a retraining cost audit: we instrumented every training job with tags capturing SKU category, retraining trigger (scheduled vs. drift-detected vs. manual), and model version. Two weeks of tagged data gave us the first granular picture of where retraining compute was going. The analysis showed that 71% of weekly retraining compute was spent on SKU categories where a monthly cadence, tested against historical accuracy data, produced forecast error within 0.4% of the weekly model ,effectively identical for planning purposes. The architecture change had three components. First, we segmented the retraining schedule: perishables and promotional SKUs remained on weekly cadence; shelf-stable and slow-moving categories moved to monthly. Second, we migrated all monthly retraining jobs to SageMaker Managed Spot Training with checkpointing: these ran on weekend nights when interruption risk was lowest and cost was highest priority. Third, we implemented warm-start HPO: rather than running full hyperparameter search on each monthly cycle, the platform now initializes from the prior cycle’s best configuration and runs a bounded search of 12 trials rather than the previous 80+. We built a unit economics dashboard: cost per training run by SKU category, cost per forecast accuracy point, that the head of supply chain and the finance controller now review together monthly. It was the first time the two had a shared metric connecting a model quality decision to a budget consequence. **What changed.** Monthly retraining cost fell 58% within two billing cycles. Forecast accuracy across the platform was statistically unchanged. The retraining budget is now a line item in the annual technology plan forecast by SKU category, reviewed quarterly, and adjusted when category composition or promotional volume shifts. The finance controller described it as the first AI/ML cost they could actually budget for. **The principle behind this engagement:** Retraining frequency is almost always set by data scientists optimizing for accuracy with no visibility into cost. The right cadence is the one that achieves acceptable accuracy at the lowest training cost – a tradeoff that requires finance and data science to be looking at the same unit economics. **Categories:** Blog **Services:** Cloud Modernization **Industry:** Financial Services, Healthcare, Insurance --- ### [Scaling e-com for Prime Day: Headless Architecture on AWS](https://dedicatted.com/insights/scaling-e-com-for-prime-day-headless-architecture-on-aws) **Published:** June 23, 2026 **Author:** Dedicatted **Content:** Every July, a familiar ritual plays out across retail operations teams. War rooms fill up. Engineers go on-call. Someone quietly refreshes the latency dashboard every ninety seconds. Slack channels that are normally quiet at 3 AM light up with incident threads. Amazon Prime Day 2026 was no different , except the numbers got bigger. Hundreds of millions of transactions compressed into 48 hours. Traffic spikes that make Black Friday look like a Tuesday. Customers arriving simultaneously from mobile apps, social commerce feeds, voice assistants, in-store kiosks, and the web.Some brands processed every order without a single incident page. Others didn’t survive the first wave. Whether the commerce system had been designed to absorb demand spikes by scaling the right layer independently, or whether it was still running on a monolithic foundation that treats the entire stack as one unit. This article explains what headless commerce architecture is, why it performs where traditional systems fail, how AWS services form the infrastructure backbone, and what a practical migration path looks like for teams that are still on legacy platforms and have the next peak event already on the calendar. ## Why Legacy Commerce Architecture Fails at Scale What makes today’s fraud environment categorically different isn’t just the technology – it’s the economics. For most of financial crime’s history, sophisticated fraud was expensive to execute. It required skilled operatives, significant preparation time, and manual effort that naturally limited scale. AI has dissolved all three constraints simultaneously. The same machine learning tools, the same large language models, the same automation capabilities available to your technology team are available to the people targeting your institution, often deployed faster, with fewer governance hurdles, and at a fraction of the cost. The result is fraud that is highly personalized, adaptive, and cross-channel by design. Four threat vectors are hitting Canadian banks hardest right now Traditional eCommerce platforms: Oracle ATG, IBM WebSphere, Magento, Demandware were built in the 1990s and early 2000s for a specific context: desktop web browsers, predictable traffic, and a single selling channel. They solved that problem well. The architecture made sense for what it was designed to do. The design was monolithic by nature. The frontend templates, the checkout engine, the product catalog, the order management logic, the tax calculation, the promotion rules – all of it lived in the same tightly coupled system, deployed together, scaled together, and maintained together. Change one layer, and you risk breaking another. Update the checkout flow, and you might inadvertently affect how promotional discounts calculate. Patch a security vulnerability in the product search service, and you need a full platform deployment. That coupling created a compounding maintenance burden over time. Every new feature required understanding the entire system. Every deployment required regression testing across the full stack. Every scaling decision required provisioning infrastructure for everything, even when only one service was under load. ### What Happens Under Traffic Spikes When Prime Day traffic arrives, the bottleneck in a monolithic system is almost never the commerce logic. The product catalog does not suddenly become computationally expensive. The order management system does not struggle with a price lookup. The bottleneck is the presentation layer: the rendering, the session management, the API calls that populate the page the customer is looking at. In a monolithic architecture, you cannot scale the presentation layer independently. To handle more storefront requests, you scale the entire application, including the checkout engine, the catalog service, the promotion engine, all of it: even though only the frontend is under pressure. That is expensive, slow, and often insufficient, because the system was not designed to be distributed in the first place. The failure modes are predictable and well-documented. Timeouts on the storefront cascade into checkout failures. Session state becomes inconsistent across instances. The database, which was designed for a single-instance deployment, becomes the global bottleneck. Teams spend the peak event managing the architecture instead of watching the revenue. ### The Channel Fragmentation Problem Beyond peak performance, there is a structural problem that accumulates over years rather than hours. Traditional eCommerce platforms were built for one channel: the desktop web. Every assumption in the architecture: the template system, the session model, the rendering pipeline was built around that single surface. As the number of customer touchpoints expanded, most organizations handled it by building separate systems for each new channel. A mobile app got its own backend. An in-store POS system ran on a different commerce engine. A marketplace integration became a separate data feed. Each channel had its own pricing logic, its own inventory view, its own promotion rules. None of them talked to each other reliably. The result is what the industry calls a scattered IT landscape: inconsistencies in product data, pricing, and inventory across channels; high total cost of ownership from distinct licensing fees and integration costs; and an IT team that spends more time synchronizing systems than building new capabilities. Gartner’s data on this is unambiguous. Organizations that have adopted a composable architecture: the broader pattern that headless commerce belongs to outpace competitors by 80% in the speed of new feature implementation. That gap does not come from working harder. It comes from removing the architectural dependencies that slow every deployment down. 47% of consumers now expect a webpage to load in two seconds or less. Our own research found that a single second of improvement in page load time corresponds to a 2% increase in conversions: a number that, at Prime Day scale, represents millions of dollars per hour. A website that performs adequately on a normal Tuesday will fail these expectations under peak load, and customers who encounter a slow or broken experience during a high-intent shopping event do not wait for the page to load. They leave. #### Serhii Semenchenko, CTO at Dedicatted Beyond performance, 73% of consumers now use multiple channels along their shopping journey. They research on mobile, compare on desktop, purchase through a social commerce integration, and return in-store. Each of those touchpoints needs to present consistent pricing, accurate inventory, and seamless cart state. A scattered IT landscape cannot deliver that. A unified commerce backend exposed through APIs can. ## What Headless Commerce Actually Is The definition is precise: headless commerce separates the frontend presentation layer (the “head”) from the backend commerce engine. The frontend is what customers see and interact with the storefront, the product pages, the checkout flow, the account dashboard. The backend is what handles the business logic: product catalog management, pricing and promotions, cart and checkout processing, order management, inventory, customer profiles, and payment workflows.In a traditional monolithic system, these two layers are tightly coupled. They share a database. They are deployed together. The frontend templates are hardcoded to expect specific data structures from the backend. They cannot evolve independently. In a headless architecture, the frontend and backend communicate exclusively through APIs, typically REST APIs or GraphQL. The backend does not know or care what the frontend looks like. The frontend does not care how the backend stores or processes data. Each side can be updated, replaced, or scaled without the other. [![iyqkbygixkkygh7otm36kscjqlo1bkv8iysiazpmru7qnre 56hf g08ekyf1s6juq2ziodrmurym4 aummhrudtxgijxwxgequyvo3dqtbbibof3fofchoyblpbjebsiios4o6z 2](https://dedicatted.com/wp-content/uploads/2026/06/iyqkbygixkkygh7otm36kscjqlo1bkv8iysiazpmru7qnre_56hf_g08ekyf1s6juq2ziodrmurym4_aummhrudtxgijxwxgequyvo3dqtbbibof3fofchoyblpbjebsiios4o6z-2-1024x388.png)](https://dedicatted.com/wp-content/uploads/2026/06/iyqkbygixkkygh7otm36kscjqlo1bkv8iysiazpmru7qnre_56hf_g08ekyf1s6juq2ziodrmurym4_aummhrudtxgijxwxgequyvo3dqtbbibof3fofchoyblpbjebsiios4o6z-2-1024x388.png)### What This Means in Practice The practical consequences of that separation are significant and immediate. **A single backend powers every channel simultaneously.** The same product catalog, pricing engine, and order management system that serves the web storefront also serves the mobile app, the in-store POS terminal, the voice assistant integration, and any future channel. A product update made once propagates everywhere in near-real time. A promotion configured once applies across all surfaces. The inventory view is consistent regardless of where the customer is looking. **Frontend and backend teams work independently.** Marketing and digital experience teams can redesign the storefront, launch campaign landing pages, run A/B tests, and ship UX improvements without waiting for backend deployments. Backend engineers can update business logic, migrate infrastructure, or integrate a new payment provider without coordinating a frontend release. Each team moves at its own pace. **Updates propagate in near-real time.** In a traditional commerce architecture, changes to the frontend interface can take minutes or hours to reflect. In a headless architecture, because the frontend fetches data from the backend through APIs at request time, updates are reflected immediately. During a flash sale, a price change or an inventory update is live the moment it is made in the backend. **The technology stack is no longer fixed.** Because the frontend and backend communicate through a standard API layer, each side can be built with the best available technology for its purpose. The frontend might be built in Next.js or React. The backend might be a custom microservices implementation. The two can evolve independently as technology improves and requirements change. ### Headless, MACH, and Composable: Understanding the Terminology These three terms are often used interchangeably and they are related but distinct. Understanding the differences matters for having precise internal conversations about architecture strategy. **Headless commerce** is specifically about the decoupling of the frontend from the backend. It is a design pattern, not a platform or a product. **MACH architecture** is the broader framework: [Microservices](https://dedicatted.com/services/glossary-of-terms#term-microservices)-based, API-first, Cloud-native, and Headless. It describes the full set of principles that modern commerce systems should be built on, of which headless is one pillar. **Composable commerce** is the strategic approach that MACH enables: the ability to assemble a commerce platform from best-of-breed services rather than purchasing a monolithic all-in-one solution. Instead of one platform that handles everything (CMS, search, checkout, loyalty, personalization), you choose the best tool for each function and compose them through APIs. ## The MACH Pillars in Detail ### Microservices: Independence at the Function Level Microservices architecture decomposes a complex application into small, independently deployable services, each responsible for a specific business function. In a commerce context, this means separate services for the product catalog, cart management, checkout, order management, pricing and promotions, loyalty, tax calculation, inventory, and customer profiles. Each service is self-contained. It has its own data store, its own deployment pipeline, and its own scaling parameters. It communicates with other services through well-defined APIs. It can be updated without requiring changes to any other service. It can be scaled independently based on actual load, if checkout is under pressure during a peak event, only the checkout service needs additional capacity. The operational benefits compound over time. A bug in the promotions engine does not take down checkout. A deployment of the loyalty service does not require a maintenance window for the catalog. A new integration with a third-party tax provider affects only the tax service. Teams can own individual services without needing to understand the entire system. [![y1DAS3k12dMjhaZukE xImlgwZ EgkUt3X8yJ9VBdtGmyEu1MH6MWHh8T4qYo5pIYSeX4qR205CQIiWL7p32orS 7vJmVcuQwCEfvNekKuKjY1V4X3izZJwnZXwgsdnsxhKQZBYLIHx5r90lsx8LU6Z9GO1aA4u27ENIN281jocD5zo0895YrH2B1XFBqLDO](https://dedicatted.com/wp-content/uploads/2026/06/y1DAS3k12dMjhaZukE_xImlgwZ-EgkUt3X8yJ9VBdtGmyEu1MH6MWHh8T4qYo5pIYSeX4qR205CQIiWL7p32orS-7vJmVcuQwCEfvNekKuKjY1V4X3izZJwnZXwgsdnsxhKQZBYLIHx5r90lsx8LU6Z9GO1aA4u27ENIN281jocD5zo0895YrH2B1XFBqLDO-1024x693.jpeg)](https://dedicatted.com/wp-content/uploads/2026/06/y1DAS3k12dMjhaZukE_xImlgwZ-EgkUt3X8yJ9VBdtGmyEu1MH6MWHh8T4qYo5pIYSeX4qR205CQIiWL7p32orS-7vJmVcuQwCEfvNekKuKjY1V4X3izZJwnZXwgsdnsxhKQZBYLIHx5r90lsx8LU6Z9GO1aA4u27ENIN281jocD5zo0895YrH2B1XFBqLDO-1024x693.jpeg)### API-First: The Integration Foundation API-first means that every capability in the commerce platform is exposed through a well-defined, consistently designed API interface before any frontend is built. The API is the product. Everything else: the storefront, the mobile app, the internal tools, the integrations consumes it. This is how brands add best-of-breed components to their commerce stack. A new personalization engine integrates through the same API layer as everything else. A new payment provider adds a new endpoint without disrupting existing ones. A new channel: TikTok Shop, a connected TV experience, an enterprise B2B portal is built on top of existing APIs without requiring backend changes. GraphQL has become the preferred API pattern for modern headless commerce because it allows the frontend to request exactly the data it needs no more, no less. Unlike REST APIs that return fixed data structures, GraphQL queries are defined by the consumer. A mobile app that only needs a product name, image, and price does not receive the full product data object. This reduces payload size, improves performance, and makes the frontend faster. ### Cloud-Native: Infrastructure That Matches Demand Cloud-native means that the application is designed from the ground up to run on cloud infrastructure: not lifted and shifted from an on-premise data center, but built to take advantage of elastic compute, managed services, global distribution, and pay-as-you-go pricing. The contrast with legacy infrastructure is stark. Traditional commerce platforms that run on on-premise infrastructure or hybrid cloud deployments require teams to provision for anticipated peak load. The standard practice was to estimate peak traffic, multiply by a safety factor, and deploy hardware accordingly. That hardware sits idle for the 340+ days per year that are not peak events, representing sunk capital cost that earns no return. Cloud-native infrastructure, particularly on AWS, eliminates this model. AWS Auto Scaling monitors application performance continuously and adds or removes capacity in real time as demand changes. A commerce platform running on AWS handles a 10x traffic spike without a pre-planned infrastructure expansion. When the spike subsides, capacity scales back down and the cost returns to baseline. The bill reflects what was actually consumed. This model also shifts expenses from capital expenditures to operational expenditures: from buying infrastructure to consuming it as a utility. Like electricity or water, you pay for what you use. For a retailer that runs a major promotion once a year, this is the difference between financing a data center that is only needed for 48 hours and paying for those 48 hours of compute at the moment they are needed. ## AWS Infrastructure for Production-Grade Headless Commerce ***Lambda*** is the serverless compute layer that powers custom business logic without the overhead of managing servers. In a headless commerce architecture, Lambda functions handle the logic that sits between the frontend and the commerce engine: tax rule enforcement, fraud signal scoring, loyalty point calculations, custom pricing logic, webhook handlers, and API extension endpoints. Lambda scales from zero to thousands of concurrent executions within seconds. When a Prime Day flash sale triggers a surge of cart additions, Lambda functions handle the spike automatically and cost nothing when the traffic subsides. Integration with API extensions means Lambda can inject custom logic synchronously into commerce operations: for example, preventing an order from exceeding a custom quantity limit, or validating a promotion code against a third-party loyalty system before allowing checkout to proceed. Practical implementation: Use Lambda for all synchronous API extensions that need to run inside the commerce transaction, and for asynchronous event handlers triggered by Amazon EventBridge. Keep each function focused on a single responsibility. Set appropriate concurrency limits to protect downstream services during unexpected spikes. ***Amazon EventBridge*** is the serverless event bus that enables event-driven architecture across the commerce stack. When something happens in the commerce platform – an order is placed, a product sells out, a cart is abandoned, a customer signs up : EventBridge routes that event to every downstream system that needs to respond. This replaces the nightly batch synchronization jobs that make legacy integrations feel sluggish. Inventory updates flow to warehouse management systems in real time. Order events trigger fulfillment workflows immediately. Cart abandonment events reach marketing automation platforms within seconds, enabling re-engagement messages while the customer is still in a decision-making window. [![Diagram showing a cart workflow: API Gateway to Cart, events routed via EventBridge to reservation and delivery-time updates across AWS components around the dashed delivery-time-service boundary.](https://dedicatted.com/wp-content/uploads/2026/06/Mathem.png)](https://dedicatted.com/wp-content/uploads/2026/06/Mathem.png)*Practical implementation:* Map your commerce events (order created, inventory updated, price changed, customer registered) to EventBridge rules during the integration design phase. Use EventBridge’s content-based filtering to route events only to the services that need them. Design downstream consumers to be idempotent capable of handling the same event more than once without producing incorrect results. ***Amazon DynamoDB*** is the managed NoSQL database service that provides the scalability and schema flexibility that modern product catalogs require. Unlike relational databases that enforce rigid schemas, DynamoDB’s schemaless model allows product attributes to vary by product type without schema migrations. A clothing product has size and color attributes; an electronic has voltage and compatibility attributes. Both live in the same database without structural conflicts. DynamoDB’s performance characteristics are designed for the kind of read-heavy workload that a product catalog generates. It handles millions of requests per second with single-digit millisecond latency, scales horizontally without downtime, and replicates globally across AWS regions for low-latency access from anywhere in the world. > Practical implementation: Design your DynamoDB access patterns before designing the data model. The primary keys and global secondary indexes that support your most frequent queries determine the performance characteristics. For product catalog use cases, model around how the frontend will query, typically by product ID, category, and search facets, rather than around how the data is organized internally. ***Amazon ECS*** is the runtime layer for containerized microservices. Each microservice in the commerce architecture: catalog, pricing, checkout, order management, loyalty runs in its own container, deployed and scaled independently through ECS. It integrates with AWS Fargate for serverless container execution, eliminating the need to manage the underlying EC2 instances. Teams define the CPU and memory requirements for each service and Fargate handles the infrastructure. Combined with Application Auto Scaling, ECS automatically adjusts the number of running containers based on CPU utilization, request rate, or custom CloudWatch metrics. *Practical implementation:* Define separate ECS services for each microservice, with independent scaling policies calibrated to that service’s specific load characteristics. Use ECS service discovery for internal service-to-service communication. Implement health checks at both the container level and the load balancer level to ensure that unhealthy instances are replaced automatically without manual intervention. [![download](https://dedicatted.com/wp-content/uploads/2026/06/download-1024x345.png)](https://dedicatted.com/wp-content/uploads/2026/06/download-1024x345.png)***Amazon Personalize*** is AWS’s managed machine learning service for real-time product recommendations and personalized user experiences. It ingests interaction data: product views, cart additions, purchases, search queries and produces recommendation models that update continuously as new data arrives. In a headless commerce architecture, Personalize connects to the backend through API calls from Lambda functions, with recommendation results surfaced through the same API layer that serves all other frontend components. During Prime Day, personalized recommendations are the mechanism that converts browsing into purchasing, showing the right product to the right customer at the moment of highest intent. *Practical implementation:* Start with an interaction dataset built from historical event data before launching the recommendation engine. Define your use case (frequently bought together, customers also viewed, personalized ranking) before selecting the Personalize recipe. Subscribe to real-time event tracking through Amazon EventBridge so that interaction data flows into Personalize models continuously rather than in scheduled batch updates. [![Scaling e-com for Prime Day: Headless Architecture on AWS](https://dedicatted.com/wp-content/uploads/2026/06/img-1024x630.jpeg)](https://dedicatted.com/wp-content/uploads/2026/06/img-1024x630.jpeg)***Amazon Pinpoint*** is the customer engagement platform that handles marketing communications across email, SMS, push notifications, and in-app messaging. In an event-driven headless commerce architecture, Pinpoint sits downstream of EventBridge, receiving commerce events and triggering engagement campaigns based on customer behavior. A cart abandonment event from the commerce platform hits EventBridge, routes to a Lambda function that evaluates the customer’s eligibility for a re-engagement message, and triggers a Pinpoint campaign that delivers a personalized email within minutes of the abandonment. A flash sale launch event triggers a push notification to all customers who have opted into the mobile app. A back-in-stock event triggers an SMS to customers on the waitlist. *Practical implementation:* Design your Pinpoint segments around commerce events rather than demographic attributes. A segment defined as “customers who viewed this product category in the last 7 days and have not purchased” is more actionable for a Prime Day campaign than a demographic segment. Use Pinpoint journey workflows to sequence multi-step engagement flows — view → abandon → remind → convert — rather than single-message campaigns. ## Architecture Patterns for Different Scales ### Pattern 1: Headless Storefront on Existing Backend (Starting Point) The minimum viable headless architecture decouples only the frontend, while leaving the existing commerce backend in place. A new storefront is built in Next.js or React, consuming the existing platform’s APIs. The customer experience is rebuilt from scratch with full frontend freedom, while backend migration is deferred. This pattern is appropriate for organizations that need immediate frontend flexibility: faster campaign deployments, performance improvements, new channel support , but are not ready for a full backend migration. It delivers value quickly and creates the decoupling that makes subsequent backend migration lower-risk. **AWS services involved:** CloudFront for global delivery, Amplify for frontend hosting and CI/CD, Lambda for any custom frontend middleware, WAF for edge security. **Timeline:** 8–16 weeks for a production-ready storefront, depending on the complexity of the existing backend’s API surface. **Limitations:** The existing backend still constrains scalability and flexibility. Backend deployments still affect the full platform. The Strangler Pattern migration should begin in parallel. ### Pattern 2: Full MACH Stack on AWS The architecture integrates EventBridge for event-driven downstream processing, Lambda for custom business logic injected through API extensions, DynamoDB for high-performance catalog caching, CloudFront for global storefront delivery, Personalize for real-time recommendations, and Pinpoint for customer engagement. **AWS services involved:** Full stack as described before. EventBridge, Lambda, DynamoDB, CloudFront, ECS, Personalize, Pinpoint, Connect, Amplify, Cognito. **Timeline:** 6–12 months for a full migration using the Strangler Pattern, depending on the number of integrations and complexity of the existing data model. **What it enables:** Independent scaling of every service during peak events. Frontend deployments in minutes rather than hours. New channel support without backend changes. Real-time inventory and pricing across all surfaces. ### Pattern 3: Composable Commerce with Best-of-Breed Stack The most advanced implementation treats every commerce capability as an independently selectable service. The commerce backend handles transactions. A headless CMS (Contentful, Sanity) handles content. An AI-powered search and discovery layer (Algolia, Constructor) handles product discovery. A personalization engine (Dynamic Yield, Amazon Personalize) handles recommendations. A loyalty platform handles rewards. Each connects through the API layer. This is the architecture that brands like Sephora, Audi, and Volkswagen Group run in production. It requires the highest level of engineering investment but delivers the highest level of flexibility: any component can be replaced without disrupting the others. **AWS services involved:** Full stack, plus integration patterns for each third-party service through EventBridge and the API layer. **Timeline:** 12–18 months for a full composable implementation. Typically phased: commerce backend first, then best-of-breed integrations, then advanced personalization and AI layers. ## When Headless Is the Right Choice and When It Is Not Headless architecture delivers its clearest value in specific conditions. Before committing to the investment, validate that these conditions apply.Strong case for headless: - You serve customers across three or more channels (web, mobile, in-store, marketplace) and need consistent data across all of them - You have annual peak events (Prime Day, Black Friday, product drops, seasonal campaigns) where traffic spikes 5x or more above baseline - Your marketing or digital experience team is blocked by developer dependencies for routine frontend changes — campaign landing pages, content updates, UX iterations - You are planning to launch in new markets or on new channels within the next 18 months - Your current platform’s licensing or customization costs are growing faster than revenue - You need to integrate best-of-breed services (search, personalization, loyalty, payments) that your current platform does not support natively **Weaker case for headless:** 1. You operate a single-channel DTC store with stable traffic and no imminent expansion plans 2. Your team lacks experience with modern frontend frameworks and API-driven development 3. You are in early stage and need to move fast with a simple platform: the additional engineering overhead of headless would slow you down more than it enables 4. Your current platform meets all requirements and has headroom for the next 2–3 years of growth The honest framing is this: headless is not the right architecture for every organization at every stage. It is the right architecture for organizations that are growing, expanding to new channels, and expect their digital presence to evolve significantly. The investment in architectural flexibility pays off over a 3–5 year time horizon. Organizations looking for a quick win in the next quarter should consider whether the returns justify the upfront cost. ## The Total Cost of Ownership Comparison The upfront cost of a headless migration is higher than staying on a legacy platform. Implementation timelines are longer. Engineering requirements are higher. The skill set needed to operate the platform is more advanced. The long-term total cost of ownership typically reverses that comparison within 12–18 months of launch, driven by: - Elimination of per-channel commerce licenses replaced by a single composable backend - Reduction in integration maintenance costs as API-first connections replace custom point-to-point integrations - Faster time-to-market for new features reducing the engineering cost per feature over time - Infrastructure cost reductions from elastic cloud replacing over-provisioned on-premise or hybrid infrastructure - Reduction in peak-event incident costs: engineering time, potential revenue loss from downtime, which are invisible in a TCO comparison until they happen ## How Dedicatted Approaches Headless Commerce on AWS **[Dedicatted is a Toronto-based AWS Premier Tier Services Partner](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/Dedicatted)** and top 2% global AWS partner, holding AWS GenAI Competency, MSP designation, and AWS Agentic AI Pilot status. Our approach to headless commerce engagements starts where it should: with a business-first audit, not a technology selection. **What our engagement process looks like:** *Assessment phase:* We evaluate your current commerce architecture against your growth objectives, channel strategy, and peak performance requirements. We identify the specific failure modes, whether they are frontend scalability issues, backend coupling, channel fragmentation, or integration debt and quantify the business impact of each. *Architecture design:* We design the target state architecture with AWS services and commerce platform selection calibrated to your specific requirements. We do not prescribe a one-size-fits-all stack. A mid-market DTC brand has different requirements than a multi-brand enterprise or a B2B distributor. *Migration roadmap:* We build the Strangler Pattern migration roadmap with milestone definitions, risk mitigation plans, and business continuity controls. Every migration milestone is designed to deliver value before the next one begins. *Implementation:* Our delivery teams execute the migration with dedicated solution architects, AWS-certified engineers, and commerce platform specialists. We own the architecture decisions and deliver the outcome. *Ongoing operations:* Post-launch, our MSP practice provides continuous cloud optimization, security management, and platform evolution as your requirements change. If you are planning for the next peak event and your current architecture is a concern, the right time to start the conversation is now. The Strangler Pattern gives you a path that does not require shutting down the revenue engine to modernize it. But it requires time that the proximity of the next peak will eventually eliminate. **[Let`s talk](https://dedicatted.com/contact)** ## Frequently Asked Questions **How long does a headless commerce migration typically take?** A phased migration using the Strangler Pattern typically runs 6–12 months for a full backend migration, depending on the number of integrations, the complexity of the existing data model, and the team’s current technical capability. A frontend-only decoupling, standing up a headless storefront on top of an existing backend can be completed in 8–16 weeks. **Does headless commerce improve SEO?** Yes, when implemented correctly. Modern frontend frameworks like Next.js support server-side rendering (SSR) and static site generation (SSG), which ensure that search engines can crawl and index content properly. Sites built with these frameworks and deployed on edge networks through CloudFront consistently outperform traditional platforms on Core Web Vitals, which directly influence search rankings. **Can headless commerce handle B2B as well as B2C?** Yes. A MACH-based commerce backend supports B2C, B2B, B2B2C, and direct-to-consumer business models through the same API layer. B2B-specific features: custom pricing per customer, tiered discounts, purchase order workflows, account-level credit limits are implemented through API extensions and custom data models without requiring separate platforms. **What is the difference between headless and composable commerce?** Headless refers specifically to the decoupling of the frontend from the backend. Composable commerce is the broader strategy of assembling a commerce platform from best-of-breed independent services: commerce engine, CMS, search, personalization, loyalty, payments each selected for its specific capability and integrated through a common API layer. Headless architecture is a prerequisite for composable commerce. **How does headless commerce handle peak traffic like Prime Day?** In a headless architecture on AWS, peak traffic is handled through independent scaling of each layer. The frontend, served through CloudFront’s edge network, absorbs traffic globally without touching origin infrastructure. AWS Auto Scaling adds backend service capacity automatically as load increases. Lambda functions scale to thousands of concurrent executions within seconds. The result is that peak events scale with demand rather than requiring pre-provisioned capacity. **Categories:** Blog **Services:** Cloud Data Migration & Modernization, Cloud Migration, Cloud Modernization **Industry:** Retail --- ### [Building a Compliance-Ready Cloud: Zivian Health AWS to Azure Migration](https://dedicatted.com/insights/building-a-compliance-ready-cloud-zivian-health-aws-to-azure-migration) **Published:** November 13, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2024 #### Industry: Healthcare, SaaS #### The service: [Cloud Migration](https://dedicatted.com/services/glossary-of-terms#term-cloud-migration) #### Overview **Zivian Health** is a U.S.-based company operating in the medical domain. Their platform helps Advanced Practice Providers (APPs) and clinics simplify compliance tracking by monitoring required documents for each state and flagging those that need to be submitted. This ensures practitioners maintain compliance and avoid the risk of losing their licenses. Their customers include independent practitioners, multi-state clinics, and enterprise healthcare providers across the United States. These clients rely on our client to reduce administrative burden, streamline regulatory compliance, and safeguard their practitioners’ ability to deliver patient care. This project, **supported by Dedicatted**, strengthens healthcare platform by modernizing its technical foundation and accelerating delivery. With initiatives such as cloud migration, design system unification, and Angular-to-React transformation, company continues to scale platform stability and enhance user experience while maintaining the compliance standards critical to the healthcare industry. ## The Challenge Our customer’s compliance tracking platform is at the heart of its business, ensuring Advanced Practice Providers (APPs) and clinics remain licensed and operational across multiple U.S. states. Each compliance document and regulatory record must be accurate, traceable, and fully aligned with state-specific requirements. Before this project, the company faced several structural bottlenecks: - **Legacy cloud environment**: Containerized workloads were running on AWS ECS, but the customer lacked a clear end-to-end migration strategy to Azure, introducing high risk of downtime during the transition. - **Supporting service dependencies** : Core services for file processing needed to be re-architected for Azure equivalents without disrupting operations. - **Operational inefficiencies**: The legacy setup demanded manual oversight for compliance-critical workflows, limiting scalability and slowing delivery of new features. - **Performance and cost challenges**: Maintaining high availability, security, and observability in the old architecture was increasingly expensive and resource-heavy. - **Client dissatisfaction**: Delayed feature releases and an outdated user experience further heightened the pressure to modernize the platform. Operationally, the company was at a crossroads: the platform needed to scale while remaining compliant, stable, and cost-efficient. Strategically, the inability to migrate smoothly and deliver updates faster risked client trust and limited growth opportunities in the competitive healthcare SaaS market. ## Want to get your copy of case study? Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## Technology & Approach We treated this as a full-scale cloud migration and modernization, ensuring **zero disruption, compliance, and cost efficiency** from the start. ### **Step 1: Platform & Containers** We began by designing a new architecture on **Azure [Container](https://dedicatted.com/services/glossary-of-terms#term-container) Apps**, tailored for scalability and high availability: - Rebuilt containerized workloads previously hosted on AWS ECS. - Adopted a **scale-to-zero** approach to reduce costs during idle periods. - Implemented **[Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform)-based Infrastructure as Code (IaC)** for reproducible, reviewable, and environment-aware deployments. ### Step 2: Observability & Operations To match and improve upon AWS CloudWatch, we introduced enhanced monitoring: - Integrated **Azure Monitor** and Log Analytics for real-time metrics, logs, alerts, and dashboards. - Created structured runbooks and health checks for pre- and post-cutover verification, ensuring stability during migration. ### Step 3: Storage & Event Processing Critical workloads relying on AWS S3 and Lambda were re-engineered for Azure: - Migrated object storage to Azure Blob Storage, maintaining lifecycle compatibility and secure access via SAS + RBAC. - Rewrote the Lambda function as an Azure Function, preserving business logic and ensuring idempotency. - Centralized all secrets in Azure Key Vault to enhance security and simplify configuration management. ### Step 4: DNS & Traffic Management We executed a near-zero downtime migration of DNS services: - Transferred zones from Amazon Route 53 to Azure DNS. - Reduced TTLs and staged NS updates to ensure a seamless cutover. - Where global routing was required, we evaluated Azure Front Door and Traffic Manager to replicate Route 53’s health checks and policies. ### Step 5: Security & Governance Security controls were rebuilt to align with compliance requirements: - Translated AWS IAM policies into Azure RBAC with strict least-privilege enforcement. - Enforced **network segmentation** with private endpoints for Blob and Key Vault. - Activated **Defender for Cloud baseline policies** for continuous compliance monitoring. ### Step 6: Migration Strategy & Cutover We minimized downtime through careful execution: - Performed incremental data syncs, followed by a **final delta sync** during a controlled maintenance window. - Ran **parity tests** functional, load, and failover before decommissioning AWS resources. [!["Architecture diagram showing a CI/CD pipeline with Git repositories, Terraform, and Azure infrastructure. On the left, Git infrastructure repositories integrate with Terraform for infrastructure deployment and manual applies. At the center, Azure components include Azure Kubernetes Service (AKS) cluster, Azure DB, Azure VPN, Web Application Firewall, Static Web App, and Azure Monitor. Azure Front Door manages external traffic. On the right, Git application repositories trigger CI pipelines for building and updating containers, as well as static web apps. Developers and users interact with the system through Git, Azure VPN, and Azure Front Door. AWS to Azure Migration](https://dedicatted.com/wp-content/uploads/2025/09/Copy-of-Zivian-new.drawio-1.png)](https://dedicatted.com/wp-content/uploads/2025/09/Copy-of-Zivian-new.drawio-1.png)## Our Solution This wasn’t just a cloud migration, it was about **building confidence in their platform’s future**. Their compliance tracking tool sits at the heart of clinical operations, so downtime or instability was not an option. We designed a solution that balanced technical precision with business impact, ensuring the transition was seamless for end users while unlocking long-term scalability. - **Cloud Migration Without the Headaches** We delivered a carefully orchestrated move from AWS to Azure with near-zero downtime. Clinicians and administrators never noticed the transition – services kept running, documents stayed accessible, and compliance tracking never skipped a beat. - **Modern Architecture, Ready for Tomorrow** Rather than lifting and shifting technical debt, we rebuilt the platform around Azure-native services. This gave our customer a cleaner, more resilient foundation, one that scales easily and supports rapid feature delivery. - **[Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) by Design** Since platform operates in a regulated healthcare environment, we ensured security and compliance were embedded into every layer, from identity and access management to data lifecycle handling [![Dashboard interface from a platform called Zivian showing a Compliance section with a list of healthcare providers, their active collaboration states, and compliance summaries marked as ](https://dedicatted.com/wp-content/uploads/2025/11/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-12T122614.745.jpg)](https://dedicatted.com/wp-content/uploads/2025/11/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-12T122614.745.jpg)## Provider Experience: AWS to Azure Migration as a Catalyst for Modernization For our customer, the transition to Azure was not simply an infrastructure project , it was a strategic investment in the future of their platform. Migration set the foundation, but modernization reshaped the way providers interact with the platform. - **Streamlined product experience**: A unified interface reduced complexity for providers and administrators, lowering support needs and increasing adoption rates. - **Faster innovation cycles**: Moving from Angular to React enabled quicker releases and the ability to respond to customer demands with greater agility. - **Operational resilience**: Automated compliance updates replaced manual processes, minimizing human error and ensuring uninterrupted service delivery. - **Scalable cost model**: Elastic workloads on Azure optimized resource consumption, cutting operational expenses while maintaining enterprise-grade performance. Through this dual focus on migration and modernization, Zivian redefined its role in the healthcare SaaS market: from a compliance tool to a modern operations platform. ## Outcome Dedicatted took end-to-end responsibility for **migration and modernization journey** – delivering a seamless transition from AWS to Azure without disrupting the clinicians and administrators who rely on the platform every day. - **Seamless migration** of all containerized workloads from AWS ECS to Azure Container Apps with near-zero downtime. - **Smooth transition** of supporting services: Route 53 to Azure DNS, S3 to Azure Blob Storage, and AWS Lambda to Azure Functions, without breaking workflows. - **Stronger compliance posture** with centralized secrets in Azure Key Vault and governance policies aligned with HIPAA and HITRUST standards. - **Reduced operational drag** through Terraform-based Infrastructure as Code, making deployments faster, safer, and repeatable. - **Future-proof security** with Azure RBAC, private endpoints, and Defender for Cloud baseline policies. **Categories:** Case study **Services:** Cloud Data Migration & Modernization, Cloud Migration, Cloud Modernization **Industry:** Healthcare --- ### [Production-Grade GenAI Lead Enrichment on AWS for KwikSave Logistics Operations](https://dedicatted.com/insights/production-grade-genai-lead-enrichment-on-aws-for-kwiksave-logistics-operations) **Published:** February 10, 2026 **Author:** Dedicatted **Content:** ## About project #### Working time: 2025-ongoing #### Industry: Travel and Hospitality, Logistics #### The service: AI-Driven [Automation](https://dedicatted.com/services/glossary-of-terms#term-automation) #### Overview **Kwiksave Logistics** is one of Canada’s largest and most experienced logistics operators, providing courier, trucking, e-commerce, and fulfillment services across the country. The company is known for its fast and reliable delivery services, offering same-day and rush fulfillment within key metropolitan areas like the Greater Toronto Area. Kwiksave partnered with Dedicatted to transform operational delivery data into a scalable source of commercial insight. As logistics operations grow, delivery workflows naturally generate signals about new potential customers – yet extracting and validating those signals traditionally requires significant manual effort. The objective of this engagement was to design and implement a production-ready lead enrichment and outreach platform that reduces manual research time, preserves CRM data quality, and applies AI in a cost-controlled, business-safe manner. Rather than pursuing full automation, the solution was intentionally architected as a hybrid system combining deterministic validation, selective GenAI enrichment, and human approval. The result is **a logistics-aware platform that integrates seamlessly with Kwiksave’s existing systems** scales predictably with delivery volume, and provides a reliable foundation for future commercial growth. ## The Challenge Kwiksave’s delivery network grew, so did the volume of valuable insights buried in their delivery data – insights that could translate into commercial opportunities. Turning these signals into actionable leads, however, was far from simple. The company faced several key challenges: - **Complex and Inconsistent Data**: The logistics data itself posed major hurdles. Metadata structures varied across different clients, recipient information was often incomplete, and delivery records were primarily designed for operational use. - ****Risks with Early AI Exploration**** : Initial experiments with fully autonomous AI agents revealed additional challenges. While promising in theory, these agents sometimes generated low-confidence or even fabricated data, which could not be trusted for commercial decision-making. Moreover, operating costs were unpredictable, introducing financial risks that Kwiksave could not afford to take on at scale - **Need for a Balanced, Pragmatic Solution**: Kwiksave needed a solution that would meaningfully reduce the manual workload while ensuring data accuracy and reliability. Any approach had to offer predictable costs, integrate seamlessly with CRM workflows, and align with marketing best practices. - **Time-Intensive Lead Qualification**: Teams had to identify the correct recipient, validate the company, discover the appropriate role, and enter all relevant details into the CRM. This repetitive process consumed significant resources and slowed the sales pipeline, making it difficult to capitalize on opportunities at scale. Operationally, the company was at a crossroads: the platform needed to scale while remaining compliant, stable, and cost-efficient. Strategically, the inability to migrate smoothly and deliver updates faster risked client trust and limited growth opportunities in the competitive healthcare SaaS market. ## Want to get your copy of case study? Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## Technology & Approach Dedicatted designed and implemented a secure, serverless, AI-enabled platform on AWS, built around an event-driven architecture to ensure transparency, scalability, and strong cost governance. The solution was created to support real-world logistics workflows, streamline enrichment processes, and enable controlled scaling without introducing infrastructure complexity or disrupting existing operational models. The architecture combines orchestration, automation, and AI-driven enrichment, fully aligned with AWS best practices for reliability, observability, and operational efficiency. Key components of the solution: ### **Serverless Orchestration Layer (AWS Step Functions)** Multi-stage workflows are coordinated through AWS Step Functions, enabling structured data processing, enrichment, and validation. This ensures full traceability across each step while maintaining predictable execution and cost control. ### **Event-Driven Compute (AWS Lambda)** Lambda components provide fine-grained control over execution logic, processing time, and AI service invocation. This modular setup supports rapid scaling as delivery volumes grow while keeping operations efficient and resilient. ### **Logistics-Specific Data Enrichment & Normalization** The platform incorporates advanced normalization of delivery metadata and configurable filtering logic. These mechanisms exclude existing customers, remove undesired vehicle types, and ensure only relevant, high-quality opportunities enter downstream workflows. ### **Lead Quality & Duplication Safeguards** Automated validation layers prevent duplicate or low-quality leads from entering campaigns, improving data reliability and operational outcomes for logistics teams. ### **Administrative Control & Governance Interface** A secure administrative environment provides visibility into enrichment flows, filtering logic, and approval decisions. Teams can monitor operations, adjust parameters, and maintain governance without modifying core infrastructure. A core design principle of the platform is **cost-aware AI usage**. By applying GenAI only after deterministic validation steps, the system minimizes unnecessary inference calls and prevents low-confidence data from entering business systems. This hybrid approach ensures that AI costs remain proportional to realized business value, while maintaining governance, auditability, and operational trust – a critical requirement for production deployment at scale. [![“Screenshot of a dark-themed ‘History’ dashboard showing an enrichment funnel for the last 7 days. Four summary cards display counts: Raw (TMS) 922, After filtering/enrichment 21, Managers verified 9, and Synced to HubSpot/Mailchimp 7. Date range selectors appear at the top. Below, a historical performance table lists daily counts by date with columns for From TMS, Filtered, Verified, and Synced](https://dedicatted.com/wp-content/uploads/2026/02/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2026-02-10T151316.173.png)](https://dedicatted.com/wp-content/uploads/2026/02/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2026-02-10T151316.173.png)## Business impact The implemented platform materially **reduced the operational burden of lead qualification.** What previously required up to 45 minutes of manual research per lead was transformed into a short, structured human review process, allowing sales teams to focus on decision-making rather than data gathering. **Structured Lead Readiness for Campaign Activation:** Integrated controls ensure that only verified, relevant, and campaign-ready leads move forward into HubSpot and Mailchimp. This improves targeting precision, supports more consistent outreach execution, and strengthens confidence in downstream marketing and sales activities. **Governed Data Quality & Process Consistency:** Embedded checks for CRM hygiene, duplication prevention, and AI-assisted validation introduced a more standardized approach to lead handling. Teams now operate within a controlled framework that reduces variability, limits manual errors, and maintains long-term data integrity. **Foundation for Continuous Optimization:** The organization now has the tools and visibility needed to refine filtering logic, enrichment criteria, and campaign readiness over time. What began as a manual, resource-intensive process has evolved into a structured, AI-supported operational capability – positioning Kwiksave to scale outreach efforts and continuously improve performance without reengineering core workflows. [![Screenshot of an 'Edit Contacts' modal window in a dark-themed interface for GenAI platform. on AWS It shows editable contact cards for 'Person 1' and 'Person 2,' each with fields for first name, last name, title, email, phone number, and social media URLs. 'Person 1' is filled in with the title 'Manager Visitor Services' and includes an email and LinkedIn URL. Each contact row has icons to confirm, cancel, or delete. Cancel and Save People buttons appear at the bottom.](https://dedicatted.com/wp-content/uploads/2026/02/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2026-02-10T152052.297.png)](https://dedicatted.com/wp-content/uploads/2026/02/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2026-02-10T152052.297.png) **Categories:** Case study **Services:** Cloud Modernization, Generative AI **Industry:** Automotive, High Tech Software --- ### [How to build your FinOps strategy for 2026](https://dedicatted.com/insights/how-to-build-your-finops-strategy-for-2026) **Published:** November 5, 2025 **Author:** Denys Yermakov **Content:** Global spending on cloud infrastructure is set to surpass $1 trillion mark by 2030, highlighting the unprecedented potential for innovation within the cloud industry. Several key trends are shaping this landscape. Cloud infrastructures are increasingly complex, with 89% of organizations opting for multi-cloud solutions. The adoption of AI-based technologies is also on the rise. In 2024, 85% of organizations utilize GenAI public cloud services. Additionally, there is a growing awareness of sustainability. 37% of organizations prioritize ecofriendly practices, indicating an increasing willingness to reduce cloud waste. These emerging trends underscore the need for new financial governance models. We have designed this comprehensive guide to provide FinOps and DevOps professionals with the essential insights required to develop a robust FinOps strategy that aligns with the expected challenges of 2026, capitalizing on the most significant trends in FinOps today. The compounding effect of these disruptions is an unprecedented rise in costs. For instance, the average price to ship a 40-foot sea container has quadrupled ; truck driver salaries are soaring due to labor shortages in major economies ; and commodity prices are the highest since 2014. And then there’s the ever-growing interest in and demand for responsible, sustainable business practices from consumers, governments, investors, and other critical stakeholders. ## **Understanding FinOps** FinOps is a cross-functional discipline focused on optimizing the business potential of cloud technology. FinOps promotes informed, data-driven decisions and cultivates financial responsibility through the engineering, finance, and business teams working together. In essence, FinOps breaks down traditional department barriers to cultivate a culture of collaboration and financial expertise. [![Three-column infographic illustrating the Inform-Optimize-Operate framework for cloud financial management. The first column, labeled Inform in blue, focuses on visibility and allocation with steps such as defining goals and KPIs, identifying data sources, creating budgets, generating forecasts, establishing reporting needs, and implementing a tagging strategy. The second column, Optimize in purple, highlights rates and usage by right-sizing underutilized resources, eliminating waste, managing cost anomalies, purchasing commitment discounts, and modernizing workloads. The third column, Operate in light violet, emphasizes continuous improvement through iterative capability enhancement, automation, and policies aligning IT with business goals.](https://dedicatted.com/wp-content/uploads/2025/11/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-05T162019.147-1.png)](https://dedicatted.com/wp-content/uploads/2025/11/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-05T162019.147-1.png)### Main Pillars The FinOps framework is grounded in four main pillars that guide organizations toward a culture of financial accountability and operational excellence in cloud spending. Understanding these pillars is critical to implementing a successful FinOps strategy. 1. **Collaboration.** At the core of FinOps lies the fundamental concept of collaboration. FinOps promotes a cooperative environment among finance, technology, and business teams. It aims to nurture open communication and mutual understanding, enabling organizations to synchronize their cloud expenditures with business results. This way, every dollar spent becomes a strategic step towards the attainment of business objectives. 2. **Visibility.** Transparency into cloud spending and usage is essential for effective FinOps. Organizations must have real-time visibility into their cloud costs and consumption patterns to make informed decisions. This involves implementing tools and processes that enable continuous monitoring and reporting of cloud expenditure, empowering teams with the data they need to optimize cloud resources efficiently. 3. **Agility.** Moving away from conventional models that rely heavily on static budgets and yearly planning cycles, FinOps brings to the table a dynamic approach. This approach endorses real-time decision-making based on the ever-changing nature of cloud costs and usage. With this agility, organizations can quickly adapt to changes in the market and progress in technology. This ensures that their financial strategies are consistently in sync with their current business objectives. 4. **Optimization.** Optimization in FinOps focuses on achieving operational efficiency and cost-effectiveness without compromising on quality or performance. This is achieved through frequent evaluation and recalibration of cloud resources and services, all in line with present and future demands. Constant optimization efforts empower organizations to cut down on waste, utilize reserved instances or savings plans, and seize cost-reduction opportunities that harmonize with their operational needs and budget limitations. While the principals above set the stage for disciplined FinOps, the true impact becomes evident when applied to a real, growing business. For instance, in our **[“Optimized Data Architecture: Transforming Data Costs into Growth”](https://dedicatted.com/insights/optimized-data-architecture-transforming-data-costs-into-growth)** case, we helped a tech client slash storage expenses by 80%, cut compute costs by 50 %, and triple search speed via managed architectural redesigns and smart automation. That’s the power of pairing FinOps strategy with expert execution – and it’s exactly what you can expect when you explore our managed services in action. ## ****Setting the Foundation**** Set the foundation for FinOps as early as possible. It will not only bear financial benefits but also accelerate the transition. Delaying until cloud expenditure reaches significant levels is not recommended as it would amplify the expenses and efforts required to transition from a traditional financial approach to efficient cloud resource management. ### Assessing Current Financial Practices Before starting your journey into FinOps, you need to understand where you stand. Start with a thorough inventory of your existing financial and operational setups. This initial assessment provides a benchmark for measuring future improvements. You need to understand current practices in cloud spending, procurement policies, and financial management. This step involves reviewing budget allocations, existing contracts, and cost management tools in use. [![A light blue background with a list of six questions about implementing and maintaining a FinOps Model, including skills, policy enforcement, change adoption, compliance risk management, cloud cost management, and staff upskilling. On the right side, an image shows a person placing a coin into a pink piggy bank, symbolizing financial management and savings.](https://dedicatted.com/wp-content/uploads/2025/11/Group-144-7-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/11/Group-144-7-scaled.png)### Identifying Key Stakeholders Success in FinOps isn’t a one-man job – it’s a team effort. Critical players in this game are high-level executives encompassing finance, IT, operations, and other business units. Each brings something unique to the table, allowing for a well-rounded strategy for cloud cost management. The finance team, for example, provides a vital understanding of budgeting and forecasting. Contrastingly, IT and operations teams play a major role in managing the technical side of cloud resources. By aligning these distinct functions with common goals, you will foster a culture of cost transparency and accountability within the FinOps framework. Explore potential advocates, champions, or executive sponsors, and engage them in personalized discussions. Identify the challenges your organization is facing and understand which groups, teams, and individuals are impacted by these challenges. Importantly, engage key business leaders from the beginning. In their survey of more than 200 business executives, McKinsey demonstrated that their early involvement in FinOps practices significantly helped in reducing unnecessary cloud expenses [![Diagram showing ](https://dedicatted.com/wp-content/uploads/2025/11/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-16T122122.131-1.png)](https://dedicatted.com/wp-content/uploads/2025/11/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-16T122122.131-1.png)### Defining Team and Governance Structure Clear governance structures are the heartbeat of successful FinOps. They involve establishing a committee tasked with supervising cloud financial operations and aligning policies and practices with the organization’s strategic goals. The optimal governance models bring together FinOps, IT operations, finance, and business leadership. The governance model should define roles and responsibilities, decision-making processes, reporting mechanisms, and collaboration strategies across various departments. This structure is pivotal in maintaining accountability, fostering continuous improvement, and scaling FinOps practices as the organization evolves. ## **Building Cost Optimization Strategies** Once the foundations are set, you can start developing strategic approaches that address all facets of your cloud investments. Building a robust cost optimization strategy requires you to walk through a few pivotal steps: ### **Identifying Cost Drivers** Understanding the primary drivers of your cloud costs is the first step toward effective management. This entails a comprehensive analysis of your cloud usage, services, and resources to identify areas where expenses are generated. Common cost drivers include: - Managed services (databases, data warehouses…) - Compute resources: VM instances, serverless computing services - Storage: Object storage, Block storage, and archival solutions - Network services: Data transfer costs, load balancing, and DNS services ### **Implementing Cost Reduction Tactics** 1. **Eliminate unused resources** that incur costs without providing value. Look into Block Storage, Load Balancers, NAT Gateway, IP addresses, Snapshot. 2. Right-size resources to match workload demands, to **prevent any overprovisioned and unnecessary wastage** 3. Adopt **Discount plans**, such as Committed Use Discounts, Reserved Instances, or Savings Plans, for predictable workloads. These not only provide significant cost advantages compared to on-demand pricing but also give you a well-planned financial strategy 4. Consider **auto-scaling services that adjust resources automatically** with fluctuating workloads. This optimizes utilization and ensures that you’re only paying for the resources you need 5. Choose the most cost-effective regions for your cloud services. This decision should be based on data locality and pricing differences to ensure the best cost-effectiveness Implementing these tactics requires continuous monitoring and analysis to ensure they remain effective over time. Yet maintaining such operational precision internally can be resource-intensive and costly. That’s why many organizations choose to partner with experts like Dedicatted for **[fully managed DevOps and FinOps support. ](https://dedicatted.com/what-we-do/devops/devops-as-a-service)** Whether you need proactive cloud optimization, application operations, or complete infrastructure management – our flexible, scalable packages provide transparent insights, predictable costs, and measurable ROI. Let us handle the complexity, while you focus on innovation and growth ## **Balancing Cost and Performance** While implementing cost reduction tactics, striking the right balance between minimizing expenses and maintaining outstanding performance and availability might be challenging. Here are some strategies to consider: - **Performance benchmarking** to establish baselines and identify optimization opportunities for your new technology. There are many tools, such as Cloudwatch or DataDog, that can help you with this task. - Adopting **modern cloud-native architectures,** such as microservices and containers. They can offer the double advantage of enhanced cost-efficiency and performance benefits. - Using **managed** or automated services to reduce the operational overhead associated with maintaining complex infrastructure, and to ensure availability at any moment ## ****Techniques for Accurate Forecasting**** The best way to enhance the accuracy of your forecasts is by combining various data sources, including operational metrics and external benchmarks. By adopting multi-dimensional forecasting models that take into account variables like seasonality, project launches, and market changes, you will be able to get a more complete picture of your future expenses. Keep your models accurate by conducting regular reviews and updates that reflect the latest data and assumptions. The FinOps Foundation provides an example of the different layers to consider when elaborating your forecast. [![Diagram showing four levels of forecasting: Simple Forecast, Trend Based Forecast, Driver Based Forecast, and Net New Workloads. Each level adds more factors to refine cloud spend predictions. The examples show how spending evolves: a simple forecast repeats last month's 100K, a trend forecast adds 10% growth (110K), a driver-based forecast accounts for a sales campaign doubling spend (210K), and a net new workloads forecast includes new project costs for a total of 230K](https://dedicatted.com/wp-content/uploads/2025/11/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-16T123644.586-1.png)](https://dedicatted.com/wp-content/uploads/2025/11/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-16T123644.586-1.png)Crafting a smart approach to FinOps is a must for companies seeking substantial cost reductions, streamlined operations, and flexible financial management. In a cloud market dynamized by AI and other advanced technologies, adaptability becomes crucial. Staying updated with industry advancements, embracing new technologies, planning carefully, and fostering a work environment that promotes continuous learning and collaboration are fundamental. This approach not only helps manage cloud costs effectively, but also optimizes the value derived from your cloud resources, and makes room for innovation. Looking ahead, we can anticipate that FinOps strategies will not only promote profitability but also adhere to environmental and social governance principles. Whereas only 19% of FinOps teams currently work alongside sustainability teams, the state of FinOps 2024 shows that 50% anticipate an increasing collaboration with sustainability teams. [![Promotional banner with text on the left reading: ](https://dedicatted.com/wp-content/uploads/2025/11/unnamed-81.png)](https://dedicatted.com/wp-content/uploads/2025/11/unnamed-81.png) **Categories:** Blog **Services:** Cloud Data Migration & Modernization, Generative AI, Security Compliance **Industry:** Automotive, Healthcare, Insurance, Manufacturing --- ### [How do you build a data warehouse in 2026: Ultimate Guide by Dedicatted’ Experts](https://dedicatted.com/insights/how-do-you-build-a-data-warehouse-in-2026-ultimate-guide-by-dedicatted-experts) **Published:** January 7, 2026 **Author:** Dedicatted **Content:** Before your data speaks visually, it must first find its order. Slick BI dashboards and visualizations look effortless, but they owe everything to data that’s been wrangled, organized, and tied together in a well-crafted data warehouse. Setting one up, however, is a significant undertaking requiring considerable investment, in-depth architectural expertise that combines mastery of both foundational and modern best practices, and a sharp eye for the traps where projects can stall. Our senior experts offer a hands-on guide through this multi-faceted terrain. With it, you’ll get an overall idea on how to build a data warehouse that provides a strong foundation for all levels of data analytics. ## **Key highlights** Building a data warehouse (DWH) involves turning scattered, inconsistent corporate data into a unified, analytics-ready foundation that fuels BI and AI initiatives. Choosing the right architecture approach (Inmon, Kimball, or Data Vault) and deployment model (cloud, hybrid, or on-prem) determines how scalable, flexible, and maintainable the warehouse will be. Most challenges in DWH projects come down to data quality, so strong data validation, governance, and continuous monitoring are what make the warehouse truly trustworthy. ## **What is a data warehouse?** A data warehouse is a centralized repository where all your company’s current and historical data, scattered across multiple systems, comes to sit down together. In other words, it’s a single, governed, and coherent source of truth that analytics and reporting can rely on. ***You might wonder:*** Why not just query those systems directly? The problem is, their data is often inconsistent: formats, time zones, naming conventions, in other words, ways of tracking the very same entity can differ. Before information lands in a warehouse, it’s cleaned from noise and duplicates, normalized into a consistent schema, enriched with contextual metadata, and sometimes aggregated to different levels of data granularity, for example, raw sales transactions might be rolled up into daily totals per store or monthly revenue per region, to suit different analytical purposes. Once a unified view of corporate data is facilitated within a warehouse, your data analytics initiatives, be it business intelligence systems or AI-driven solutions, get a solid, dependable foundation. To deliver reports that are not just visually appealing but genuinely reliable, a well-designed data warehouse must enforce multiple layers of validation. These checks ensure that data arrives accurately. Let’s assume there are ten orders in the CRM. When the DWH pulls these, it should capture all of them along with the payments linked to each order from the financial system. A mature DWH automatically validates that every order and its matching payment have been successfully ingested, and that no data is duplicated. If any inconsistencies are detected, the system flags them before they cascade into reporting #### Serhii Semenchenko, CTO at Dedicatted ## ****Data warehouses vs data lakes vs data lakehouses vs database vs data marts**** There are many ways to store data, and the choice of suitable ones usually comes down to one question: what does the business expect to get from it? Driven by the type(s) of data stored and the way it’s organized, a tangle of terms of different data storage systems has emerged. Let’s untangle it. **Database** is a structured collection of data used for day-to-day operations and transactional processes. It can be of two types: relational (~structured tables with predefined relationships) and non-relational, or NoSQL, (handle semi-structured or unstructured data like documents or [JSON](https://dedicatted.com/services/glossary-of-terms#term-json) files). **Data warehouse** is essentially a relational database, but it’s designed to store preprocessed data from various corporate databases, mainly for analytical purposes. **Data lakes** can be described as data warehouses where no strict rules for data schema design or preprocessing apply. It’s a great place to dump all sorts of raw data (structured, semi-structured, or unstructured) from an unlimited number of data sources in a quick way to clean and organize later. **Data lakehouses** combine the discipline of a data warehouse with the flexibility of a data lake. If a business needs a single environment for both analytics-ready data and advanced data science workloads, data lakehouses are the way to go. **Data mart** is basically a subset of a data warehouse that provides data for specific needs of a particular business unit, for example for HR, sales, or marketing teams. ### **Why might companies need to build a data warehouse?** Sooner or later, every business runs into the same problem: data piles up across different systems, scattered and disconnected, making it hard to understand what’s actually happening in the company. That’s usually when the C-suite decides they need to keep the big picture in focus. In practice, this is the moment when most BI projects get the green light. Getting data into a BI tool quickly and reliably and then opening the door to historical, real-time, predictive, prescriptive, in short, every kind of insight, is exactly what a solid data warehouse is built for. But apart from faster and more reliable decision-making across the organization, consolidating enterprise data brings side benefits: Improved collaboration: teams across departments can quickly access and confidently work with the same curated datasets. - Single, trustworthy version of truth: everyone is aligned as data quality, consistency, and accuracy are maintained across all systems and reports. - Operational efficiency: less manual work reconciling or cleaning data. - Better compliance and governance: it’s easier to track data lineage, enforce data policies, and meet regulatory requirements. - Smoother integration with new tools: simpler and more reliable connections to any corporate systems or AI/ML models. ## Data warehouse design approaches Before comparing design approaches, it helps to map what a warehouse is made of. From a functional perspective, which focuses on the data lifecycle within the warehouse, the architecture breaks into four layers: 1. **Source layer:** an entry point of the data warehouse architecture, where data from databases, corporate systems, external APIs, and other sources originates. 2. **Staging layer:** an intermediate transition area that temporarily stores data as it moves from source systems toward the warehouse. This is where quality checks, error assessment, and integrity validation happen to prevent inconsistencies, duplicates, missing values, or anomalies from reaching the storage layer. 3. **Storage layer / warehouse layer:** a central repository where processed, cleaned and structured data is stored for long-term use. 4. **Presentation layer / consumption layer:** a final layer, where users access the data via user-friendly interfaces within BI tools or data visualization platforms. [![Visual diagram explaining data warehouse architecture, including data source layer, staging layer for processing, storage layer with sales and HR data marts, and presentation layer using BI, reporting, and business applications.](http://dedicatted.com/wp-content/uploads/2026/01/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-18T141658.404.jpg)](https://dedicatted.com/wp-content/uploads/2026/01/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-18T141658.404.jpg)Depending on how these layers are distributed, a data warehouse architecture can be: - Single-tier. Everything, from source to consumption layer, exists in a single tier. - Two-tier. A presentation layer is separated. - Three-tier. Source, storage, and presentation layers have their own isolated tiers. As data sources grow more diverse, analytics more complex, and user concurrency higher, the need for separation also increases. While a single- tier architecture works fine for tiny warehouses (<100 GB), large, high-complexity storage systems benefit from a three-tier architecture, which is more scalable, performant, and manageable. With the layers of a data warehouse unpacked, here are the common modeling approaches and how they shape the architecture. ### ****Inmon (top-down)**** The Inmon approach employs a schema strategy centered on normalized 3NF (Third Normal Form) structures. Data is organized by concept: each subject area (customers, orders, products) exists in its own table with carefully structured relationships through primary and foreign keys. While 3NF schemas ensure data integration and consistency, they’re not designed for direct business user access. Extracting insights from normalized tables requires query structures that are too complex, which makes them less suitable for end-user analytics without additional transformation layers. [![Architecture diagram of the Inmon approach, showing OLTP data sources, ETL processes into a centralized data warehouse, downstream data marts, OLAP cubes, and a reporting layer accessed through a cube browser.](http://dedicatted.com/wp-content/uploads/2026/01/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-18T141854.592.jpg)](https://dedicatted.com/wp-content/uploads/2026/01/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-18T141854.592.jpg)### **Kimball (bottom-up)** As opposed to Inmon’s, a bottom-up approach created by Ralph Kimball focuses on creating data marts initially, then integrating them into a cohesive data warehouse. Fundamentally, it’s built around dimensional modeling, which prescribes the use of star schemas or snowflake schemas. Those dimensional schemas allow for rapid query execution and user-friendly analysis due to their inherent advantages: - high performance on relational databases - flexibility to slice and dice data easily - extensibility to accommodate business changes [![Architecture diagram of the Kimball approach, where OLTP data sources load individual data marts via ETL, data marts feed a centralized data warehouse, and OLAP cubes support the reporting layer through a cube browser.](http://dedicatted.com/wp-content/uploads/2026/01/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-18T141901.336.jpg)](https://dedicatted.com/wp-content/uploads/2026/01/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-18T141901.336.jpg)### **Data vault (DV)** However, today, organizations most often turn to a third data modeling approach – Data Vault, introduced by Dan Linstedt. It’s considered a hybrid model, as it combines elements of Inmon’s enterprise-wide, normalized architecture and Kimball’s subject-area-focused, dimensional design. [![Example of a Data Vault data warehouse architecture on AWS showing source systems loading into a Raw Data Vault on Amazon Redshift, transforming into a Business Data Vault, and serving BI dashboards and data science consumers. Diagram highlights Redshift RA3 clusters, managed storage layer, raw and business vault schemas, data marts, and on-demand processing schedules](http://dedicatted.com/wp-content/uploads/2026/01/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-18T142302.781.jpg)](https://dedicatted.com/wp-content/uploads/2026/01/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-18T142302.781.jpg)A distinct **modular schema structure** beneath this approach consists of: - **Hubs**, containing core business concepts identified by business keys and surrogate keys - **Links**, representing associations between hubs - And **satellites**, holding descriptive attributes grouped by source or change frequency. Such a structure is built so that adding new data sources doesn’t require model reconstruction. [Query](https://dedicatted.com/services/glossary-of-terms#term-query) efficiency is accompanied by flexibility, scalability, and rapid adaptability of new business relationships. [![Comparison table of data warehouse design approaches showing Inmon, Kimball, and Data Vault methodologies with their core ideas, strengths, and best use cases, including enterprise data governance, fast analytics, and compliance-ready architectures.](http://dedicatted.com/wp-content/uploads/2026/01/table-15-scaled.png)](https://dedicatted.com/wp-content/uploads/2026/01/table-15-scaled.png)## **Four steps to setting up a data warehouse** Even though every project has its own flavor, these are the steps you need to generally follow when creating a data warehouse. **1. Discovery.** The discovery phase is where the whole foundation gets set. Everything that comes after, from design to deployment, rests on it. The first thing to nail down here is the business objectives the company is trying to reach. Pain points, priorities, and expectations are mapped against how processes currently operate and which data sources are available. When a company has hundreds of data sources, it takes time to dig into what exists in each and how it’s meant to support analytical needs. Jumping straight into building a data warehouse without these exploratory activities is a recipe for costly missteps caused by poorly designed data models or redundant ETL/ELT pipelines. #### Alex Rozdolskyi, AI Lead Engineer at Dedicatted With every source of data examined inside out, you then decide on the number of tiers, how data flows between layers, and where data transformation happens (whether to use ETL or ELT further). Besides, here you determine whether the warehouse will be deployed on-premises, in the cloud, or using a hybrid environment. Although a fully on-premises deployment is rare for modern projects, it remains a highly reliable option when full ownership and control of all data is a must, for example, in strict data compliance environments. For most organizations, though, cloud or hybrid setups offer greater scalability, faster deployment, and lower operational overhead, while still allowing tight control over critical data where necessary. The market is full of cloud-based solutions like Snowflake, Amazon Redshift, Google BigQuery, etc., capable of deploying data warehouses quickly and handling diverse workloads with minimal infrastructure management. #### Serhii Semenchenko, CTO at Dedicatted 2\. **Designing both logical and physical data model**. First comes the logical data model. Data engineers walk the documented processes and agree on the core entities, for example, customer, order, device, shipment, claim, and the exact relationships between them, pinning down business keys and the few rules that must always be true. Once that picture is stable, experts cast it into the physical model: - each entity becomes a table or a set of tables - keys are translated into primary-key columns or composite hashes - data types are chosen to match the source precision while keeping storage and compute costs in view This is where decisions are made about how each piece of information gets stored, so it can be found fast, stay accurate, and grow over time without breaking the budget. It’s also where the first rules of security are set (who can see which fields). **3. Implementing data pipelines, testing and deploying a data warehouse** Here, your data warehouse begins to breathe. Data starts flowing automatically from sources to the warehouse. For this flow to be consistent and reliable, a whole lot of measures need to be taken: 1. Setting up logging and alerting for failures 2. Writing transformation scripts (SQL or dbt models) 3. Configuring orchestration (e.g., Airflow DAGs for daily runs). 4. Implementing incremental loads (only process new/changed data) 5. Building data validation checks (record counts, nulls, referential integrity) Special attention should be given to testing. Hit it from every angle to verify all aspects of data quality. ***Data accuracy:*** Does total revenue in DW match source systems? ***Data completeness:*** Are all records loaded each day? ***Transformation logic:*** Are derived metrics (e.g., average order value) computed correctly? ***Performance:*** Are queries fast enough for users? **4. After-launch support and maintenance**. After going live, the warehouse should hum along under constant watch. Its health needs to be continuously monitored and issues – flagged and resolved as they arise. When new data sources have to be added or ETL pipelines adjusted, maintenance specialists are supposed to take care of it all. [![Call-to-action banner promoting data warehouse consulting services by Dedicatted, featuring text 'Get started on building data warehouse journey with Dedicatted experts' alongside a modern data center server room illustration.](http://dedicatted.com/wp-content/uploads/2026/01/Group-141-19-scaled.png)](https://dedicatted.com/wp-content/uploads/2026/01/Group-141-19-scaled.png)## **Where to take it next** Even the smartest data warehouse can hit the same roadblocks every enterprise faces: messy data, fragile pipelines, compliance bottlenecks, and rising costs. **[Our team can blueprint a warehouse tailored to your stack](#contact-form)**, covering sources, ETL/ELT flows, security, and KPIs and get a working prototype live in weeks, not months. Turn your scattered data into a foundation that actually drives decisions. **Categories:** Blog **Services:** Data Architecture **Industry:** Financial Services, Healthcare, Insurance, Retail --- ### [From Zero to Developer-Ready Platform in Two Weeks](https://dedicatted.com/insights/from-zero-to-developer-ready-platform-in-two-weeks) **Published:** April 1, 2025 **Author:** Dedicatted **Content:** ## **Introduction** Building quickly is always a challenge. Building quickly and securely – without sacrificing scalability – is where most growing companies hit a wall. Especially for teams operating in high-compliance environments, the pressure to move fast while keeping everything airtight is real. This case study covers our engagement with a fast-scaling SaaS company in the data integration space. Operating in North America and serving enterprise clients, their product helps software vendors integrate with dozens of business systems through a single API layer. To support their growth, they needed a cloud platform that was fast to build, easy to replicate across regions, and secure enough to meet the demands of compliance-heavy customers. We were brought in as both a **DevOps consulting partner** and the team responsible for their **security and compliance readiness**. The objectives were clear: - Stand up a secure, developer-ready platform from scratch. - Enable quick geographic expansion through automation. - Ensure infrastructure and practices were audit-ready for SOC 2. ## **The Challenge** #### Working time: 2 weeks #### Industry: High Tech Software #### The service: Security [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance), DevOps Consulting #### Overview The client came to us at a pivotal stage in their growth: no legacy infrastructure to work around, but also no existing platform to build on. They needed to move quickly, but without compromising on security or compliance expectations. Their key challenges were: **Development [Environment](https://dedicatted.com/services/glossary-of-terms#term-environment)**: There was no cloud infrastructure in place. They needed a full AWS platform set up from the ground up – secure, scalable, and production-ready. **Regional Expansion**: The team anticipated fast entry into new markets, so the infrastructure had to be portable and reproducible with minimal effort. **SOC 2 Compliance**: Their customers required proof of maturity, which meant technical readiness for SOC 2 from day one – everything from access control to audit trails needed to be properly implemented and documented. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## **Approach** We stepped in with a clear plan: build it once, build it right, and build it to scale. #### **Infrastructure from Day One, Designed for Growth** We deployed a clean, production-grade AWS environment using Infrastructure as Code. This included VPCs, EKS clusters, S3, IAM, and DNS – all automated, versioned, and deployed consistently across accounts. The setup followed AWS security best practices and was built to support rapid iteration without the usual growing pains. #### **Regional Rollouts in 48 Hours** From the beginning, the platform was designed to support expansion. By using [Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform) modules and [GitOps](https://dedicatted.com/services/glossary-of-terms#term-gitops) workflows, we ensured the entire infrastructure could be deployed in new AWS regions with minimal lead time. When the business was ready to expand, we delivered a working environment in two days – no rework, no reconfiguration. #### **SOC 2 Readiness From the Inside Out** Security wasn’t a checklist; it was baked into every layer of the platform. We implemented strict IAM boundaries, encrypted everything at rest and in transit, set up centralized logging, and built alerting into key services. We also supported documentation, policies, and internal workshops – everything the business needed to meet technical requirements for SOC 2 certification. #### **Governance with AWS Control Tower and Organizations** To future-proof the setup, we deployed AWS Control Tower and Organizations to create a scalable, governed multi-account structure. It allowed the engineering team to move quickly while staying aligned with the company’s long-term security posture and compliance model. ## **The Two-Week Impact** By the end of the second week, the development environment was fully operational. CI/CD pipelines were live. Developers were shipping code. There were no blockers, no last-minute infrastructure fixes, and no missing pieces. We brought in our own internal playbooks – standardized templates, security baselines, and GitOps automation – so we could deliver fast without cutting corners. The development team didn’t have to wait on infrastructure. Instead, they were building, testing, and moving forward. This wasn’t a temporary boost. It was a head start that shaped the entire trajectory of the platform. #### If you find this case interesting, we recommend taking a closer look at [ App modernization ](https://dedicatted.com/what-we-do/cloud-services/app-modernisation) [ Managed services ](https://dedicatted.com/what-we-do/devops/devops-as-a-service) ## **Results** - A complete, secure, and compliant development environment was deployed in under **two weeks** - The client successfully achieved **SOC 2 certification**, with every technical control documented and audit-ready - Expansion into a new region was completed in just **two days** Ongoing platform operations and compliance management are now handled under our **Managed Services model**. ## **Let’s Build Yours** We partner with high-growth teams who need security and speed to coexist. If your platform needs to move fast without missing the fundamentals – whether it’s SOC 2, scalable infrastructure, or both – we’re ready when you are. **[Let’s build the right foundation together](https://dedicatted.com/contact).** **Categories:** Case study **Services:** Cloud and Infrastructure Security, DevOps Consulting, Security Compliance **Industry:** High Tech Software --- ### [Transforming Platform Reliability and Achieving SOC 2 Compliance for a High-Volume Link Management Service](https://dedicatted.com/insights/transforming-platform-reliability-and-achieving-soc-2-compliance-for-a-high-volume-link-management-service) **Published:** April 4, 2025 **Author:** Dedicatted **Content:** ## About #### Working time: April 2024 – ongoing #### Industry: Web-based Tech Service #### The service: Security [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) #### Overview The client is a long-standing and widely recognized URL shortening service used by individuals, developers, and enterprises around the globe. Handling over 300 billion data records and supporting extremely high request volumes, the platform is a core part of many digital workflows. With its large-scale operations and history rooted in legacy on-premises infrastructure, the client faced growing pressure to meet modern standards of platform reliability and compliance. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## The Challenge As the platform matured, several operational and compliance issues became more pressing: - **SOC 2 Compliance on a Self-Managed Infrastructure** The client aimed to achieve SOC 2 compliance while operating on a complex, self-managed, on-premises infrastructure. This introduced significant hurdles in terms of visibility, access control, data handling, and operational maturity. - **Data Security and Control Coverage** With more than 300 billion highly-requestable data records, there was a clear need for comprehensive controls around data storage, processing, access, and protection. At the time, key areas such as encryption at rest were missing or underdeveloped. - **Low SLA Target** The system’s existing Service Level Agreement (SLA) was capped at 90%, which fell short of expectations for both internal stakeholders and end users relying on consistent uptime. ![Web application architecture diagram showing high availability setup with CloudFlare load balancer at the top connecting to multiple server racks containing Laravel applications running on Lighttpd with Caddy for failover, RabbitMQ for messaging, Redis for caching, Elasticsearch for search, and MariaDB in master-slave configuration](https://dedicatted.com/wp-content/uploads/2025/04/TinyURL-Infrastructure-Diagram-light.png) ## Our Approach To meet the ambitious goal of making the platform SOC 2 compliant while modernizing and stabilizing it, we assumed full responsibility for the technical side of the project – from infrastructure design to security operations. Our strategy included: - **Platform Ownership**: We took over the full technical ownership of the platform, allowing for better coordination and faster execution of architectural and security initiatives. - **Security and Compliance Implementation**: The platform was brought in line with SOC 2 requirements through major improvements in access control, logging, and alerting. We implemented protective monitoring across all infrastructure layers, deployed automated vulnerability scanning, and introduced incident response workflows including detection, reporting, and resolution processes. - **System Hardening and Stability Work**: Significant work was invested in stabilizing the platform. This included database clusterization, capacity planning, and hardware resource management to ensure reliability and performance under high traffic loads. ## DevOps and Observability Solutions One of the most technically rewarding aspects of the engagement was the level of customization achieved in both DevOps automation and observability: We developed **custom metric and log exporters** specifically designed for the client’s bare-metal infrastructure. These tools provided visibility into system performance metrics that had never been tracked before, enabling proactive issue detection and capacity forecasting. To support rapid development cycles, we built **a [GitOps](https://dedicatted.com/services/glossary-of-terms#term-gitops)-based solution** that allowed developers to spin up dynamic environments on-demand inside the on-prem Kubernetes cluster. These ephemeral environments were isolated, secure, and lifecycle-managed based on feature branches – reducing testing overhead and increasing deployment speed without sacrificing control. Together, these custom tools significantly advanced the client’s operational maturity, providing not just compliance, but a measurable boost to developer efficiency and platform reliability. ## The Results The project delivered strong outcomes across both compliance and engineering performance: [![Results of SOC 2 compliance project: 99.95% uptime, full observability, secure automated environments, and improved infrastructure resilience for link management platform](https://dedicatted.com/wp-content/uploads/2025/04/results-Transforming-Platform-Reliability-and-Achieving-SOC-2-Compliance-for-a-High-Volume-Link-Management-Service-1-1-scaled.jpg)*Key outcomes: SOC 2 compliance, enhanced SLA performance, full system observability, and DevOps automation delivering greater platform stability and developer efficiency.*](https://dedicatted.com/wp-content/uploads/2025/04/results-Transforming-Platform-Reliability-and-Achieving-SOC-2-Compliance-for-a-High-Volume-Link-Management-Service-1-1-scaled.jpg) **Categories:** Case study **Services:** Cloud and Infrastructure Security, DevOps Consulting, Security Compliance **Industry:** High Tech Software --- ### [Data Architecture: Key components, tools, frameworks, and strategies](https://dedicatted.com/insights/data-architecture-key-components-tools-frameworks-and-strategies) **Published:** April 10, 2025 **Author:** Andriy Kopachevskyy **Content:** *(This is the second article in our series on data architecture, where we explore its key components, types, and strategies for building an effective framework. If you missed the first article, be sure to check it out for a deep dive into [data infrastructure 101](https://dedicatted.com/insights/data-infrastructure-101-building-for-scalability-and-security "data infrastructure 101"))* ## What Is Data Architecture? Data is the lifeblood of modern enterprises, fueling everything from strategic decisions to AI-driven innovations. But raw data, scattered across multiple sources and formats, is chaotic and difficult to leverage effectively. Without a structured approach, businesses risk drowning in an ocean of disconnected information. Data architecture is the foundational framework that defines how an organization collects, stores, manages, and utilizes its data. It translates business needs into data assets and manages organizational data flow. By structuring data architecture, organizations can enhance data management, governance, and utilization across various applications. As we continue advancing in the digital era, the sheer volume of data keeps expanding – like an ever-growing library collection. Without a structured approach, chaos would be inevitable. But with a strong data architecture in place, everything stays in order, accessible, and ready to support business success. Sounds essential, right? [ ![Data Infrastructure 101: Building for Scalability and Security](https://media.dedicatted.com/wp-content/uploads/2025/04/Designing-Platform-for-Instant-Market-Penetration-2025-11-14T123012.084-300x157.png) BlogSerhii Semenchenko 5 min to read Also read ### Data Infrastructure 101: Building for Scalability and Security Automotive Healthcare High Tech Software Insurance Cloud Security Data Architecture ](https://dedicatted.com/insights/data-infrastructure-101-building-for-scalability-and-security)## Key Components of Data Architecture Data architecture may seem vast and intricate, but at its heart, it’s built on a set of essential components that work together to create a seamless, efficient, and reliable data ecosystem. To truly understand what data architecture is and why it matters, we first need to explore these foundational building blocks. **1. Data Models** Data models define how information is structured, stored, and accessed within databases. They serve as blueprints, shaping the flow and relationships of data across systems. - Conceptual models focus on high-level relationships between entities. - Logical models dive deeper, detailing attributes, keys, and relationship types. - Physical models, as the name suggests, translate these structures into tangible database elements like tables and columns. **2. Data warehouses and data lakes** It is a common assertion that data warehouses are a good fit for small- to medium-sized businesses, while data lake use cases are more common for larger enterprises. However, everything depends on the type of data you are dealing with and its sources. That said, there are few questions to help you make the right decision: **Do you have a set-up structure?** If you use an SQL database, CRM, ERP, and/or HRM systems, a data warehouse will fit well into your business environment. If you need a from-scratch solution, proceed to the next question. **How unified is your data?** For companies that are dealing with well-structured information or the one that can be structured, a data warehouse will work perfectly. If your data comes from diverse data sources (e.g., IoT logs and telemetry, binary data, analytics), data lakes are probably a better choice **Are your business needs predictable?** If you can deal with reports that are generated by running a predetermined set of queries against the table(s) that is regularly updated, a DW will probably suffice. However, if you are working with more experimental cases, such as ML, IoT, or predictive analytics – it’s better to store raw data in its initial format. **3. ETL processes** ETL is a data integration process that: **E**xtracts raw data from various sources and formats **T**ransforms that data using a secondary processing server **L**oads the transformed, structured data into a target database – usually a data warehouse We can add a picture here showing the process, like this: **4. Data marts** Consider data marts as smaller, more focused data warehouses. They cater to specific business area, for example, you can create a data mart to support reporting and analysis for the marketing department. This limitation also has some benefits. By limiting the data to specific departments (such as marketing), the business unit does not have to shift through irrelevant data. You can feed data directly from data sources or an existing data warehouse in a data mart. They use highly structured schema optimized for reporting and analysis, commonly dimensional modeling such as snowflake or star schema. **5. Metadata management** To help users confidently understand and utilize data, metadata management is crucial. Metadata provides context about the data’s purpose, consumption readiness, and applicability to specific use cases. Effective metadata management links users with the correct sources of information, promoting data reliability and enhancing user trust. **Example:** A retailer might use metadata to track the source of a customer’s transaction, whether it came from an in-store POS system or the online checkout process, granting analysts key context to use the right data in targeted marketing campaigns. **6. Data governance** [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) audits are the biggest challenge addressed by data governance. 52% of executives have reported difficulties with compliance audits, and 40% have stated that they failed to comply at least once. Data governance involves establishing policies, procedures, and controls for data quality, privacy, and security; implementing data management technologies and systems; and ensuring that data across the organization is consistent, accessible, and properly used. **7. Data Security** In a world where data breaches make headlines, data security is non-negotiable.This component focuses on protecting IT and digital assets, including hardware, software, storage devices, user devices, access, and administrative controls, from corruption, theft, or unauthorized access throughout their entire life cycle. This ensures an organization’s data confidentiality, authenticity, integrity, and availability are consistently maintained in alignment with its unique risk management profile and business goals. **8. Data integration** As enterprises increasingly adopt a plethora of tools and platforms, the need for seamless data flow becomes paramount. Data integration ensures that data from disparate sources can be cohesively viewed and accessed. Through a combination of middleware, APIs, and connectors, it stitches together various data silos, creating a unified data ecosystem. ## Types of Data Architecture Organizations adopt different data architectures depending on their needs. As the name suggests, **microservices** architecture disassembles the application into smaller, independent services. Each microservice runs its process, communicating through APIs or messaging systems.Developers can quickly iterate or modify a single service without disrupting the entire system, A **centralized** data architecture consolidates all data into a single repository, providing consistency and streamlined management. In contrast, a **decentralized** (federated) architecture, such as domain-driven design, data mesh, and distributed data architectures, distributes data across multiple independent sources while allowing centralized access when necessary. For businesses leveraging the cloud, **cloud-based** data architecture provides scalability and flexibilit. Cloud-native technologies, like serverless computing and containerization, further enhance the efficiency of data processes in modern architectures. Companies operating in fast-paced environments may prefer an **event-driven** architecture, which focuses on real-time data streaming and processing. From stock trading platforms to instant messaging, its applications are vast. Meanwhile, deeply entrenched in the realm of big data, both **Lambda and Kappa** architectures prioritize swift data processing, where **Lambda** architecture supports both batch and real-time data processing, ensuring fault tolerance and scalability and **Kappa** simplifies this by solely relying on stream-processing, making it nimbler but necessitating a robust streaming platform. A powerful example of modern data architecture comes from [**Dedicatted’s collaboration with a global leader in wearable sensor analytics** ](https://dedicatted.com/insights/building-scalable-data-architecture-for-iot-with-aft-managed-landing-zones)for clinical trials. To handle vast streams of physiological data, our team built a **secure, AWS-native platform** featuring a Data Lake with AWS Lake Formation, real-time streaming with MSK and Flink, and automated infrastructure using Account Factory for [Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform) (AFT). This architecture enabled the client to process and analyze IoT data from wearable devices in real time while maintaining full compliance and scalability across more than 20 AWS accounts. It’s a perfect illustration of how thoughtful data architecture transforms raw sensor data into actionable insights that advance healthcare research and innovation. #### If you find this article interesting, we recommend taking a closer look at [ Data Architecture ](https://dedicatted.com/services/data/architecture) [ Cloud Data Migration & Modernization ](https://dedicatted.com/services/data/cloud-data-migration) ## Proven Strategies to craft the perfect data architecture framework - ***Start with the business goals*.** Data architecture should be built around business and user needs rather than solely technical preferences. Moreover, having these objectives articulated ensures that all stakeholders, from IT specialists to business leaders, are aligned and working cohesively towards a shared vision. - ***Prioritize data quality.*** Implement data validation, cleaning, and enrichment processes to maintain high-quality datasets. - ***Architect for access.*** Effective data architecture should make data access fast, easy, and intuitive. It should empower users to select the right data for their needs, backed by well-documented metadata and a user-friendly discovery layer. A data catalog serves as a discovery layer for users to browse well-curated metadata, understand the lineage, confidence levels, and purposes the data supports, and ensure the right data is used for their purpose. - ***Ensure Data Security.*** The need for data security and privacy is a common thread across all listed components. Here are some examples of data security tools: - Data classification tools help categorize data based on its sensitivity and relevance to privacy regulations. - Access control systems ensure that only authorized personnel can access sensitive data. They employ measures such as user authentication, role-based access control (RBAC) - [Encryption](https://dedicatted.com/services/glossary-of-terms#term-encryption) and Data Masking obscure specific data within a database, hiding sensitive information from users without the necessary access rights. - IDPS (Intrusion Detection and Prevention Systems) can help detect and prevent breaches by identifying suspicious activities that could indicate a security threat. - ***Encourage collaboration.*** The world of data isn’t an isolated island; it’s a bustling metropolis where IT professionals, data scientists, business analysts, and leaders converge. Each group brings a unique perspective, and when these viewpoints intertwine, magic happens. By fostering a culture of collaboration, you ensure that your data architecture framework is not only technically sound but also aligned with business objectives. Hold regular brainstorming sessions, workshops, and feedback rounds. The IT team might be building the framework, but its users span across departments. By ensuring everyone has a say, the end product becomes more holistic, intuitive, and user-friendly. - ***Leverage [Automation](https://dedicatted.com/services/glossary-of-terms#term-automation) & AI.*** Integrating AI and ML technologies into data pipeline management allows engineers and analysts to focus on strategic initiatives while cutting the time spent on manual operations by automating processes such as data ingestion, analysis, and visualization. ## Strong Data Architectures Make Strong Companies Data architecture offers a myriad of benefits, ranging from enhancing performance and reducing costs on existing systems to empowering users with self-service analytical capabilities and facilitating the development of data-driven applications. However, navigating the complexities of implementation requires expertise and experience. At Dedicatted, we specialize in designing and implementing robust data solutions tailored to meet each client’s unique needs. **Categories:** Blog **Services:** Data Architecture **Industry:** Automotive, High Tech Software, Insurance, Manufacturing --- ### [Data Infrastructure 101: Building for Scalability and Security](https://dedicatted.com/insights/data-infrastructure-101-building-for-scalability-and-security) **Published:** April 8, 2025 **Author:** Serhii Semenchenko **Content:** The world of data is filled with promising quotes, such as “information is the oil of the 21st century” or “data is becoming the new raw material of business.” However, the value ingrained in data only becomes vivid when analytics solutions come into play. Before analytics can start, data needs to be prepared. This stage is a foundation for efficient and effective analysis. And this is where data infrastructure enters the battlefield. ***Data architecture*** is the high-level blueprint that defines how data is collected, stored, integrated, and utilized across an organization. It sets the standards, rules, and policies for managing data assets, ensuring consistency, security, and scalability. ***Data infrastructure***, on the other hand, is the underlying framework of hardware, software, and networks that bring this architecture to life. Together, they form the backbone of modern data-driven enterprises. In this first article of our Data Architecture Series, we explore how a well-designed data architecture enables an efficient data infrastructure, paving the way for reliable and insightful analytics. ## Break down two significant barriers to your data analysis with a robust data infrastructure Data in an ideal world is perfect and structured, but in reality, several challenges hinder its usability. Two major barriers to an effective data analytics process are: ![management infographic with two main elements: on left side shows stacked servers with shield icon labeled Data Accessibility & Governance, on right side shows server with expandable outline and directional arrows labeled Scalability & Volume of Data](https://dedicatted.com/wp-content/uploads/2025/04/data-infrastructure-101-icons-2-1024x455.png) While scalability primarily affects growing companies, accessibility and governance issues impact businesses of all sizes, making them essential considerations in any data architecture strategy. ### Data accessibility No matter how clean and organized your information is, if access is restricted to engineers or data scientists, your organization is not truly data-driven. A recent survey by IDC revealed that 68% of enterprise data goes unused for analytics due to poor accessibility and fragmentation. Without a well-defined data architecture, companies often struggle with fragmented data silos, leading to inefficiencies in decision-making. Nevertheless it ensures that: - Data is standardized across the organization. - Security policies are embedded at every level. - Access is granted based on roles and responsibilities. - Metadata management is in place for data lineage and discovery. For companies aiming for true data democratization, a centralized yet flexible data architecture allows teams to securely access and analyze the information they need, when they need it. ### Growing Data Volume The total amount of data created in 2024 reached 147 zettabytes, while in 2020 it was only 64. This situation became a challenge for data engineers, who are expected to build a new infrastructure to handle such a huge amount of data and get ready for exponential growth in the future in such a short space of time. The more information you have, the more complex the architecture of the serving infrastructure will be. Data engineers need to seamlessly combine two tasks: rebuild existing data infrastructure and try not to become buried under the ever-increasing data flow. The amount of data will naturally grow over time – that’s a fact! According to Statista, by 2027, data creation all over the world is expected to increase threefold. Without implementing a proper data infrastructure in your organization, it will be troublesome to grow and stay competitive among other companies. So the best time to start preparing your data infrastructure is now. ## Five must-do tips for building a robust data infrastructure There is no one-size-fits-all solution for data architecture, but focusing on these five areas will help create a robust and scalable foundation: **1. Define your Data Infrastructure Strategy** A concise data infrastructure strategy will save you a lot of effort in the future. First, think of where you will manage your data: in the cloud or on-premises. The prospect of maintaining your own data center may seem unprofitable, but this is only applicable to small companies. If your organization has enough resources to contain hardware, it may even prove to be more cost-effective. In terms of reliability, there is no difference between the two options. [Let our cloud expert guide your best choice ](https://dedicatted.com/contact) **2. Design a scalable Data Storage System** At the core of data architecture lies data storage design. Organizations must carefully choose between data lakes, data warehouses, or a hybrid model based on their analytical needs: **Data Lake** **Data Warehouse****Type of data**Structured, semi-structured and raw data from multiple sourcesStructured data in quantitative metrics**Data volumes**Up to hundreds of petabytes (PB)Mostly in terabytes (TB)**Data curation**Storing any type of data for big data analytics, machine learning, deep learning, and moreSelected data for read-only queries and data summarization**Purpose**Big data storage at a very low costData analytics for business intelligence**Intended users**Data scientistsBusiness users**Schema**Schema-on-readPredefined schemas-on-write**Cost**InexpensiveExpensive**Security**Generally minimal security measuresHigh grade of data protectionThat said, this doesn’t have to be an either-or decision. There’s also the option to go for a hybrid solution. You can keep the data with minimal business meaning in a lake while storing the useful and relevant data in a warehouse; or use a data lake to collect data and a DWH to structure it. But keep in mind, that these repositories use different technologies: data lake – NoSQL, data warehouse – SQL. So you have to resolve this contradiction when deciding to build your data infrastructure. **3. Ensure Data Quality & Optimization** Data integrity is paramount for accurate analytics. Organizations must establish automated data cleansing and validation mechanisms to maintain high data quality. A robust data architecture should incorporate: - Real-time data validation pipelines. - Automated anomaly detection systems. - Standardized data governance policies for consistency. Last but not least – be aware of your information quality. The data must always satisfy six conditions: **Completeness.** All data sets and data items must be recorded. **Uniqueness**. This parameter is kept if data has only been registered once. **Timelessness.** This is about how useful or relevant your data is according to its age. **Validity**. The data you’ve recorded must reflect the type of data you set out to record. **Accuracy.** This metric determines whether the information you hold is correct or not. **Consistency.** You can compare data across data sets and media, if it’s all recorded in the same way. #### If you find this article interesting, we recommend taking a closer look at [ Data Architecture ](https://dedicatted.com/services/data/architecture) [ Cloud Data Migration & Modernization ](https://dedicatted.com/services/data/cloud-data-migration) **4. Develop a Resilient ETL [Pipeline](https://dedicatted.com/services/glossary-of-terms#term-pipeline)** Extract, Transform, Load (ETL) pipelines serve as the foundation of an organization’s data architecture, enabling seamless data flow between different sources and storage systems. However, businesses often face challenges such as data format inconsistencies, broken data connections, and system integration failures. A well-engineered ETL pipeline ensures that data is ingested, transformed, and stored efficiently, facilitating real-time analytics and decision-making. **5. Implement Strong Data Governance & Security** All the actions above make little sense without proper data governance. It increases efficiency by giving your business a solid database to work from and saving time on correcting the existing data. Besides, it helps to avoid risks associated with dirty and unstructured data and avoid regulatory and compliance issues. A robust data governance framework should address: - Data ownership and stewardship. - Access control mechanisms (role-based and attribute-based security). - Regulatory compliance (GDPR, CCPA, HIPAA). - Data lineage and cataloging for transparency and traceability. ## Solid data infrastructure empowers in-depth analysis A well-designed data architecture is the foundation of a highly efficient, scalable, and secure data infrastructure, that smooths the road for data science efforts. To benefit from it, you need to care about collecting raw data, cleaning it, and making it accessible. ### Real-World Example: Driving Platform Stability Through Managed Support Services One vivid example of how the right infrastructure and support model boost operational efficiency comes from **[our collaboration with a leading provider of unified entity management solutions. ](https://dedicatted.com/insights/advancing-infrastructure-stability-and-operational-capabilities-with-support-service)**As the company scaled its platform to enterprise level, it faced challenges with fragmented processes, lack of 24/7 support, and limited operational visibility. By embedding their environment into our Managed Support Services (MSP) framework, Dedicatted introduced **round-the-clock monitoring, ITSM-based process structure, and proactive incident management**. The result was a **99.97% SLA compliance rate**, rapid issue resolution, and significantly improved uptime. This transformation not only stabilized the platform but also empowered the client’s internal team to focus on innovation, knowing their critical infrastructure was fully supported and future-ready. In the next article of our **Data Architecture Series**, we will explore the principles of designing scalable and modular data architectures, diving deeper into best practices and emerging technologies. [![Data architecture consultation banner with dark text on light background featuring server room image and call-to-action button for booking free expert guidance](https://dedicatted.com/wp-content/uploads/2025/04/Group-141-3.png)](https://dedicatted.com/services/data/architecture) **Categories:** Blog **Services:** Cloud Security, Data Architecture **Industry:** Automotive, Healthcare, High Tech Software, Insurance --- ### [Managing cloud data migration: Tools, Tips, and Expert Help](https://dedicatted.com/insights/managing-cloud-data-migration-tools-tips-and-expert-help) **Published:** August 1, 2025 **Author:** Serhii Semenchenko **Content:** The cloud has now been with us for a while. AWS EC2 first became available in 2006 and Google’s AppEngine arrived in 2008. Since then, what the cloud is and does have only evolved and matured. If you are an organization considering a cloud migration, you are free of the burden of consuming all the first-time risk and now take up the burden of analyzing which path is best to take. You don’t have to be first to land on the moon; turns out the moon is now a well-colonized and popular destination. You do have to analyze the right trajectory and best implementation for landing your particular rocket there This article is going to help you figure out what beginning you need to take when starting your cloud migration journey along with identifying various decision points along the way. Some of the decision points may seem like simple left or right turns but mask long term implications. We will try to point out as many of the specific impacts these decisions can have [![Close-up on the futuristic block with the cloud on it, highlighting cloud environment importance. The text on the picture is embracing migration to the cloud](https://dedicatted.com/wp-content/uploads/2025/08/Group-144-4-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/08/Group-144-4-scaled.png)## ******What are the benefits of moving your data to the cloud?****** **1.** **Greater [Scalability](https://dedicatted.com/services/glossary-of-terms#term-scalability) and Flexibility** One of the main drivers for cloud data migration is the ability to scale effortlessly. Unlike on-premises servers that require costly hardware upgrades, cloud environments let you adjust resources up or down based on actual demand. This means you can seamlessly manage seasonal traffic spikes without over-investing in infrastructure. With features like autoscaling, resources expand when needed and contract when they’re not automatically. **2. Lower Costs, Both Direct and Indirect** Cloud storage follows a pay-as-you-go model, so you’re billed only for the resources you use. This eliminates the overhead of maintaining physical servers, including costs for upkeep, staffing, and space. By shifting to a cloud provider, you’re leveraging their infrastructure and scale – often at a much lower total cost than managing your own systems. Our proven frameworks help businesses migrate **30% faster on average**, and we’ve helped clients cut infrastructure costs by up to **40% within the first year** post-migration. [![A statistics made by Mckinsey "Budget for cloud migration vs actual spend" highlighting that in fact, 75% of businesses end up blowing way past the intended budget, and 38% exceed the expected completion timeline.](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-31.jpg)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-31.jpg)**3. Improved performance and user experience** Cloud providers improve performance in two key ways: low latency and high availability (HA). Globally distributed data centers decrease latency because content delivery is closer to your end users. And built-in redundancies – techniques like load balancing, data replication, HA architectures, and more, ensure high availability. In short, **[cloud services](https://dedicatted.com/services/data/cloud-data-migration)** are much less likely than on-premises servers to be impacted by local disruptions. Better performance means end-users get a better experience at the same time as you’re increasing operational efficiency. One example of putting these principles into action is [**Zivian Health**,](https://dedicatted.com/insights/building-a-compliance-ready-cloud-zivian-healths-aws-to-azure-migration) a healthcare SaaS platform that ensures Advanced Practice Providers stay compliant across multiple U.S. states. When they needed to migrate from AWS to Azure, Dedicatted delivered a seamless transition – rebuilding core services like ECS workloads, S3 storage, and Route 53 DNS on Azure-native equivalents with zero downtime. The project went beyond migration: Infrastructure as Code with [Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform) and a move from Angular to React modernized delivery, while Azure’s governance tools reinforced HIPAA and SOC 2 alignment. The outcome was a faster, safer, and more scalable platform – proof that the right cloud strategy can fuel both innovation and compliance. [![Comparison chart showing benefits of moving from on-premises to cloud. Highlights: Cloud infrastructure is elastic and on-demand; maintenance is handled by provider; architecture becomes strategic and unified; technologies are continuously updated, reducing costs and risks](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-32-1.jpg)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-32-1.jpg)## ************6 strategies for migrating your data to the cloud************ Since cloud technologies have been with us for more than a decade, a handful of migration options have coalesced into six primary paths for an application moving to the cloud. These are known as the 6 Rs. Your overall cloud strategy will dictate which 6 R path, or likely combination of paths, an organization will use for moving their applications to the cloud. [![An image showcasing The "6 Rs" of cloud migration refer to six common strategies organizations use when moving applications and data to the cloud: Rehost, Replatform, Repurchase, Refactor/Rearchitect, Retire, and Retain.](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-33.png)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-33.png)- Retain This option means you do nothing. The application in question may gain little from any sort of migration. There could be several reasons for Retaining applications, including a product line reaching end-of-life, or the technology of the solution space has not evolved. You may also just need to revisit the solution in question at a later date. As the rest of the organization migrates to the cloud, changes in the surrounding ecosystem will impact the value proposition of choosing to retain an application, and you can reassess from there. - Retire You may just retire a solution. This means you no longer need the solution. It may be discovered during a migration that redundant IT solutions are simply still out there consuming resources in the enterprise. It could be that in the process of performing a repurchase to replace another system, the new system also performs the function of another existing IT solution, which you then decide to retire. - Rehost This is the classic “life-and-shift” approach. You rehost your current application in the cloud without using any of the cloud provider’s extended capabilities. Often enterprises choose this tactic because it is simple and obvious. This strategy is no different than moving your workloads into a co-location facility. It may be a simple matter of moving VMs and there are several tools that can perform this action. Often, there is still some installation and configuration changes in order for applications to function in the cloud - Replatform This approach is a variation on Rehost. Instead of an almost pure lateral move that Rehosting implies, when you Replatform you make low-cost, low-complexity changes to the application to leverage features from the cloud. For example, when Replatforming you might keep the core application as-is, but use a cloud provider’s managed database service or managed file server instead of moving the on-premise capability into the cloud. - Refactor The refactoring process involves redesigning or modifying an application to leverage cloud-native capabilities. Significant development work is required to break down monolithic infrastructure into microservices, implement new architectures, and adopt cloud-specific services. While this approach requires significant resources upfront, it often yields the greatest long-term benefits. Refactoring enables organizations to capitalize on cloud technology’s advantages fully. - Repurchase Performing a repurchase often refers to completely abandoning an existing system for a new functional equivalent. When the functional capabilities of an existing solution provide no strategic differentiation when compared to a new cloud variant, repurchase can be a good choice. A common path is to discard an existing on-premise application for a SaaS solution. Repurchases could also be changing to a different managed offering from a competing vendor, or choosing a new SaaS provider. ****Relocation: A Growing Choice for Cloud-Ready Organization**** As more organizations adopt portable runtimes, a new migration approach is gaining traction- **Relocation**. This method resembles Rehosting, but is particularly effective when most applications are already containerized – essentially a cloud-native mindset before cloud adoption. If your organization is using a container orchestration platform like [**Kubernetes**](https://dedicatted.com/solutions/key-technologies/kubernetes), you’re well-positioned to shift workloads directly to a cloud provider’s managed container service. This not only simplifies the move but also lays the groundwork for a multi-cloud strategy from day one. Beyond containers, many cloud infrastructure providers now offer mature VMware Cloud solutions, making it easier than ever to move traditional workloads to the cloud with minimal disruption. [![Banner with a gradient background from purple to teal showing a tablet on the left with the cover of a whitepaper titled 'MAP to the Cloud: Navigating Migration with Dedicatted on AWS.' Main text reads: 'Facing unexpected hurdles in your migration journey? Dive deeper into best practices and real-world strategies in our AWS cloud migration whitepaper.' On the right side is a green button labeled 'Discover.](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-34.png)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-34.png)**************Hidden Refactoring Opportunities************** Refactoring isn’t limited to applications – it can also apply to business processes during cloud migration. As cloud capabilities become more visible across the organization, teams often identify spontaneous opportunities to streamline outdated workflows. Ongoing communication is key, as it helps surface these opportunities organically. A real-world example comes from a retail company migrating to the cloud. Initially focused on upgrading backend authentication, they discovered the real issue was an overly complex identity process for employees. Instead of solving it with a traditional approach, a developer used a newly available PaaS single sign-on solution to build a simple identity services app. This app unified multiple systems, reduced friction for users, and cut support calls to HR and IT. It wasn’t part of the original plan, but it delivered immediate business value by leveraging refactored APIs and cloud-native tools. Meanwhile, the identity team working on a separate complex solution realized they could achieve their goals more efficiently using the same PaaS. ## **Steps to create a cloud data migration strategy for your company** ****Step 1: Identify which data to migrate**** Not all data deserves a ticket to the cloud. Start by categorizing your data – critical, obsolete, and everything in between. Prioritize high-value data that supports active workflows or regulatory requirements. Cleaning up redundant or outdated datasets before migration saves storage costs and improves system performance from day one. ***Tip:*** Conduct a data audit with key business units to align on what truly needs to move. ****Step 2: Define the target environment**** Your choice of cloud (public, private, or hybrid) will impact everything from compliance to cost-efficiency. Consider your long-term business model, scalability goals, and data sensitivity. Hybrid models often offer the flexibility enterprises need when dealing with legacy systems and regulatory constraints. ***Tip:*** If you’re in a highly regulated industry, consider hybrid or private cloud options that allow granular control over sensitive data. ****Step 3: Choose a migration approach**** There’s no one-size-fits-all method. Rehosting (lift-and-shift) gets you to the cloud quickly. Replatforming gives you some optimization along the way. Refactoring unlocks full cloud-native benefits, but takes more time and planning. Choose the strategy that balances your risk tolerance, budget, and timeline. ***Tip:*** A phased, hybrid approach is often best – start with quick wins (like rehosting) and evolve into refactoring as your team gains cloud maturity. ****Why** [**Amazon Redshift**](https://aws.amazon.com/marketplace/pp/prodview-t2szuj6h2xc2m?sr=0-11&ref_=beagle&applicationId=AWSMPContessa) **is your perfect choice for cloud data migration?**** It is a fully managed cloud data warehouse built on PostgreSQL, designed for fast and scalable analytics. It stands out among competitors like Snowflake and BigQuery, holding the largest market share at 18.7%, with over 8,000 companies using it. Redshift supports a wide range of use cases – from business intelligence dashboards to operational and predictive analytics. One of its main advantages is its familiar SQL syntax, making migrations from PostgreSQL environments faster and smoother. Redshift also integrates seamlessly within the AWS ecosystem, connecting easily with services like S3, DynamoDB, Kinesis, and EMR. It supports machine learning directly within your data warehouse through Redshift ML, enabling you to build and deploy ML models using SQL. In short, migrating to Redshift means scalable performance, seamless [**AWS integration**](https://dedicatted.com/our-partners/aws), ML capabilities, and support for both structured and semi-structured data – all within a secure, fully managed environment. [ ![Platform & Data Migration with Security Enhancement](https://media.dedicatted.com/wp-content/uploads/2025/03/Tecnic.-Cover-2-300x157.webp) Case studyDedicatted Petlichenko 5 min to read Also read ### Platform & Data Migration with Security Enhancement Automotive Cloud and Infrastructure Security Cloud Data Migration & Modernization Cloud Migration Cloud Modernization Cloud Operations DevOps Consulting Security Compliance ](https://dedicatted.com/insights/platform-data-migration-with-security-enhancement)****Step 4: Assess risks and define a timeline**** Unplanned downtime or integration issues can derail even the best migrations. Conduct a risk analysis early and build a detailed timeline that includes stakeholder sign-offs, testing checkpoints, and a rollback plan. Make sure business operations can continue even during transitional phases. ***Tip:*** Involve both IT and business leaders in timeline planning to ensure alignment on critical milestones and system availability ****Step 5: Define validation procedures**** Successful migration doesn’t stop at “go-live.” Build in time for post-migration validation: test data integrity, system performance, user access, and security controls. This final step ensures the cloud environment functions as expected and delivers business value from day one. ***Tip:*** Run pilot migrations for select workloads and gather feedback before scaling the process organization-wide. [![Infographic titled "From opportunity to operations" explaining support for cloud data platform migrations. Icons represent stages: Cloud migration business case, Tech POCs/evaluations, Cloud migration planning, Cloud migration execution, Cloud platform operations. Key points: realize value early, focus on decommissioning, involve stakeholders, build skills in the cloud, integrate security and privacy from the start, and minimize business disruption. Background gradient purple to teal.](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-35.jpg)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-35.jpg)## **Conclusion** Data is a new form of capital at the heart of everything an enterprise aspires to do – from innovative new business models, to more efficient operations, to deeper partnerships with its ecosystem Cloud enables organizations to break free from the constraints of on-premises data storage and compute. Its cost-effectiveness and flexibility, combined with its scalability and innovation potential, mean you can optimize your data platform far more effectively while simultaneously opening up the possibility of new data-driven business models and revenue streams. Don’t let the prospect of cloud overwhelm you without sitting down and chatting with folks that have made the journey before. [Dedicatted](https://dedicatted.com/contact) has helped architect a number of successful cloud migrations for small and large clients. We’d like to help. We’re not only highly experienced, but highly empathetic. We specialize in listening to exactly where clients are at and charting out where they could go within the cloud. We like to make rocket science not seem so rocket-sciencey. Want a tailored roadmap for your use case? Request your custom[ **Data migration review**](https://bit.ly/3GVakrz) **Categories:** Blog **Services:** Cloud Data Migration & Modernization, Cloud Migration, Cloud Modernization **Industry:** Financial Services, Healthcare, High Tech Software, Manufacturing --- ### [Winning the Shopper’s Mind: How AI Personalization Drives Loyalty](https://dedicatted.com/insights/winning-the-shoppers-mind-how-ai-personalization-drives-loyalty) **Published:** August 13, 2025 **Author:** Denys Yermakov **Content:** [**Artificial intelligence**](https://dedicatted.com/services/artificial-intelligence) isn’t just transforming retail; it’s disrupting it at its core, unlocking game-changing opportunities while forcing retailers to rethink how their entire business functions, from product design through to customer engagement. AI is the ultimate double agent – on one hand, it gives retailers the power to craft immersive, hyper-personalized experiences that captivate customers and streamline operations. On the other, smarter tools empower consumers to hunt for better deals, compare products instantly and align purchases with their personal values – all with just a few clicks. The customer experience is now a high-stakes battleground, where loyalty can be won or lost in seconds. As consumer expectations escalate, retailers face relentless pressure to deliver seamless, frictionless and deeply personalized interactions across digital and physical spaces. It’s critical though that this personalization feels natural. According to Salesforce , 84% of customers say that being treated like a person and not a number, is of utmost importance to earning their business. Ready to produce experiences that feel intuitive, build loyalty, and drive growth? Discover how **[AI-led personalization transforms retail operations](https://dedicatted.com/insights/genai-for-retail-the-secret-weapon-of-customer-first-brands?utm_source=Website&utm_medium=insight-informational-genaivsagenticai&utm_campaign=ad-organic)** across the entire customer journey. [![Dedicatted has achieved AWS Retail Services Competency. We are among the top 5% of AWS Consulting Partners in Canada to hold the AWS Retail Services Competency](https://dedicatted.com/wp-content/uploads/2025/08/Group-317-2-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/08/Group-317-2-scaled.png)## ****Why retailers love AI**** Improving customer service, making physical shopping smarter, rationalizing inventory and supply chain management became easy thanks to a range of retail technology innovations such as: - **Conversational AI:** AI-powered tools that work 24/7 and can deal with routine customer service inquiries and provide personalized product recommendations, streamlining the service process. - **Computer Vision Systems**: detecting misplaced items, and monitoring shelf inventory levels in real-time to optimize inventory, cutting the need for time-consuming manual checks. - **Natural Language Processing (NLP) Tools:** scanning of customer reviews and social media sentiment to gain the latest information on consumer trends, helping to inform the development of future products. - **Machine Learning Algorithms:** analyzing customer data at vast scale and pace, to make accurate predictions on purchasing trends and preferences, so that inventory and marketing campaigns alike can be perfected. Imagine a customer asking for a gift in Arabic and instantly getting the perfect match – with tone, context, and cultural nuance intact. That’s not a vision anymore. In one of our recent AI-driven retail projects, we used AWS Bedrock and LangChain to create a **bilingual assistant** that helps shoppers explore, compare, and decide through natural conversation. The impact: **20% daily adoption**, **22% conversion growth**, and **6% higher order value**, all while keeping operational costs under 1% of new revenue – a powerful testament to secure, explainable, and high-performing AI. [**See how this innovation came to life.**](https://dedicatted.com/insights/ai-that-understands-you-taw9eels-bilingual-shopping-assistant-delivers-results) [![Diagram explaining computer vision, machine learning, and NLP workflows in AI, from data input to predictions and responses.](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-40.jpg)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-40.jpg)## ****What is the journey to becoming an intelligent retailer**** **Phase One**: Enabling people and establishing the foundations for AI adoption. At the function level, businesses pilot AI solutions across various areas, building skills, fostering innovation and learning from these initial implementations. At the foundation level, organizations use cloud platforms and pre-trained AI models from strategic providers with limited customization. This phase focuses on creating awareness, experimentation and alignment to ensure the organization is prepared for broader AI integration The first step in becoming an intelligent retailer is empowering employees with AI tools that make every interaction more personal, relevant, and seamless. By reducing routine workloads and delivering real-time insights, AI enables staff to focus on what matters most – creating experiences that keep shoppers coming back. [![Gen AI complexity by function: finance, HR, IT, services, cyber, risk, supply chain, marketing, sales, and front office.](https://dedicatted.com/wp-content/uploads/2025/08/AI-2025-08-13T161332.650.png)](https://dedicatted.com/wp-content/uploads/2025/08/AI-2025-08-13T161332.650.png)- **Personalized Selling at Scale:** These AI systems, integrated into apps or websites, handle repetitive customer queries like order tracking, product availability and return policies. This enhances the customer experience with faster, more personalized interactions - **Dynamic Pricing for Trust & Value:** By adjusting prices in real time (e.g., Amazon), AI ensures customers always feel they’re getting the best deal without sacrificing brand perception. Automotive retailers , for instance, use live pricing to set pre-owned automobile prices - **Conversational AI Service:** AI-powered chatbots can handle a range of basic customer inquiries, from store hours to product details, while also providing information on returns, package tracking, loyalty/points balances and refund policy, reducing the load on human agents and improving response times for customer support - **In-Store Personalization Devices:** With just a few taps, associates gain real-time access to customers’ buying history, loyalty or rewards program status, payment and shipping preferences, preferred sizes, and even their online browsing activity. This rich, AI-driven data enables associates to provide highly personalized service that narrows the gap between in-store and online experiences. The Human Connection, Enhanced by AI AI doesn’t replace the human touch – it amplifies it. By automating the repetitive and surfacing real-time customer insights, it allows associates to focus on genuine connections, informed recommendations, and service that feels truly personal. This is how personalization moves from a marketing tactic to a loyalty engine. Read one of our own [**retail success stories**](https://dedicatted.com/insights?industry=retail) here, then take a look at our [**AI solutions**](https://dedicatted.com/industries/retail) in more detail **Phase Two**: Embedding AI in the Flow of Work In Phase One, AI equips employees with targeted tools. In Phase Two, it becomes the backbone of retail operations- embedded into day-to-day workflows, strategic decision-making, and customer interactions across the enterprise. At this stage, AI is no longer a separate project; it’s part of the operating model, guiding how value is created, delivered, and sustained. Instead of separate teams for in-store, e-commerce, and fulfillment, AI helps retailers reorganize around value streams – end-to-end processes that directly create value for the customer. This breaks down barriers between departments, enabling data sharing, faster collaboration, and a unified customer experience. [![Enterprise value framework connecting customer engagement, predictive operations, support services, AI, and cloud foundations.](https://dedicatted.com/wp-content/uploads/2025/08/AI-2025-08-13T161840.896.png)](https://dedicatted.com/wp-content/uploads/2025/08/AI-2025-08-13T161840.896.png)****Key value streams enhanced by AI include:**** Customer Acquisition & Engagement : AI personalizes marketing campaigns, loyalty offers, and product recommendations across channels, including emerging spaces like social commerce. This builds emotional connections and keeps shoppers engaged over time. Purchases & Transactions : AI enhances both in-store and online checkout experiences with secure payments, intelligent cart suggestions, and frictionless processes that reduce abandonment. Presence on Shelf : AI-driven supply chain optimization ensures product availability while supporting ethical, sustainable sourcing practices that build brand trust. Loyalty: AI turns loyalty programs into true personalization engines, analyzing behavior to deliver rewards, discounts, and experiences that feel tailored, not generic. Returns – Automated return processes and predictive tools help reduce return rates, improve processing times, and recommend alternative products, turning a potential churn point into a loyalty opportunity. **Ethics, Trust, and Customer Connection** At this scale, the stakes are higher – decision-makers must ensure AI recommendations are explainable, data is handled responsibly, and customer privacy is protected. In retail, personalization only works if shoppers trust you with their data. That’s why embedding AI must go hand-in-hand with robust security: protecting applications, cloud infrastructure, and compliance frameworks like SOC 2, ISO 27001, HIPAA, and GDPR. With **[Dedicatted’s Security Services](https://dedicatted.com/services/security)**, you can build zero-trust environments, automate compliance, and secure every layer of your AI-driven operations -turning data protection into a loyalty advantage. [**Learn more**](https://dedicatted.com/services/security) #### Order security audit from Dedicatted [ Schedule your strategy call ](#contact-form) [ Visit our Security page ](https://dedicatted.com/services/security) **Phase Three**: Evolving Your Retail Ecosystem In the third and most advanced stage, the retailer becomes a fully AI-enabled, seamlessly connected ecosystem that integrates effortlessly into customers’ daily lives. This is where personalization reaches its peak – AI draws from vast streams of behavioral, lifestyle, and contextual data, including purchase history, real-time location, weather, and even mood signals, to deliver instant, relevant, and highly individualized interactions. Physical and digital retail converge into a truly unified “phygital” experience. Stores transform into immersive hubs where augmented reality (AR), virtual reality (VR), and autonomous service technologies create fluid, omnichannel journeys. Customers no longer have to choose between online and offline; instead, they experience a single, continuous ecosystem designed entirely around their preferences and lifestyles. Ideally, the retailer operates with a circular economy model, ensuring that every product can be recycled, refurbished or repurposed. AI tracks the lifecycle of products and proactively offers trade-ins or upgrades, contributing to a zero-waste ecosystem Key considerations to take into account: - Design an AI strategy that declares a clear statement of intent and aligns with core competencies . Retailers should adopt robust performance measurement frameworks to track AI’s impact on outcomes such as customer retention, inventory turnover or supply chain efficiency, ensuring AI efforts align with strategic goals. - Establish robust AI governance frameworks. Prioritize privacy by design – implementing encryption, anonymization and secure data-sharing practices as standard. This is where a reliable partner as [**Dedicatted**](https://dedicatted.com/services/artificial-intelligence) would come in handy. - Focus on modular and interoperable solutions: open APIs, interoperable software and vendor-agnostic solutions allow retailers to experiment with new innovations without locking themselves into specific ecosystems Dedicatted is a trusted digital partner, operating at the cutting edge of technology. We reveal, transform, accelerate, and optimize the way large enterprises and software companies do business. With expertise across healthcare, retail, financial services, software, and more, we implement end-to-end solutions to deliver the innovation, quality, and speed that our clients’ users expect. Let Us help you identify the highest ROI opportunities, build tailored GenAI workflows, and deploy with confidence. [**Book a free AI-readiness consultation** ](https://dedicatted.com/contact)We can help you assess where AI-driven automation fits best and how to implement it with purpose, not pressure. [![Dedicatted is a first and inly generative AI and MSP partner in Canada](https://dedicatted.com/wp-content/uploads/2025/07/plashka-4-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/07/plashka-4-scaled.png) **Categories:** Blog **Services:** Generative AI, Managed Services **Industry:** Retail --- ### [No More Black Boxes: Making AI Agents Understandable, Auditable, and Accountable](https://dedicatted.com/insights/no-more-black-boxes-making-ai-agents-understandable-auditable-and-accountable) **Published:** August 20, 2025 **Author:** Denys Yermakov **Content:** [**Artificial intelligence**](https://dedicatted.com/services/artificial-intelligence) is powering a technological flywheel where each advancement drives the development of even more sophisticated systems. While generative AI has driven the AI narrative since ChatGPT’s launch in late 2022, we believe that 2025 is shaping up to be the year of agentic AI, marking a shift from passive information processing towards proactive and actionable AI. The central question isn’t whether to adopt this technology, but how swiftly organizations can integrate it to stay ahead of the competition. This executive playbook explores how organizations can leverage this technology to boost operational efficiency, enhance customer experience, and drive revenue growth. It provides real-world success stories spanning industry sectors and organizational functions, strategic insights, tactical blueprints, and best practices to guide your journey into this revolutionary landscape. ## ****Why retailers love AI**** [**Agentic AI** ](https://dedicatted.com/services/artificial-intelligence/agentic-ai)refers to fully autonomous software capable of understanding user inputs and executing complex tasks independently. Unlike traditional chatbots that are limited to short-term goals and simply guide users through actions, AI agents complete long tasks on their own, continuously learning and adapting from interactions. They can leverage external resources, such as datasets, web searches, and even other AI agents, to fill information gaps and refine their knowledge base as needed to complete a task. In global survey of nearly 1,500 IT leaders, 96% of organizations said they plan to expand their use of AI agents next year, and 84% believe agents are essential to staying competitive. What was once emerging tech is now a strategic imperative. But while interest is high, scaling agentic AI isn’t simple. Fifty-three percent cite data privacy and compliance as their top concern. Others are held back by integration (40%), implementation complexity (39%), and gaps in governance (30%). These barriers aren’t stopping adoption but are forcing leaders to rethink how they go from pilots to production: [![Diagram showing how an intelligent agent interacts with the environment. On the left, the Agent has internal components: State, How the world evolves, What my actions do, and Utility. These feed into Sensors, producing information such as 'What the world is like now,' 'What it will be like if I do action A,' 'How happy I will be in such a state,' and 'What action I should do now.' The Sensors connect to Precepts, which link to the Environment. The Agent also uses Actuators, sending outputs to Actions that influence the Environment. The flow represents decision-making based on perception, prediction, and utility evaluation.](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-46-1.jpg)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-46-1.jpg)## ******Why should organizations consider early adoption and avoid being late movers?****** The speed of adoption often determines market leadership. According to McKinsey, companies that adopt new technologies early can achieve up to 2x faster revenue growth than those who delay. Early movers not only capture customer attention but also establish competitive advantages that become increasingly difficult, and costly , for late entrants to overcome. The AI market is a prime example. Businesses that implement AI ahead of the curve report an average 20-30% boost in operational efficiency and significantly higher customer retention rates. Waiting for the “perfect moment” to adopt is often a costly mistake. Forrester research shows that late adopters spend up to 30% more trying to catch up with established competitors, due to retrofitting technology into outdated processes and losing ground in customer loyalty. By the time they enter, the early movers have already optimized, scaled, and built trust with their audience. The choice is clear: early adoption isn’t just about embracing innovation – it’s about securing market position before the window of opportunity closes. [![Comparison table of early adopters of AI solutions and late movers , highlighting barriers and cost to entry, operational efficiency, learning curve as a comparative criteria](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-47.png)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-47.png)## **What obstacles can arise, and what’s our plan to resolve them?** Scaling agentic AI isn’t just a technical lift – it’s a trust test. As enterprises move from limited pilots to real-world workflows, concerns around data privacy, system integration, and ethics come into sharper focus. **Data privacy** tops the list. With agents accessing sensitive systems like financial records, patient data, and proprietary insights, organizations must lock down what they can access and infer. The stakes are high: IBM reports the average data breach cost is $4.45 million, a figure expected to only keep climbing. One misstep can lead to compliance violations and a breakdown in public trust. Curious how your AI strategy measures up when it comes to privacy? Let’s talk. **[Book a free consultation with our team](#contact-form)** – and partner with us to build compliant, future-proof AI solutions that inspire trust and drive innovation. What if company’s cybersecurity team detects unusual traffic patterns? An investigation reveals a data breach where customer information, including personal details and purchase histories, has been compromised. Apparently, the breach occurred through vulnerabilities in the API connecting to the vendor’s LLM. *Considerations:* When companies rely on vendor models via API, they expose their data to external threats. Data breaches can occur if the API is not adequately secured, allowing hackers to access or manipulate sensitive information. Insider threats also pose a significant risk; vendor employees or contractors might intentionally or accidentally leak data. Moreover, compliance with data protection regulations such as GDPR or HIPAA becomes complex. These laws often require data to be processed and stored within specific geographic boundaries, and using an external API might inadvertently send data across borders or into less secure environments. *Resolution:* Self-hosting AI models keep data under direct control, reducing the risk of unauthorized access and simplifying compliance with global data protection laws. [![Flowchart showing a cybersecurity attack detection system. On the left, Data Sources include network activity, database activity, application activity, and user activity. Data flows into the Training Phase with steps: Data Collection, Data Preparation, and Model Training. A Testing Phase follows with Data Collection and Data Preparation. Both phases feed into an Attack Detection Model. On the right, Visualization outputs include dashboard, report, and email notification. The final step is User Takes Required Action.](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-48-1.jpg)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-48-1.jpg)**Technical complexity** follows close behind. Forty percent of leaders cite integration with legacy systems as a significant challenge, especially in sectors like telecom or finance, where infrastructure spans decades. More urgently, enterprises face a talent gap. Seventy-six percent of large companies report a shortage of AI-skilled talent, and 44% say it’s slowing them down. Agentic AI requires hybrid teams who understand both the tech and the business. Without that bridge, even well-funded projects can stall. What if a company launches an AI-powered customer service assistant? Initial demos work flawlessly, but once rolled out, it struggles with high request volumes, misinterprets customer queries, and fails to integrate with legacy CRM systems. Developers are bogged down in patching the system instead of improving it, while customers grow frustrated with inconsistent responses – eroding trust in the brand. *Considerations:* AI deployment requires robust infrastructure capable of scaling with demand, seamless integration with existing tools, and ongoing model retraining to maintain accuracy. Legacy systems may not easily connect with modern AI frameworks, requiring middleware or significant refactoring. Additionally, model performance can degrade over time without continuous tuning, leading to subpar outputs and wasted investments. *Resolution:* Start with a scalable architecture and integration roadmap. A vision without execution is hallucination – align your GenAI strategy with actionable plans and meticulous execution. This philosophy of building explainable, scalable, and trustworthy AI systems is best reflected in our partnership with **Taw9eel**, one of the region’s largest online retailers. To elevate its digital shopping experience, Dedicatted developed an AgenticAI architecture powered by AWS Bedrock, ECS, IAM, OpenSearch, and S3, and orchestrated via custom MCP servers that managed product search, trending insights, and semantic reasoning. Within months, Taw9eel achieved a **22% conversion increase**, **faster product discovery**, and a **positive ROI in Year 1**, proving how practical, ethical AI can redefine customer engagement. **[Read the full case study.](https://dedicatted.com/insights/ai-that-understands-you-taw9eels-bilingual-shopping-assistant-delivers-results)** [![A six-step business process flow diagram. Step 1: Vision alignment. Step 2: Assess capabilities. Step 3: Assess meticulous execution. Step 4 (shown as a purple section): Scale up with a rocket icon. Step 5: Risk management. Step 6: Organizational change. The steps are shown in a curved, connected path](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-49.jpg)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-49.jpg)Vision alignment: - Define clear objectives for the AI initiative – whether that’s faster response times, reduced ticket resolution costs, or improved customer satisfaction. - Align AI projects with business goals so integration with the CRM directly supports revenue growth or service efficiency. - Secure executive sponsorship to ensure resources for technical upgrades and cross-department alignment. - Start with a high-impact pilot : such as integrating AI into one customer service channel – to demonstrate ROI early. Assess capabilities: - Technology infrastructure: Is your IT environment ready for AI integration? - Platform options: Weigh-in commercial and open-source AI solutions and make build-vs-buy decisions based on your organization’s requirements, budget, and technical expertise. - Consider integration: Ensure the chosen platform can integrate seamlessly with your existing systems and workflows, both upstream and downstream. - Data readiness: Do you have access to quality, multimodal data? Meticulous execution: - Start small: Begin with small pilot projects to test the effectiveness of agentic AI in your business environment. - Measure success: Define clear metrics for success and monitor the performance of the pilot projects. Gather feedback from stakeholders and make necessary adjustments. - [Agile](https://dedicatted.com/services/glossary-of-terms#term-agile) methodology: Be flexible, nimble and adaptive in your implementations. - Iterate and improve: Use the insights gained from pilot projects to refine your approach and address any challenges Scale up: - Gradual expansion: Once the pilot projects are successful, gradually scale up the implementation of agentic AI across more areas of your operations. - Ensure support: Provide adequate training and support to your team to ensure a smooth transition and adoption of the new technology. - Monitor and optimize: Continuously monitor the performance of agentic AI systems and optimize them for better results Then there’s the **ethical dimension**. Fifty-one percent of leaders are concerned about bias in AI systems. A Yale study, cited in Cloudera’s report, showed that diagnostic agents trained on non-diverse datasets performed worse for underrepresented patients, leading to delays and misdiagnosis. Bias can surface at any stage – data collection, model design, or deploymen and scale quickly without strong oversight. [![Modern banner with bold typography "Count on our experience to get it right from the first time" on a blue background, representing innovation and readiness for artificial intelligence.](https://lh7-rt.googleusercontent.com/docsz/AD_4nXfy7ykVTdh58goMwGM_xy3UzMnHczqqHC2c22TM20X7VyJVtq92SSK1uqIe3n4Jd83_67sjFUAD4r-FSEZWq1At8kHUP5Mkr8TnggFoju-WlJMkp2mQo7s8-n9cBTndsyrJdTn9qw?key=LJ8m2vqwzgWDXGXy53F7Ow)](https://lh7-rt.googleusercontent.com/docsz/AD_4nXfy7ykVTdh58goMwGM_xy3UzMnHczqqHC2c22TM20X7VyJVtq92SSK1uqIe3n4Jd83_67sjFUAD4r-FSEZWq1At8kHUP5Mkr8TnggFoju-WlJMkp2mQo7s8-n9cBTndsyrJdTn9qw?key=LJ8m2vqwzgWDXGXy53F7Ow)What if, in response to new ethical guidelines and compliance policies, the vendor company updates its LLM’s internal system prompt, inadvertently affecting yourCorp’s agentic AI behavior? Due to a nuanced legal situation, the updated prompt contradicts the organization’s AI operational goals. As a result, Corp’s AI systems behave erratically, promoting out-of-stock products or suggesting business strategies that conflict with corporate social responsibility policies. *Considerations*: If the AI relies on client models for decision-making, changes in vendor ethics or compliance policies can bleed through to client AI systems. Such updates might not be communicated effectively, or the implications could be misunderstood due to the complexity of AI behavior. In this case, the vendor’s new ethical stance might prioritize different values or interpret laws in ways that conflict with Corp’s business model or operational ethics. This misalignment can lead to AI outputs or decisions detrimental to Corp’s objectives or customer relations. *Resolution:* Self-hosting gives Corp the autonomy to ensure that any AI model updates align with their specific business ethics, compliance requirements and operational goals. In [**Dedicatted**](https://aws.amazon.com/marketplace/pp/prodview-5ukdz3n6oviqc) , we understand the importance of balancing innovation, data privacy, and ethical considerations in AI development lies in ensuring sustainable technological progress while safeguarding individual rights and societal norms. [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) fosters transparency and accountability in AI operations, leading to more reliable technology. **[Contact us](#contact-form)** to create more responsible and user-centric AI solutions that are viable in a global market. **Categories:** Blog **Services:** Agentic AI, Managed Services **Industry:** Healthcare, Insurance, Manufacturing, Retail --- ### [Top 5 DevOps Pitfalls - And How Dedicatted Expert Consulting Helps You Avoid Them](https://dedicatted.com/insights/top-5-devops-pitfalls-and-how-dedicatted-expert-consulting-helps-you-avoid-them) **Published:** September 3, 2025 **Author:** Serhii Semenchenko **Content:** Did you know that companies embracing DevOps can deploy software 200x more frequently and recover from failures 24x faster than their competitors? But here’s the catch: while DevOps has become the cornerstone of modern IT, driving speed, agility, and reliability, many organizations stumble along the way. Common DevOps mistakes, like skipping automation, neglecting testing, or overlooking security can quickly derail progress, dilute results, and block you from realizing its full potential. The truth is, successful DevOps adoption isn’t just about tools – it’s about strategy. Recognizing and fixing these pitfalls early is the difference between a smooth journey and endless roadblocks. That’s where partnering with the right experts comes in. At [**Dedicatted**](https://dedicatted.com/contact) , we specialize in helping organizations navigate the complexities of DevOps implementation. As a trusted DevOps services company, our expert guidance ensures you avoid common mistakes while empowering your DevOps Practitioners to embrace best practices and customized strategies that achieve impactful results and foster sustainable growth. ## ********5 Common DevOps Mistakes to Avoid By DevOps Practitioners in 2025******** ### Mistake #1: Misjudging the Finish Line Many IT and DevOps teams proudly describe their deployment pipeline as a smooth journey: *“The application moves from Development, through Test/QA, into Pre-Production, and finally into Production.”* But here’s the catch – in over 80% of cases, no one mentions a Post-Production environment. **Why it happens**: Pre-Production is often treated as a sandbox, freely accessible to developers, QA, and anyone who needs to test a change. In practice, this means configurations get tweaked, applications get “fixed” on the fly, and Pre-Production slowly drifts away from being a true mirror of Production. Teams then mistakenly believe they don’t need anything beyond it. ****The Impact:**** When Pre-Production isn’t a locked-down replica of Production, comparing environments becomes unreliable. That’s when hidden discrepancies, like subtle config differences or untested tweaks creep into Production. The result: Delays, unpredictable failures, and firefighting when systems behave differently in the real world than they did in testing ****What Dedicatted Recommends:**** Ask yourself: Is Pre-Production truly hands-off? Who has access and are changes strictly controlled? Does it *always* look identical to Production? If the answer is “no,” it’s time to introduce a dedicated Post-Production environment. 1. Maintain server, middleware, and application configurations consistently across Post-Production and Production 2. Automate comparisons to catch discrepancies before they cause failures 3. Secure and govern access, ensuring Post-Production remains reliable With the right approach, Post-Production becomes your safety net, reducing risk and ensuring every deployment lands smoothly in Production. [ !["Flowchart of DevOps Continuous Integration/Deployment (CI/CD) process flow showing five stages: Development, Peer Review, QA, Pre-Prod, and Production. In Development, code is pulled from the master branch, new feature branches are created, developed, and pull requests submitted. In Peer Review, code undergoes bug checks, security analysis, and build validation; failures return to development. Successful reviews move to QA, where QA testing, user bug analysis, and security analysis occur. Passing QA triggers auto-deployment to Pre-Prod, with load balancer and availability zones A and B. Another auto-deploy step moves to Production, where load balancers distribute traffic across availability zones. Snapshot backups are created in GCP for disaster recovery.](https://media.dedicatted.com/wp-content/uploads/2025/09/devops_continuous_integration_deployment_process_flow_software_development_and_it_operations_methodology_slide01.jpg) ](https://media.dedicatted.com/wp-content/uploads/2025/09/devops_continuous_integration_deployment_process_flow_software_development_and_it_operations_methodology_slide01.jpg)### Mistake #2: Overloading with “Nice-to-Have” Requirements When choosing tools for a DevOps project, teams often create massive requirement lists – sometimes 10+ pages of spreadsheets with hundreds of line items. While some are essential, many end up being future-oriented “nice-to-haves.” One common example: “The tool must integrate with our CMDB ([Configuration Management](https://dedicatted.com/services/glossary-of-terms#term-configuration-management) Database).” **Why it happens** Teams want to “future-proof” their solutions. They think: “We’ll eventually need this, so let’s make sure it’s built-in now.” The problem is, many of these future capabilities like a fully functional CMDB are still years away, or may never materialize. Instead of focusing on immediate goals, the team anchors on features that don’t yet exist. **The Impact:** This mindset makes perfect the enemy of good. By forcing vendors and internal teams to meet requirements that aren’t urgent (or realistic), organizations waste time, inflate costs, and delay releases. Instead of solving today’s problems, DevOps projects get bogged down chasing hypothetical futures **What Dedicatted Recommends:** Don’t build your DevOps strategy around features you might need in three years – focus on what creates value today. Here’s a tangible way to do that/ Prioritize requirements into three buckets: - Must-have (critical for solving today’s problem) - Nice-to-have (useful but not urgent) - Future idea (not actionable yet) Cut anything in buckets 2 and 3 from your initial tool selection. Keep them documented for future review, but don’t let them slow you down now. Example: Instead of requiring “CMDB integration,” ask: “What information would we want to pull from a CMDB, and do we already store it elsewhere?” If the answer is unclear, drop the requirement. ## Count on our experience to deliver a system that works [ Book a free meeting ](https://dedicatted.com/contact) [ DevOps Consulting ](https://dedicatted.com/what-we-do/consulting/devops-consulting) ### Mistake #3: Managing “Server #17” Instead of the Application Too many DevOps teams still think in a server-centric way: monitoring server health, tweaking server configs, and asking “What’s happening with Server #17?” But customers and CIOs don’t care about servers – they care about applications. No one logs into an ecommerce site and says, “Wow, Server #17 is running great today!” They just want the site to load quickly on Black Friday. Yet many DevOps solutions are still built around managing servers instead of managing the applications those servers power. **Why It Happens:** Historically, applications had a 1:1 relationship with servers, if the server failed, the app failed. It made sense to obsess over server health. But in today’s world of virtualization, cloud, and microservices, one application might run across dozens of physical and virtual servers. Old habits die hard, and teams still default to server-first thinking **The Impact:** This outdated mindset leads to: - Teams drowning in low-level server monitoring instead of focusing on app performance. - Slower incident response, since symptoms are tracked per server instead of at the application level. - A disconnect between IT and business goals – CIOs care about customer experience, not CPU metrics. In short: managing servers instead of applications keeps DevOps reactive, not strategic. **What Dedicatted Recommends:** Shift the focus from “server health” to application health. That’s where business value lives. Here’s how to make it tangible: 1.Ask application-centric questions: - Instead of “What’s going on with this server?” → ask “Is the ecommerce app running smoothly across all its servers?” - Instead of “What are the configs of this server?” → ask “What are the configs of the application across its environment?” 2\. Adopt application-first monitoring tools that aggregate server data into application views (APM tools like New Relic, Datadog, or AWS X-Ray). 3\. Map dependencies: Identify how critical apps rely on supporting services. This way, if one microservice lags, you see its impact on the whole system – not just on one machine. [ ![Transforming Platform Reliability and Achieving SOC 2 Compliance for a High-Volume Link Management Service](https://media.dedicatted.com/wp-content/uploads/2025/04/TinyURL-cover-300x157.png) Case studyDedicatted Petlichenko 5 min to read Also read ### Transforming Platform Reliability and Achieving SOC 2 Compliance for a High-Volume Link Management Service High Tech Software Cloud and Infrastructure Security DevOps Consulting Security Compliance ](https://dedicatted.com/insights/transforming-platform-reliability-and-achieving-soc-2-compliance-for-a-high-volume-link-management-service)### ****Mistake #4: The “Two-Pizza Rule” and Death by Meeting**** When teams have a lot to accomplish, the instinct is often to get more “hands on deck.” The assumption is: “Together Everyone Achieves More.” But in practice, adding more people to working meetings almost guarantees that nothing meaningful will get done. If your meeting requires more than two pizzas to feed everyone, Jeff Bezos would say it’s too big. And science backs him up – organizational psychologist Richard Hackman found that communication inefficiencies grow as group size expands. The optimal working meeting size? 5 people – 10 at most. Anything bigger becomes an announcement, not a working session. **Why It Happens:** - Corporate politics: Leaders feel obligated to invite every stakeholder “just in case.” - Fear of exclusion: Teams don’t want to leave anyone out who might have an opinion. - Confusion about roles: Without clarity, invitations expand to anyone even loosely connected. **The Impact:** Bloated meetings waste time, dilute accountability, and slow down decisions. Instead of solving problems, teams get stuck in endless discussions where no one owns the outcome. Meanwhile, actual progress is delayed sometimes by weeks. **What Dedicatted Recommends:** First of all apply the Two-Pizza Rule: If your team needs more than two pizzas, your meeting is too big. Limit working meetings to the essential few. Use the R-A-P-I-D framework to decide who belongs in the room: R – Recommender: The person driving the decision (e.g., Ops Manager). A – Approver: Only if regulatory or compliance sign-off is needed. P – Performers: The people who will actually do the work. I – Influencers: Those with valuable context or expertise. D – Decider: The ultimate tie-breaker or approver. [![Visualization showing how the number of communication links increases with team size. First, a triangle for 3 members with 3 links. Second, a hexagon-shaped network of 6 members with 15 links. Third, a dense circular network of 12 members with 66 links. Finally, a chaotic web representing 50+ members with over 1,225 links, illustrating exponential growth in complexity as team size increases." Do you want me to also make a short version optimized for accessibility (for screen readers) or keep it descriptive for presentations and documentation?](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-41.png)](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-41.png)### Mistake #5: Mixing Legacy Practices with DevOps Some organizations try to adopt DevOps while still clinging to outdated tools, manual processes, or siloed workflows. This creates a “hybrid DevOps” approach that may feel safer during the transition – but in reality, it slows everything down and leaves teams stuck halfway between old and new. **Why It Happens:** - Resistance to change: Teams are comfortable with legacy tools they’ve used for years and hesitate to switch. - Budget limits: Leadership may delay investments in modern infrastructure, hoping to “make do” with what’s already in place. - Fear of disruption: Organizations worry that changing too much too quickly will break existing systems. **The Impact:** This hybrid approach undermines everything DevOps is supposed to deliver: 1. Slower workflows because old manual steps remain in place. 2. Inconsistency between teams using modern CI/CD pipelines and those stuck with legacy tools. 3. Missed opportunities for innovation, automation, and faster delivery. Instead of reaping DevOps benefits like agility and reliability, companies end up with extra complexity and frustrated teams. **What Dedicatted Recommends:** A successful DevOps journey requires going all in, but that doesn’t mean tearing everything down overnight. Here’s a practical way forward: Audit your toolchain: Identify where legacy tools create bottlenecks. For example, is manual deployment still slowing releases even though CI/CD is in place? Prioritize modernization by impact: Start replacing the tools or processes that block automation first. Don’t waste energy modernizing systems that don’t affect delivery. Upskill teams gradually: Provide training so people understand not just how to use DevOps tools, but why they matter. Bridge, then replace: If a legacy system must stay for now, ensure it integrates cleanly with DevOps workflows – then set a clear timeline to phase it out. ## **Case study: Enabling Security Compliance and DevOps Agility in a Regulated Healthcare Environment** Many healthcare organizations struggle with the complexities of DevOps adoption, particularly when operating under strict compliance requirements. Challenges such as siloed teams, manual workflows, and limited automation often slow innovation and increase operational risk. A leading healthcare provider chose to partner with **[Dedicatted](https://dedicatted.com/contact)**, leveraging our expertise in DevOps strategy for regulated environments. This collaboration proved transformative, enabling the client to overcome common DevOps pitfalls while ensuring security and regulatory compliance. The results were immediate and impactful: - **Seamless Cloud Transition:** The client successfully migrated to Azure with zero user disruption, ensuring continuous service for thousands of active users. - **Enhanced [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance):** Security controls and infrastructure policies were fully aligned with high-trust compliance standards, satisfying internal audits and regulatory requirements. - **HIPAA Assurance:** Full coverage of technical HIPAA controls provided peace of mind and a clear roadmap for future audits and certifications Want the full story? Read the full case study:[ **Enabling Security Compliance and DevOps Agility in a Regulated Healthcare Environment**](https://dedicatted.com/insights/enabling-security-compliance-and-devops-agility-in-a-regulated-healthcare-environment) **Categories:** Blog **Services:** DevOps Consulting, Managed Services **Industry:** Automotive, Healthcare, High Tech Software, Insurance, Retail --- ### [HealthTech in Action: OkDoc App Enhances Patient Experience and Efficiency](https://dedicatted.com/insights/healthtech-in-action-okdoc-app-enhances-patient-experience-and-efficiency) **Published:** September 16, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2023 – 2024 #### Industry: HealthTech #### The service: App Development #### Overview **OkDoc** is a digital healthcare platform designed to transform the way patients and doctors interact. At its core, the application enables patients to book medical appointments online, communicate with their doctors through secure chat or video calls, share sensitive medical files, and receive digital prescriptions and discharge notes – all within one mobile app. By bringing these capabilities together, OkDoc eliminates the traditional pain points of healthcare: long wait times, fragmented communication, and paperwork-heavy processes. The app was built with a patient-first philosophy: for patients, it provides instant access to appointments, reminders, and medical records , no phone calls or queues required. For doctors and healthcare staff, OkDoc reduces administrative burdens, automates repetitive tasks, and ensures that important information is always available in one secure system. Behind the patient-facing simplicity, OkDoc integrates with the hospital’s legacy scheduling and records system, which had been in place for decades and was never designed for modern digital services. This project, carefully crafted by Dedicatted, became a key to unlocking the hospital’s digital transformation and laid the foundation for a scalable telehealth ecosystem that could evolve with the needs of hospitals, clinics, and healthcare providers. ## Want to get your copy of case study? Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## The Challenge The hospital network that launched OkDoc relied on a decades-old scheduling system with no modern APIs and limited data accessibility. They wanted to provide a patient-friendly mobile app, but several challenges stood in the way: - **Legacy Integration:** Connecting the app with a scheduling system never designed for mobile access. - **Data Security:** Managing sensitive healthcare records under strict compliance requirements (HIPAA/GDPR). - **Real-Time Communication:** Ensuring patients could message or call doctors without delays or dropped connections. - **[Scalability](https://dedicatted.com/services/glossary-of-terms#term-scalability):** Preparing the system to support thousands of patients simultaneously. - **User Experience:** Delivering a clean, intuitive interface for both patients and doctors, despite the complexity behind it. ## Our Approach We treated this project as a full-scale digital transformation, ensuring great patient experiences could be delivered seamlessly while connecting to a legacy hospital system from day one: ### Step 1: Discovery & System Mapping We began by analyzing the hospital’s legacy scheduling infrastructure and existing workflows: - Mapped dependencies between the old system and staff operations. - Conducted patient journey workshops to identify UX gaps. - Defined integration touchpoints to enable secure, real-time data exchange. ### Step 2: Designing Patient & Doctor Journeys We created intuitive, secure interfaces for both user groups: - Accessibility and compliance baked into every design decision. - A patient app for booking, communication, file sharing, and prescriptions. - A doctor dashboard for managing schedules, consultations, and digital notes. ### Step 3: Building a Middleware Integration Layer To bridge the old and new systems without disruption, we developed middleware that: - Translated legacy data into modern APIs. - Enabled real-time synchronization of appointments and availability. - Allowed hospital staff to continue using their familiar tools while unlocking new capabilities. ### Step 4: Secure HealthTech App Development We delivered a feature-rich iOS app with: - Encrypted chat and video calls. - File sharing for lab results and medical documents. - Push notifications and reminders for appointments and prescriptions. - Automated digital prescriptions and discharge notes. ### Step 5: Deployment & Compliance The final system was deployed on AWS, ensuring resilience and compliance: - Auto-scaling infrastructure for peak loads. - End-to-end encryption across data flows. - [Monitoring](https://dedicatted.com/services/glossary-of-terms#term-monitoring) and logging to meet HIPAA/GDPR standards. - 24/7 availability with zero disruption to hospital operations. [![Promotional image of the OKDOC mobile app displayed on three smartphones, showing features such as doctor profiles, appointment booking, and scheduling calendar.](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-92-1.jpg)](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-92-1.jpg)## Our Solution Dedicatted’s approach to building OkDoc was centered on one principle: modernizing patient-doctor interactions without disrupting hospital operations. The challenge of integrating with a decades-old scheduling system shaped every decision we made, from architecture to UX. We began by creating a custom middleware integration layer that acted as a bridge between the hospital’s legacy system and the new mobile app. This layer translated outdated system logic into modern APIs, enabling real-time synchronization of appointments, cancellations, and doctor availability. This ensured the hospital staff could continue using the tools they already knew, while patients gained access to a completely new, digital-first experience. On the patient side, we developed a feature-rich iOS application that delivered all core healthcare interactions in one place: - **Online appointment booking** with instant confirmations. - Secure chat and video calls, protected with end-to-end encryption. - File sharing capabilities, allowing patients to send test results or images directly to their doctor. - **Digital prescriptions and discharge notes**, stored safely within the patient’s profile for easy access. - Push notifications and reminders, reducing missed appointments and ensuring treatment adherence. For doctors, we built a **dedicated dashboard** integrated directly into the app ecosystem. This streamlined schedule management, provided instant access to patient documents, and introduced automation tools that reduced repetitive administrative tasks. Doctors could issue prescriptions or generate discharge notes with a few taps, allowing them to spend more time focusing on care rather than paperwork. [!["Comparison chart showing the impact of digital healthcare solutions on hospitals and patients. Categories include Appointment Booking, Medical Records & Prescriptions, Doctor-Patient Communication, Operational Efficiency, and Data Security & Compliance. Benefits for hospitals include reduced administrative load, centralized records, secure messaging, better resource planning, and compliance. Benefits for patients include convenient booking, instant access to medical records, direct chat with doctors, automated reminders, and peace of mind on data security.](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-89-2-1.png)](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-89-2-1.png)To support OkDoc’s full range of features, we built the solution on AWS with a focus on **reliability, compliance, and patient trust**. The infrastructure was designed to scale effortlessly during peak usage, protect sensitive data with advanced encryption, and enforce strict access controls so only authorized personnel could view patient information. Continuous monitoring and logging ensured full compliance with HIPAA, GDPR, and healthcare regulations, creating a secure and seamless telehealth ecosystem that empowers patients while maintaining the hospital’s operational integrity. **Categories:** Case study **Services:** Managed Services **Industry:** Healthcare --- ### [Launching a Flexible, SEO-Optimized Website for Adomik’s Marketing Teams](https://dedicatted.com/insights/launching-a-flexible-seo-optimized-website-for-adomiks-marketing-teams) **Published:** September 19, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: March 2025 – July 2025 #### Industry: AdTech #### The service: Website Modernization #### Overview **Adomik** is a successful SaaS company with a global footprint, specializing in advertising analytics for digital publishers. Its platform aggregates and normalizes data from ad servers, SSPs, and header bidding partners, providing publishers with unified insights to optimize revenue, streamline operations, and strengthen transparency across their programmatic stack. Adomik’s customers include leading global publishers such as Microsoft, eBay, TripAdvisor, and Rakuten. These organizations rely on Adomik to harmonize fragmented ad data, detect revenue leakage, improve pricing strategies, and make faster, more informed business decisions. This project, supported by Dedicatted, enhances Adomik’s digital presence by delivering a scalable, SEO-optimized website built for long-term flexibility. With initiatives such as modular Elementor structures, custom navigation features, and integrated SEO best practices, Adomik now has a robust platform that empowers its teams, strengthens brand visibility, and supports continued growth in the highly competitive ad tech industry. ## The Challenge Adomik’s mission is to bring order to the chaos of programmatic advertising. Its platform makes sense of scattered, messy data so publishers can see clearly and act with confidence. The company’s website had become a stumbling block – hard to maintain, invisible in search, and out of sync with the innovative SaaS platform it represented. For a business built on transparency, this disconnect was more than cosmetic; it risked credibility with the very publishers who depend on Adomik for insight and trust. - **Unclear website foundation**: The existing stack was poorly documented, making the site unmaintainable and unscalable. - **Weak SEO baseline**: Missing structure, planning, and best practices prevented organic visibility and discoverability. - **Incomplete design wireframes**: Key UI elements such as headers and menus evolved mid-implementation, creating inconsistencies. - **Content delays**: No final content was available during the build phase, slowing progress. - **Undefined sitemap and redirect strategy**: Lack of clarity on domain and navigation decisions risked SEO performance. ## Want to get your copy of case study? Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## Technology & Approach Dedicatted designed and deployed a modern, scalable website for Adomik, using a modular **Elementor-based structure** on a flexible CMS to support both marketing and operational needs. The solution focused on maintainability, SEO optimization, and empowering non-technical teams to manage content independently. Key components of the solution: - **Modular, Reusable Elements**: Every UI component, from sticky headers to CTA buttons, was built as a reusable module. This structure ensures consistent branding across the site while giving the team flexibility to make updates without disrupting existing layouts. - **Dynamic Navigation & Sticky Header**: The navigation bar adapts as users scroll, creating a seamless experience. Dropdown menus are styled individually per page with editable color logic in Elementor, ensuring visual clarity while maintaining brand consistency. - **SEO-Optimized Framework**: SEO was integrated from the ground up. Meta annotations, heading structures, URL strategy, and on-page optimization were implemented during development and continuously validated with expert SEO consultation. This ensures the site is fully indexable, discoverable, and capable of performing well in search rankings from day one. - **Flexible Content Components**: Interactive modules such as logo carousels, sliders, and adaptive CTA buttons were developed to provide both aesthetic appeal and functional flexibility. Marketing teams can now rotate logos, adjust slider content all through the admin panel. - **Iterative, [Agile](https://dedicatted.com/services/glossary-of-terms#term-agile) Workflow**: Design wireframes were not fully complete at project start, so development needed to remain flexible. Dedicatted coordinated asynchronous handoffs between design and development, iterating quickly on evolving UI elements. This minimized delays and ensured that even late-stage design changes were implemented efficiently. - **Knowledge Transfer & Empowerment**: Throughout the project, Dedicatted worked closely with Adomik’s team, explaining each technical decision and SEO strategy. This collaboration built internal capacity, enabling the client’s marketing team to maintain and enhance the website independently post-launch. By combining flexible technology, robust SEO practices, and a user-first design philosophy, **Dedicatted delivered a website** that not only supports Adomik’s current needs but also positions the company for future growth. The platform now mirrors the clarity and efficiency that Adomik provides to its customers in the programmatic advertising space, turning a previously rigid, hard-to-manage website into a dynamic, scalable, and high-performing digital hub. #### If you find this case interesting, we recommend taking a closer look at [ Engineering services ](https://dedicatted.com/services/engineering) [ App modernization ](https://dedicatted.com/what-we-do/cloud-services/app-modernisation) ## Our Solution Dedicatted approached Adomik’s website project as a chance to turn a rigid, hard-to-manage site into a dynamic digital platform. The goal was to empower the marketing team, improve SEO visibility, and make every visitor interaction seamless – without requiring constant developer intervention.To achieve this, the team focused on practical, high-impact interventions: - **Agile, Iterative Development:** With designs evolving in real time, development kept pace through rapid cycles, ensuring no delays despite incomplete wireframes. - **Empowering Marketing Teams:** Custom logic and flexible modules were built so non-technical users could update sliders, CTAs, and menus independently, giving Adomik full control over content and campaigns. - **SEO Embedded in Workflow:** SEO considerations weren’t an afterthought, they were integrated at every stage, with meta annotations, heading clarity, and ongoing validation alongside development. - **Safe Experimentation with New Tools:** Limitations in Elementor beta features were carefully analyzed, and fallback solutions were implemented to maintain stability while future-proofing the site. - **Smooth Coordination Across Teams:** Design and development handoffs were managed without waiting, allowing feedback loops to be efficient and avoiding bottlenecks in approvals. - **Custom, Interactive Features:** Dynamic navigation, sticky headers, editable logo carousels, and adaptive CTAs were built to enhance both usability and brand presentation. These features were specifically scoped to support branding clarity, user engagement, and lead-generation effectiveness. Adoption has been robust throughout the laboratory. Managers highly commended the role-based controls, which ensured compliance while maintaining workflow efficiency. Additionally, the direct updating of the knowledge base has facilitated the system’s adaptation to evolving research priorities and regulatory standards. [![Infographic with four metrics showing the impact of an AI assistant. Efficiency: 1 in 5 daily active users adopted the assistant in the first months of rollout. Productivity: 22% increase in conversion rate for AI-assisted sessions (2.3% → 2.8%). Revenue: 6% uplift in average order value through smarter bundling and cross-sell. Speed: 2 minutes saved per shopping session with faster product discovery.](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-09-19T111459.213-1.png)](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-09-19T111459.213-1.png) **Categories:** Case study **Industry:** High Tech Software --- ### [AWS Activate for startups: a bonus program to save you up to 100K](https://dedicatted.com/insights/aws-activate-for-startups-a-bonus-program-to-save-you-up-to-100k) **Published:** January 8, 2024 **Author:** Denys Yermakov **Content:** Amazon Web Services is the largest cloud service provider globally, with 32 regions and 102 availability zones in operation. Also, AWS has more services and features than any other cloud provider, making building your product or service more convenient and cost-efficient. And the good news is that startups have an opportunity to try its services. For example, with the [AWS Activate](https://aws.amazon.com/activate/activate-landing/) program, you can get up to $100,000 in free credits, which act as a trial period, allowing you to experience the platform’s features and capabilities. What must you know about AWS Activate, how do you enable it, and what do you spend the credits on? Let’s discover. ## What is AWS Activate? AWS Activate is a program started in 2013 to help new startups and early-stage companies. It gives them free tools, resources, and discounts to help build and scale their businesses using Amazon Web Services. With AWS Activate, startups get: - Access to the AWS Activate Console – your personalized hub of tools, resources, and content adapted to your startup needs - Expert tips on business and technical matters - Training and support materials through Learn on AWS - Over 40 pre-built infrastructure templates - Exclusive discount codes and free trials - Access to personalized tools and content through the AWS Activate Recommendations feed - **The chance to apply for up to $100,000 in AWS credits over a startup’s lifetime to pay for AWS services** **Hint:** You don’t have to apply for all credits at once since it can be better to apply for what you need for your plans. This approach will help you to avoid overspending and ensure credits don’t expire unused. These benefits allow startups to learn more about AWS and create essential plans before fully committing. However, many startups are particularly interested in AWS Activate Credits. **So, what are AWS Activate Credits?** They’re like special coupons that you can use to pay for AWS services. It simplifies joining AWS without significant investments. These credits are free and valid for one or two years from the moment you get them. They are applied to your account until they run out or expire. You will have the expiration date on the credits page of your AWS Billing and Cost Management Console or your AWS Activate Console. AWS cannot extend credits after expiration, so pay attention to it. ## Criteria to apply for AWS Activate Credits The AWS Activate program is available for self-funded startups or funded up to their series A round. However, there are some conditions: 1. Startups must have been established within the last 10 years. 2. Startups need to have a working website or public profile. ## How to apply for AWS Activate Credits? If you’re a startup looking to benefit from AWS Activate credits, here’s how to make it. The program has [two tiers](https://aws.amazon.com/startups/credits#packages): 1. **Activate Founders** Self-funded and bootstrapped startups can get $1,000 in credits at this stage. In addition to the criteria we mentioned before, qualifications include being an AWS Activate Founders newbie and having an active AWS account. To unlock these credits, you must complete your Activate Console registration and submit your application. Once approved, you’ll be eligible for this amount in AWS credits, other AWS Activate features, and the chance to get more credits later. 2. **Activate Portfolio** It provides startups associated with venture capital firms, accelerators, or other startup-enabling organizations with up to $100,000 in AWS credits. Qualifications include being funded or unfunded up to Series A, as well as having an active AWS account, company website, and LinkedIn page. Remember that if you’ve already received AWS credits, you need to apply for a larger amount. Your application may be rejected if you’ve reached the maximum $100,000 credit. You can check your credit balance in your Billing and Cost Management Console, then – Credits. You also can start with a smaller amount: you can get $300 in credits through the [AWS Connected Community](https://aws-experience.com/emea/smb/exclusive-offers/aws-credits). ## What do you spend the AWS Activate Credits on? AWS Activate Credits are used to reduce charges for eligible AWS services, from compute and storage to AI and automation tools. Many startups leverage them to experiment with scalable architectures, integrate AI solutions, and streamline operations before moving into full-scale production. A real-world example is **Hercules Crane & Lifting Supplies**, Canada’s largest national rigging company. Their manual remittance workflows caused slow processing, frequent errors, and limited scalability. Using AWS and GenAI tools like Amazon Bedrock and AWS Comprehend, we automated document processing and integrated it with Hercules’ Epicor Prophet ERP. The results were impressive: - **40% cost reduction** in financial operations - Faster, error-free document handling - Scalable workflows supporting nationwide growth This case shows how AWS credits can help companies automate processes, save costs, and scale efficiently. **[Read the full case study](https://dedicatted.com/insights/automating-remittance-workflows-and-reducing-costs-with-genai-and-aws)** ## Summary If you’re a startup that meets the criteria above and has ambitions for rapid growth – try the AWS Activate program. You can start with $300 or $1000 in AWS credits to acquaint yourself with this cloud provider’s features and determine whether it fits your business well. We can help you make this decision and prepare your startup for it if you already use another cloud provider and are considering migrating to AWS. Reach out to us via , and we’ll consult you. **Categories:** Blog **Services:** Cloud and Infrastructure Security, Cloud Data Migration & Modernization, Cloud Migration, Cloud Modernization, Managed Services **Industry:** Financial Services, Healthcare, Insurance, Retail --- ### [AWS Well-Architected Framework and Review: best practices to boost your business in the cloud](https://dedicatted.com/insights/aws-well-architected-framework-and-review-best-practices-to-boost-your-business-in-the-cloud) **Published:** February 28, 2024 **Author:** Andriy Kopachevskyy **Content:** Amazon Web Services is a leading cloud provider with a 32% market share. One of the reasons for this success is that AWS offers clients different tools and services to help create the best cloud-based systems for their business needs. For example, to help businesses make the most out of its services, AWS designed a Well-Architected Framework. It assists in building secure, high-performing, resilient, and efficient infrastructure for various applications and workloads. Let’s look closer at this framework and its components and define how your business can benefit from it. ## What is AWS Well-Architected? AWS Well-Architected Framework is a set of best practices, design principles, and guidelines to ensure that your cloud infrastructure helps you achieve business goals. There is an AWS Well-Architected Framework Review, or WAFR, for that purpose. It is a systematic review of the cloud infrastructure to help you determine the areas for improvement to align with the framework. Both framework and review are based on six pillars, each covering a specific area. Let’s discover them. ## The six pillars ### Security Pillar This pillar ensures the confidentiality, integrity, and availability of your data and applications in the cloud. It covers network security, access management, data encryption, threat detection and response, and implementing controls for detecting security events. ### Reliability Pillar This pillar is responsible for workloads: they must be highly available and perform intended functions. It also ensures that your cloud-based systems can recover quickly in case of a failure to satisfy demands. It includes distributed system design, recovery planning, and adapting to changing requirements. ### Performance Efficiency Pillar The Performance Efficiency pillar focuses on optimizing the cost and performance of your cloud-based systems. It covers selecting suitable instance types, optimizing storage and database performance, and using caching and content delivery networks (CDNs) to improve application performance. ### Cost Optimization Pillar The [Cost Optimization](https://dedicatted.com/services/glossary-of-terms#term-cost-optimization) pillar focuses on reducing costs. Key topics include: - Understanding spending over time - Choosing the proper type and quantity of resources - Scaling without overspending - Implementing cost-saving measures. ### Operational Excellence Pillar This pillar is about enhancing the efficiency of your cloud-based systems by automating changes, monitoring your systems, and continuously improving processes and procedures. ### Sustainability Pillar The Sustainability pillar concentrates on how businesses can be responsible and positively contribute to the environment, economy, and society. It helps companies to understand their impact and set sustainability goals. ## What’s in it for your business? Industry changes rapidly: new standards and best practices appear, as well as new challenges and threats. Even the most secure and high-performing systems have to stay up with updates. AWS regularly upgrades the WAFR, so it can help you to achieve it. Also, there is a point not just to conduct the review once, but to perform it systematically. With the review, you’ll be able to: - Keep up to date with the framework’s best practices - Determine and monitor any issues to find better solutions to possible future problems - Identify the unnecessary costs of cloud infrastructure; allocate costs wisely to reduce them - Optimize and improve the reliability and performance of your systems - Get insights about security and enhance it so you will be more prepared for different security threats - Lower the effort for operation and maintenance A great example of these principles in action comes from our work with Kwiksave, a travel and hospitality platform that relied on AWS to scale its operations efficiently. We helped Kwiksave optimize cloud performance, cut manual coordination by 35%, reduced operating costs by 30%, and achieved ROI in just 12 months – all while maintaining high security standards across multiple services and regions. This collaboration demonstrates how following the framework can turn complex cloud infrastructure into a streamlined, business-boosting engine. You can explore the full details of this success story**[ here and see concrete examples of best practices in action.](https://dedicatted.com/insights/smarter-logistics-for-travel-and-hospitality-kwiksave-aws-cloud-success)** If these benefits sound like a good deal for you, let’s focus on the WAFR process. ## The review process: what to know You should conduct the review every 6 to 12 months. The assessments should concentrate on critical AWS workloads for your business operations and functions. It involves supply chain and logistics, inventory management systems, payment gateways, and more. And there are two options to organize it: - You can do it by yourself for free with the [AWS Well-Architected Tool](https://aws.amazon.com/well-architected-tool/). But remember that AWS’s underlying technologies are complex. So, if you need help performing it correctly, another option exists. - Ask an AWS Well-Architected Partner to review your systems. AWS WA Partners have comprehensive training on the framework, so they have the expertise to implement best practices, measure workload states, and make improvements where required. *We are an AWS Well-Architected Partner, so we can conduct the review for your business and help to implement the best practices to improve it. You can reach us via* [*contact@dedicatted.com*](mailto:contact@dedicatted.com) *for consultation and further cooperation.* If you choose to move with the second option with Dedicatted. It includes: - initial review - all reports and documentation preparation - remediation of high-risk issues - applying for credits **And as a result, you’ll get $5000 of Amazon credits.** So in the end, you’ll improve your cloud systems and get $5000 AWS Credits on your AWS needs. The AWS WA Review with Dedicatted consists of 5 stages: 1. Initial review. We start with a kick-off meeting to identify your expectations and learn more about your business goals and needs. 2. The review. It’s a long call with your technical team to assess the identified areas. Together, we answer the questions in the Well-Architected Framework Tool. 3. Findings and reports. At this stage, we present them to you. We offer an improvement plan that highlights high-risk issues that should be addressed. 4. Remediation. We help you remediate the selected high-risks issues, either via IaC, manual changes, or by providing detailed instructions. 5. Final. You receives a credit code from AWS for 5000$. This process will take approximately one month. After that, when you feel the need, you can conduct another review to develop continuously. ## Conclusion AWS Well-Architected provides a complex approach to evaluate architectures and implement designs that can scale over time. With the best AWS Well-Architected practices, your business can enhance the security, reliability, efficiency, and cost-effectiveness of your cloud-based systems. AWS Well-Architected Framework is a good help for small businesses and startups. With it, you can optimize costs and IT infrastructure to maximize the outcomes of your investments. But also, it could be useful for large businesses to monitor and cut unused resources and avoid overspending. If you are interested in taking advantage of this opportunity – contact us via for consultation. **Categories:** Blog **Services:** Cloud and Infrastructure Security, Cloud Data Migration & Modernization, Managed Services **Industry:** Automotive, Insurance, Manufacturing, Retail --- ### [How can DevOps help ML train models cheaper](https://dedicatted.com/insights/how-can-devops-help-ml-to-train-models-cheaper) **Published:** February 17, 2023 **Author:** Dedicatted **Content:** Machine learning algorithms can be beneficial for a wide variety of applications such as in medicine, email filtering, speech recognition, computer vision, and others. It is an inevitable decision if no fully satisfactory algorithm is available. Creating and training a machine learning model can be costly. But only if you don’t know how to optimize your costs. You can achieve it with DevOps practices. It may sound as if DevOps is the answer to everything. But you have to try in order to make sure: start by improving the infrastructure, continue with automation, then CI/CD, and so on. Between these stages, check how much your costs had changed. But for starters, here’s how DevOps can help to cut costs on training: ## Infrastructure It is the most important part: DevOps can optimize your costs on the infrastructure. Here are some best practices to do it in order to improve your work with ML: 1. Infrastructure as Code or IaC allows organizations to automate infrastructure provisioning and management. Define infrastructure as code to get rid of manual infrastructure management and easily replicate it and scale. 2. [Cloud Computing](https://dedicatted.com/services/glossary-of-terms#term-cloud-computing): DevOps practices can be leveraged to take advantage of cloud computing, which can reduce the cost of infrastructure for ML projects. Cloud providers offer flexible, pay-as-you-go models, which can optimize the upfront costs of infrastructure and allow organizations to scale it as needed. 3. Containers and Kubernetes: These are powerful tools for managing infrastructure. They simplify the deployment and scaling of applications and services. 4. Auto-scaling: Allows organizations to automatically adjust the resources required to support ML models in production. By automatically scaling the infrastructure, organizations can cut the costs of unused resources and ensure that models have enough of them to perform optimally. ## Automation Since automation is at the core of DevOps, it can help organizations to reduce the time and resources required to build, deploy, and maintain ML models. Automating repetitive tasks, such as data preparation and model deployment, can save data scientists and engineers time to focus on more valuable tasks. [ ![Automating remittance workflows and reducing costs with GenAI and AWS](https://media.dedicatted.com/wp-content/uploads/2025/03/Hercules.-Cover-300x157.webp) Case studyDedicatted Petlichenko 5 min to read Also read ### Automating remittance workflows and reducing costs with GenAI and AWS Manufacturing Cloud Modernization Cloud Security Generative AI ](https://dedicatted.com/insights/automating-remittance-workflows-and-reducing-costs-with-genai-and-aws)## Continuous Integration and Delivery DevOps practices, such as continuous integration and delivery, can help to quickly and consistently deploy ML models to production. This can reduce the time and costs of manual deployment and improve the reliability of ML models in production. ## Collaboration Collaboration between development and operations teams is crucial for DevOps and can help organizations rapidly identify and resolve issues with ML models. By breaking down silos between teams and promoting open communication, DevOps can help to decrease the time and costs associated with troubleshooting and maintenance. ## Monitoring and Maintenance [Monitoring](https://dedicatted.com/services/glossary-of-terms#term-monitoring) and maintenance allow your ML models to run smoothly in production. By proactively monitoring models and making updates as needed, you can save the time you would need to fix problems and maintain models. ## Conclusion DevOps practices are useful for reducing costs associated with ML projects. Automate processes, improve collaboration, and streamline deployment and maintenance to optimize your expenses. It is also very important to pay attention to the infrastructure of your project. With Infrastructure as Code, cloud computing, containers and Kubernetes, and auto-scaling you can perform better and more efficiently. If you consider DevOps practices to enhance your ML project, we share your idea. It can help your organization to deliver ML models faster and more reliably. **Want to implement DevOps in your project to get the most out of it? Reach out to us via .** **Categories:** Blog **Services:** DevOps Consulting --- ### [Top 6 Solutions for Kubernetes Intrusion Protection with IDS and IPS](https://dedicatted.com/insights/top-6-solutions-for-kubernetes-intrusion-protection-with-ids-and-ips) **Published:** April 25, 2023 **Author:** Denys Yermakov **Content:** The distributed nature of a Kubernetes containerized environment can present significant challenges. In fact, according to a recent survey, 42% of developers consider security to be the primary issue in container orchestration platforms. Furthermore, 55% report that release delays occur due to security concerns. However, with effective solutions such as IDS and IPS for Kubernetes, you can enhance your environment’s visibility and mitigate more threats. This article will explore intrusion prevention (IPS) and intrusion detection systems (IDS) for Kubernetes (K8s), explaining how they can help you identify suspicious activities, intercept attacks, and optimize performance. As an added bonus, we will review some of the most popular solutions available on the market. ## **Intrusion detection and prevention systems: what do they entail? If you’re not well-versed in this area, let’s start by reviewing the definitions of intrusion detection and prevention systems for Kubernetes. ## **Systems for detecting intrusions** An intrusion detection system is a type of software that monitors your Kubernetes environment and produces alerts when it detects anomalies. These anomalies may include unauthorized system calls, suspicious requests to the Kubernetes API, malicious traffic patterns, DDoS attack patterns, or other types of vulnerability exploits. The system acts as a sort of watchdog, keeping a constant eye on your Kubernetes environment and raising the alarm it detects any suspicious behavior. The methods used by IDS solutions to analyze activities in your K8s cluster include: [![Blog section with light blue background detailing signature-based protection, abnormality identification, examination of protocol states featuring key approach features, protocols etc](https://dedicatted.com/wp-content/uploads/2023/04/Group-146-5-1-scaled.png)](https://dedicatted.com/wp-content/uploads/2023/04/Group-146-5-1-scaled.png)Numerous IDS products employ a combination of detection methods, typically including signature-based, anomaly detection, and stateful techniques. By using a hybrid approach, these products can detect and identify a wider range of threats. It’s important to note that IDS is a Kubernetes intrusion detection solution that operates in a passive manner. These systems are capable of sending alerts to security information and event management (SIEM) systems, as well as security teams, for additional analysis. However, unlike IPS products, they do not actively protect your endpoints or address vulnerabilities. ## **Cyber threat prevention system Similar to IDS for Kubernetes, intrusion prevention systems monitor and identify potential threats. However, in addition to detection capabilities, they provide measures to safeguard your environment, such as: [![Blog section with light blue background with the checklist of actions for disaster recovery with the turquoise checkpoint signs along side and a picture on the right of the software engineer creating models with AI](https://dedicatted.com/wp-content/uploads/2023/04/Group-144-2-1-scaled.png)](https://dedicatted.com/wp-content/uploads/2023/04/Group-144-2-1-scaled.png)IPS can be seen as a supplement to IDS since they both identify potentially malicious activity and network traffic within your K8s clusters, pods, and applications. However, in addition to detection, IPS is capable of halting an attack as soon as it is detected. Let’s go to the next point. ## **Is it advisable to deploy IDS and IPS for Kubernetes? You may be wondering whether it’s necessary to deploy network intrusion prevention and detection systems for Kubernetes when you already have a firewall or built-in tools from your provider. However, the answer is **“yes”** for the following reasons. The core principle of DevSecOps suggests that any system can be compromised given the attacker’s expertise, time, and motivation. Therefore, you require multiple techniques to prevent as many attacks as possible and respond to breaches quickly to minimize their impact. While firewalls provide a basic level of security against some traffic, advanced attacks can evade them. Additionally, they do not safeguard your systems within the cluster if an attack breaches the perimeter. Despite the robust access control, authentication, and authorization mechanisms provided by popular cloud computing platforms such as Amazon Web Services, Google Cloud, and Microsoft Azure, Kubernetes distribution is not completely immune to failure. In addition to this, monitoring a large number of containers, pods, and underlying code efficiently can be a daunting task, which becomes even more challenging when deploying K8s across multiple cloud services. By implementing IPS and IDS for Kubernetes, you can monitor your containers in real time, which helps you detect more threats and attacks, allowing you to patch up vulnerabilities promptly. Additionally, an IPS can isolate malicious traffic and repair the damage automatically. Let’s move on from the theory and take a closer look at the available solutions that you can deploy to secure your Kubernetes infrastructure. #### If you find this article, we recommend taking closer look at [ Kubernetes Consulting Services ](https://dedicatted.com/solutions/key-technologies/kubernetes) [ Compliance & Security ](https://dedicatted.com/services/security) ## **6 best-recommended intrusions detection and prevention solutions for K8s** Below you can see the best tools created in order to guard your collection and containerized applications against security breaches, zero-day attacks, and other unusual incidents. ### **1. Aqua The Aqua CNDR platform offers a range of tools for detecting, preventing, and automating responses to intrusions and anomalies across your K8s infrastructure. It leverages behavioral detection and eBPF (Berkeley Packet Filter) to identify network attacks, evasion techniques, and unfamiliar malware. This platform can prove highly beneficial to DevSecOps teams. Aqua CNDR evaluates and scores your workloads, helping your experts pinpoint vulnerable deployments, and enabling you to prioritize mitigation efforts. Additionally, it allows you to visualize inbound and outbound connections for each deployment, facilitating the identification of probable entry points for attacks. Furthermore, Aqua can identify containers that were not part of your pipeline or that were modified post-deployment. This capability enables you to block containers and unauthorized access attempts effectively. However, the platform falls short in its role-based access control (RBAC) granularity, necessitating manual permission settings for each cloud integration. It also does not group alerts, making it difficult for enterprises with a huge amount of microservices. ### **2. Datadog Datadog is a monitoring tool that keeps an eye on every node in your K8s infrastructure, even if they are distributed across several clouds. It detects attacks against infrastructure, monitors clusters for security misconfigurations, and automatically reports on CIS benchmarks. Along with intrusion detection, it tracks resource metrics such as CPU, memory, and traffic load and logs autoscaling events. This software supports over 500 integrations with common monitoring and log management tools, allowing you to have real-time visibility into your infrastructure. You also get application performance monitoring (APM) and distributed tracing to gain transaction-level insight into your activities. All logs and reports are automatically tagged and grouped for your convenience. For example, logs from Redis containers are tagged with *service:redis* and *source:redis*. However, Datadog has limited documentation on its integrations and configurations, making it difficult to understand how to use it effectively. Additionally, its billing panel does not show your charges until the end of the month, which can be challenging for budgeting purposes. Furthermore, there is no way to set caps or limits, making it impossible to predict how much you will be charged. ### **3. Falco Falco is a an open-source, runtime security tool designed for Kubernetes. It can be run directly in your K8s environment or kept separate for additional security measures in case of an attack. With this tool, you can continuously monitor both your cluster and cloud logs for suspicious behavior, configuration changes, or potential data breaches. Falco can identify abnormal activity or intrusions based on system calls, Kubernetes audit logs, arguments, and properties of the calling process. Falco is a popular solution with a strong community of developers that provides customizable threat detection policies and APIs. You can also find various SDKs and documentation to help you create custom plugins. While Falco serves as an intrusion detection system, it lacks preventive capabilities, which means that your security team must manually analyze the alerts. Deploying at scale can be challenging without extra automation tools, especially if you have a large number of microservices. Moreover, the alerting configuration options are limited for containers of the same type. [![Group of the logos lined up: the logos of datadog, waqzuh , falco, aqua logo](https://dedicatted.com/wp-content/uploads/2023/04/Group-150-1-scaled.png)](https://dedicatted.com/wp-content/uploads/2023/04/Group-150-1-scaled.png)### **4. Prisma Cloud Prisma Cloud, formerly known as Twistlock, is a comprehensive solution for real-time intrusion prevention in Kubernetes environments. It provides virtual firewalls that inspect network traffic and only allow safe content to pass through, creating secure network boundaries across clusters. The workload identifier feature assigns each container a unique cryptographic identity, blocking network access for unverified or unauthorized workloads. Prisma Cloud also includes compliance checks to prevent application misconfigurations throughout the application lifecycle. With over 400 customizable checks for common regulations such as GDPR, PCI DSS, and HIPAA, as well as various pre-built compliance templates, you can generate detailed reports on traffic, application, and threat detection for compliance audits. However, regular updates to the platform may require reconfigurations and additional maintenance. Also, Prisma Cloud could benefit from more detailed technical documentation and responsive support. ### **5. Tigera Calico Tigera Calico is a comprehensive security solution that uses machine learning algorithms and a rule-based engine to detect and mitigate data breaches and advanced persistent threats in microservices. The platform monitors all traffic flowing through your microservices and alerts you when it detects unusual behavior, allowing you to take remediation measures before any harm is done. The networking plugin is easily scalable and has a low overhead, making it suitable for large-scale deployments. It also includes Honeypods, which are fake pods in your K8s cluster that capture malicious traffic and trigger in-depth analysis for known signatures. Tigera Calico offers advanced encryption for extra protection of sensitive data, and a DNS dashboard helps you confirm and eliminate connectivity issues in your cluster. However, the installation process can be complex, as it requires deploying numerous namespaces with dozens of pods. Additionally, the architecture consists of multiple sub-projects, which may make troubleshooting a bit tricky. ### **6. Wazuh Wazuh is a platform designed for preventing threats in containerized environments that is available for free as open-source software. Its approach is based on signatures and a ruleset, which enables it to detect compromise indicators and security violations. In addition, the platform allows for analyzing endpoint configurations to minimize the cluster’s attack surface. This platform offers various incident response solutions to address active threats, such as a robust search engine and visualization tool to assist security teams in processing flagged incidents. Furthermore, it includes security controls to ensure compliance with industry regulations. On the other hand, Wazuh’s interface is somewhat clunky, and its documentation is lacking. Additionally, while the software is free, tools like CIS-CAT scanning require external licenses. Finally, users must configure monitoring capabilities and alerts manually. It is worth noting that any IDS or IPS product requires users to configure network policies, signatures, and baseline behavior for their clusters. Otherwise, there is a risk of overwhelming the team with false alerts, overlooking real threats, or impeding productivity by blocking trustworthy traffic. [Discuss your project with Dedicatted technical experts ](https://dedicatted.com/contact) ## **Summary** Safeguarding containerized environments is critical, and intrusion detection and prevention systems (IDS/IPS) offer adaptable solutions. For Kubernetes, IDS tools analyze network traffic within defined parameters, log activity records, and alert you to potential security threats. For even stronger network security, IPS can automatically block malicious activities and remediate anomalies. However, to ensure optimal performance and protection, these tools require careful setup of security policies and remediation rules. If you need assistance, consider engaging an experienced software development company. At Dedicatted, we have extensive technical expertise in container orchestration platforms like Kubernetes and DevSecOps. Our team can help you select, set up, and manage IDS/IPS solutions that suit your needs. [Contact us today](https://dedicatted.com/contact) to learn how we can enhance the security of your K8s cluster. **Categories:** Blog **Services:** Cloud and Infrastructure Security, Security Compliance --- ### [Unlocking the Power of Cloud Migration: Why enterprises are adopting it?](https://dedicatted.com/insights/unlocking-the-power-of-cloud-migration-why-enterprises-are-embracing-the-future) **Published:** July 6, 2023 **Author:** Dedicatted **Content:** In today’s rapidly evolving digital landscape, big and small enterprises recognize the immense potential of cloud migration. The process of moving digital operations to a cloud computing environment has become synonymous with growth and innovation. With careful planning and the right expertise, businesses can unlock a multitude of benefits that pave the way for long-term success. As an [AWS Partner](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/Dedicatted), we understand the importance of showcasing our expertise and dedication to cloud migration. That’s why we provide a wide array of public-facing materials, including captivating blog posts, informative press articles, and engaging videos. In each of these examples, we highlight our profound knowledge and proficiency in cloud migration while emphasizing the specific AWS services we utilize to drive successful outcomes. ## The Shift Towards Cloud Migration Cloud migration encompasses a variety of approaches, including transitioning from on-premises or legacy infrastructure to the cloud and transferring critical data and applications between different cloud platforms. It’s no surprise that businesses are increasingly considering this shift, given the remarkable benefits it offers. According to a recent Gartner survey, public cloud spending is projected to surpass 45% of all enterprise IT spending by 2026, a significant surge from less than 17% in 2021. Moreover, cloud investments have emerged as a top-three priority for over a third of businesses, highlighting the growing importance of cloud migration in the modern business landscape. ## Prerequisites for a Successful Cloud Migration While cloud migration presents numerous advantages, it also comes with its share of challenges. To ensure a smooth transition, businesses need to lay the groundwork and address critical prerequisites. [![Flowchart moving left to right showing cloud migration steps. Starts with 'Assess application readiness' with an icon above, followed by 'Analysis of cost implications,' 'Cloud preferences,' and 'Skilled resources,' each with its relevant symbol. in gradient](https://dedicatted.com/wp-content/uploads/2023/07/Group-140-7.png)](https://dedicatted.com/wp-content/uploads/2023/07/Group-140-7.png)## Reasons to Embrace Cloud Migration Enterprises face a myriad of challenges when it comes to operating and maintaining existing servers. Cloud migration provides a compelling solution to address these pain points and unlock new opportunities. Here are some key reasons why businesses are opting for cloud migration: 1. **[Scalability](https://dedicatted.com/services/glossary-of-terms#term-scalability):** The cloud offers the ability to upscale or downscale resources based on business needs, eliminating the need to pay for unused data and optimizing cost-efficiency. 2. **Cost-effectiveness:** By eliminating the need for local servers and on-site staff, cloud migration enables significant cost reductions in maintenance and support. 3. **Remote Collaboration:** Cloud accessibility enables seamless collaboration among teams, allowing for efficient work from anywhere, especially during these times of remote work and ongoing pandemic challenges. 4. **Better Data Storage:** Cloud solution providers offer highly secure data storage at minimal costs, with the flexibility to expand or reduce storage per evolving requirements. 5. **Enhanced Security:** Cloud environments provide robust security measures to safeguard against data theft, hacking, and unauthorized intrusions. 6. **Disaster Recovery:** Cloud-based backup solutions enable efficient recovery processes, saving time and reducing costs associated with in-house disaster recovery operations. 7. **[Automation](https://dedicatted.com/services/glossary-of-terms#term-automation) of Tasks:** Cloud platforms simplify the process of updating and integrating applications, automating repetitive tasks and streamlining operations. 8. **Flexibility:** Cloud storage allows for easy management of varying bandwidth demands, providing the ability to scale up capacity as needed. Testing and deployments become hassle-free in the cloud environment. 9. **Agility:** Cloud services empower quicker business decisions, reducing infrastructure-related time and effort, and improving overall efficiency. 10. **Zero Risk of Failure:** Cloud service providers ensure high uptime and seamless workload performance, minimizing the risk of system failures even during unforeseen events. [ ![The Importance of Disaster Recovery in Cloud Migration](https://media.dedicatted.com/wp-content/uploads/2023/07/Designing-Platform-for-Instant-Market-Penetration-99-300x157.png) BlogAndriy Kopachevskyy 5 min to read Also read ### The Importance of Disaster Recovery in Cloud Migration Automotive High Tech Software Insurance Manufacturing Cloud and Infrastructure Security Cloud Migration Security Compliance ](https://dedicatted.com/insights/the-importance-of-disaster-recovery-in-cloud-migration)## Crafting Your Cloud Migration Roadmap While there is no one-size-fits-all roadmap for cloud migration, businesses can follow a set of fundamental steps to navigate the process effectively. These steps include meticulous planning, selecting the most suitable cloud environment (public, private, hybrid, or multi-cloud), executing data and application migration with a focus on security and backup, and monitoring and validating migration efforts to ensure both technical and business success. At our AWS Partner organization, we specialize in assisting enterprises throughout their cloud migration journey. With our expertise and guidance, we ensure a seamless and efficient migration process, enabling businesses to concentrate on their core goals while we handle the complexities of application and data migration to the cloud. ## Conclusion Cloud migration has become an indispensable step for enterprises seeking growth, agility, and innovation. As businesses embark on this transformative journey, it is crucial to partner with trusted AWS Partners who possess the expertise and experience to deliver successful outcomes. Through our compelling public-facing materials, we demonstrate our commitment to helping enterprises unlock the power of cloud migration. Each example we showcase highlights the specific AWS services we employ, offering tangible evidence of our focus on and proficiency in cloud migration. Embrace the future of cloud migration and propel your business toward unprecedented heights. Let us be your trusted partner on this transformative journey, as we guide you through every step, ensuring a seamless migration experience that allows you to focus on achieving your core business goals. Together, let’s unlock the true potential of cloud migration and revolutionize the way you operate in the digital realm. [![Dedicatted is your trusted partner in your migration journey](https://dedicatted.com/wp-content/uploads/2023/07/core-service-card.png)](https://dedicatted.com/wp-content/uploads/2023/07/core-service-card.png) **Categories:** Blog **Services:** Cloud Migration, Cloud Modernization --- ### [The Importance of Disaster Recovery in Cloud Migration](https://dedicatted.com/insights/the-importance-of-disaster-recovery-in-cloud-migration) **Published:** July 14, 2023 **Author:** Andriy Kopachevskyy **Content:** Disaster recovery refers to an organization’s ability to quickly and effectively respond to and recover from a disruptive event that impacts its normal business operations. It involves implementing strategies and techniques that enable the organization to resume using critical systems and IT infrastructure as soon as possible after a disaster occurs. ## Why Is Disaster Recovery Important? Disaster recovery holds significant importance due to the potential damage that disasters can cause. Whether it’s a natural calamity like a hurricane or a minor network outage, the consequences can range from complete destruction of facilities to dissatisfied clients and financial losses for businesses. ## Financial impact for disasters The costs associated with disasters can be substantial. According to the [Uptime](https://dedicatted.com/services/glossary-of-terms#term-uptime) Institute’s Annual Outage Analysis 2021 report: ![Financial impact statistics for disasters: 40% of outages cost between $100,000 and $1 million; 17% exceed $1 million; average data breach cost was $3.86 million.](https://dedicatted.com/wp-content/uploads/2023/07/Group-135-5-scaled.webp) ## AWS Partner Expertise in Migration To showcase an AWS Partner’s expertise and focus on migration, it is essential to have public-facing materials such as blog posts, press articles, videos, and more. These materials should highlight the AWS Partner’s proficiency in migration and specifically mention the AWS services utilized in each example. ## Key Benefits of a Disaster Recovery Plan Having a robust disaster recovery plan and data backup strategy offers several benefits: 1. **Avoiding Disruptions and Safeguarding Information:** In the face of increasing cyber threats, data loss can lead to significant financial and operational disruptions. Implementing data backup and recovery plans as preventive measures ensures business continuity during a disaster. 2. **Minimizing [Downtime](https://dedicatted.com/services/glossary-of-terms#term-downtime) and Revenue Loss:** Whether caused by a cyber attack or technological breakdown, data loss can disrupt business operations and result in revenue loss. A data backup strategy enables quick data recovery and minimizes downtime, allowing businesses to resume normal operations promptly. 3. **Maintaining Reputation and Customer Satisfaction:** Downtime not only affects business operations but also has an impact on customer satisfaction and brand reputation. A backup and recovery plan with minimal downtime helps maintain a positive reputation and keeps customers satisfied. 4. **Retaining Customers and Avoiding Losses:** Extended periods of downtime due to data loss increase the likelihood of customers seeking alternative goods and services from competitors. By swiftly recovering critical information, businesses can continue serving their customers and prevent revenue loss. ## The Cost of IT Disruptions and Downtime IT disruptions can be financially devastating, leading to reduced productivity and lost revenue. Key statistics highlight their impact: [![Infographic with three sections highlighting business risks of downtime and data loss. Business Continuity Risks: 54% of companies face prolonged downtime, 40-60% of small businesses never reopen after a disaster, 90% fail if they don't reopen quickly, 93% go bankrupt after prolonged data loss, and 43% fail without a disaster recovery plan. Financial and Security Implications: Downtime costs range from $10,000 to $5 million per hour, 28% of companies have experienced data loss, 43% of data breaches involve small businesses, 19% have had security breaches, 34% of breaches involve internal actors, and ransomware downtime costs have increased by 200%. Operational and Infrastructure Challenges: 45% of companies report downtime from hardware failure, natural disasters cause 5% of disruptions, 70% of small businesses see any data loss as harmful, and 96% of companies with disaster recovery solutions fully recover.](https://dedicatted.com/wp-content/uploads/2023/07/Group-137-6-1-1-1-scaled.png)](https://dedicatted.com/wp-content/uploads/2023/07/Group-137-6-1-1-1-scaled.png)## Conclusion In summary, a disaster recovery strategy is crucial for safeguarding data and ensuring business continuity. By implementing effective recovery plans, businesses can mitigate risks, minimize financial losses, and maintain their reputation in an increasingly digital world. **Categories:** Blog **Services:** Cloud and Infrastructure Security, Cloud Migration, Security Compliance **Industry:** Automotive, High Tech Software, Insurance, Manufacturing --- ### [Exploring the Top 10 AWS Cloud Migration Tools and Services in 2023](https://dedicatted.com/insights/exploring-the-top-10-aws-cloud-migration-tools-and-services-in-2023) **Published:** July 19, 2023 **Author:** Serhii Semenchenko **Content:** ## Introduction As cloud adoption continues to soar, organizations are increasingly turning to Amazon Web Services (AWS) for their cloud migration needs. ## Why Choose AWS for Cloud Migration? AWS offers a wide range of tools and services designed to facilitate seamless and efficient migration to the cloud. In this article, we will delve into the top 10 AWS cloud migration tools and services available in 2023. These resources not only demonstrate an AWS Partner’s dedication to migration expertise but also showcase their ability to leverage specific AWS services to deliver successful migration outcomes. ## Top 10 AWS Cloud Migration Tools and Services 1. **AWS Server Migration Service (SMS):** AWS SMS enables organizations to automate the migration of their on-premises servers to AWS. It provides a simple, agentless solution for migrating physical, virtual, and cloud servers, helping businesses achieve faster and more efficient migrations. AWS SMS supports various AWS services such as Amazon EC2, Amazon EBS, and Amazon S3. 2. **AWS Database Migration Service (DMS):** With DMS, AWS facilitates the smooth and secure migration of databases to the cloud. This service supports both homogeneous and heterogeneous migrations, allowing businesses to easily migrate their databases to AWS. DMS supports popular databases like Oracle, MySQL, Microsoft SQL Server, and more, making it a versatile tool for AWS Partners to showcase their database migration expertise. 3. **AWS Snowball:** For organizations dealing with large-scale data transfer challenges, AWS Snowball offers a physical data transport solution. It allows businesses to securely migrate petabytes of data to and from AWS, eliminating common bottlenecks associated with network-based transfers. AWS Snowball ensures data integrity and supports various AWS services such as Amazon S3 and Amazon Glacier. 4. **AWS CloudEndure Migration:** AWS CloudEndure Migration simplifies and expedites the migration of physical, virtual, and cloud-based servers to AWS. It provides continuous replication, ensuring minimal downtime during the migration process. AWS CloudEndure Migration supports a variety of operating systems and integrates seamlessly with AWS services like Amazon EC2 and Amazon RDS. 5. **AWS Application Discovery Service:** Before embarking on a migration journey, understanding the existing application landscape is crucial. AWS Application Discovery Service helps organizations assess their on-premises applications and plan their migration strategies accordingly. It provides insights into application dependencies and resource utilization, enabling a smooth migration experience. This service is an excellent way for AWS Partners to showcase their expertise in planning and executing large-scale migrations. 6. **AWS Migration Hub:** AWS Migration Hub offers a centralized location to monitor the progress of multiple migration projects. It provides a comprehensive view of application readiness, migration status, and resource utilization across various AWS services. AWS Partners can leverage Migration Hub to exhibit their ability to manage complex migration projects effectively. 7. **AWS Transfer Family:** For organizations migrating file transfer workflows to AWS, the AWS Transfer Family simplifies the process. It provides fully managed services for transferring files over SFTP, FTPS, and FTP directly into Amazon S3 storage. AWS Transfer Family enables secure and scalable file transfers while showcasing an AWS Partner’s expertise in migrating file-based workloads. 8. **AWS Application Migration Service:** AWS Application Migration Service helps businesses migrate their existing applications to AWS without code modifications. It simplifies the migration process by automatically identifying and configuring the required AWS resources. AWS Partners can highlight their application migration capabilities by utilizing this service and showcasing successful migrations to AWS. 9. **AWS Database Migration Assessment Tool:** To ensure a smooth and efficient database migration, AWS offers the Database Migration Assessment Tool. This tool analyzes on-premises databases, assesses their compatibility with AWS database services, and provides recommendations for migration. AWS Partners can leverage this tool to demonstrate their expertise in database migration planning and execution. 10. **AWS Professional Services for Migration:** In addition to the various tools and services, AWS Partners can also showcase their expertise through AWS Professional Services. These services offer consulting and migration assistance tailored to organizations’ unique requirements. AWS Partners can emphasize their experience and success stories in executing complex migration projects with the support of AWS Professional Services. ## Zivian Health’s Seamless Journey from AWS to Azure The power of AWS migration tools becomes truly visible when they help organizations build a foundation for long-term growth – just like in our work with **Zivian Health**, a U.S.-based healthcare technology company. Zivian’s platform simplifies compliance tracking for Advanced Practice Providers (APPs) and clinics across multiple states, ensuring every license and credential stays up to date – a mission where downtime simply isn’t an option. Initially hosted on AWS, Zivian relied on **Amazon ECS, AWS S3, and Lambda** to run critical workloads that handled millions of compliance documents daily. With the help of AWS Migration Hub and CloudEndure Migration, Dedicatted designed a robust, secure architecture that kept performance high and operational costs low. This stable AWS foundation later became the launchpad for Zivian’s next evolution – a full-scale modernization and migration to Microsoft Azure. Zivian’s story perfectly illustrates how mastering AWS migration tools not only ensures immediate success but also sets the stage for multi-cloud readiness, innovation at scale. Explore the case: [**Building a Compliance-Ready Cloud – Zivian Health’s AWS-to-Azure Migration**](https://dedicatted.com/insights/building-a-compliance-ready-cloud-zivian-healths-aws-to-azure-migration) ## Conclusion: Elevate Migration with AWS Expertise The top 10 AWS cloud migration tools and services in 2023 provide AWS Partners with a robust toolkit to showcase their focus on and expertise in migration. By utilizing these tools and services and highlighting the specific AWS resources employed, AWS Partners can demonstrate their ability to deliver successful migration outcomes and assist organizations in realizing the full benefits of cloud adoption. **Categories:** Blog **Services:** Cloud Data Migration & Modernization, Cloud Migration, Cloud Modernization --- ### [Smart Environmental Reporting with GenAI](https://dedicatted.com/insights/smart-environmental-reporting-with-genai) **Published:** March 5, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2024 – ongoing #### Industry: Health Tech #### The service: Generative AI #### Overview **CASSEN** Testing Laboratories, our client in the chemical analysis sector, struggled with manual data processing bottlenecks that limited timely report delivery. For any testing laboratory, delayed analysis impacts regulatory compliance, legal proceedings, and client operations across multiple industries. Our AI-powered solution reduced their report generation time by 80%, tripled chemical marker analysis capacity, and enabled B2C market expansion – positioning them for sustained growth while maintaining their commitment to accuracy. ## The Challenge Our client’s reliance on manual lab data processing created significant workflow bottlenecks. Their team spent excessive time processing raw data and generating reports, while facing increasing demand from the end customers. The manual nature of their process limited their capacity to analyze higher number of chemical markers and restricted the ability to scale. **Inefficient Workflow & Time-Consuming Processes** - Manual lab data processing created bottlenecks, with excessive time spent on raw data analysis and report generation. ****Limited Expansion Ability**** - Expanding into the B2C channel was blocked by the limited capacity to serve a growing customer base. **Limited Analysis Capacity & [Scalability](https://dedicatted.com/services/glossary-of-terms#term-scalability)** - The manual approach restricted chemical marker analysis to 50 markers and prevented operational scaling to meet complexity demands. ****Growth Constraints**** - Manual report generation hindered market expansion and revenue growth. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## **Technology & Approach** We developed an **AI-driven solution using Bedrock GenAI to streamline lab data processing.** Leveraging OpenSearch, we implemented a RAG (Retrieval-Augmented Generation) architecture for intelligent incident analysis and chemical marker correlation. [![Architecture diagram of an AI-powered document processing pipeline using AWS services, including Amazon SageMaker, API Gateway, ECS Cluster, and a vector database for generating summarized outputs.](https://dedicatted.com/wp-content/uploads/2025/02/Cassen-diagram.png)](https://dedicatted.com/wp-content/uploads/2025/02/Cassen-diagram.png)**Implementation** To ensure a smooth transition, Dedicatted rolled out the solution in phases, minimizing disruptions to existing workflows. - **Phase 1:** We started by implementing automated report generation, reducing manual effort and improving data consistency. - **Phase 2:** The RAG-based analysis system was integrated, enabling deeper insights and intelligent correlation of chemical markers with potential causes. - **Phase 3:** Continuous monitoring and optimization were introduced to refine accuracy and efficiency. ## **Customization & Client Adaptation** To ensure seamless integration into CASSEN’s existing processes, we customized the solution to match the specific workflow requirements and industry standards. ### ********Precision in Chemical Marker Analysis******** The system was fine-tuned to accurately process and analyze chemical markers, aligning with CASSEN’s forensic and environmental investigation needs. ### **************Compliance & Regulatory Alignment************** AI-generated reports are structured to comply with industry regulations, ensuring data accuracy, security, and adherence to established protocols. ### ******************Leveraging Historical Data for Better Insights****************** Integrating CASSEN’s historical data into the system, allowed for pattern recognition, predictive analysis, and better incident correlation over time. ### ******************Operational Efficiency & Scalability****************** The solution was designed to streamline workflows, reduce manual effort, and enhance efficiency while remaining scalable to support future growth and new data sources. #### If you find this case interesting, we recommend taking a closer look at [ Generative AI Solutions ](https://dedicatted.com/services/generative-ai) [ Innovation for Healthcare ](https://dedicatted.com/services/generative-ai/healthcare) ## **The Outcome** The solution accelerated report generation from days to hours (**7x faster**), tripled analysis capacity (**50 to 150+ markers**), and cut human error by 3x (**10% to** **3%**). [Automation](https://dedicatted.com/services/glossary-of-terms#term-automation) enables continuous data processing, reducing manual effort and speeding up client deliverables. **Long-Term Value** - Scalable infrastructure supports future growth, including the B2C market expansion. - Faster service delivery and broader analysis capabilities. - Continuous improvement enabled via MLOps best practices and automated feedback loop. - Easily adaptable to integrate new chemical markers and analysis parameters. - Lower operational costs with automated processing and reporting. ****Business Impact: Smarter, Faster and more Scalable**** 1. Freed up skilled lab staff to focus on complex analysis and client consulting rather than manual data processing. 2. Enabled simultaneous handling of multiple client requests, eliminating bottlenecks. 3. Enhanced report quality with deeper insights and more comprehensive chemical marker correlations. 4. Improved customer satisfaction through faster turnaround times and detailed analytical insights. 5. Expanded service capacity, allowing for new client onboarding without compromising quality. **Categories:** Case study **Services:** Cloud Modernization, Cloud Security, Generative AI **Industry:** Healthcare --- ### [Automating remittance workflows and reducing costs with GenAI and AWS](https://dedicatted.com/insights/automating-remittance-workflows-and-reducing-costs-with-genai-and-aws) **Published:** March 12, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2024 – ongoing #### Industry: Wholesale #### The service: Generative AI #### Overview **Hercules Crane & Lifting Supplies** is Canada’s largest national rigging company with over 400 experts across 13 branches. Their extensive distribution network supplies everything from custom fabrication to essential safety equipment nationwide. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## The Challenge Financial operations were bottlenecked by manual remittance processing through Outlook, requiring staff to handle diverse document formats individually. Without automated document parsing capabilities, the team spent excessive time on data entry and document management. As a growing industry leader handling complex distribution operations across Canada, streamlined financial processes were crucial for maintaining their competitive edge. 1\. ********Operational Bottlenecks******** Manual document handling through Outlook created significant workflow delays. 2\. **************Data Processing Constraints************** Team spent excessive time on data entry across diverse document formats. 3\. ****************System Integration Gaps**************** Disconnect between manual processes and their Epicor Prophet ERP system. 4\. **********************Quality & Accuracy Concerns********************** Increased risk of errors and delayed payment cycles affected customer service. 5\. ******************************Growth Limitations****************************** Manual processes unable to scale efficiently to support business expansion. ## **Technology & Approach** We developed an advanced tech stack to automate and streamline financial data processing. Our solution leverages AWS services to extract, analyze, and process data from various document formats, turning it into structured information that integrates seamlessly with Hercules’ ERP system. [![AWS-powered invoice automation workflow showing data path from Outlook emails through AWS services (SES, Bedrock, Lambda, Extract, Comprehend, S3, SQS) to ERP system with security components (GuardDuty, Secrets Manager, KMS) | Dedicatted](https://dedicatted.com/wp-content/uploads/2025/03/Hercules-architecture.png)](https://dedicatted.com/wp-content/uploads/2025/03/Hercules-architecture.png)### **Implementation** To ensure a smooth transition and minimize disruption to Hercules’ operations, we implemented the solution in strategic phases: **Phase 1: Foundation Building** Established core infrastructure using AWS services for document processing. Created reliable pipeline for automated document handling and storage. **Phase 2: Intelligence Integration** Implemented GenAI through Amazon Bedrock and AWS Comprehend. Enhanced the system’s ability to understand and extract data from complex financial documents. **Phase 3: ERP Connection & Optimization** Created custom integration layer with Epicor Prophet ERP. Optimized the entire pipeline for performance, security and scalability. ## **Customization & Client Adaptation** To ensure maximum value and seamless integration, we tailored the solution specifically to Hercules’ unique operational requirements and industry demands. **Intelligent Document Processing** The system was fine-tuned to accurately process diverse financial document formats with confidence scoring to identify potential issues. **ERP Integration Excellence** We built a custom integration layer between AWS infrastructure and Epicor Prophet ERP to ensure reliable data flow across all financial operations. ****Security & [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance)**** Integrated cloud-native audit and security tools protect sensitive financial data throughout the processing pipeline. ****Scalable Architecture**** The solution was designed to handle increasing document volume without performance degradation, supporting Hercules’ growth across Canada. #### If you find this case interesting, we recommend taking a closer look at [ Generative AI Solutions ](https://dedicatted.com/services/generative-ai) [ Innovation for Manufacturing ](https://dedicatted.com/services/generative-ai/manufacturing) ## **The Results** Our AWS and GenAI solution transformed Hercules’ financial operations with measurable results: **40% cost reduction across financial operations** with **significantly reduced processing time.** **Long-Term Value** - Standardized financial processes across all 13 national branches. - Enhanced reporting enabling better financial planning and strategic decisions. - Adaptable system that evolves with changing business requirements. **Business Impact: Operational Improvements** - Eliminated reconciliation errors, strengthening vendor relationships. - Freed skilled staff to focus on strategic initiatives rather than manual processing. - Accelerated financial workflows, improving overall operational efficiency. - Positioned Hercules for continued nationwide expansion with robust, automated processes. **Categories:** Case study **Services:** Cloud Modernization, Cloud Security, Generative AI **Industry:** Manufacturing --- ### [AI Governance Framework: A Practical Guide for Financial Services and Healthcare](https://dedicatted.com/insights/ai-governance-framework-a-practical-guide-for-financial-services-and-healthcare) **Published:** May 22, 2026 **Author:** Dedicatted **Content:** Due to the increasing adoption of AI in enterprise operations, the risks associated with data privacy, ethical considerations, and regulatory compliance have become more evident. The lack of AI governance and risk management solutions is a significant barrier to AI adoption after the cost barriers. These statistics highlight a critical problem: while AI adoption is rising, effective and responsible implementation still needs to be achieved. [![AI Adoption](https://dedicatted.com/wp-content/uploads/2026/05/AI-Adoption-1024x576.png)](https://dedicatted.com/wp-content/uploads/2026/05/AI-Adoption-1024x576.png)The core problem is the complexity of AI governance. Enterprises integrating [AI, ML and Data Science](https://dedicatted.com/services/artificial-intelligence) encounter numerous challenges, such as confirming data quality, preventing biases, and complying with evolving regulations. Without a solid governance framework and expert AI consulting services, these issues can lead to inefficiencies, legal risks, and loss of trust from stakeholders. Moreover, unclear guidelines and lack of accountability can hinder innovation and the successful scaling of AI initiatives. The gap between having AI principles and living them is the defining governance challenge of this decade. For organisations in financial services, healthcare, insurance, and other regulated industries, it is also increasingly a legal one. This article is about closing that gap – practically, measurably, and before the regulator closes it for you. ## Defining enterprise AI governance Enterprise AI governance integrates ethical, transparent, and accountable policies, procedures, and practices into deploying and operating AI systems. At the same time, AI initiatives align with the organization’s strategic goals and values while mitigating risks and fostering trust among stakeholders. It covers traditional governance principles like policy and accountability with modern requirements such as ethics reviews, bias testing, and continuous monitoring. ## **Why most AI governance programmes stall** A mid-sized Canadian property and casualty insurer we worked with had done everything right on paper. They had a published AI ethics statement, a cross-functional working group, and a data science team that genuinely cared about fairness. What they didn’t have was a single person with the authority or the mandate – to stop a model from going to production if it failed their own stated standards. When their claims prioritisation model showed measurably different outcomes across postal codes that mapped closely to income and ethnicity, the working group flagged it. The flag went into a report. The report went into a queue. The model went live. This is not unusual. Practitioner research across 26 organisations on four continents found that roughly 75% of Responsible AI projects were more than a year old, yet in almost all cases had stagnated or remained incomplete. The reason is consistent: organizations underestimate the technical complexity and the scale of people-and-process change required to move from principle to practice. A policy document is not a governance framework. An ethics committee that meets quarterly to review slide decks is not accountability. **For Canadian organizations:** The regulatory urgency is immediate, not theoretical. Quebec’s Law 25 has been in force since September 2023, requiring Privacy Impact Assessments before using personal information to train AI models and mandating human review rights for automated decisions. OSFI has made algorithmic oversight an active supervisory priority for federally regulated financial institutions. And the Artificial Intelligence and Data Act (AIDA), advancing through Parliament , will impose binding impact assessment and human oversight requirements on high-impact AI systems when enacted. Organizations waiting for AIDA to pass before building governance infrastructure are already operating outside current obligations and will be structurally unprepared when binding legislation arrives. ## What the failures actually look like Before describing what good governance looks like, it is worth being specific about how it fails, because the failure modes are not the ones most commonly discussed in policy documents. **The leadership shield.** A major Canadian bank piloting an AI-assisted mortgage underwriting tool received internal analysis showing that the model’s approval rates differed significantly across applicants whose first language was neither English nor French. The finding was escalated. Leadership acknowledged it was “worth monitoring” and approved the pilot expansion. Eighteen months later, the same pattern appeared in a complaint filed with the Financial Consumer Agency of Canada. The model had been generating discriminatory outcomes at scale while governance documentation showed the risk had been “noted.” When executives are shielded from the specifics of AI failures, governance becomes a paper exercise. **The metric trap.** A health technology company deploying an AI-powered patient triage tool in three Ontario hospitals evaluated its data science team on model accuracy and deployment speed. Nobody’s performance review mentioned fairness outcomes. When an internal audit found that the model performed measurably worse on patients over 75, recommending lower-acuity care for a demographic that frequently presents with atypical symptom profiles – it took four months and a near-miss incident to trigger a model review. The incentive structure had been quietly working against responsible AI the entire time. **The one-time assessment.** A national Canadian retailer implemented an AI-driven credit product and conducted a thorough algorithmic fairness assessment at launch. The model performed well. Twelve months later, following a shift in their customer acquisition strategy that brought in a significantly younger and more geographically concentrated customer base, the model’s performance on minority demographic groups had degraded substantially. There was no monitoring framework to detect it. The first indication was a pattern in customer complaints , not in any internal governance system. Each of these failures carries specific Canadian legal exposure. The mortgage underwriting scenario implicates the Canadian Human Rights Act prohibition on discrimination in the provision of services, FCAC’s fairness expectations for financial products, and potentially OSFI’s model risk guidance. The healthcare scenario raises obligations under Ontario’s PHIPA and, depending on the nature of the near-miss, potential reporting requirements under provincial patient safety legislation. The retail credit scenario engages Quebec’s Law 25 ongoing monitoring obligations and PIPEDA’s accountability principle. In Canada, AI governance failures are not abstract – they map to specific statutes with specific consequences. ## **The four foundations that actually work** Our work across regulated industries has produced a framework built on four pillars that must be built simultaneously. Organizations that focus on one at the expense of others reliably stall. The pillar that gets neglected is almost always the first one. [![Three-layer enterprise AI governance framework shown beside a transparent pyramid graphic. The top green layer, labeled ‘Enterprise,’ includes investments and budgeting, business goals, and environmental, social, and governance priorities. The middle purple layer, labeled ‘AI governance,’ includes AI strategy and roadmap, methodology and processes, regulatory compliance, and risk management. The bottom blue layer, labeled ‘AI team,’ includes AI performance metrics, technology, data and tools, integration to IT operations, teams and skilling, and executing the strategy.](https://dedicatted.com/wp-content/uploads/2026/05/Framework-1024x576.png)](https://dedicatted.com/wp-content/uploads/2026/05/Framework-1024x576.png)### 1. Organizational: Culture before committee A credit union was one of the more thoughtful organizations we’ve worked alongside on AI governance. What made them effective wasn’t their tooling or their budget – it was that their Chief Executive had personally chaired the first three sessions of their AI ethics committee, had asked the data science team to walk her through every model assumption in their member loan product, and had created explicit protection for any employee who raised a governance concern. When a junior analyst flagged that their small business lending model was producing approval rates that correlated with the gender of the primary account holder, that flag reached the CEO’s desk within 48 hours. The model was paused. The issue was investigated and resolved. And the analyst was publicly recognized at the next all-hands. That is what governance culture looks like in practice. It is not a code of conduct, it is senior leadership demonstrating, through specific decisions, that raising hard questions about AI is valued above shipping quickly. What this requires: - Responsible AI success criteria that are separate from product KPIs: fairness outcomes and risk reduction metrics tracked alongside revenue - Role-specific training that is genuinely calibrated to what each function actually needs to do, not a single annual compliance module - Incentive structures that reward ethical behavior alongside business performance: including explicit recognition when governance processes prevent a harmful deployment - Active protection for internal dissent – practitioners who push back on model assumptions are preventing failures, not creating obstacles Every CEO out there we advise you to make governance visible through your own behavior. Chair the first ethics committee meeting. Ask the hard questions about specific models in public forums. The signal you send by engaging personally with the details of AI risk is more powerful than any policy document your organization will ever publish. ### 2. Operational: Authority, not advisory An insurance company that came to us after a difficult regulatory examination had a governance structure that looked comprehensive on paper. They had an AI ethics committee, a responsible AI policy, and a model review process. What they didn’t have was clarity on who could actually stop a model from deploying. When we mapped their governance workflow, we found seven points in the model development lifecycle where a fairness concern could be raised and zero points where raising one would automatically pause deployment. Every escalation path led to a committee that could recommend further review but could not compel it. We helped them rebuild the framework around a single principle: governance authority must match governance responsibility. Within four months they had a restructured committee with explicit sign-off authority at three model lifecycle gates, a Responsible AI playbook that mapped every stage of model development to both the relevant ethical principle and the named individual accountable for it, and an escalation channel that any employee could use without routing through their direct manager. The first model that failed a fairness gate under the new framework was paused, reviewed, and redeployed with a modified feature set. The process took three weeks and prevented what would have been a significantly more expensive problem in production. Every Chief Risk Officer should audit your governance structure for the presence of actual authority, not just advisory function. If your ethics committee cannot compel a model deployment to pause, it is not a governance body – it is a review forum. The distinction matters enormously when something goes wrong and you need to demonstrate to a regulator that your oversight was real. **For Canadian organizations:** Canada’s federated regulatory structure means a single governance playbook is unlikely to be sufficient. A financial institution operating nationally needs to align with OSFI at the federal level while accounting for provincial consumer protection legislation that varies meaningfully by jurisdiction. A health technology company deploying across multiple provinces is simultaneously subject to federal device regulations, PHIPA in Ontario, the Health Information Act in Alberta, and British Columbia’s E-Health Act – each with different requirements around automated decision-making, data residency, and patient rights. The operational implication: build a federated governance model with common enterprise-level principles and jurisdiction-specific annexes. Your ethics committee needs legal representation with genuine expertise in provincial health and privacy law, not just federal regulatory knowledge. ### 3. Technical: Fairness is an operational property, not a launch condition A national Canadian lender deployed an AI-driven personal loan product in 2022 following what they considered a thorough pre-launch fairness assessment. The model performed well on every metric they had defined. What they hadn’t defined was a monitoring framework for post-deployment performance and they hadn’t considered that their model’s training data was drawn almost entirely from applicants in Ontario and British Columbia, making it structurally under representative of Atlantic Canada demographics. Eighteen months after launch, a pattern emerged in their Atlantic Canada portfolio: approval rates and interest rate assignments were consistently less favourable for applicants in that region, in ways that correlated with demographic characteristics the model had never been explicitly tested against. There was no automated alert. There was no monitoring dashboard. The pattern was identified by a regional sales manager who noticed it in her quarterly portfolio review and escalated it manually. The fix required a full model retrain, a six-week deployment pause, and a proactive disclosure to OSFI. The cost, in time, money, and regulatory relationship was substantially higher than the cost of building a monitoring framework at the outset would have been. What technical governance actually requires: - Fairness metrics defined before model development begins, not after and calibrated to the specific context and population of the deployment - Training data that is representative of the actual population the model will serve, not the population that was most convenient to collect data from - Adversarial testing that attempts to surface failure modes before they appear in production - Continuous monitoring pipelines with automated alerting when model performance or fairness metrics drift beyond defined thresholds - A comprehensive AI system inventory , including vendor-embedded AI and supplier tools, because you cannot govern what you haven’t mapped Treat model monitoring as infrastructure, not as an optional reporting layer. Every AI system in production that makes or influences decisions affecting individuals should have automated fairness monitoring with defined alert thresholds. If it doesn’t, you don’t have responsible AI in production, you have responsible AI at launch, which is a very different thing. ## The implementation road to responsible AI innovation **Phase one – inventory and risk prioritisation (weeks 1 – 4).** Before governing anything, know what you have. Map every AI system: internally developed models, vendor products with embedded AI, open-source components, contractor tools. Apply a risk matrix, likelihood of harm, severity, scale of affected population. High-risk systems get full governance treatment immediately. Lower-risk tools can operate under lighter monitoring while you build capability. **Phase two – governance infrastructure (weeks 4 – 10).** Establish a cross-domain ethics committee with genuine authority to pause or halt deployments. Build a Responsible AI playbook that maps every lifecycle stage to the relevant ethical principle and the named accountable individual. Create role-specific training plans. Establish an escalation channel that any employee can use. **Phase three – technical embedding (from week 6, ongoing).** Integrate fairness assessment checkpoints into your existing model development workflow, not as a separate process, but as embedded as code review. Define fairness metrics before development begins. Build monitoring into production infrastructure from day one. **Phase four – communicate and scale (ongoing).** Internal governance dashboards, regular cross-functional updates, and proactive external transparency as maturity grows. As the framework proves itself on the first use case, expand it across the model inventory systematically. ## The C-suite view: advice on putting principles into practice AI governance fails when it is owned by everyone in principle and nobody in practice. Here is how we think about it by role: ***CEO:*** Governance culture is set by your visible behavior. Chair the first ethics committee meeting. Ask specific questions about specific models. Protect the people who raise hard questions. The tone you set in those moments determines whether your governance framework is real or decorative. ***Chief Risk Officer:*** Audit your governance structure for actual authority. If your ethics committee cannot compel a deployment to pause, rebuild it. Own the model risk inventory. Ensure that AI risk sits within your enterprise risk framework with the same rigour as credit risk or operational risk. ***CTO:*** Model monitoring is infrastructure. Every AI system in production that affects individuals needs automated fairness monitoring with defined alert thresholds. Own the technical inventory. Define the fairness metrics before model development begins. Make continuous monitoring a deployment prerequisite, not an optional enhancement. ***Chief Legal Officer :*** Map your AI inventory against your specific regulatory obligations: federal and provincial. Don’t assume US-derived governance frameworks are sufficient for Canada. They are not. Own the PIA process under Quebec’s Law 25. Ensure your ethics committee has provincial legal expertise, not just federal regulatory knowledge. ## The next step: From practice to proof with Dedicatted AI governance is not a project with an end date. It is an organizational capability: built, maintained, and improved over time, in the same way that financial controls or data security are capabilities. For regulated industries in Canada, the window for treating governance as a future consideration has closed. Quebec’s Law 25 is in force. OSFI is watching. AIDA is advancing. The organizations that build genuine governance capability now, not governance documentation, governance capability will navigate the next five years of regulatory evolution with confidence. The ones that don’t will be explaining their model inventory to a regulator under circumstances they would have preferred to avoid. The difference between those two futures is not technical complexity. It is organizational will: starting at the top, and demonstrated through specific decisions about specific models on specific days. At Dedicatted, this is the work we do. If you want to understand where your AI governance stands today and what it would take to get it where it needs to be, we’d be glad to **[start that conversation.](https://dedicatted.com/contact)** We collaborate closely with clients to monitor, manage, and enhance their AI systems. Our team assists you in establishing clear AI governance frameworks, implementing robust monitoring and management tools, and cultivating a culture of responsible AI use. Our team of over 50 dedicated AI, Data, and ML experts has successfully executed AI projects across various sectors. We are an **[AWS Premier Tier Partner](https://aws-marketplace-catalogue.dedicatted.com/)** with the Generative AI Competency, MSP designation, and a place in the AWS Agentic AI Pilot program (one of about 60 partners worldwide). We are the only Canadian partner with that combination. We have built and operated agentic systems across financial services, healthcare, manufacturing, and SaaS Book a working session with our team to map your use case scope the build, and price it honestly. [**Talk to us.**](#contact "Talk to us.") [![Hero banner promoting cloud-powered business innovation with Dedicated AWS Competencies branding on a dark tech background, and service categories below.](https://dedicatted.com/wp-content/uploads/2026/05/Group-274ng-1024x267.png)](https://dedicatted.com/wp-content/uploads/2026/05/Group-274ng-1024x267.png) **Categories:** Blog **Services:** Agentic AI, Generative AI, Security Compliance **Industry:** Financial Services, Healthcare, Insurance --- ### [Dedicatted became AWS Delivery Partner in 7 categories to Support Cloud and IoT Solutions](https://dedicatted.com/insights/dedicatted-became-aws-delivery-partner-in-7-categories-to-support-cloud-and-iot-solutions) **Published:** December 16, 2025 **Author:** Denys Yermakov **Content:** The real test of any cloud project doesn’t happen at kickoff. It doesn’t happen at the architecture review. It doesn’t even happen at go-live. It happens the day after – when real users log in, real data flows through the system, and real business pressure begins. And this moment matters more than ever. According to industry research, over 70% of cloud incidents happen within the first 30 days after launch – not because of bad tools, but because of fragile delivery practices. This is the moment our latest milestone is really about. Dedicatted has officially earned **7 AWS Delivery Partner Competencies**, recognizing our ability to design, implement, and operate production-grade AWS platforms across infrastructure, containers, streaming, analytics, and IoT. But beyond recognition, these competencies validate something far more practical: We build cloud systems that stay standing when reality arrives. [![A graphic displaying seven AWS Partner badges against a purple cloud-themed background. Each badge features the AWS logo and the word “Partner,” with specific delivery designations: Amazon ECS Delivery, AWS IoT Core Delivery, Amazon Redshift Delivery, Amazon Kinesis Delivery, Amazon EC2 for Windows Server Delivery, Amazon MSK Delivery, and AWS IoT Greengrass Delivery. The badges are arranged in two rows, creating a showcase of multiple AWS service competencies.](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-10T103016.489.jpg)](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-10T103016.489.jpg)## **Amazon EC2 for Microsoft Windows Server Delivery Competency** Despite all the innovation in cloud-native development, more than 60% of enterprise workloads worldwide still run on Windows-based systems. These platforms power finance, identity, logistics, and operational cores of businesses. The challenge isn’t just moving them. The challenge is moving them without business interruption. Our AWS delivery recognition in Windows environments reflects our ability to: - Migrate live production workloads with minimal downtime - Preserve business continuity during modernization - Improve availability and disaster recovery - Reduce operational overhead without rewriting core systems In real terms, this means modernization that feels controlled – not risky. ## **Amazon ECS Delivery Partners** Containers can accelerate development – but production containers are a different story. Without a reliable delivery process, scaling, monitoring, and operational continuity can become a headache. With our **Amazon ECS Delivery Partner** status, Dedicatted delivers: - Fully managed, production-ready container platforms - Zero-downtime deployments, even during peak business hours - Operational visibility and alerting, so teams detect issues before they impact users - Cost-efficient scaling, automatically adjusting resources to match demand For example, a SaaS client running multi-tenant applications on ECS experienced frequent scaling challenges. By applying production-grade ECS architectures, automated scaling policies, and proactive monitoring, their latency could be reduced up to 40% and eliminated downtime during peak usage. ## **Amazon Kinesis Delivery Competency** Achieving the AWS Kinesis designation differentiates **Dedicatted as an AWS Partner Network (APN) member** that is well-equipped to assist AWS customers with services, such as real-time data streaming on AWS, building scalable data lakes, loading and analyzing streaming data, building custom streaming data applications, and more. For instance, if a retail client wanted live monitoring of e-commerce activity to adjust pricing and inventory dynamically. Using Kinesis, we can build a pipeline that processed millions of events per hour, delivering insights in seconds instead of hours – helping the client increase revenue during peak sales periods. ## **Managed Streaming for Kafka (MSK) Delivery Competency** In a world where data flows constantly – user activity, events, logs, sensor telemetry, business transactions , keeping up means more than occasional batch jobs. You need a streaming backbone: something robust, scalable, and manageable. That’s where Amazon MSK comes in – and why Dedicatted’s **MSK Delivery Partner competency** matters. - **Reduced operational overhead & risk:** MSK provisions and manages brokers and metadata nodes, handles patching and upgrades, replaces failed servers, and ensures data durability. - **Scalable, cost-efficient streaming:** [Serverless](https://dedicatted.com/services/glossary-of-terms#term-serverless) or right-sized provisioned clusters adapt to load, saving up to 40% TCO compared to self-managed Kafka. - **Enterprise-grade availability & compliance:** Multi-AZ deployment, encryption, IAM controls, private VPC – all built in. - **Storage & processing:** Tiered storage, automated scaling, and robust throughput management let your cluster grow with your business. [![A comparison chart showing responsibilities for managing Apache Kafka across three environments: On-Premises, AWS EC2 (Self-Managed Kafka), and AWS MSK (AWS Managed). Each column lists operational tasks such as app development/optimization, scaling, high availability, Kafka installation and patching, rolling upgrades, broker/Zookeeper maintenance, within-cluster data transfer cost, encryption, OS patching, OS installation, hardware maintenance, hardware lifecycle, and power/network/HVAC. The chart highlights that On-Premises and AWS EC2 require full self-management, while AWS MSK handles more tasks through AWS-managed services](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-10T114353.563.jpg)](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-10T114353.563.jpg)## **Amazon Redshift Delivery Competency** Data drives decisions , but only if you can analyze it fast, reliably, and at scale. With **Amazon Redshift**, Dedicatted helps clients transform sprawling data into actionable insights, while cutting costs and boosting team productivity. **AWS & IDC Research Found Redshift Provides:** - **408% five-year ROI** – massive returns from your data investment - **30% increase in analytics team productivity** – analysts spend more time on insights, less on infrastructure - **47% lower five-year data warehouse costs** – cost-efficient, scalable, and future-ready - **27% faster report delivery** – get insights to business lines when they’re needed most **As an Premier Tier AWS Partner and Redshift adopter** with an experienced team of AWS Certified Solutions Architects and Developers, Dedicatted will help you modernize and protect your data warehouses with Amazon Redshift. For instance, with this tool we can easily analyze all your data across your data warehouse and data lake with consistent security and governance policies. With the Redshift Spectrum feature, you can query data in your lake and with Data Lake Export, you can save the results of a Redshift query back into the lake. Another good example of Amazon Redshift usage would be gaining faster insights at a lower cost. AWS Redshift lets us analyze petabytes of data up to 16x faster and 8x cheaper than other cloud data warehouses. Redshift provides great value as you scale your data warehouse, and you can start small and pay only for what you use. ## AWS IoT Core Delivery Competency In the world of IoT, success is about reliably ingesting, managing, and acting on device data at scale, from anywhere in the world. AWS IoT Core does exactly that: it’s a **fully managed, protocol‑agnostic gateway and message broker** that enables secure, real-time communication between devices and cloud applications and it scales automatically, so you don’t have to manage infrastructure. With AWS IoT Core you can connect nearly any device type : from low-power sensors to industrial machines , using standard protocols like MQTT, MQTT over secure WebSockets, or HTTPS. Secure communication is guaranteed by mutual authentication (X.509 certificates or AWS signature-based authentication) and TLS encryption. Imagine a factory with hundreds of production machines sending telemetry every second – temperature, vibration, usage metrics. Previously, maintenance was reactive: issues were noticed only after breakdowns, causing costly downtime. That’s where AWS IoT Core comes in, and why Dedicatted’s **IoT Core Delivery Partner competency** matters for clients looking to transform operations: 1. All machines securely stream real-time telemetry into AWS IoT Core. 2. The data feeds automated analytics pipelines, detecting anomalies and predicting maintenance needs. 3. Operations teams receive instant alerts for preventive actions, while dashboards update live with performance metrics. 4. Integration with other AWS services (Lambda, Kinesis, Redshift) enables advanced analytics and reporting. **Outcome:** minimized downtime, increased operational efficiency, and better resource planning, all while maintaining security and scalability. ## AWS IoT Greengrass Delivery Competency Many applications require real-time processing, local decision-making, and offline resilience and that’s where AWS IoT Greengrass shines. As a **Greengrass Delivery Partner**, Dedicatted helps clients extend AWS capabilities to the edge, enabling devices to act intelligently and autonomously, even when disconnected from the cloud. - Local compute and messaging: Devices can process data, run Lambda functions, and communicate with other devices locally, reducing latency and dependency on the cloud. - Offline operation: Greengrass enables devices to operate autonomously when internet connectivity is intermittent, buffering data and syncing once connection is restored. - Secure device-to-device and device-to-cloud communication: End-to-end encryption, mutual authentication, and fine-grained access control ensure robust security at the edge. - Machine learning at the edge: Deploy ML models directly to devices, allowing for intelligent processing e.g., predictive maintenance, anomaly detection, or smart automation , without sending all raw data to the cloud. ## Dedicatted Partnership with AWS Dedicatted joined the APN early and extended its strategic partnership with Amazon to the **AWS Premier Tier Partner status**, demonstrating deep expertise in AWS technologies and proven ability to develop industry-leading cloud solutions. [![We have achieved the AWS Retail Services Competency Innovate faster and improve customer experiences with HCLTech and AWS – 2025-11-28T140549.838](http://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-28T140549.838.png)](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-28T140549.838.png)Together with AWS, Dedicatted guides organizations through cloud adoption, offering a full range of services starting from AWS cloud strategy development to on-demand implementations and maintenance. Among the companies that benefited from our AWS expertise are Tecnic, Zivian Health , Adomik , Taw9eel, to name a few. Whether your next project involves Windows workloads, containers, streaming pipelines, analytics, or IoT, Dedicatted delivers systems that survive the day after go-live and every day after that. **Categories:** Blog **Services:** Cloud Migration, Cloud Modernization, Cloud Security **Industry:** Financial Services, Healthcare, High Tech Software, Manufacturing --- ### [Amazon Bedrock Best Practices for Building and Scaling Generative AI](https://dedicatted.com/insights/amazon-bedrock-best-practices-for-building-and-scaling-generative-ai) **Published:** May 12, 2026 **Author:** Andriy Kopachevskyy **Content:** More than 70% of organizations are already experimenting with or investing in generative AI, yet most enterprise AI projects still struggle to move beyond the pilot stage. The challenge is rarely the model itself. It is the complexity of turning prototypes into secure, scalable, production-ready systems without driving up infrastructure costs or operational overhead. That is where Amazon Bedrock comes in. Built by Amazon Web Services, Amazon Bedrock gives organizations access to leading foundation models through a fully managed service, helping teams accelerate development while maintaining security, governance, and scalability. Combined with AgentCore, AWS also enables businesses to build and orchestrate agentic AI workflows without stitching together an entire ecosystem from scratch. This article explores how Amazon Bedrock works, its key capabilities and enterprise use cases, and what organizations should consider when building and scaling generative AI solutions on AWS. ## **What Amazon Bedrock actually is** **[Amazon Bedrock](https://aws.amazon.com/ru/bedrock/)** is a managed service from AWS that gives you access to foundation models from multiple providers through a single API. That includes Anthropic Claude, Meta Llama, Mistral, Amazon Nova and Titan, AI21 Jamba, Cohere, DeepSeek, and others. You can use them out of the box, customize them with your own data, and ship applications without managing the infrastructure underneath. The pitch is simple. Instead of stitching together a model provider, a vector database, an orchestration layer, a security layer, and a deployment pipeline, you get one platform that handles most of it for you. Your team focuses on the use case. AWS handles the rest. With Amazon Bedrock, **[Dedicatted](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/Dedicatted)** can provide clients with a cost-effective service to help examine large volumes of data to customise models and utilise natural language for various use ### What Bedrock gives you - *Choice of models:* A growing catalogue of foundation models from leading providers, all behind one API. Switch between them without rewriting your application. - *Customization without exposure:* Fine-tune or use retrieval-augmented generation (RAG) on your proprietary data without sending it outside your AWS environment. - *[Serverless](https://dedicatted.com/services/glossary-of-terms#term-serverless) infrastructure:* No clusters to provision. Bedrock scales with demand and bills based on what you actually use. - *Knowledge Bases:* A managed RAG service that connects models to your documents, websites, or databases without writing your own retrieval pipeline. - *Guardrails:* Configurable safety controls for content filtering, topic blocking, and personally identifiable information (PII) redaction. - *A Playground for non-developers:* Marketing, product, and operations teams can experiment with models in a graphical interface before engineering writes a line of code. ## **Why Foundation Models Matter** A foundation model is a large, general-purpose model trained on enormous datasets. The point is reuse. The same model can write a customer email, summarise a 40-page report, draft SQL, or extract entities from a contract, with no task-specific training. That changes the economics. Instead of building and training a separate model for each use case, your team adapts one model to many. This is why Bedrock matters. It puts a catalogue of these models behind a single, governed API, so you can pick the right one for each task. Use a small, cheap model for classification and routing. Use a larger model for reasoning and code. Switch between them as the work demands. ## How Bedrock works in practice [![Diagram of an AWS Cloud workflow: a client sends a request to Amazon API Gateway, which triggers AWS Lambda. Lambda processes the request and calls Amazon Rekognition for image analysis and Amazon Bedrock for AI/ML capabilities. The results are returned through Lambda and API Gateway back to the client, with numbered steps indicating the request-response flow.](https://dedicatted.com/wp-content/uploads/2026/05/unnamed-4-1024x576.jpg)](https://dedicatted.com/wp-content/uploads/2026/05/unnamed-4-1024x576.jpg)There are three steps from idea to working application. 1. Bedrock offers models for text, image, and multimodal tasks. Anthropic Claude for reasoning and conversational depth. Amazon Nova or Mistral for cost-efficient text generation. Stability AI for image creation. Llama for open-weight flexibility. The right choice depends on the workload, the budget, and the data residency requirements. - ***Amazon Titan:*** Use for text tasks for writing, summarizing, or translating. - ***Stability AI:*** Use when high-quality image creation from text description is required. - ***Meta Llama 2:*** Perfect for multilingual conversational AI, enabling advanced communication in multiple languages. 2\. Most production deployments do not need fine-tuning. They need RAG. Connect a model to your knowledge base, supply the right context at runtime, and you get answers grounded in your business data without retraining anything. Fine-tuning is for the cases where tone, format, or domain language really do need to be baked in. 3\. Your application sends prompts and gets back text, images, or embeddings. The same Converse API works across most models, so swapping a model is a configuration change, not a rewrite. ## Where Bedrock earns its keep Bedrock’s unique offering is one of the most fascinating solutions for businesses venturing into generative AI: - Flexibility: It can address numerous tasks, ranging from customer service chatbots to content creation, which provides flexibility in covering multiple use cases. - Accessibility: Provides an effortless way to adopt generative AI through accessible pre-trained models that do not require deep technical knowledge to implement. - Saves Resources: Prototype and deploy applications rapidly, decreasing the time taken to develop and market AI-enabled products and services. - Cost-Effectiveness: You can save on the training expenses associated with creating an AI by using pre-trained models that take much less time and resources to integrate. - [Scalability](https://dedicatted.com/services/glossary-of-terms#term-scalability): Take advantage of AWS’s strong infrastructure to add capacity based on the demand of the workload. ## Use Cases for Amazon Bedrock Employing the generative solutions of AI is powerful for businesses and an absolutely unique feature of Amazon Bedrock is enabling businesses to leverage that ability. Here are some practical ways it can be applied: 1\. Customer Service: With Amazon Bedrock, businesses can create chatbots to provide order updates or assist with troubleshooting common issues. This reduces the workload based on repetitive queries, allowing agents to take on complex tasks and ensuring accurate and timely customer support. 2\. Marketing and Content Creation : Bedrock aids marketing professionals with the creation of detailed blog post, and social media posts descriptions, as well as aiding in email creation. Efforts are timed and utilized to ensure effective strategies and innovations are implemented instead of mundane tasks. 3\. Product Personalization : With Bedrock businesses can create unique applications resulting in dynamic, AI-enabled product recommendations tailored to individual customer tastes. This improves consumer engagement and repays the business with increased sales, thus enhancing consumers’ overall shopping experience. 4\. Analytics and Insights: Bedrock can be used to summarize long analytics processes into short, clear descriptions or focus on identifying specific trends through summarization and executive summary generation. The remaining usable information allows decision-makers to derive correct assumptions and fully use them accurately. 5\. Design and Creative Projects : Automated processes in Bedrock support design teams by performing creative functions such as custom image generation and branding asset development. It commences the design process, which allows teams to iterate more effectively while concentrating on creating powerful visuals. [![Architecture diagram showing an Amazon Bedrock model deployment workflow. An app team user uploads model binaries to Interim S3, submits endpoint and model configuration metadata through App HAR GIT, and triggers a deployment pipeline via MR and Spinnaker. The Deployment SDK pulls Docker artifacts and model binaries, assembles a model package, uploads it to ModelStore, and imports the model into Amazon Bedrock. The workflow also provisions endpoints through Amazon SageMaker and verifies deployment through a Prediction Service.](https://dedicatted.com/wp-content/uploads/2026/05/image-1-28-1024x497.png)](https://dedicatted.com/wp-content/uploads/2026/05/image-1-28-1024x497.png)## Where AgentCore changes the game Models on their own are useful but limited. They generate text. They do not call APIs, query a database, remember what happened in last week’s conversation, or coordinate multiple steps to complete a task. That is what an agent does, and that is what AgentCore is built for. Amazon Bedrock AgentCore became generally available in October 2025, with the Policy and Evaluations services reaching GA in March 2026. AWS reports more than 2 million SDK downloads in the first five months of preview, and customers including PGA TOUR, Epsilon, Ericsson, Thomson Reuters, and Cox Automotive are already running production workloads on the platform. This is not a roadmap announcement. It is a working agent infrastructure. AgentCore is not one product. It is a set of services that work together or independently with any framework, including open-source options like CrewAI, LangGraph, LlamaIndex, Strands Agents, and Google ADK, and any model in or outside Bedrock. ## The Services worth knowing Every agent task is a loop. The model reasons, decides on an action, takes it, observes the result, and decides what to do next. Five steps in practice: ***Managed Agent [Orchestration](https://dedicatted.com/services/glossary-of-terms#term-orchestration):*** Consider an agent to be a manager of a project. Once an agent gets a request, the first step is to decompose it into smaller and simpler units. Then, the agent decides in the sequence to perform and execute the units of work. AgentCore, without supervision, solves this reasoning problem and takes over the entire complex workflow. ***Multi-Model & Multi-Agent Support:*** You aren’t locked into a single AI model. AgentCore lets you mix and match various foundational models, such as Claude (Anthropic), Llama (Meta), or Titan (Amazon), all within the same agent. This is beneficial in using the most appropriate model to perform a task, for instance, one model for creative text and another for analysis of the data. ***Secure Tool and API Integration:*** One of the most important features for an agent to be effective is their ability to communicate with other systems. AgentCore enables agents to connect with external APIs, AWS Lambda Functions, and internal databases, creating a safe conduit for agents to leverage existing services as tools. ***Memory and Knowledge Integration:*** With no context, an agent would be ineffective. AgentCore integrates with Amazon Knowledge Bases and allows your agent to access your company’s information. This is done through Retrieval-Augmented Generation. It also has short-term memory to remember the details of the conversation and functions long-term to draw knowledge from past conversations. **Guardrails, Safety, and Governance:** Control is very much needed in an enterprise environment. AgentCore has strong governance capabilities and allows for creating rules on what an agent can and cannot do. You can set these rules using free text and make sure your agents work within their safe boundaries and task compliance. ***[Observability](https://dedicatted.com/services/glossary-of-terms#term-observability) and [Monitoring](https://dedicatted.com/services/glossary-of-terms#term-monitoring):*** Once your agent is live, it is essential to understand how it is performing. AgentCore contains performance and issue debugging logs, traces, and metrics. It also has systems for quality assurance of your agent based on various parameters, such as correctness, helpfulness, and safety, to name a few. ## How Amazon Bedrock AgentCore Works Understanding AgentCore systems and processes works best when looking at the workflow of a specific task. The workflow consists of reasoning and action. It is a recursive process. [![Diagram showing an AgentCore architecture workflow: agents built with various frameworks (e.g., CrewAI) using AgentCore SDK and Starter Toolkit are packaged and pushed to a container registry, then deployed via AgentCore Runtime and Gateway. The system manages tools, memory, and identity, connects to LLM models for reasoning, and integrates observability through OpenTelemetry.](https://dedicatted.com/wp-content/uploads/2026/05/CoreComponents-AgentCore-jpg.webp)](https://dedicatted.com/wp-content/uploads/2026/05/CoreComponents-AgentCore-jpg.webp)When an agent is assigned a user request, the agent moves through a series of steps coordinated by Bedrock: 1. Input and pre-processing. The agent receives the user’s request and adds context (user ID, session history, relevant knowledge). 2. Planning. The foundation model breaks the request into steps and decides on a sequence. 3. Tool use or knowledge retrieval. The agent calls an API, queries a database, or pulls from a knowledge base, depending on what the step requires. 4. Observation and iteration. The agent reads the result, adjusts the plan, and continues until the task is complete. 5. Output. The final response is formatted and returned to the user. A simple example. A customer asks: “check the status of my recent order and send me the tracking link.” The agent plans two steps, calls one API to look up the order, calls a second to fetch the tracking URL, and composes the reply. AgentCore orchestrates that sequence without you writing the orchestration code. ## What it actually costs Bedrock and AgentCore both bill on consumption. There is no minimum commitment and no idle charge for AgentCore [Runtime](https://dedicatted.com/services/glossary-of-terms#term-runtime) when an agent is waiting on an external API. That matters more than it sounds. Most agent workloads spend the majority of their time idle, waiting on something else. ### For AgentCore specifically, here is how the pricing breaks down (current as of mid-2026): ***Runtime*** CPU: $0.0895 per vCPU-hour, billed only when the agent is actively processing. Memory: $0.00945 per GB-hour, with a 128 MB minimum. ***Gateway*** API invocations: $0.005 per 1,000 calls. Search API: $0.025 per 1,000 calls. Tool indexing: $0.02 per 100 tools indexed per month. ***Memory*** Short-term memory: $0.25 per 1,000 events. Long-term storage: $0.75 per 1,000 events per month. Retrieval: $0.50 per 1,000 retrievals. On top of these, you pay for the model inference itself (input and output tokens, per the model you choose) and for any AWS resources your tools consume, such as Lambda or third-party APIs. ***Knowledge Retrieval Costs:*** If your agent has an integration with a knowledge base, Amazon Bedrock RAG retrieval costs apply. ***Tool-Related Charges:*** Costs related to the tools your agent uses, such as AWS Lambda execution, third-party APIs, and others, are charged separately. ## A Worked Example A small internal assistant handling 10,000 monthly requests, 300 input tokens and 200 output tokens per request, using a cost-efficient model: - Model inference (Nova Micro range): around $0.39 per month. - Runtime (CPU and memory across 10,000 sessions): around $28.74 per month. - Gateway invocations: around $0.05 per month. - Memory events and retrievals: around $6.00 per month. **Total:** roughly $35 per month. At this scale, runtime dominates. As you move to higher-tier models or higher-volume workloads, model inference becomes the main cost driver. The 100x price gap between Nova Micro and Claude Opus matters more than any other lever. ## How to Keep the bill down 1. Right-size the model. Use Nova Micro or smaller Llama variants for classification and routing. Reserve premium models for reasoning and code. 2. Engineer prompts properly. Shorter system prompts, summarised memory, and structured outputs reduce token spend significantly. 3. Cache aggressively. Bedrock prompt caching can cut costs by up to 90% on repeated inputs. Most teams underuse it. 4. Use intelligent prompt routing. Bedrock can automatically route simple queries to smaller models and complex ones to larger models in the same family. AWS reports up to 30% cost reduction without quality loss. 5. Batch where you can. Batch inference is 50% cheaper than on-demand for asynchronous workloads. ## The Architecture in plain terms At a high level, AgentCore uses a layered architecture in which each component has a distinct role, allowing organizations to swap or scale individual pieces without rebuilding the entire system. The agent layer runs the core agent logic through AgentCore Runtime, while the reasoning engine relies on foundation models to handle planning and decision-making. Tool and model routers determine which model or external tool should be called for each task, helping optimize performance and cost. Knowledge and memory layers combine Retrieval-Augmented Generation (RAG) capabilities with both short-term and long-term memory to provide contextual responses. Finally, AgentCore Gateway enables secure integrations with AWS services and external APIs, allowing agents to interact with enterprise systems and third-party platforms seamlessly. ## Where Dedicatted fits Bedrock and AgentCore lower the barriers, but they do not remove the hard parts. Picking the right model for the workload, designing a knowledge base that retrieves accurately, writing guardrails that hold up under adversarial input, and operating an agent in production while staying within audit and compliance boundaries: those still take experience. We are an **[AWS Premier Tier Partner](https://aws-marketplace-catalogue.dedicatted.com/)** with the Generative AI Competency, MSP designation, and a place in the AWS Agentic AI Pilot program (one of about 60 partners worldwide). We are the only Canadian partner with that combination. We have built and operated agentic systems across financial services, healthcare, manufacturing, and SaaS, and we run them after launch under our MSP practice. ## We do not just build it. We run it. If you are evaluating Bedrock for the first time, or you have a working prototype that needs to graduate into a production-grade, governed deployment, we can help you skip the costly missteps. ## Get in touch Book a working session with our team to map your use case to Bedrock and AgentCore, scope the build, and price it honestly. [**Talk to us.**](#contact "Talk to us.") [![Banner with Dedicatted logo on a dark, abstract network background. Large headline reads: ](https://dedicatted.com/wp-content/uploads/2026/05/Group-2751-1024x307.png)](https://dedicatted.com/wp-content/uploads/2026/05/Group-2751-1024x307.png) **Categories:** Blog **Services:** Agentic AI, Cloud Modernization, Managed Services **Industry:** Automotive, Financial Services, High Tech Software, Insurance --- ### [Automated Budget Management for AWS: Introducing Budget Controls for Resource Governance](https://dedicatted.com/insights/automated-budget-management-for-aws-introducing-budget-controls-for-resource-governance) **Published:** September 30, 2025 **Author:** Denys Yermakov **Content:** Did you know that over 30% of cloud spending is wasted each year due to idle or mismanaged resources? According to the State of the Cloud Report 2025, cloud waste continues to rank among the top concerns for both enterprises and startups. The irony is clear: while the cloud promises scalability and flexibility, it can also become an uncontrolled drain on your budget if not managed properly. AWS itself highlights cost optimization as one of the five pillars of its Well-Architected Framework – right alongside security and performance. Yet, too often, teams focus on building and scaling while neglecting financial governance. The result: Unexpected bills, project delays, and frustrated stakeholders. This is where Budget Controls for AWS comes in: an open-source solution that ensures you stay in control of your spending without slowing down innovation. ## ********What it is and How It Works: Smart Tagging and Automated Actions******** The solution applies a special tag called **`BudgetControlAction`**, which governs how resources behave once your budget thresholds are reached. It supports three case-sensitive values: **Inform**, **Stop**, and **Terminate**. - **Inform (default)**: At 80% and 90% of your budget, the system will send alerts but won’t interrupt operations. This is a low-risk way to monitor usage without disrupting services. - **Stop**: When triggered, resources with this setting are paused, which stops further compute charges. The resource can be restarted at any time. However, it’s important to note that connected services, like storage volumes, may continue to generate some costs. - **Terminate**: This permanently deletes the resource when budget limits are reached. It’s best suited for development or test workloads that can be easily recreated, as the action is irreversible. For leadership, this means budget compliance is enforced automatically without relying solely on manual oversight. With cloud overspending affecting nearly three out of four organizations, proactive controls like this help avoid “bill shock” while giving your teams the freedom to experiment and scale. Cost optimization isn’t just about monitoring what you already use, it’s also about testing new services without financial risk. That’s where the AWS Proof of Concept (POC) Program comes in. It lets businesses experiment with AWS services for free (up to $25,000 in funding), helping you validate ideas before committing resources.Learn how to apply (and how we can help you get started) in our article: [ **AWS Proof of Concept: how to try new cloud services for free**](https://dedicatted.com/insights/aws-proof-of-concept-how-to-try-new-cloud-services-for-free) ## Reviewing the Budget Controls for AWS Architecture At its core, the Budget Controls for AWS solution relies on two key services: - **[AWS Budgets](https://aws.amazon.com/aws-cost-management/aws-budgets/?nc1=h_ls):** Tracks overall spend against customer-defined thresholds. At 80% of the budget, an alert is sent via email. At 90%, automated workflows are triggered to take the actions defined by the account owner (e.g., Inform, Stop, or Terminate). - **[AWS Config](https://aws.amazon.com/config/?nc1=h_ls):** Continuously monitors your resources to ensure they are properly tagged with BudgetControlAction. If tags are missing or invalid, Config flags the resource as NON\_COMPLIANT. There are several other AWS services deployed as part of the solution, as shown in the full architecture diagram below. [![AWS cloud architecture diagram for budget control with 13 labeled callouts. The flow starts from AWS Config (1) sending data to a custom Lambda rule (2), which stores information in Amazon DynamoDB. Amazon EventBridge (3) triggers reporting and remediation functions (5, 4) for resources including Amazon EC2, SageMaker, Aurora, and OpenSearch. AWS Budgets (6) publishes alerts to an SNS alert topic, which routes to an SNS action topic (7) and then to a Lambda trigger (8) connected to Parameter Store. AWS Step Functions (9) orchestrate workflows, interact with Parameter Store (11), and manage resource actions (10). Reporting function (13) also connects to DynamoDB. The architecture shows monitoring, alerting, and automated remediation flow for cost and resource management.](https://dedicatted.com/wp-content/uploads/2025/09/fig-1-Budget-Control-architecture-callouts-v2-1-1024x576-1.jpg)](https://dedicatted.com/wp-content/uploads/2025/09/fig-1-Budget-Control-architecture-callouts-v2-1-1024x576-1.jpg)In addition to AWS Budgets and AWS Config, several other AWS services integrate to **automate enforcement and reporting**. The workflow unfolds as follows: **1. [Continuous Monitoring](https://dedicatted.com/services/glossary-of-terms#term-continuous-monitoring) & [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) Validation** 1. - AWS Config serves as the foundation, constantly monitoring every supported resource for configuration changes. - A custom-built Lambda rule evaluates these resources against the required BudgetControlAction tag. - Compliant resources are written to Amazon DynamoDB, ensuring there’s a single source of truth for resource status and budget alignment. Business impact: This ensures that every resource in your environment is accounted for, eliminating hidden cost drivers – a challenge that 73% of organizations face when managing cloud budgets. Business impact: This ensures that every resource in your environment is accounted for, eliminating hidden cost drivers – a challenge that 73% of organizations face when managing cloud budgets. #### Dedicatted, AWS Premier Tier Partner **2. Automated Remediation.** Amazon EventBridge detects non-compliant resources and triggers the Lambda Remediation Function. 2. This function enforces governance by: - Adding the missing tag (BudgetControlAction = Inform by default). - Recording the update in DynamoDB for audit purposes. - Notifying stakeholders immediately through Amazon SNS. - All notifications are encrypted with AWS KMS, guaranteeing security and compliance with enterprise-grade standards. For example, in one of our case studies: our client’s manual remittance workflows were slow, error-prone, and costly. Our AWS and GenAI solution transformed their financial operations with measurable results: 40% cost reduction across financial operations with significantly reduced processing time. Just like Budget Controls automatically manage cloud resources to prevent overspending, smart automation in financial workflows ensures efficiency, compliance, and predictable costs- letting teams focus on growth, not manual tasks. Read the full story: [**Automating Remittance Workflows with GenAI and AWS**](https://dedicatted.com/insights/automating-remittance-workflows-and-reducing-costs-with-genai-and-aws) Business impact: This creates self-healing governance. Instead of waiting for engineers to manually fix issues, the system remediates instantly, saving both time and money. #### Dedicatted, your trusted AWS partner **3. Intelligent Budget Thresholds** 3. - During deployment, customers define a monthly budget cap. - When spending reaches 80%, AWS Budgets proactively issues an alert via email. - At 90%, the solution escalates automatically, triggering the SNS Action Topic. Business impact: This provides a dual safeguard – early warnings for managers to course-correct, and hard enforcement to prevent budget overruns. #### Dedicatted, 10 year of experience in the Cloud 4\. **Automated Decision Execution.** A Lambda function, subscribed to the Action Topic, invokes an AWS Step Function workflow. The Step Function cross-references DynamoDB to determine which action applies to each resource: 4. - Inform – Notify without interruption. - Stop – Suspend the resource, halting compute charges. - Terminate – Permanently delete test or dev resources to prevent waste. Every action is precisely logged back into DynamoDB, building a tamper-proof operational record. Business impact: This provides financial discipline at scale – resources are automatically adjusted based on business rules, removing human error and ensuring cost predictability. #### Dedicatted, Your Cloud & Generative AI Partner **5. Transparent Reporting & Accountability** 5. - Once actions are completed, the Step Function triggers a reporting Lambda. - The system aggregates data from DynamoDB and produces a comprehensive report. - A final summary email is sent via SNS, providing decision makers with clear visibility into what actions were taken, why, and when. ## Crunching the Numbers: What This Solution Will Cost You When evaluating any cloud governance framework, understanding the financial footprint is just as important as the technical setup. Here’s how this solution translates into real-world AWS costs. Fixed Costs. The only predictable, recurring cost comes from **AWS Key Management Service (KMS)**. A single encryption key used for securing notifications costs **$1 per month** , essentially the price of a coffee for enterprise-grade encryption. Variable costs. The bulk of the costs depend on how dynamic your environment is, in other words, how often resources change and how tagging is managed. AWS Config: - Every time a supported resource (EC2, Aurora, SageMaker, OpenSearch) changes configuration, AWS Config records the change. - Cost: **$0.003 per change.** - Formula: `Number of resources × Number of configuration changes per month × $0.003` BudgetControlAction Tag Changes: - Each time the tag value is updated, AWS Config re-evaluates compliance. - Cost: **$0.001 per evaluation.** - Formula: `Number of resources × Number of tag changes per month × $0.001` Other AWS Services - DynamoDB, Lambda, EventBridge, and SNS generate only negligible costs. - Combined monthly charges: **less than $0.01.** For most organizations, AWS Config will be the primary contributor to costs. The rest is almost invisible on your bill. [![An announcement of Dedicatted new whitepaper "How to get the most of Amazon Q for business "](https://dedicatted.com/wp-content/uploads/2025/09/Group-158-2.png)](https://dedicatted.com/wp-content/uploads/2025/09/Group-158-2.png)## Limitations of the Solution While this solution brings powerful automation and cost control, it’s important to note a few key constraints before rolling it out broadly. 1\. Single Account [Monitoring](https://dedicatted.com/services/glossary-of-terms#term-monitoring). Currently, the solution operates on a per-account basis. Multi-account setups require deploying the stack individually in each account. 2\. Single Region Coverage. The solution is designed to function within **one AWS region per deployment**. If your infrastructure spans multiple regions, deployment must be repeated across them. 3\. Supported Resource Types. At the time of writing, the solution supports: **EC2**, **RDS Aurora, SageMaker (instances and domains)**, **OpenSearch clusters** 4\. Email-Based Notifications. Notifications are delivered through **Amazon SNS email alerts.** Subscription confirmation is required, and there’s no out-of-the-box integration with collaboration tools like Slack or Microsoft Teams. Want to dive deeper into the mechanics of Budget Controls for AWS? The official AWS Cloud Financial Management blog breaks down how this solution automatically tracks, tags, and takes action on your cloud resources to prevent overspending. From detailed examples of resource tagging to automated stop and terminate workflows, it’s a must-read for anyone serious about financial governance in the cloud. Explore the full insights here: **Introducing Budget Controls for AWS: Automatically Manage Your Cloud Costs.** **Categories:** Blog **Services:** DevOps Consulting, Managed Services **Industry:** Automotive, Financial Services, Manufacturing, Retail --- ### [ARM vs x86: How AWS Graviton Can Save up to 40% on Cloud Costs](https://dedicatted.com/insights/arm-vs-x86-how-aws-graviton-can-save-up-to-40-on-cloud-costs) **Published:** December 5, 2025 **Author:** Maksim Lukhanin **Content:** The story of cloud servers used to be simple: same architecture, legacy support, backward compatibility, and years of optimization – x86 was (and still is) the standard for cloud infrastructure. But in 2018, things started to change once [**Amazon introduced their first ARM-based server CPU**](https://aws.amazon.com/blogs/aws/new-ec2-instances-a1-powered-by-arm-based-aws-graviton-processors) – AWS Graviton, promoted as “the most energy-efficient cloud chip yet.” [![A rounded rectangular banner with a purple-to-teal gradient background containing white text that states: ](http://dedicatted.com/wp-content/uploads/2025/12/Screenshot-472.png)](https://dedicatted.com/wp-content/uploads/2025/12/Screenshot-472.png) At the same time, x86 vendors keep upgrading server CPUs, pushing power and IPC improvements and publishing their own metrics. This means the gap in ARM’s benefits could be closing quickly by 2025, as Intel and AMD tune microarchitecture, system memory, and compiler/toolchain optimizations. At **Dedicatted**, we decided to run our own test and make an independent AWS Graviton4 review through our own benchmarks, and direct competitor comparison. We wanted to know: “**Does Graviton4 actually deliver those 40% of cost savings in 2025, and if so, under what workload conditions?**“. But first, let’s take a closer look at the actual market differences between the 2 CPU architectures: ARM and x86. ## **ARM or x86 CPU for Cloud in 2025?** Today the enterprise cloud services market narrows down to 3 families you see everywhere: AWS Graviton (ARM), Intel Xeon (x86) and AMD EPYC (x86). Below we list the concrete advantages of the top models from each vendor. [![A comparison table of three 16xlarge cloud processor options - AWS Graviton4, Intel Xeon 8488C, and AMD EPYC 9R14 - showing CPU architecture, core density, legacy software support, and on-demand pricing. AWS Graviton4 has 64 vCPUs with 64 physical cores and supports arm64 software stacks at $3.6/hr. Intel Xeon 8488C has 64 vCPUs with 32 physical cores (with SMT) and broad x64 legacy software support at $4.7/hr. AMD EPYC 9R14 has 64 vCPUs with 64 physical cores and supports common databases and runtimes at $5.3/hr.](https://dedicatted.com/wp-content/uploads/2025/12/table-14.png)](https://dedicatted.com/wp-content/uploads/2025/12/table-14.png)### **AWS Graviton4** Graviton4 is built for density and efficiency: more useful cores per dollar and lower energy draw for scale-out services. AWS keeps expanding Graviton support across managed services infrastructure, so ARM-based software bottlenecks keep improving with each release. ### **Intel Xeon 8488C** Xeon is still the ‘safe option’ for workloads where single-thread speed matters the most. [Latency](https://dedicatted.com/services/glossary-of-terms#term-latency)-sensitive apps or older enterprise stacks usually “just work” on Xeon, without any porting issues. That’s the reason Xeon stays relevant even when cost-per-core isn’t the best. ### **AMD EPYC 9R14** AMD EPYC sits in a strong position too with higher per-core performance and memory bandwidth. This makes EPYC the go-to option for some heavy workloads (analytics, HPC, or big database servers). ## **Benchmarks: AWS vs Intel vs AMD** We compared 3 same-sized 16xlarge instances (64 vCPUs / ~512 GB memory)**: AWS Graviton4, Intel Xeon 8488C and AMD EPYC 9R14**. Every processor was running Ubuntu 24.04, Linux 6.8 with identical storage class and networking tier for all instances to avoid I/O or NIC bias. We ran performance tests using sysbench on the 3 instances to perform basic OLTP read-write tests. The very first results we got were surprising: [![A horizontal bar chart titled ](https://dedicatted.com/wp-content/uploads/2025/12/Screenshot-473.png)](https://dedicatted.com/wp-content/uploads/2025/12/Screenshot-473.png)This chart shows the average latency under an OLTP workload. Graviton consistently comes out ahead: about 38% lower latency than Xeon and 20% lower than AMD EPYC. [![A horizontal bar chart titled ](https://dedicatted.com/wp-content/uploads/2025/12/Screenshot-474.png)](https://dedicatted.com/wp-content/uploads/2025/12/Screenshot-474.png)Both metrics (TPS & latency) were captured during fixed-length steady-state runs (200s throughput windows). Here, **Graviton4 delivered roughly 13% higher than Intel and 8% higher** than AMD for sustained transactional work. [![Bar chart titled 'Raw operations (I/O, random reads/writes)' showing AWS Graviton4 r8g.16xlarge with the highest throughput (~5.6M ops/s), AMD EPYC 9R14 r7a.16xlarge slightly lower (~5.17M ops/s), and Intel Xeon 8488C r7i.16xlarge significantly lower (~3.74M ops/s). Higher ops/s is better.](https://dedicatted.com/wp-content/uploads/2025/12/Screenshot-475.png)](https://dedicatted.com/wp-content/uploads/2025/12/Screenshot-475.png)Here we captured raw I/O/ops capability (random reads/writes) under heavy load (a proxy for datastore and cache headroom). AWS again sits ahead, but the gaps versus AMD are smaller here (memory, IO, single-thread speed change the picture). [![Bar chart titled 'Performance-per-dollar (sustained TPS ÷ hourly cost)' showing AWS Graviton4 r8g.16xlarge with the highest TPS-per-dollar value (~11.36B), followed by Intel Xeon 8488C r7i.16xlarge (~10.44B), and AMD EPYC 9R14 r7a.16xlarge (~9.64B). Higher values indicate better cost-efficiency.](https://dedicatted.com/wp-content/uploads/2025/12/Screenshot-476.png)](https://dedicatted.com/wp-content/uploads/2025/12/Screenshot-476.png)Finally, we converted throughput into a per-dollar metric by **dividing sustained TPS by a normalized hourly cost** (snapshot at the time of testing). The result is a normalized “TPS per $” that lets you compare cost directly. AWS shows about 35% better value & performance versus AMD and 15% better versus Intel. #### Head of DevOps, George Levytskyy ## ****How to Choose the Graviton4 RDS Instance?**** Does switching the server to ARM64 make any sense in 2025? Yes, Graviton4 architecture does justify the migration work with stable price/performance and energy-efficiency wins. But the savings won’t magically appear unless you pick an instance that matches your workload profile. If you pick the wrong size (too little memory, wrong EBS/network footprint, or a workload that’s heavily single-threaded), you can actually lose TPS and money. R8g is the most popular solution across AWS’s Graviton4 family. It’s the most memory-optimized CPU line (DDR5), tuned for DBs, in-memory caches and big data paths: [![Table showing AWS r8g instance sizes with columns for vCPUs, memory (GiB), network bandwidth, and Amazon EBS bandwidth. Instances range from r8g.medium with 1 vCPU and 8 GiB RAM to r8g.48xlarge and r8g.metal-48xl with 192 vCPUs and 1,536 GiB RAM](https://dedicatted.com/wp-content/uploads/2025/12/Screenshot-477.png)](https://dedicatted.com/wp-content/uploads/2025/12/Screenshot-477.png)AWS also provides less popular instances: **X8g** (for high memory workloads); **C7g** (for compute-optimized tasks); and **r8g.metal** for hardware scale-up (bigger single instances) or scale-out (more instances) control. What’s also important is the AWS cost-control toolkit (**On-Demand, Savings Plans, Reserved Instances** & **Spot**). Here you should choose the plan that fits your workflows. It often saves more than micro-optimizing instance specs. ## **What to Look at? RDS Instance Checklist** Before you cutover, run a production-like pilot and measure more than synthetic TPS: profile p99 latency, GC/CPU stalls, memory bandwidth and EBS/network behavior. Below are the factors that actually change the optimal instance choice: • **Memory Type & Bandwidth** For in-memory DBs and caches, DDR5 and higher memory bandwidth (what R8g exposes) materially improves tail latency and sustained throughput. • **Network & EBS bandwidth** Check expected network/TPS and EBS throughput. A NIC or EBS bottleneck will erase CPU gains. Use the instance bandwidth configs if you need to bias toward EBS or network. • **Binary / Dependency Compatibility** Verify critical native libs and drivers run on **aarch64** (or are containerized). Managed services support for Graviton is growing, but double-check specific engine/plugin versions you use • **Pricing Commitment Strategy** Combine On-Demand for short/experimental runs with Savings Plans / Reserved for steady state and Spot for interruptible batch. ## **Ready to migrate? Dedicatted can help with the whole migration process, including:** - review of your current cost management setup; - building a cost governance and implementation roadmap; - running a Graviton4 migration pilot; - technical assessment of your system architecture. Let’s talk about how **we** can help you save on cloud expenses and get migrated to AWS products quickly. [![A promotional banner for a case study titled ](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-09-17T135516.806-1.png)](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-09-17T135516.806-1.png) **Categories:** Blog **Services:** Cloud Data Migration & Modernization, Cloud Migration, Cloud Modernization, Cloud Security **Industry:** Financial Services, Healthcare, High Tech Software, Manufacturing --- ### [AWS Proof of Concept: how to try new cloud services for free](https://dedicatted.com/insights/aws-proof-of-concept-how-to-try-new-cloud-services-for-free) **Published:** January 26, 2024 **Author:** Dedicatted **Content:** Migration to the cloud can cut businesses’ infrastructure costs. But before doing this, one wants to make sure of it. And here, the largest cloud provider – Amazon Web Services – comes to help. Amazon offers different programs to help businesses build effective systems in the cloud to make the most out of the cloud. One of these possibilities is the AWS Proof of Concept (POC) Program, which allows you to check for free if these cloud solutions suit your business. Let’s discover how it works and what amount you can count on. ## What is AWS Proof of Concept? A Proof of Concept (POC) works as ‘try before you buy’. It lets you start with a small-scale project to understand AWS services’ feasibility and functionality in the context of your business. In the result, you will verify whether it is the best cloud option for your goals. You can experiment with AWS technologies in a controlled environment before committing to a full-scale implementation. AWS will cover all the costs itself. If you are questioning why would they do that, here is the answer. When you have tried these tools, migrated part of your workload to AWS, and got used to working with it, there is little probability of the decision to start over with another cloud provider. Business will likely continue cooperation. Now, let’s get to the numbers. ## What funding can I expect for my business? Proof of Concept funding can cover 10% of the expected total Annual Recurring Revenue (ARR) for your test workload. If your company expects to spend $75K per year, you can get $7,5K for a proof of concept. But there is a limitation: funding can be at most $25K, even if the potential workload will cost you more than $250K annually. However, there are no limits on the type of workload you want to fund within the POC: AWS Cloud Applications, cloud migrations, Machine Learning projects, or others. ## How do you apply for AWS Proof of Concept? Some steps are awaiting you within the AWS Proof of Concept: - Defining the objectives you aim to address using AWS services. - Selecting AWS services that align with set goals. - Defining the scope of the POC and determining the duration of it. - Setting up the necessary AWS infrastructure. - Configuration and integration, testing, monitoring, and many more. That’s why, **for this funding, the cloud provider requires the participation of a differentiated AWS Partner**, who will make your journey smooth and productive. We at Dedicatted successfully passed all the stages to become a differentiated Partner, so you can start experiencing AWS services with us. Just contact us via [email](mailto:contact@dedicatted.com), and we’ll consult you about this opportunity. ## Sum-up If you’re looking to migrate your workload to the cloud, the AWS Proof of Concept can help you test the benefits of this decision for free. With the involvement of AWS Partner, you can count on funding up to $25K to check if cloud services are a good match for your business. *To try AWS cloud services with POC – reach out to us via* [*contact@dedicatted.com*](mailto:contact@dedicatted.com)*. We will support you at each step of your cloud experience.* **Categories:** Blog **Services:** Cloud and Infrastructure Security, Cloud Data Migration & Modernization, Cloud Migration, Cloud Modernization, Managed Services **Industry:** Automotive, Healthcare, High Tech Software, Manufacturing --- ### [Why security compliance is important](https://dedicatted.com/insights/why-security-compliance-is-important) **Published:** January 12, 2023 **Author:** Dedicatted **Content:** Security isn’t a trend, but a necessity. The amount of cyberattacks only increases. 560k new malware pieces are detected every day. And it costs businesses a lot of money and customers’ trust. For example, in 2022, the average cost of a data breach globally hit $4.35 million. How to ensure that your product is protected? Security compliance will help you with it. There is a set of standards created by a third party, like [**ISO**](https://www.iso.org/home.html) or [**PCI SSC**](https://www.pcisecuritystandards.org/), that guides you in protecting your business. Meeting these standards means that various types of data and consumers’ rights to control and access their data are secured. ## Antivirus won’t save you If you don’t pay enough attention to your product security, you’re risking. Don’t think that cybercriminals aren’t interested in your business if it’s small or its data may seem useless. But you’re not alone in this way of thinking. [![Infographic with four statistics shown as percentage bars. 20% of companies have more than 1 million files accessible to every employee. 60% of small to medium businesses could not continue operating if hit by ransomware. 78% of businesses expect annual increases in regulatory requirements. 89% of companies do not have an incident response plan in place.](https://dedicatted.com/wp-content/uploads/2023/01/Group-140-8-1.png)](https://dedicatted.com/wp-content/uploads/2023/01/Group-140-8-1.png)60% of small business owners don’t consider themselves a target for cybercriminals. That’s why they are popular cyberattack victims. Large companies also have their reasons to care about security, since the bigger you are, the more damage cybercrime can cause. **So what to do?** ## Consider security assessments They help you to understand the level of your business safety, to find weaknesses, and ways to eliminate them. Assessments test your systems and processes for vulnerabilities and provide you with ways to lower the risk of cyberattacks. They are also useful because help to keep your systems and policies up to date. It usually conducts every month or sometimes even every week. It is needed to make sure that your product is still in compliance with the security requirements established in the standards. During the assessment, the specialists that test IT systems and networks, follow a standard pattern. First, they observe the system and its components to determine the requirements. Then they create an action plan and conduct vulnerability scans, penetration tests, and other methods to test the security level. After that, an evaluation of the results comes into place. Based on it, developing a plan for increasing systems’ security. And a very important part – the report. Report includes: - the initial state of the system or network, - methods that were used to determine potential problems and weaknesses, - recommendations for eliminating found issues. You can assess the product with your internal team or entrust it to a third party that specializes in it. The second option can cost you more, but in this case, you can be sure that the assessment will be competent and cover all your systems and processes. #### Reach out to us if you want to have a competent technical partner to ensure your systems are secured [ Contact us ](https://dedicatted.com/contact) [ Compliance & Security ](https://dedicatted.com/services/security) ## What is a security audit, and why do you need it? A security audit is a complex assessment of your system that measures its security compliance to an audit checklist of industry best practices, externally established standards, or federal regulations. Audit discovers whether your system is corresponding to a set of internal or external criteria regulating data security. Internal criteria are about IT policies and procedures in your company. External criteria include federal regulations and standards set by **[ISO](https://www.iso.org/ru/home.html)** or **[NIST](https://www.nist.gov/)**. It is essential for developing risk assessment plans and mitigation strategies for businesses that work with sensitive and confidential data. If you want to create a secure and reliable product, here are a few standards to explore: - **[SOC 2](https://soc2.co.uk/)** is a security framework that specifies how organizations should protect customer data from unauthorized access, security incidents, and other vulnerabilities. - **[GDPR](https://gdpr.eu/)** is a regulation in European law on data protection and privacy in the EU and the European Economic Area. - **[PCI DSS](https://www.pcisecuritystandards.org/)** is a standard for collecting, transmitting, and storing users’ card payment information and sensitive data. [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) measures provide you with a set of clear frameworks, checklists, and best practices to make your business more secure. If you want to ensure compliance of your product to standards actual for your industry – don’t hesitate to contact us. ## Where to start? Now you understand the importance of security compliance and assessments. But where to start your way to secured systems and processes? [![Security audit services banner with gradient background showing cybersecurity interface and expert examining digital systems with call-to-action button to speak with Dedicatted security specialists about comprehensive security assessment services](https://dedicatted.com/wp-content/uploads/2023/01/Group-141-1.png)](https://dedicatted.com/what-we-do/consulting/devops-consulting#devops-and-security) **Categories:** Blog **Services:** Cloud and Infrastructure Security, Security Compliance --- ### [Strengthening Platform Reliability and Service Continuity Through Managed Support Services](https://dedicatted.com/insights/strengthening-platform-reliability-and-service-continuity-through-managed-support-services) **Published:** March 12, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2024 – ongoing #### Industry: E-commerce #### The service: Managed Services #### Overview A fast-growing e-commerce company Tawseel operating in a high-demand market partnered with us to enhance the reliability, continuity, and operational maturity of their digital platform. With increasing platform usage and pressure to meet customer expectations during high-traffic periods, the client needed a structured, scalable support model. They turned to us to implement a comprehensive **Managed Support Services (MSP)** engagement, designed to provide 24/7 coverage, incident management, process consistency, and continuous service improvement. ## **Challenges Addressed Through MSP** As the platform scaled, several key operational and support gaps emerged, all of which aligned directly with the capabilities offered through our MSP framework: **********Lack of 24/7 Support and Operational Coverage********** - The absence of round-the-clock monitoring and support left the platform vulnerable to unaddressed incidents during nights, weekends, and holidays – creating delays in resolution and compromising availability. **********Limited Operational Ownership and Service Desk Capabilities********** - The internal team lacked dedicated resources to manage routine operational tasks such as user access handling, patching, or platform maintenance. Permissions were assigned inconsistently, and platform responsibilities were fragmented. ********No Structured Incident Management Process******** - Incident handling was inconsistent and reactive without formal escalation workflows or response protocols. There was no centralized alerting, tracking, or reporting mechanism, which affected issue visibility and operational control. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## ******Our MSP-Based Approach****** We onboarded the client into our **Managed Support Services (MSP)** program, designed to provide long-term platform stability, structured operational processes, and continuous service excellence. - **24/7 Support & Major Incident Management (MIM)** We delivered continuous L2/L3 support coverage with formal MIM protocols for high-severity events. This ensured consistent uptime and rapid response times, even during peak traffic periods [![Incident response workflow diagram showing escalation path from initial alert through resolution, including decision points for automated fixes, support team involvement, and client communication](https://dedicatted.com/wp-content/uploads/2025/03/Копия-Support-L2-work-flow.drawio.png)](https://dedicatted.com/wp-content/uploads/2025/03/Копия-Support-L2-work-flow.drawio.png)- **Service Desk and Operational Process Ownership** Our MSP team assumed responsibility for day-to-day tasks including on/offboarding personnel, reviewing Git merge/deploy permissions, patch management, and maintaining system access workflows – restoring structure and accountability to platform operations. [![Service desk ticket review flowchart depicting decision process for handling open tickets, including escalation paths to L3 support and timing rules for client communication](https://dedicatted.com/wp-content/uploads/2025/03/Копия-Support-L2-work-flow.drawio-1.png)](https://dedicatted.com/wp-content/uploads/2025/03/Копия-Support-L2-work-flow.drawio-1.png)- **Standardized Maintenance and Upgrades** We implemented an ongoing upgrade and patch cycle for EKS clusters, OS, and platform add-ons – minimizing technical debt and reducing operational risks tied to outdated infrastructure components. - **Structured Reviews and Continuous Improvement** In line with our MSP service model, we conducted annual **Well-Architected Reviews**, **Disaster Recovery Simulations**, and regular **infrastructure vulnerability scans**, ensuring the platform remained secure, stable, and optimized. ## ********AWS Services Deployed******** - **Compute & Containers:** Amazon EKS (Kubernetes) for microservices; EC2 Auto Scaling Groups for legacy monoliths - **Networking & Security:** Application [Load Balancer](https://dedicatted.com/services/glossary-of-terms#term-load-balancer) (ALB) with AWS WAF and AWS Shield Advanced; Amazon Route 53 health-based DNS failover - **[Monitoring](https://dedicatted.com/services/glossary-of-terms#term-monitoring) & Alerting:** Amazon CloudWatch Alarms and Events, AWS CloudTrail, AWS X-Ray for distributed tracing - **Configuration & Patching:** AWS Systems Manager (SSM) Patch Manager and State Manager for OS and container host remediation - **[Backup](https://dedicatted.com/services/glossary-of-terms#term-backup) & Recovery:** AWS Backup for centralized snapshot orchestration; cross-Region replication of Amazon S3 and EBS snapshots - **Identity & Access:** AWS IAM with granular roles, AWS Single Sign-On for centralized user management, AWS Secrets Manager for credential rotation ## ******Business Milestone: First Black Friday Without Service Interruptions****** As a direct result of the platform maturity and support provided through our MSP engagement, the client successfully navigated their most critical commercial period – **Black Friday – without a single service disruption**. This marked a major milestone for the business and demonstrated the impact of consistent support, structured processes, and operational discipline. ## Outcome & Impact - **Service Level Availability increased to 99.97%**, driven by 24/7 monitoring, structured incident response, and proactive support. - **DDoS and operational incident risks were mitigated**, reducing business disruption and support overhead. - **Platform operations were stabilized**, with full ownership of support processes, user access, patching, and upgrade management. - **Disaster Recovery readiness was validated**, giving the client confidence in infrastructure resilience and business continuity. ## Summary By adopting our **Managed Support Services (MSP)** model, the client transitioned from a reactive, fragmented operational structure to a mature, reliable, and scalable support framework. With continuous platform oversight, proactive maintenance, and structured service delivery processes, the business is now equipped to grow with confidence – while ensuring service continuity and reliability, even during the most demanding commercial periods. **Categories:** Case study **Services:** Cloud and Infrastructure Security, Managed Services, Security Compliance **Industry:** Retail --- ### [Advancing Infrastructure Stability and Operational Capabilities with Support Service](https://dedicatted.com/insights/advancing-infrastructure-stability-and-operational-capabilities-with-support-service) **Published:** March 14, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2023-2024 #### Industry: Legal Tech #### The service: Managed Services #### Overview **The Customer** is a leading provider of unified entity management solutions for global enterprises. As their platform scaled to meet the demands of enterprise-grade clients, The Customer identified the need for a mature and reliable operational support model to enhance service quality, maintain system availability, and drive continuous platform improvement. The company partnered with us to implement a structured **Managed Support Services (MSP)** framework to ensure long-term platform stability, process maturity, and operational excellence. ## **Challenges: The Growing Need for MSP** The Customer’s rapid growth and expanding infrastructure brought increasing complexity to their platform operations. Without a dedicated operations team or formal service processes in place, the need for **Managed Support Services (MSP)** became essential to ensure platform reliability, security, and business continuity. Key pain points included: ### ******Lack of 24/7 Operational Coverage****** - **The Customer** required a trusted partner to provide round-the-clock platform support and incident management – a critical gap in their internal capabilities as platform usage scaled globally. ### ******Lack of Structured Support Processes and Permissions Management****** - The client operated without formal support processes, resulting in inconsistent incident handling and ad hoc request management. Permissions were assigned chaotically, creating accountability gaps and limiting scalability to meet enterprise standards. ### ********Gaps in Operational Ownership and Process Maturity******** - Following internal restructuring, responsibilities across critical infrastructure and platform processes became fragmented. The absence of a structured ownership model made day-to-day operations reactive and inconsistent. ### ******Lack of Monitoring and Operational Insights****** - Without structured monitoring, incident tracking, or regular reporting processes, the customer had limited insight into platform health and operational performance. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## **Our Approach** To address these challenges, we embedded customer into our **Managed Support Services (MSP)** framework, delivering structured support, proactive problem management, and continuous platform improvement under clearly defined service tiers. #### **24/7 On-Call Support & Incident Management** We introduced full **24/7 support coverage for L2 and L3 incidents**, along with structured **Major Incident Management (MIM)** for P1/P2 events. This allowed for rapid escalation, resolution, and reporting – ensuring consistent SLA adherence and service availability at all times. #### **Built-in ITSM Processes** A formalized ITSM process set was implemented, covering **incident management, request fulfillment, change management**, and structured reporting. This provided the customer with a scalable support model that aligned with enterprise-grade service expectations. #### **Proactive Problem Management** Proactive measures were introduced to identify and address recurring issues early. We allocated capacity for **ongoing problem analysis and resolution tracking**, ensuring better system resilience and reducing downstream incidents. #### **Continuous Service Improvement** Under our MSP model, we rolled out a **continuous improvement process** focused on platform performance, resource utilization, and service efficiency. This included regular reviews, reporting, and system optimization roadmaps to evolve the platform alongside business needs. #### **Structured Reporting & Visibility** We enabled regular performance and service reporting on a monthly cadence, giving the customer team full transparency into operational metrics, ticket resolution, incident trends, and improvement opportunities. #### If you find this case interesting, we recommend taking a closer look at [ Managed Services ](https://dedicatted.com/what-we-do/devops/devops-as-a-service) [ DevOps Services ](https://dedicatted.com/what-we-do/consulting/devops-consulting) ## **Results & Impact** - **Operational stability** significantly increased through structured 24/7 support and incident management processes. - **SLA compliance improved to 99.97%**, with average response time 13 minutes 25 sec. - **Recurring system issues were proactively addressed**, improving uptime and platform performance. - **Full platform visibility and service maturity were established**, enabling the customer’s internal team to focus on strategic innovation while we handled day-to-day platform operations. ## **Summary** Through our **Managed Support Services**, the customer transitioned to a structured, mature, and scalable operational model that supports their enterprise growth trajectory. With continuous support, proactive management, and clear service ownership, the platform now delivers consistent performance, high availability, and operational efficiency – fully aligned with the demands of global enterprise clients. **Categories:** Case study **Services:** Cloud and Infrastructure Security, Managed Services **Industry:** High Tech Software --- ### [Optimized Data Architecture: Transforming Data Costs into Growth](https://dedicatted.com/insights/optimized-data-architecture-transforming-data-costs-into-growth) **Published:** March 25, 2025 **Author:** Dedicatted **Content:** #### Working time: 2023 – ongoing #### Industry: High Tech #### The service: Data Architecture #### Overview The client is a **prominent technology company** based in Southern Europe, specializing in advanced analytics and consumer insights powered by artificial intelligence (AI). Established to transform consumer reviews and feedback into actionable business intelligence, the company operates across multiple continents including Europe, North America, and Asia. Their innovative platform provides comprehensive and real-time insights, significantly impacting product development, marketing strategies, and overall business performance for leading brands across diverse sectors. Their services encompass sophisticated data analysis solutions that convert vast amounts of customer-generated content into meaningful market intelligence. Leveraging cutting-edge machine learning algorithms, the client’s platform helps businesses quickly interpret complex consumer behaviors, preferences, and evolving market trends. Having experienced considerable growth, the company now collaborates with major global brands, further establishing its reputation in the analytics industry. A recent significant growth equity investment underscores their ambitious plans for continued expansion and technological advancement. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## **Challenge** As the client rapidly expanded their customer base and entered new markets, the volume and complexity of the data they managed grew exponentially. This rapid growth highlighted critical weaknesses within their existing data architecture, limiting their ability to efficiently scale and adapt to market demands. Specific challenges included: ****Fragmented Data Ecosystem**** The client faced multiple duplicated datasets across various storage solutions, making it difficult to maintain data accuracy and consistency. This increased the complexity and cost of data governance and storage. ****Inefficient Compute Resource Management**** The client experienced frequent issues with over-provisioned and poorly optimized computational resources. High operational expenses incurred from maintaining these resources did not translate into proportional analytical improvements, significantly straining their financial resources and affecting overall profitability. ******Limited Data Search and Retrieval Capabilities****** The absence of a scalable and robust search infrastructure led to significant bottlenecks in data analysis workflows, causing delays in critical processes. This limitation reduced business agility and responsiveness, hindering the company’s ability to quickly adapt to market changes and opportunities. ******Extensive Manual Data Processing****** The client heavily relied on manual data cleaning, normalization, and migration processes. Skilled analysts and engineers were burdened by these repetitive tasks, diverting their valuable time and expertise away from strategic and innovative projects. Consequently, this negatively impacted productivity and slowed down overall business growth. ## **Solution** To address the challenges and streamline the client’s data architecture, our team devised and executed a comprehensive, technically robust approach, which incorporated several strategic initiatives: #### **1. Adoption of DeltaLake (Apache Iceberg)** We implemented DeltaLake using Apache Iceberg to drastically reduce data duplication by shifting from storing entire data snapshots to capturing only incremental changes. This approach significantly minimized storage requirements and enhanced data consistency and accuracy across multiple datasets. [!["Architecture diagram showing Amazon EMR integration with Delta Lake UniForm tables. Users access Amazon EMR Studio and Workspace, which connects through a VPC to Amazon EMR running on an EC2 cluster. Amazon EMR interacts with Amazon S3 for Delta Lake and Iceberg data, and with AWS Glue Data Catalog for UniForm tables. Data can be read and written via Delta Lake UniForm, and the same table can be read as an Iceberg table in Amazon Redshift.](https://dedicatted.com/wp-content/uploads/2025/03/solution-overview-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/03/solution-overview-scaled.png)#### **2. Dynamic Spark Clusters on Kubernetes** To improve computational efficiency and optimize resource utilization, we established dynamic Spark clusters orchestrated via Kubernetes. This strategy allowed for real-time scaling of compute resources, leveraging cost-effective spot instances. As a result, the client experienced substantial cost savings while maintaining optimal performance for intensive data computations. #### **3. Migration to AWS OpenSearch** We replaced the existing data search infrastructure with AWS OpenSearch to significantly enhance data retrieval and analytics capabilities. AWS OpenSearch provided robust, scalable, and real-time search functionalities, effectively eliminating bottlenecks and enabling quicker, more efficient data-driven decision-making. [!["Architecture diagram of an Amazon OpenSearch migration workflow. Client traffic enters the Source Cluster (Amazon OpenSearch or self-managed) and passes through a Capture Proxy ALB Target Group on Amazon ECS. Data is streamed to Amazon MSK for event streaming and captured for replay. A Source Cluster Snapshot is stored in Amazon S3 and reindexed using Reindex-from-Snapshot (RFS) tasks on Amazon ECS. Data flows into the Target Cluster (Amazon OpenSearch or self-managed). Migration Console manages operations, and Replay tasks reapply captured events to the Target Cluster. Monitoring and analysis are handled by Amazon CloudWatch and Amazon EFS. Numbered steps (1-6) illustrate the sequence of client traffic flow, snapshotting, reindexing, streaming, replay, and monitoring.](https://dedicatted.com/wp-content/uploads/2025/03/download-1.png)](https://dedicatted.com/wp-content/uploads/2025/03/download-1.png)#### **4. Creation of Robust Data Pipelines** We developed automated data pipelines to streamline data movement, cleansing, and normalization processes. These pipelines reduced manual workload and allowed the client’s skilled analysts and engineers to focus on high-value strategic tasks, accelerating the pace of innovation and enhancing overall productivity. ## **Intelligent Data Lifecycle Management** A standout feature of this project was the implementation of sophisticated data lifecycle rules, designed explicitly to optimize storage efficiency and significantly reduce operational expenditures. By carefully defining and automating these lifecycle policies, we ensured that data was stored precisely where it offered maximum value at minimum cost. These customized rules dynamically transitioned data between storage tiers based on access frequency and business relevance. Frequently accessed data remained instantly available, while older or less critical data seamlessly moved to cost-effective, long-term storage solutions. This strategy greatly minimized unnecessary expenses related to data storage, aligning storage costs directly with the value extracted from the data. For the business, this meant more predictable budgeting, reduced infrastructure costs, and enhanced clarity regarding their data assets. Ultimately, the intelligent lifecycle management empowered the client to focus financial resources more strategically, directly supporting their business objectives and growth. #### If you find this case interesting, we recommend taking a closer look at [ Data Architecture ](https://dedicatted.com/services/data/architecture) [ Managed Services ](https://dedicatted.com/what-we-do/devops/devops-as-a-service) ## **Results** The comprehensive overhaul of the data architecture delivered tangible and remarkable outcomes for the client, directly addressing their earlier inefficiencies and driving substantial business benefits. These measurable improvements have had a lasting impact, creating a solid foundation for future growth and scalability. #### **1. Significant Storage Cost Reduction** The implementation of sophisticated data lifecycle management rules dramatically optimized storage usage. By moving to incremental change-set storage with DeltaLake (Apache Iceberg) and automating transitions between storage tiers, the client successfully achieved an 80% reduction in data storage costs. This significant decrease allowed the business to allocate funds previously spent on storage to innovation and strategic initiatives, further accelerating company growth and profitability. #### **2. Optimized Compute Resource Utilization** Establishing dynamic Spark clusters on Kubernetes and strategically leveraging spot instances significantly enhanced compute efficiency. This initiative resulted in a remarkable 50% reduction in compute resource costs, freeing up substantial budgetary resources for reinvestment in innovation and growth. The client now benefits from flexible, scalable compute resources that adapt to workload demands, effectively eliminating resource wastage. #### **3. Enhanced Data Search Efficiency** Migrating to AWS OpenSearch greatly improved the client’s data retrieval capabilities, tripling data search speeds. This substantial acceleration enabled the business to perform real-time analyses and swiftly respond to evolving market demands and opportunities. Employees across various departments now enjoy quicker, more efficient access to critical insights, significantly improving productivity and enabling more informed decision-making processes. #### **4. Fully Automated Data Processes** Automated data pipelines replaced previously manual, labor-intensive tasks of data movement, adjustment, and cleaning. The new pipelines operate continuously, delivering rapid, error-free data processing 24/7. This automation allowed the client’s data teams to focus exclusively on strategic and innovative projects, significantly boosting overall productivity and operational agility. Moreover, the automation substantially reduced human error, increased data reliability, and provided enhanced consistency across all datasets. Collectively, these results positioned the client to sustain and scale their operations efficiently, fostering continued competitive advantage and supporting ongoing business expansion. The improvements delivered not only addressed immediate operational inefficiencies but also strategically positioned the company to tackle future challenges and seize emerging market opportunities confidently. **Categories:** Case study **Services:** Cloud and Infrastructure Security, Cloud Modernization, Data Architecture, Managed Services **Industry:** High Tech Software --- ### [Building Scalable Data Architecture for IoT with AFT-Managed Landing Zones](https://dedicatted.com/insights/building-scalable-data-architecture-for-iot-with-aft-managed-landing-zones) **Published:** March 26, 2025 **Author:** Dedicatted **Content:** #### Working time: 6 months #### Industry: Digital Health #### The service: DevOps consulting, IoT & Data Architecture #### Overview The client is a global leader in data analysis consulting and software, specializing in wearable sensor physiological monitoring solutions for clinical trials, healthcare research, and consumer wellness. Their core expertise lies in developing regulatory-compliant digital biomarkers from wearable sensor data – used to accurately measure drug and treatment effects in regulated clinical trials. As the first analytics company to create novel digital biomarkers from wearable sensors, the client supports several international pharmaceutical trials, including those focused on rare disease indications already in the open-label phase with top-tier pharma companies. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## **Challenges** The client initiated an AWS migration strategy with the goal of finding a dependable partner to manage cloud infrastructure, security, compliance audits, monitoring, logging, and CI/CD. To optimize operations built around wearable IoT data, the client required: - A secure and cost-optimized Data Lake for raw IoT data. - A robust Data Warehouse for processed data. - Scalable ETL pipelines to support data processing and analytics. - Selection and implementation of a streaming platform for real-time processing. ## **Our Approach** To meet the client’s goals, Dedicatted built a secure, scalable AWS-native platform leveraging modern infrastructure and data processing tools. ### **Data Architecture** - AWS Lake Formation: Foundation for the Data Lake, supporting raw (images, sound files, CSV) and processed data. - AWS MSK (Kafka): Primary streaming platform for ingesting IoT data. - Managed Service for Apache Flink: For real-time stream data processing. - AWS Glue: Used to build the data catalog. - AWS Athena: Enabled serverless analytics on structured data. ### **DevOps & Infrastructure Automation** - [Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform): Infrastructure provisioning as code. - AWS CodePipeline & AWS CodeBuild: CI/CD pipeline implementation. - AWS Control Tower & Landing Zones: Centralized AWS account management. - AWS AFT (Account Factory for Terraform): Enabled automated, scalable Control Tower management. - AWS EKS & ArgoCD: For managed Kubernetes and [GitOps](https://dedicatted.com/services/glossary-of-terms#term-gitops) deployment workflows. [![Architecture diagram of AWS Account Factory for Terraform (AFT) setup. The workflow starts with an engineer who installs AWS Control Tower in the management account and creates an AFT-management account. The diagram shows AWS Control Tower, AWS Organization, and AWS Service Catalog working together to provision new AWS accounts. Roles such as AWSAFTExecutionRole, AWSAFTAdminRole, and AWSControlTowerExecution are created and assumed for account customization. The AFT framework uses Lambda functions, Step Functions, CodeBuild jobs, and CodePipelines for automation. On the right, IAM trust relationships and policies for AFT roles are displayed.](https://dedicatted.com/wp-content/uploads/2025/03/unnamed-4-1.png)*AWS Account Factory for Terraform (AFT) architecture used to automate AWS Control Tower landing zone provisioning and account governance.*](https://dedicatted.com/wp-content/uploads/2025/03/unnamed-4-1.png)## **Outcome** Dedicatted assumed full ownership of the client’s AWS infrastructure, implementing AWS Control Tower according to industry best practices and aligning the platform with both security and scalability goals. **Key achievements:** - Fully operational AWS Data Lake built with Lake Formation and integrated into ETL pipelines within 3 months. - Clear separation of raw and processed data via isolated Data Lakes and Data Ponds, with experiment-level access controls. - Provisioned real-time ETL pipelines using AWS MSK and Apache Flink. - Automated CI/CD pipelines integrated with infrastructure and data processing services. - AWS AFT enabled scalable management of over 20 AWS accounts. - Full automation of account creation with GuardDuty SCP policies applied at the organization level. The outcome of this collaboration is a secure, automated, and scalable AWS environment built from the ground up to support the client’s IoT data platform and long-term growth. **Categories:** Case study **Services:** Cloud Security, Data Architecture, DevOps Consulting, IoT Platform, Managed Services **Industry:** Healthcare --- ### [The Intelligent Edge: Where IoT meets Machine Learning](https://dedicatted.com/insights/the-intelligent-edge-where-iot-meets-machine-learning) **Published:** March 28, 2025 **Author:** Denys Yermakov **Content:** In today’s hyperconnected business landscape, the marriage of Internet of Things (IoT) devices with machine learning (ML) algorithms is creating unprecedented opportunities for innovation and efficiency. This powerful combination transforms raw data into actionable intelligence, enabling organizations to predict outcomes, automate decisions, and discover insights impossible to detect with human analysis alone. ## **Understanding the partnership** The Internet of Things encompasses the vast network of physical devices embedded with sensors, software, and connectivity that enables them to collect and exchange data. From industrial equipment to consumer wearables, these devices generate massive amounts of information that would overwhelm traditional analysis methods. This is where machine learning becomes essential. ML algorithms can process this torrent of data, identifying patterns, anomalies, and relationships that drive business value. When IoT provides the eyes and ears, machine learning provides the brain – creating systems that not only monitor but understand, predict, and adapt. ### **The Fundamental Differences** **FEATURE****INTERNET OF THINGS (IOT)****MACHINE LEARNING (ML)****COMBINED VALUE**Primary FunctionData collection through connected devicesPattern recognition and predictionAutomated intelligence and optimizationData HandlingGenerates vast amounts of operational dataConsumes large datasets to train modelsContinuous improvement through feedback loopsDecision MakingLimited to programmed rulesLearns from data to make flexible decisionsAutonomous systems that adapt to changing conditionsBusiness ImpactVisibility into operationsInsights from complex dataPredictive capabilities driving strategic advantageThe synergy between these technologies creates a whole greater than the sum of its parts. IoT devices generate the rich, real-time data that machine learning algorithms need to become increasingly accurate, while ML provides the intelligence that makes IoT implementations truly valuable. ## **Transforming industries through intelligent IoT** The impact of this technological convergence extends across virtually every sector, though some industries are particularly well-positioned to benefit from early adoption. ### **Manufacturing: The Smart Factory** Manufacturing facilities were among the first to embrace IoT sensors for monitoring equipment. Now, they’re integrating machine learning to move from simple monitoring to predictive capabilities. Predictive maintenance algorithms analyze vibration patterns, temperature fluctuations, and other variables to identify potential failures before they occur. A McKinsey study found that manufacturers implementing these technologies reported 10-15% increases in production efficiency and 4-5% improvements in EBITDA. The combination of sensor data with intelligent algorithms minimizes downtime, optimizes production scheduling, and ensures consistent quality control. ### **Urban Infrastructure: Smarter Cities** Municipal governments worldwide are deploying IoT networks to manage everything from traffic flow to energy usage. Dubai’s smart city initiative exemplifies how machine learning can transform this data into practical applications. Their network of edge AI devices monitors traffic patterns, automatically detecting incidents and optimizing signal timing without requiring constant human oversight. The results are impressive: incident monitoring improved by 83%, emergency response times decreased by 30%, and overall journey times reduced by 20%. By processing data locally at the edge, these systems maintain privacy while enabling real-time decision making. ### **Healthcare: Predictive Patient Care** The Internet of Medical Things (IoMT) encompasses everything from wearable health monitors to hospital equipment. When combined with machine learning, these devices enable a shift from reactive to preventive healthcare. Remote monitoring systems now analyze patient data to detect subtle changes that might indicate deteriorating conditions, allowing intervention before critical situations develop. ML algorithms can identify patterns across thousands of patients, helping medical professionals develop more effective treatment protocols tailored to specific patient profiles. [ ![Building Scalable Data Architecture for IoT with AFT-Managed Landing Zones](https://media.dedicatted.com/wp-content/uploads/2025/03/VivoSense-Cover-300x157.webp) Case studyDedicatted Petlichenko 5 min to read Also read ### Building Scalable Data Architecture for IoT with AFT-Managed Landing Zones Healthcare Cloud Security Data Architecture DevOps Consulting IoT Platform Managed Services ](https://dedicatted.com/insights/building-scalable-data-architecture-for-iot-with-aft-managed-landing-zones)## **From concept to deployment** Organizations looking to harness the power of IoT and machine learning should approach implementation strategically, following a structured methodology to maximize success. ### **Assessing opportunity areas** The first step involves identifying specific business challenges that could benefit from data-driven insights. Rather than implementing technology for its own sake, successful organizations focus on clear business outcomes, whether cost reduction, quality improvement, or enhanced customer experience. ### **Technical implementation framework** **IMPLEMENTATION PHASE****KEY ACTIVITIES****SUCCESS FACTORS****COMMON PITFALLS**DiscoveryAssess existing infrastructure, identify data sources, define business goalsClear executive sponsorship, cross-functional teamTechnology-driven approach without business alignmentPilot [Deployment](https://dedicatted.com/services/glossary-of-terms#term-deployment)Implement limited-scope project, establish metrics, validate approachFocused use case, adequate data quality, realistic expectationsAttempting too much complexity in initial implementationScalingExpand successful pilots, standardize approaches, build internal capabilitiesDocumented procedures, knowledge transfer, infrastructure planningUnderestimating integration complexity with legacy systemsOptimizationRefine algorithms, enhance data collection, automate processesContinuous monitoring, regular retraining of models, feedback mechanismsFailing to update models as conditions change### **Managing implementation challenges** Every organization faces unique challenges when implementing IoT-ML initiatives. However, several common hurdles deserve special consideration: **Data Quality and Volume** IoT sensors generate massive amounts of data, but quality issues can undermine ML effectiveness. Successful implementations include data governance frameworks that ensure consistency, completeness, and accuracy. **Security Concerns** Connected devices create new attack surfaces for potential security breaches. Comprehensive security strategies must address device authentication, data encryption, and ongoing vulnerability management. **Integration Complexity** Many organizations struggle to integrate IoT-ML systems with existing infrastructure. The most successful approaches involve phased implementation with careful attention to API management and data standardization. ## **The business value equation** The ultimate measure of success for any IoT-ML initiative is the business value it creates. Organizations should establish clear metrics aligned with strategic objectives before implementation begins. ### **ROI framework for IoT-ML initiatives** **VALUE CATEGORY****EXAMPLE METRICS****MEASUREMENT APPROACHES****TYPICAL TIMEFRAME**Operational EfficiencyMaintenance cost reduction, Equipment uptime improvement, Energy usage optimizationComparative analysis (before/after), Control group testing6-12 monthsCustomer ExperiencePersonalization effectiveness, Response time improvement, Product quality metricsCustomer satisfaction surveys, Usage analytics, Defect rates3-9 monthsNew Revenue StreamsSubscription services adoption, Data monetization opportunities, New product developmentRevenue attribution analysis, Market share metrics12-24 monthsRisk Reduction[Downtime](https://dedicatted.com/services/glossary-of-terms#term-downtime) avoidance, [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) violation reduction, Security incident preventionPredictive model accuracy, Incident frequency comparison6-18 months## **What’s next for intelligent IoT** As organizations master current implementations, several emerging trends will shape the future of IoT and machine learning integration: - **Edge Intelligence** Processing will continue moving toward the edge, with more sophisticated ML capabilities running directly on IoT devices, reducing latency and bandwidth requirements. - **Federated Learning** Privacy concerns are driving development of techniques that allow ML models to train across distributed devices without centralizing sensitive data. - **Autonomous Systems** The combination of IoT sensors, ML intelligence, and automated controls is enabling fully autonomous systems that can operate independently with minimal human oversight. - **Digital Twins** The creation of comprehensive digital replicas of physical systems allows for simulation, optimization, and testing without disrupting operations. ## **Conclusion: strategic imperative for competitive advantage** The integration of IoT and machine learning represents more than a technological upgrade – it’s a strategic imperative for organizations seeking competitive advantage. Those who successfully harness this powerful combination gain the ability to predict market shifts, optimize operations, enhance customer experiences, and develop innovative business models. The most successful implementations share common characteristics: clear business alignment, phased deployment approaches, cross-functional teams, and robust security frameworks. By focusing on these elements, organizations can navigate the challenges of implementation while capturing the tremendous value these technologies offer. As we move into an increasingly connected and data-driven future, the question is no longer whether to implement IoT and machine learning, but how quickly and effectively organizations can leverage these capabilities to transform their businesses. ![IoT data analytics marketing banner showing industrial automation system with touchscreen control panel and call-to-action button to speak with Dedicatted experts about leveraging IoT data for business insights](https://dedicatted.com/wp-content/uploads/2025/03/Group-141-2.png) **Categories:** Blog **Services:** Generative AI, IoT Platform **Industry:** Automotive, Financial Services, Healthcare, Manufacturing --- ### [What It Takes to Make a SaaS Platform Compliant and Secure](https://dedicatted.com/insights/what-it-takes-to-make-a-saas-platform-compliant-and-secure) **Published:** April 1, 2025 **Author:** Dedicatted **Content:** ## **Introduction** Security can’t be an afterthought – especially for platforms that handle sensitive corporate data across global markets. One of our clients, a fast-growing software provider in the **legal technology sector**, came to us with a critical need: to bring their infrastructure up to the standards required by both internal stakeholders and external auditors, without slowing down their product roadmap. Their team had built something innovative, but like many scaling companies, they were moving quickly – and their security posture hadn’t kept up with the pace. They needed a partner to help assess the risks, rebuild the foundation, and guide them toward sustainable compliance. That’s where we stepped in. ## The Client and Their Challenge #### Working time: 2023-2024 #### Industry: Legal Tech #### The service: Security [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) #### Overview This North American SaaS company offers a platform designed to help large enterprises manage complex legal structures, automate compliance filings, and centralize governance across jurisdictions. Their customers operate in regulated industries and expect strong security practices by default. By the time we were brought in, the company’s technical debt was beginning to impact reliability and compliance. Key issues included: - **Publicly exposed cloud resources** that didn’t align with compliance requirements - **A network lacking high availability** or proper segmentation - **Cloud storage misconfigurations**, with data accessible from public endpoints and no secure file transfer mechanisms in place - **A self-managed database** that was outdated, lacked backups, and wasn’t properly secured The goal wasn’t just to patch things up. The task was to deliver a security-first platform that would stand up to audits, scale with the business, and support a fast-moving engineering team without adding friction. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## **Our Approach** We treated this as a full security rebuild – with compliance built in from the start. ### **Step 1: Network Re-Architecture** We began by designing a new AWS network environment focused on isolation and control: - All public-facing services were removed or routed internally using VPC endpoints - **Intrusion detection and prevention systems (IDS/IPS)** were implemented to monitor traffic patterns and identify potential threats early - VPN access was rebuilt around **role-based controls**, ensuring only the right individuals had access based on their responsibilities ### **Step 2: Locking Down Storage and File Transfers** Cloud storage was reconfigured to enforce privacy and encryption: - All buckets were set to **private access only**, with strict IAM policies and encryption enforced at rest and in transit - We introduced a **custom SFTP solution** restricted by IP allowlists and backed by dedicated IAM roles, giving the client full control over who could move data and how ### **Step 3: Database Hardening** The self-managed database was replaced with **MongoDB Atlas**, giving the client: - Built-in **high availability and backups** - **Automated patching and maintenance** A **secure VPC peering connection** that ensured data never traveled across the public internet ## **Security That Scales** One of the most valuable outcomes was the implementation of a **layered security model**, designed to reduce reliance on any single control. This included: - **AWS WAF**, filtering malicious traffic before it reached the application - **AWS Network [Firewall](https://dedicatted.com/services/glossary-of-terms#term-firewall)**, enforcing fine-grained rules inside the VPC - **AWS GuardDuty**, providing continuous threat detection and monitoring - **VPN access with strict RBAC policies**, defining clear access boundaries across teams and environments Together, these controls created a secure-by-default platform with visibility, accountability, and enforcement built into every layer. ## **Results** With these changes in place, the client’s platform is now compliant, resilient, and ready for continued growth – with confidence. - All public resources were secured or eliminated - A **multi-layered security architecture** is now actively protecting the environment - Storage is encrypted and tightly access-controlled, with secure file transfer protocols in place - The database is now **fully managed, backed up, and highly available** - VPN access is governed by role and environment, providing security without operational slowdowns Everything is structured, documented, and auditable. Most importantly, the engineering team didn’t need to become compliance experts – we took that off their plate, so they could stay focused on product and delivery. #### If you find this case interesting, we recommend taking a closer look at [ Managed Services ](https://dedicatted.com/what-we-do/devops/devops-as-a-service) [ DevOps Services ](https://dedicatted.com/what-we-do/consulting/devops-consulting) ## **Let’s Build Yours** If your platform is growing and you’re feeling the pressure to get security and compliance under control – we can help. Whether you need a complete rebuild or a clear path to audit-readiness, we’ll meet you where you are and help you move forward with confidence. Let’s build a cloud environment that your team can rely on, your customers can trust, and your business can scale with. **Categories:** Case study **Services:** Cloud and Infrastructure Security, Security Compliance **Industry:** High Tech Software --- ### [Enabling Security Compliance and DevOps Agility in a Regulated Healthcare Environment](https://dedicatted.com/insights/enabling-security-compliance-and-devops-agility-in-a-regulated-healthcare-environment) **Published:** April 9, 2025 **Author:** Dedicatted **Content:** #### Working time: February 2024 – ongoing #### Industry: Digital Healthcare #### The service: Security [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance), DevOps Consulting #### Overview Our client is a fast-growing digital healthcare provider operating across the United States, offering remote patient engagement and mental health services. With a focus on personalized care coordination, the platform supports thousands of active users, including clinicians, therapists, and healthcare professionals. Operating in a highly regulated industry, the client places significant emphasis on data privacy, infrastructure reliability, and continuous compliance with healthcare-specific security standards. ### Want to get your copy of case study? Download it here. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## The Challenge As part of a strategic modernization effort, the client initiated a cloud migration from AWS to Microsoft Azure. This shift introduced critical requirements that aligned with two of their core priorities: **Security Compliance and DevOps Enhancment.** ****[Cloud Migration](https://dedicatted.com/services/glossary-of-terms#term-cloud-migration) Without Compromise**** Migrating core infrastructure and services to Azure was a foundational part of the client’s modernization plan. It had to be done with no interruption to care delivery, no data loss, and without impacting end users – many of whom rely on the platform daily for vital health support. **Maintaining High-Trust Compliance Post-Migration** As a healthcare organization, the client must comply with strict regulatory frameworks such as HIPAA and other trust certifications. The move to Azure needed to preserve – and ideally strengthen – their existing security posture without introducing risk to sensitive health data. ******Enabling Scalable and Secure DevOps Practices****** The client’s engineering team required support in building a DevOps foundation that could handle a high velocity of feature development. This meant enabling secure, dynamic environments that would automatically spin up and tear down in response to GitHub development branch activity – ensuring that compliance and security remained intact throughout the CI/CD lifecycle. [![Comparison of compliance-oriented vs. scalable HIPAA-compliant infrastructure highlighting Azure-powered security and virtual patient engagement](https://dedicatted.com/wp-content/uploads/2025/04/scalable-agile-and-fully-compliant.png)](https://dedicatted.com/wp-content/uploads/2025/04/scalable-agile-and-fully-compliant.png)## Our Approach To meet the dual objectives of security and development agility, we delivered a comprehensive solution that addressed both infrastructure and process design: - **Zero-[Downtime](https://dedicatted.com/services/glossary-of-terms#term-downtime) Cloud Migration**: The platform was migrated from AWS to Azure with no service disruption or degradation in user experience. - **Security-First Architecture**: We ensured full encryption in transit and at rest, deployed compute resources within private subnets, and implemented fine-grained network controls using firewalls and security groups. - **Ownership of Technical Controls**: We assumed full responsibility for implementing and managing all technical controls required under HIPAA. This included every technical preparation needed to meet compliance – from data protection and access control to network design and operational monitoring. - **Dynamic [Environment](https://dedicatted.com/services/glossary-of-terms#term-environment) Management**: We integrated GitHub workflows with Azure infrastructure to enable on-demand creation of feature environments. Each development branch triggered the provisioning of a dedicated, fully isolated environment, which was automatically destroyed upon branch closure or merge. [![DevOps architecture diagram showing Git repositories triggering CI pipelines, Terraform Enterprise for infrastructure provisioning, and an Azure-based production environment with VPN, Azure DB, Kubernetes services, monitoring tools, and a Front Door and WAF for web app traffic control.](https://dedicatted.com/wp-content/uploads/2025/04/Zivian-Diagram.png)](https://dedicatted.com/wp-content/uploads/2025/04/Zivian-Diagram.png)## GitOps-Driven Ephemeral Environments One of the standout features of this project was the implementation of **ephemeral environments powered by GitHub and [Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform)**. For every active development branch, a secure and fully functional environment was spun up automatically – complete with networking, compute, and storage. Once the feature branch was merged or closed, the environment was automatically destroyed. This approach enabled safe experimentation, rapid iteration, and cost control, all while maintaining the high level of security required by healthcare regulations. This [GitOps](https://dedicatted.com/services/glossary-of-terms#term-gitops)-based mechanism allowed the client to streamline their development process while keeping infrastructure clean and compliant – an important achievement in a security-sensitive industry. ![AWS ephemeral environments setup enabling efficient DevOps, automated CI/CD, and secure cloud-based app development](https://dedicatted.com/wp-content/uploads/2025/04/ephermal-environments-in-aws-1024x576.png) ## The Results The outcomes of this engagement were measurable and impactful: - The client successfully transitioned to Azure with no user disruption, preserving service continuity for thousands of active users. - Security controls and infrastructure policies were aligned with high-trust compliance requirements, meeting internal audit criteria and regulatory expectations. - We delivered full coverage of technical HIPAA controls, giving the client peace of mind and a clear path through future audits and certifications. - Development operations became significantly more agile, with automated, short-lived environments that empowered teams to test and release features faster, while still maintaining a secure perimeter. - With infrastructure fully codified, the client gained improved visibility, consistency, and control – reducing manual overhead and minimizing the potential for configuration drift. This project demonstrates how careful planning, secure architecture, and automation can work together to support business growth while staying compliant in a highly regulated environment. **Categories:** Case study **Services:** Cloud and Infrastructure Security, Cloud Migration, DevOps Consulting, Security Compliance **Industry:** Healthcare --- ### [Retail Data Modernization: Elevating In‑Store & Digital Operations with AWS](https://dedicatted.com/insights/retail-data-modernization-unlocking-agility-through-scalable-cloud-architecture) **Published:** June 4, 2025 **Author:** Dedicatted **Content:** #### Working time: 2025 – ongoing #### Industry: Retail #### The service: Data Architecture & Analytics #### Overview The client is a premium regional grocery chain known for its curated selection of high-quality food and beverage products. With a growing presence in suburban and urban neighborhoods, the brand is built on personalized service, specialty assortments, and a strong commitment to local sourcing. To stay competitive in a fast-evolving retail environment, the company needed to modernize how it managed and used data. Challenges like inconsistent reporting, limited real-time visibility, and siloed operations made it difficult to scale insights and support data-driven decisions across merchandising, store operations, and marketing. We partnered with the client to build a cloud-native, fully automated data platform on AWS – designed to improve inventory accuracy, unlock customer insights, and establish a foundation for future AI-driven innovation. ## Challenge #### Siloed Operational Data Sales, inventory, and supplier data were dispersed across different systems. This fragmentation made it difficult to track product performance across store locations or measure the success of new promotions and product launches. #### Inflexible Reporting Processes Most reporting relied on end-of-day batch data, with limited ability to view store performance or inventory trends in real time. Teams were slow to act on supply issues or customer demand signals. #### Limited Scalability and Visibility The company’s data infrastructure was not designed to support expansion. As new locations opened and operations became more complex, scaling the existing reporting tools became costly and inefficient. #### Rising Operational Costs Manual ETL processes, underutilized compute resources, and on-prem storage systems contributed to rising data platform costs – without delivering proportional business value. ## If you find this case interesting, we recommend taking a closer look at [ Cloud Data Migration & Modernization ](https://dedicatted.com/services/data/cloud-data-migration) [ Data Architecture ](https://dedicatted.com/services/data/architecture) ## Solution Our team designed and implemented a flexible AWS-based data architecture tailored to the client’s retail operations and growth objectives. The core solution included: #### Centralized Data Lake on Amazon S3 All point-of-sale, inventory, pricing, and supplier data was consolidated into a structured Amazon S3-based data lake. Using AWS Glue, we created standardized data catalogs and automated ETL pipelines to streamline ingestion and transformation. #### Real-Time Data Pipelines with Amazon Kinesis We implemented real-time data pipelines using Amazon Kinesis to capture live transaction and stock-level events from all store locations. This enabled dynamic dashboards that reflect up-to-the-minute store activity, including sales spikes and low-stock alerts. #### Scalable Analytics with Amazon Redshift We deployed an analytical warehouse on Amazon Redshift, giving business users and analysts self-serve access to curated datasets. Redshift supports customer segmentation, campaign performance reporting, and cross-store trend analysis – all from a single source of truth. #### Secure Access with AWS Lake Formation To ensure data governance and security, we used AWS Lake Formation to define role-based access policies and enforce compliance across sensitive business and customer data. This allowed for secure collaboration between operations, finance, and marketing teams. #### Cost Optimization and Lifecycle Management With intelligent S3 lifecycle rules, we reduced long-term storage costs by transitioning infrequently accessed data to lower-cost tiers. We also implemented usage tracking and cost tagging to monitor spend across departments and data workloads. [ ![Data Infrastructure 101: Building for Scalability and Security](https://media.dedicatted.com/wp-content/uploads/2025/04/Designing-Platform-for-Instant-Market-Penetration-2025-11-14T123012.084-300x157.png) BlogSerhii Semenchenko 5 min to read Also read ### Data Infrastructure 101: Building for Scalability and Security Automotive Healthcare High Tech Software Insurance Cloud Security Data Architecture ](https://dedicatted.com/insights/data-infrastructure-101-building-for-scalability-and-security)## Outcomes #### Inventory Visibility in Real Time Store managers and inventory teams now have immediate visibility into stock levels across locations, allowing them to react faster to trends and avoid product shortages – particularly in high-demand categories like fresh food and beverages. #### 50% Faster Business Insights Reporting timelines were reduced by half, enabling teams to measure the impact of pricing changes, supplier promotions, and product assortment decisions within hours instead of days. #### 65% Reduction in Data Management Overhead Manual data processing has been replaced by fully automated pipelines, freeing up technical teams and reducing dependency on legacy reporting systems. #### Single Source of Truth Across Business Functions All stakeholders now operate from a unified data foundation – supporting accurate, consistent decision-making across merchandising, supply chain, and executive reporting. #### AI-Ready Infrastructure The client is now equipped to experiment with machine learning and GenAI solutions, including dynamic product recommendations, demand forecasting, and personalized customer experiences – without needing to re-architect their data foundation. **Categories:** Case study **Services:** Cloud Modernization, Data Architecture **Industry:** Retail --- ### [Data lake vs data warehouse: Which one to choose for your business?](https://dedicatted.com/insights/data-lake-vs-data-warehouse-which-one-to-choose-for-your-business) **Published:** July 3, 2025 **Author:** Andriy Kopachevskyy **Content:** [!["Comparison chart illustrating the differences between a Data Lake and a Data Warehouse. The Data Lake side highlights data as raw, large, and undefined, typically unstructured, used by data scientists and analysts for stream processing, machine learning, and real-time analytics. The Data Warehouse side shows data as refined, smaller, and relational, typically structured, used by business analysts for batch processing and BI reporting.](https://dedicatted.com/wp-content/uploads/2025/06/Data-L-vs-Data-W.png)](https://dedicatted.com/wp-content/uploads/2025/06/Data-L-vs-Data-W.png)In today’s digital-first world, data is the new oil – the lifeblood powering Industry 4.0 and driving business intelligence. But like any valuable asset, data needs to be stored, protected, and refined to unlock its true potential. That’s where data lakes and data warehouses come into play. Though they might sound similar – after all, both are massive data repositories – their purposes and structures are fundamentally different. A data lake is like a vast reservoir, holding raw, unprocessed data in its native format until it’s ready for use. In contrast, a data warehouse is more like a well-organized storage facility, structured and optimized for quick analysis. Both have their place in modern data strategies, and understanding how they work – both individually and together – can help you make informed decisions to enhance your data platform and strengthen your data governance framework. This guide will walk you through the key differences, benefits, and industry-specific use cases of data lakes and data warehouses, empowering you to choose the right solution for your business needs. Ready to navigate the world of data storage? Let’s dive in! [![Image titled "Executive foresight starts with informed infrastructure" with an image of the man using laptop to watch stiock market](https://dedicatted.com/wp-content/uploads/2025/06/Group-141-10-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/06/Group-141-10-scaled.png)## ******What is a Data Lake? Definition and Purpo******se In the expansive world of big data, a data lake serves as a centralized, scalable repository designed to store all types of data in its native format – as it arrives from a variety of sources. Unlike traditional databases that rely on rigid structures and predefined schemas, data lakes offer the flexibility to ingest structured, semi-structured, and unstructured data without the need for immediate transformation. Think of it as an open ecosystem of data – versatile, adaptable, and rich with opportunity. Data lakes can seamlessly accommodate everything from social media streams and IoT sensor data to high-resolution videos and PDF documents. Just as a natural lake gathers water from diverse sources, a data lake consolidates information from numerous channels, preserving its original format for future exploration. This flexibility makes it an invaluable foundation for data scientists and analysts who aim to drive innovation through advanced analytics and large-scale machine learning models. Wondering which data solution is right for your business, do not hesitate to **[contact our experts](https://dedicatted.com/contact)!** [![Diagram showing Data Lake features: low-cost storage options, real-time updates, accepts data from all sources](https://dedicatted.com/wp-content/uploads/2025/06/AI-2025-06-19T163402.425.png)](https://dedicatted.com/wp-content/uploads/2025/06/AI-2025-06-19T163402.425.png)When considering a data lake for your business, understanding its core attributes is crucial: - **[Scalability](https://dedicatted.com/services/glossary-of-terms#term-scalability):** Data lakes are designed to scale horizontally, accommodating massive datasets at a fraction of the cost of traditional storage. For businesses dealing with ever-expanding data – like IoT logs, financial transactions, or customer interactions – this is a practical, future-proof solution. - **Agility:** Data lakes support rapid experimentation, making them perfect for data scientists and analytics teams exploring machine learning models, deep learning, and predictive analytics. This agility can accelerate innovation cycles and speed up time-to-market for new services. - **Cost Efficiency:** Built on low-cost storage solutions, data lakes offer an economical choice for archiving large datasets. For businesses aiming to optimize costs while maintaining robust data capabilities, this architecture is highly attractive. If your business is in industries like **healthcare**, **finance**, **manufacturing**, or **retail** – where unstructured data is abundant and real-time analysis is critical – a data lake can significantly enhance your ability to drive insights and maintain a competitive edge. [![Data lake architecture diagram with sources feeding into ingestion pipelines, centralized storage zones, processing and analytics, governance, and end-user consumption through BI, dashboards, and machine learning.](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-9-1.jpg)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-9-1.jpg)## ******Data Warehouse: Parameters or options****** If a data lake is the *Wild West* of data storage, then a **data warehouse** is its sophisticated counterpart – a neatly organized library where information is carefully structured, cataloged, and ready for quick retrieval. In a data warehouse, data from various sources is collected, processed, and stored in predefined schemas, making it highly accessible for analysis and decision-making. The purpose of a data warehouse is clear: to transform raw data into valuable business intelligence. Unlike traditional databases that handle real-time transactions, data warehouses are designed specifically for large-scale analysis. This enables business leaders and analysts to extract insights, identify trends, and make data-driven decisions with confidence. For companies focused on **operational efficiency, forecasting, and strategic planning**, data warehouses provide a reliable foundation for deep business insights. [![Diagram showing Data Warehouse features such as integrated, non-volatile, scalable](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-10.jpg)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-10.jpg)Understanding the core features of data warehouses is essential when considering the right solution for your business: - **Centralized Repository**: Data warehouses unify data from multiple systems, eliminating silos and offering a 360-degree view of business operations – essential for consistent reporting and analytics. - **Structured Format**: With strict schemas and table organization, data warehouses function like a well-organized library – making queries faster and ensuring consistency for BI, audits, and compliance. - **RDBMS Backbone**: Powered by relational databases, they deliver data accuracy, consistency, and integrity – crucial for generating trusted insights. [![Real-word Enterprise Data Warehouse setup: from data types (CRM, ERP, Legacy) to enterprise Data Warehouse (ETL, Operational and data marts)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-11.jpg)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-11.jpg)## ****Data lake or data warehouse: Checklist to choosing the right solution for your business**** [!["Checklist-style infographic comparing data lakes and data warehouses based on business needs. It asks four questions: 1) Is data retention an issue? - Data lakes are more cost-effective for long-term storage. 2) Are your business needs predictable? - Warehouses suit fixed reports; lakes support dynamic use cases like ML, IoT, or predictive analytics; many use both. 3) Do you have a set-up structure? - Warehouses integrate with SQL-based systems, while lakes offer flexibility for fresh setups. 4) How unified is your data? - Warehouses work for structured data, while lakes handle varied sources such as logs, media, or telemetry.](https://dedicatted.com/wp-content/uploads/2025/07/AI-2025-07-02T143826.669.png)](https://dedicatted.com/wp-content/uploads/2025/07/AI-2025-07-02T143826.669.png)Imagine turning chaotic, sprawling datasets into a high-speed engine for insights. That’s exactly what we did for a top AI analytics company in Southern Europe – combining DeltaLake, dynamic Spark clusters, and AWS OpenSearch to slash storage costs by 80%, cut compute spend in half, and triple data retrieval speed. The result: Teams focused on strategy, not manual data work. See how we did it: **[Optimized Data Architecture: Transforming Data Costs into Growth](https://dedicatted.com/insights/optimized-data-architecture-transforming-data-costs-into-growth).** What’s exciting is that this transformation isn’t just about cost savings or faster queries – it’s about unlocking new opportunities. With a modern, flexible data architecture, companies can experiment with advanced analytics, build predictive models, and respond to market trends in real time. Imagine spotting shifts in consumer behavior the moment they happen, or launching new products with data-driven confidence. The possibilities expand as your data becomes not just a resource to store, but a strategic asset that fuels innovation and growth across your entire business. **Categories:** Blog **Services:** Data Architecture, IoT Platform **Industry:** Financial Services, High Tech Software, Insurance, Retail --- ### [LLMOps for Enterprises: What It Is and How to Scale GenAI in Production](https://dedicatted.com/insights/llmops-for-enterprises-what-it-is-and-how-to-scale-genai-in-production) **Published:** July 17, 2025 **Author:** Denys Yermakov **Content:** Generative AI may be stealing the spotlight – but behind every production-grade [AI experience](https://dedicatted.com/services/generative-ai) lies an unsung hero: LLMOps.If your organization is struggling to scale LLMs beyond the prototype phase, you’re not alone Struggling to bring LLMs into production? Dedicatted helps data-driven companies build, operate, and scale GenAI systems that perform – securely and efficiently. Book a free [LLMOps discovery session](#contact-form) In fact, **over 80% of AI projects never make it into production** – a staggering statistic that reveals a harsh truth: deploying LLMs is easy; operating them effectively is not. That’s where Large Language Model Operations, or LLMOps, comes in. It’s the secret sauce that transforms experimental models into reliable business tools. In this article, we’ll break down what LLMOps actually is, how it differs from MLOps, and why it’s quickly becoming a non-negotiable for enterprises aiming to compete in the AI economy. Ready to stop experimenting and start scaling? Let’s crack LLMOps together. [![Graphic titled "Explore tech innovation with Generative AI" that is positioned on the gradient background](https://dedicatted.com/wp-content/uploads/2025/07/Perebivka-with-icon-1-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/07/Perebivka-with-icon-1-scaled.png)## **Large Language Model Operations: The Key to Managing Large Language** Deploying large language models (LLMs) goes far beyond building flashy prototypes. While LLMs offer rapid experimentation and plug-and-play functionality that can generate impressive results in minutes, turning that prototype into a production-ready, scalable solution is a whole different challenge. From sourcing clean, domain-specific data to crafting effective prompts, fine-tuning models, and ensuring real-time responsiveness – every step introduces complexity. Most importantly, LLMs are not static systems; they require continuous updates and refinements as user behavior shifts and data evolves. That’s where LLMOps becomes indispensable. You’re not just deploying a model – you’re setting up an evolving AI capability that demands frequent iteration, monitoring, and compliance. Unlike traditional software or even classical machine learning systems, managing LLMs calls for new thinking around deployment pipelines, versioning, and user feedback loops. Below we break down how LLMOps compares with MLOps, showing how each supports different AI operational needs and where LLMOps steps in to handle the demands of modern, generative language applications. [![Comparison table of LLMops and MLops in 5 categories: data handling, model versioning, automated testing, performance tracking, feedback loops](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-17.jpg)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-17.jpg)## ********Mastering the LLM Lifecycle: The Crucial Role of LLMOps******** Large Language Models (LLMs) bring massive potential – but managing them in production is no small feat. As these models evolve, ensuring they remain reliable, scalable, and aligned with business goals becomes increasingly complex. Think of it as the engine room of your generative AI efforts, quietly powering everything behind the scenes to make sure your model doesn’t just run – it thrives. 1\. **[Deployment](https://dedicatted.com/services/glossary-of-terms#term-deployment) Done Right.** LLM versions evolve rapidly – with changing APIs, configurations, and behaviors. A careless rollout can break downstream applications or introduce bias. At [Dedicatte](https://dedicatted.com/)[d](https://dedicatted.com/), our LLMOps engineers design battle-tested deployment pipelines with automated versioning, rollback control, and compliance auditing – ensuring every model update is safe, fast, and traceable. This is how we help teams release weekly without fear of breaking production. [ ![Automating remittance workflows and reducing costs with GenAI and AWS](https://media.dedicatted.com/wp-content/uploads/2025/03/Hercules.-Cover-300x157.webp) Case studyDedicatted Petlichenko 5 min to read Also read ### Automating remittance workflows and reducing costs with GenAI and AWS Manufacturing Cloud Modernization Cloud Security Generative AI ](https://dedicatted.com/insights/automating-remittance-workflows-and-reducing-costs-with-genai-and-aws)2\. **Real-Time [Monitoring](https://dedicatted.com/services/glossary-of-terms#term-monitoring) at Scale.** Once the model is live, the real work begins. LLMs can react unpredictably to shifts in input data or user behavior. We are leaning towards integrating **real-time monitoring tools to catch anomalies early** – tracking performance, surfacing drift, and alerting teams when things veer off course. From dashboards to A/B testing and anomaly detection, monitoring isn’t an afterthought – it’s a continuous discipline. 3\. **Continuous Maintenance & Retraining** Over time, even top-performing models degrade. Data drifts, user needs change, and underlying infrastructure evolves. Our recommendation: try maintaining a **transparent record of datasets**, code, hyperparameters, and performance metrics, this gives opportunity for teams to retrain and refine models with confidence – preserving quality while adapting to change. 4\. **Building the Foundations for GenAI Success** Implementing LLMOps isn’t plug-and-play. It starts with assessing your organization’s readiness – do you have the infrastructure, data governance, and team skills in place? From here, [Dedicatted](#contact-form "Dedicatted") takes the lead – covering everything from data collection and preprocessing, to inference optimization, prompt engineering, and version management. [![Graphic titled "Simplify, scale with Dedicatted on AWS " with the graphic og the Dedicatted team at the right corner](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-18.png)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-18.png)5\. **Governance, [Automation](https://dedicatted.com/services/glossary-of-terms#term-automation), and Adaptability** LLMOps is more than operations – it’s about establishing governance, automation, and adaptability in the age of intelligent systems. That includes: - Scalable infrastructure with auto-scaling and low-latency support - Prompt and model registries with rollback capabilities - Evaluation pipelines with fairness, bias detection, and expert review - Strategies like RAG (retrieval-augmented generation) or agent-based approaches for cutting-edge performance For insurers like Xodus, GenAI automation had to meet strict compliance standards. We delivered a claims system with versioned prompts, bias checks, and full traceability – LLMOps built for real-world accountability: [ Read the full story](https://dedicatted.com/insights/genai-powered-claims-processing?utm_source=chatgpt.com) [!["Diagram of the LLM (Large Language Model) lifecycle showing three main phases: Development, Deployment, and Monitoring. Each phase is represented in a circular flow. Development includes data management and preparation (data processing, collection, augmentation), evaluation and validation (expert review, performance metrics, bias and fairness checks), and model selection, approach selection, and prompt engineering. Deployment covers latency and throughput optimization (real-time inferencing, batch processing), model serving and scaling (inference optimization, API integration, infrastructure scaling), and model and prompt versioning and rollback (registry, releases, rollback mechanisms). Monitoring focuses on security and reliability monitoring (anomaly detection, failover mechanisms), ethical and compliance monitoring (bias detection, compliance audits), and performance monitoring (response accuracy, user feedback integration, model and prompt drift detection).](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-19.jpg)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-19.jpg)## **Production-Ready LLMs? We Make It Happen on AWS** When it comes to building scalable, secure, and high-performing LLMOps infrastructure, AWS sets the gold standard. With over 70% of GenAI workloads running on AWS, it’s the go-to ecosystem for enterprises. But AWS gives you the tools – not the blueprint. That’s where Dedicatted comes in. We applied LLMOps principles to help Xodus, a travel insurance provider, streamline claims processing. By integrating GenAI into their adjudication workflow on AWS, we cut manual document handling by 70%, dramatically reducing labor and accelerating throughput. #### Want to explore what LLMOps on AWS could look like for your business? [ Schedule your strategy call ](https://dedicatted.com/contact) [ VISIT OUR AWS PAGE ](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/) **Built for AWS, Tailored to You** We work natively within the AWS ecosystem – using SageMaker, Lambda, Step Functions, EKS, and more – to create automated pipelines that fit your unique setup. No bloated solutions, no extra complexity. Just scalable, secure, and clean architecture that supports your team’s speed and flexibility. **Full Visibility, Right from the Start** LLMs evolve fast – and so should your observability. We bake in real-time monitoring from day one using AWS-native tools like CloudWatch and SageMaker Model Monitor, and extend it with WhyLabs or Arize AI when needed. You’ll always know how your models are performing, where drift is creeping in, or when retraining is needed – before it affects results. **Faster Updates, Less Risk** When you’re ready to ship updates, we make sure you can do it without second-guessing. With CI/CD pipelines designed specifically for model workflows, canary deployments, rollback paths, and version tracking, you can experiment and improve continuously – without putting production at risk. **Smart Management of Models and Prompts** We help you stay in control of every iteration. From prompt tuning to full model versioning, we set up registries and testing workflows that let you compare, track, and revert easily. With SageMaker endpoints and real-time A/B testing, it’s easy to scale what works – and drop what doesn’t. **Speed That Doesn’t Break the Bank** LLMs can be resource-heavy – but they don’t have to be expensive. We tune performance using techniques like model compression, optimized inference with AWS Inferentia, and intelligent scaling strategies that align with your actual usage – not your worst-case scenario. [![Graphic titled "Bring LLMOps from chaos to clarity"](https://dedicatted.com/wp-content/uploads/2025/07/core-service-card-1-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/07/core-service-card-1-scaled.png) **Categories:** Blog **Services:** Generative AI, Managed Services **Industry:** Financial Services, High Tech Software, Insurance, Manufacturing --- ### [The privacy dilemma in AI: Identifying risks and building trust](https://dedicatted.com/insights/the-privacy-dilemma-in-ai-identifying-risks-and-building-trust) **Published:** July 24, 2025 **Author:** Denys Yermakov **Content:** Artificial intelligence is often hailed as a superpower, driving unprecedented innovation across nearly every industry – and for good reason. Yet, with this extraordinary progress comes a pressing question: is AI encroaching on our privacy? AI has captivated the global imagination, promising to transform business, reshape society, and unlock new levels of efficiency. But behind the enthusiasm lies growing unease. Concerns around job displacement, consumer manipulation, and malicious applications have begun to surface. The speed of AI advancement has outpaced our collective understanding, highlighting the urgent need for clear governance and responsible implementation. Lately, privacy concerns have dominated headlines, reinforcing one undeniable truth: safeguarding consumer data must be a top priority in every AI strategy. Curious how your AI strategy measures up when it comes to privacy? Let’s talk. **[Book a free consultation with our team](#contact-form)** – and partner with us to build compliant, future-proof AI solutions that inspire trust and drive innovation. [![Dedicatted is a first and inly generative AI and MSP partner in Canada](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-23-1024x121.png)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-23-1024x121.png)## ******A pulse check on AI and privacy in 2025****** The heady growth of generative AI tools has revived concerns about the security of AI technology. The data chills it triggers have been long plaguing AI adopters – except they’re now exacerbated by unique gen AI capabilities. Inaccuracy, cybersecurity problems, intellectual property infringement, and lack of explainability are some of the most common generative AI privacy concerns that refrain 50% of organizations from scaling gen AI responsibly. [![bar and line chart showing generative AI risks that organizations consider relevant in 2024 vs 2025. Top concerns in 2025: inaccuracy (63%), intellectual property infringement (53%), and cybersecurity (52%). Other risks include regulatory compliance (45%), personal privacy (43%), explainability (40%), equity and fairness (30%), workforce displacement (27%), organizational reputation (24%), national security (13%), environmental impact (13%), political stability (12%), and physical safety (8%). 'None of the above' remains at 1%.](https://dedicatted.com/wp-content/uploads/2025/07/AI-2025-07-18T204748.641.png)](https://dedicatted.com/wp-content/uploads/2025/07/AI-2025-07-18T204748.641.png)The worldwide community is echoing a security-focused approach of AI leading players, with a sweeping set of new regulations and acts advocating for more responsible AI development. These global efforts are initiated by actors ranging from the European Commission to the Organization for Economic Co-operation and Development to consortia like the Global Partnership on AI. ## ************The dark side of AI: how can it jeopardize your organization’s data security?************ No matter what type of AI solutions you are integrating into your business, prebuilt AI applications or self-built ones, the adoption of AI systems demands a heightened level of vigilance. When left unattended, AI-related privacy risks can metastasize, potentially causing a range of dire consequences, including regulatory fines, algorithmic bias, and other pitfalls. **Unclear control over data usage and access** Once your data flows into a generative AI system, it becomes challenging to track how it’s being handled and who can access it. Vague data ownership and undefined permissions – especially within third-party systems – make it difficult to enforce privacy. Add in the “black box” nature of many models, and you’re relying on external security practices that may not meet your company’s standards. **Your data, their training fuel** By signing up for a vendor-powered AI solution, organizations often unknowingly allow their data to be reused for training broader foundational models. Instead of being confined to your use case, your inputs can be absorbed into the vendor’s system – raising red flags around privacy and intellectual property. This practice can also backfire, introducing outside biases into your model’s behavior. **Re-identification of anonymized data** Even when personal data is scrubbed of identifiers, it doesn’t guarantee safety. Advanced AI systems can reconstruct user identities through behavioral patterns, effectively reversing anonymization. Worse yet, some models struggle with anonymizing data accurately, leading to potential breaches and compliance failures. **Weak links in the AI supply chain** AI ecosystems are made up of various layers – from the hardware and data sources to the model itself. Each component must be secured. Without holistic protection, one compromised element can corrupt the entire system – resulting in skewed outputs, biased decisions, or poisoned data sets. While companies grapple with AI privacy challenges, real-world solutions show what’s possible. **McFarland Johnson** faced an overwhelming volume of documents across multiple divisions, slowing decision-making and risking inconsistencies in client deliverables. With Amazon Q, Dedicatted automated routine reports, centralized historical knowledge, and implemented role-based access controls, all while keeping sensitive data secure. Employees now save hours each week, workflows are more accurate, and AI-generated insights come with traceable links to source documents for full transparency. Explore the full AI transformation in [**our case study here**](https://dedicatted.com/insights/from-manual-to-automated-mcfarland-johnsons-ai-powered-workflow-revolution-with-amazon-q-2) [![Join our expert-led workshop to identify where gen ai services fit into your operations](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-24-1024x147.jpg)](https://dedicatted.com/wp-content/uploads/2025/07/unnamed-24-1024x147.jpg)## **6 practices to wipe out AI data privacy concerns** While some companies grapple with AI risk management, 68% of high performers address gen-AI-related concerns head-on by locking risk management best practices into their AI strategies. [![Bar chart showing organizations engaging in risk-related best practices for generative AI. Comparison between all other respondents and high-performing organizations. Results: 68% of high performers vs 34% of others say Gen AI risk awareness and mitigation are required skills for technical talent. 44% of high performers vs 23% of others have clear processes to embed risk mitigation in AI solutions. 43% of high performers vs 18% of others design AI models to allow audits, bias checks, and risk assessment. 24% of high performers vs 18% of others have an enterprise-wide council or board for responsible AI governance.](https://dedicatted.com/wp-content/uploads/2025/07/1727703786590.jpg)](https://dedicatted.com/wp-content/uploads/2025/07/1727703786590.jpg)Standards and regulations provide a strong ground zero for data privacy in smart systems, but putting foundational principles in action also requires practical strategies. Below, our AI team has curated six battle-tested practices to effectively manage AI and privacy concerns. **1. Establish AI vulnerability management strategy** Just like any tech solution, an AI tool can have technology-specific vulnerabilities that spawn biases, trigger security branches, and reveal sensitive data to the prying eyes. To prevent this havoc, you need a cyclical, comprehensive vulnerability management process in place that focuses on the three core components of any AI system, including its inputs, model, and outputs. - *Input vulnerability management* – by validating the input and implementing granular data access controls, you can minimize the risk of the input vulnerability. - *Model vulnerability management* – threat modeling will help you harden your model by mitigating known documented threats. If you have commercial generative AI models in your infrastructure, make sure to perform close inspection of data sources, terms of use, and third-party libraries to prevent bias and vulnerabilities from permeating your systems. - *Output vulnerability management* – strip the output of sensitive data or hidden code to make sure nobody can infer sensitive information and to mitigate cross-site vulnerabilities. **2. Take a hard stance on AI security governance** Along with vulnerability management, you need a secure foundation for your AI workloads, rooted in the wraparound security governance practices. Thus, your security policies, standards, and roles shouldn’t be confined to proprietary models but also extend to commercial and open-source models. Water-tight security starts with a strong AI environment, amplified with encryption, multi-factor authentication, and alignment to best industry frameworks such as NIST AI RMF. **3. Build in a threat detection program** To defend your AI set-up against cyber attacks, you should apply a three-sided threat detection and mitigation strategy that addresses potential data threats, model weaknesses, and involuntary data leaks in the model’s outputs. Such practices as data sanitization, threat modeling, and automated security testing will help your AI team to pinpoint and neutralize potential security threats or unexpected behaviors in AI workloads. **4. Secure the infrastructure behind AI** Manual security practices might do the trick for small environments, but complex and ever-evolving AI workloads demand an MLOps approach. The latter provides a baseline and tools to automate security tasks, usher in best practices, and continuously improve the security posture of AI workloads. Among other things, MLOps helps companies integrate a holistic API security management framework that solidifies authentication and authorization practices, input validation, and monitoring. You can also design MLOps workflows to encrypt data transfers between different parts of the AI system across networks and servers. Using CI/CD pipelines, you can securely transfer your data between development, testing, and production environments. [![A picture with the text "Order an audit from Dedicatted" and the image of the scared cybersecurity specialist warned about the error](https://dedicatted.com/wp-content/uploads/2025/07/Group-141-1-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/07/Group-141-1-scaled.png)**5. Keep your AI data safe and secure** Data that powers your machine learning models and algorithms is susceptible to a broader range of attacks and security breaches. That’s why end-to-end data protection is a critical priority that should be implemented throughout the entire AI development process – from initial data collection to model training and deployment. Here are some of the data safeguarding techniques you can leverage for your AI projects: - Data tokenization – protect sensitive data by replacing it with non-sensitive data tokens as surrogates for the actual information. - Holistic data security – make sure you secure all data used for AI development, including at-rest, in-transit and in-use data. - Loss prevention – apply data loss prevention (DLP) techniques to prevent sensitive or confidential data from being lost, stolen, or leaked outside the perimeter. - Security level assessment – continuously monitor the sensitivity of your model’s outputs and take corrective actions if the sensitivity level increases. Extra vigilance won’t hurt when using new input datasets for training or inference. **6. Emphasize security during AI software development lifecycle** Last but not least, your ML consulting and development team should create a safe, controllable engineering environment, complete with secure model storage, data auditability, and limited access to model and data backups. Security scans should be integrated into data and model pipelines throughout the entire process, from data pre-processing to model deployment. Model developers should also run prompt testing locally in their environment and also in the CI/CD pipelines to assess how the model responds to different user inputs and nip potential biases or unintended behavior in the bud. ## **Balancing innovation and privacy** To remain top of the game amidst the growing competition, companies in nearly every industry are venturing into AI development to tap its innovative potential. But with great power comes great responsibility. As they pioneer AI-driven innovation, organizations must also address the evolving risks associated with AI’s rapid development. In [**Dedicatted**](https://aws.amazon.com/marketplace/pp/prodview-5ukdz3n6oviqc) , we understand the importance of balancing innovation, data privacy, and ethical considerations in AI development lies in ensuring sustainable technological progress while safeguarding individual rights and societal norms. [Compliance](https://dedicatted.com/services/glossary-of-terms#term-compliance) fosters transparency and accountability in AI operations, leading to more reliable technology. **[Contact us](#contact-form)** to create more responsible and user-centric AI solutions that are viable in a global market. **Categories:** Blog **Services:** Agentic AI, Generative AI, Managed Services **Industry:** Automotive, High Tech Software, Insurance, Manufacturing --- ### [How enterprise automation works: Detailed roadmap for your business](https://dedicatted.com/insights/how-enterprise-automation-works-detailed-roadmap-for-your-business) **Published:** August 6, 2025 **Author:** Andriy Kopachevskyy **Content:** Imagine cutting operational costs by 30% while boosting productivity across every department, without hiring a single new employee. That’s the power of enterprise automation. Once considered a futuristic luxury, it’s now a critical driver of agility, innovation, and scalability. Global industrial automation investment grew from around $160 billion in 2019 to upward of $205 billion in 2022-24, with forecasts expecting it to surpass $350-400 billion by 2030, forward-thinking companies are already turning it into a competitive advantage. But a path to automation is a thorny one. To pan out, your automation strategy should sit on an enterprise approach and make technology a single fluid experience within your organization. Here’s how to reach this tough bar. In view of the rapid expansion of automation across industries , should we be concerned? Apprehensive? Excited? Enthusiastic? Perhaps all of the above. Book our **[free Discovery Session](#contact-form "free Discovery Session")** – a conversation designed to help you understand the practical impact of emerging capabilities. [![Dedicatted is your trusted partner in enterprise automation](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-25-1.png)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-25-1.png)## **What is enterprise automation ?** Enterprise automation builds on the idea of basic task automation – like using Excel macros or invoice processing tools – and scales it across the entire organization. It goes beyond isolated efficiencies to strategically streamline, integrate, and automate complex, interrelated business processes. By connecting core systems like Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Supply Chain Management (SCM) systems, thereby addressing complex, interrelated processes that span various business units, enterprise automation breaks down silos and enables end-to-end process orchestration. But it’s not just about adding tools. True enterprise automation requires a centralized, scalable approach aligned with broader business goals: transforming automation from a quick fix into a long-term advantage. [![Stacked bar chart showing steps organizations have taken to automate business processes by region: Developing markets, Europe, North America, Asia-Pacific, and global average. Categories include: scaled automation across multiple parts of business, fully automated processes in at least one function, piloted automation in at least one function, have not automated but plan to within next year, and have not automated with no plans. Developing markets show highest pilot automation at 30%. Asia-Pacific has highest non-automation with no plans at 26%. Average across all regions: 16% scaled automation, 13% fully automated, 28% piloted, 18% plan to automate, 20% no plans.](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-26.jpg)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-26.jpg)## **6 Types of enterprise automation , explained** There are several different types of enterprise automation. You can combine different types to develop your own enterprise automation strategy. **1. **Basic enterprise automation**** Basic automation refers to automating basic, repetitive tasks and processes. A basic automation that people are familiar with is out-of-office auto-replies to emails. Monthly sales reports that are emailed on a schedule is another example of basic enterprise automation. Basic automations are the building blocks of a more comprehensive enterprise automation system. **2. ****Rule-based automation****** Rule-based automation is a type of automation where tasks and processes are executed based on predefined rules and conditions. A popular example of rule-based automation is email You set up “rules,” such as the sender’s name and keywords, and the email is automatically filtered into the appropriate folder. **3. ******Robotic process automation (RPA********) Robotic process automation is considered to be one of the approaches to Business Process [Automation](https://dedicatted.com/services/glossary-of-terms#term-automation) that includes the implementation of software bots. Following AI insights, bots complete high-volume, rule-based tasks, emulating human interaction with software applications. Although RPA is technically a form of process automation, its focus is placed on automating the types of tasks traditionally performed by humans. Because of its non-intrusive nature and ease of deployment for specific repetitive tasks, RPA can often serve as a quick and effective automation solution that doesn’t require extensive changes to the existing IT infrastructure. [![Infographic showing the top 5 RPA business case criteria with circular percentage charts. Better quality: 79%, Speed to market: 75%, Faster process speeds: 74%, Reduced need to hire employees in the future: 73%, Greater employee engagement from less repetitive activities: 73%](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-27.jpg)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-27.jpg)**4. **Business process automation (BPA)**** Unlike basic automation, the turf of BPA spans across entire functions. In particular, process automation streamlines, and orchestrates repetitive business processes, for which it requires a multi-step automation workflow. The main differentiator of enterprise process automation is its reliance on integration, API, and data exchange across internal or external systems. Automation of a new employee onboarding process is a fine specimen of BPA. In this case, a company deploys a workflow automation tool to manage the entire process from job offer acceptance, document submission, and verification, to provisioning work equipment and scheduling orientation sessions, with minimal human intervention. **4. ****Integration automation****** Enterprise automation strategies based on the integration-first approach mean connecting different business software systems and using this connectivity to automate manual tasks. In this case, connected business systems operate on shared data, reducing errors and eliminating the need for manual data entry. For example, ERP systems hinge on integration automation to exchange data with finance, sales, and customer success departments. By connecting it with a CRM platform like Salesforce, businesses can streamline order management and execution. Once an order is added to a CRM, it’s automatically transferred to an ERP system and fed to the warehouse. The integration works backward: an ERP then provides sales insight into order statuses right from a CRM dashboard. **5. ******Artificial intelligence (AI) and machine learning (ML) automatio**n****** AI and ML can be used to automate tasks that rely on pattern recognition and predictive analysis. In addition, AI can learn from data and adapt to new situations. For example, AI can use consumer patterns to send personalized recommendations automatically. AI automation can refine its recommendations as it receives more data. ## ******How to win enterprise automation: a detailed roadmap****** Enterprise automation path might differ from company to company depending on their size, IT structure, and automation level. However, the overall strategy remains the same and can be summarized as follows: [![Infographic titled 'Deliver automation across domains' showing a cycle of continuous improvement for business process automation. At the top, operations management seeks efficiency and visibility, leading to process mining and modeling, then to process/business analysts who model processes and communicate requirements. Across the middle, under 'Connected enterprise,' multiple stages capture, classify, and extract data from content. On the right, decision-making drives automation execution. At the bottom, IT authors and developers build apps with low-code automation services, while end users define needs and requirements. The cycle loops back into operational intelligence and optimization, illustrating a closed loop of discovery, decision, action, and optimization](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-28.jpg)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-28.jpg)****Step 1: Identify processes that are worth automating**** Start with a process audit. Look for high-volume, repetitive workflows that are rules-based, prone to human error, or create bottlenecks. These are strong candidates for automation. Focus first on areas with clear KPIs- such as invoice processing, customer onboarding, or order management , where automation can show measurable impact quickly. We use process mining and log analysis to uncover inefficiencies that aren’t visible in standard workflows, helping you find automation potential where others don’t. ****Step 2: Select the right tools for your stack**** Choose technologies that match your business environment. RPA is great for legacy systems; AI/ML works for decision-making; and cloud-native tools are key for modern, scalable systems. Think ecosystem, not isolated tools. Choose platforms that integrate well with your existing ERP, CRM, and data infrastructure. We specialize in building automation on AWS using services like Step Functions for orchestration, Lambda for logic, and EventBridge for event-driven workflows -all secured and cost-optimized. [ ![Automating remittance workflows and reducing costs with GenAI and AWS](https://media.dedicatted.com/wp-content/uploads/2025/03/Hercules.-Cover-300x157.webp) Case studyDedicatted Petlichenko 5 min to read Also read ### Automating remittance workflows and reducing costs with GenAI and AWS Manufacturing Cloud Modernization Cloud Security Generative AI ](https://dedicatted.com/insights/automating-remittance-workflows-and-reducing-costs-with-genai-and-aws)****Step 3: Build a cross-functional automation team**** Automation isn’t just IT’s job. Create a team that includes business analysts, developers, operations leaders, and end-users. This ensures solutions are practical, usable, and aligned with real-world needs. Empower a governance team or automation Center of Excellence (CoE) to define standards, track performance, and drive adoption. Our engineers work alongside your team using Infrastructure as Code ([Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform), AWS CDK) and [GitOps](https://dedicatted.com/services/glossary-of-terms#term-gitops) practices so automations are versioned, tested, and repeatable. ******Step 4: Develop and test automation solutions****** Build small-scale pilots and proof-of-concept projects. These should be low-risk but valuable – perfect opportunities to test your tools and refine your processes before full-scale deployment. Use this phase to build trust internally. Highlight wins, document learnings, and gather feedback from early users. We design modular automations using [Docker](https://dedicatted.com/services/glossary-of-terms#term-docker) or serverless components, so they’re portable across teams and easy to monitor and update ********Step 5: Roll out automation across the enterprise******** Once a pilot is successful, gradually expand its reach to other departments and workflows. Standardize deployment practices to ensure consistency and reduce friction during scale-up. Keep communication strong. Train staff, address change resistance, and emphasize that automation is there to empower, not replace. With reusable code libraries and AWS-native automation blueprints, we help clients reduce delivery time and rollout risks, without sacrificing flexibility. If you’re exploring where AI can make the biggest impact in your organization, let’s talk. [**Book a call** ](https://bit.ly/40KUczR)with our team to discuss your goals and how agentic AI can help you get there faster. ****Step 6: Monitor, optimize, and evolve**** Automation isn’t set-and-forget. Continuously track ROI, process speed, error rates, and employee feedback. Use insights to improve existing automations and uncover new opportunities. Make automation part of your innovation cycle. Schedule regular reviews and tie outcomes to business goals, not just technical performance. We integrate tools like AWS CloudWatch, OpenTelemetry, and Grafana into every solution, so you get real-time insights and alerts across all automation layers. **Categories:** Blog **Services:** Generative AI, Managed Services **Industry:** Automotive, Financial Services, High Tech Software, Manufacturing --- ### [Smarter Science: How Dedicatted Brought Agentic AI to Chemical Reporting at Cassen Laboratories](https://dedicatted.com/insights/smarter-science-how-dedicatted-brought-agentic-ai-to-chemical-reporting-at-cassen-laboratories) **Published:** August 13, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2025 #### Industry: Health Tech #### The service: AgenticAI #### Overview **Cassen Testing Laboratories** is a Canadian, enterprise-level, ISO 17025-accredited chemical and environmental health laboratory based in Toronto, Ontario. Active in the ecological services, inspection, and testing industry, Cassen provides accredited testing for VOC profiling, indoor air quality, environmental forensics, and customizable analytical packages. Its customers include government agencies, healthcare providers, industrial hygiene firms, environmental consultants, and private sector companies based in Canada and around the world. These clients rely on Cassen’s reports to meet strict compliance standards, guide safety and remediation decisions, and protect public health. This project, led by Dedicatted, is among Canada’s first deployments of agentic AI in the environmental testing field. It introduces a multi-agent AI platform, partially powered by Amazon Nova models, into a highly regulated, precision-focused environment. Combining domain expertise with advanced AI, Cassen has not only improved its back-end processes but also opened new opportunities for services in both B2B and B2C markets. ## The Challenge Cassen’s reporting function is core to its business, delivering the scientific results on which clients subsequently base their actions. Each Safety Data Sheet and chemical report must be precise, comprehensive, and fully traceable to proven sources. Before this project, an earlier GenAI pilot already showed promise but also revealed several structural bottlenecks: - **Throughput limitations** – Every analysis run was capped at approximately 50 chemical markers, which constrained the complexity of cases that could be tested simultaneously. - **Manual regulatory testing** – Manual verifications of environmental and safety compliance slowed down shipments. - **Multi-day turnaround times** – Lengthy processing had the potential to delay client decision-making and response efforts. - **Massive per-report cost** – The labor involved made expansion expensive and laborious. - **B2C barrier** – The process wasn’t architected for a consumer-based model, where fast, simple reports are paramount to adoption. Operationally, the lab was at maximum capacity, with growing client demand and increasingly sophisticated test cases putting pressure on its systems. Strategically, not being able to expand into consumer markets meant leaving money on the table. ## If you find this case interesting, we recommend taking a closer look at [ Agentic AI ](https://dedicatted.com/services/artificial-intelligence/agentic-ai) [ Generative AI ](https://dedicatted.com/services/generative-ai) ## Technology & Approach Dedicatted designed and deployed a secure, AWS-hosted agentic AI solution that was appropriate for Cassen’s needs, using Amazon Bedrock with Amazon Nova models for top-level reasoning, summarizing, and accuracy in a compliant environment. Solution components of note: 1. **Multi-Agent [Orchestration](https://dedicatted.com/services/glossary-of-terms#term-orchestration)** – Multiple AI agents perform in sequence to execute pattern detection, retrieval from past reports (through RAG), internet enrichment at the discretion of the user, and auto-assembly into a formatted output. 2. **Compound→Pattern→Source Knowledge Base** – A relational database that manages and stores compounds as relationships to identified patterns, risks, and source references, ensuring outputs remain valid and traceable. 3. **Role-Based, Chat-First Interface** – Chemists interact conversationally with the platform to guide and influence outputs. Administrators oversee the knowledge base, control internet access for compliance, and manage approved source lists. 4. **Domain-Specific Fine-Tuning Loop** – An ongoing improvement process involving chemist-reviewed output, which fine-tunes AI to Cassen’s workflows, vocabulary, and regulatory environment. 5. **Scalable AWS Architecture** – A modular setup that allows for future expansion, such as integration with lab instruments or the addition of specialized analytical models. [![AI-powered chemical reporting architecture on AWS using Bedrock, SageMaker, OpenSearch, and ECS.](https://dedicatted.com/wp-content/uploads/2025/08/cassen-agentic-ai-5-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/08/cassen-agentic-ai-5-scaled.png)\*\* – Report Generation module is built on AWS AgentCore, where the controller agent acts as the AgentCore Supervisor and the other agents are implemented as AgentCore sub-agents with their own tools Impact on workflows: - The system can now analyze over 150 markers per run – three times its previous capacity. - Advanced features, such as VOC signature analysis, provide new forensic insights, like identifying potential fire origins. - The platform produces comprehensive, compliant reports and streamlined, consumer-ready summaries from a single dataset, targeting various market segments without requiring duplicate effort. ## User Experience & Adoption Dedicatted set usability as a design driving principle from day one. Chemists were not accustomed to using AI tools in the first place, so the system had to feel natural from the outset. The chat interface enables users to interact with the platform like a trusted colleague – asking for analysis, requesting specific information, and facilitating real-time editing. Training needs were minimal: - The majority of chemists were self-assured after 1-2 sessions. - Interactive refinement proved to be a significant boost in productivity, enabling chemists to see and modify the AI’s outputs instantly instead of starting from scratch. - Built-in citations removed the need for manual reference checks separately. Adoption has been robust throughout the laboratory. Managers highly commended the role-based controls, which ensured compliance while maintaining workflow efficiency. Additionally, the direct updating of the knowledge base has facilitated the system’s adaptation to evolving research priorities and regulatory standards. ## Outcome Quantitative results: [![Infographic highlighting improvements: Efficiency - 7x faster report generation, Productivity - over 70% less manual effort, Capacity - 3x more markers per run, Accuracy - 70% reduction in error rate.](https://dedicatted.com/wp-content/uploads/2025/08/AI-2025-08-15T163300.767.png)](https://dedicatted.com/wp-content/uploads/2025/08/AI-2025-08-15T163300.767.png)Qualitative results: - Reports are more comprehensive, fully sourced, and consistent, which boosts confidence among regulators and clients. - The consumer-facing summary layer opens a new pathway for market expansion without impacting regulated B2B processes. - The modular, AWS-based architecture enables quick adaptation to changing testing needs and regulatory updates. For Dedicatted’s Cassen Laboratories, this initiative is about more than just an efficiency gain – it’s a foundation for growth. By scaling AI-driven workflows and providing the flexibility to serve both institutional and consumer markets, Cassen is now a Canadian leader in applying cutting-edge AI to environmental testing. **Categories:** Case study **Services:** Agentic AI **Industry:** High Tech Software --- ### [Webinar | Practical AI Solutions Every Business Leader Needs to Know](https://dedicatted.com/insights/webinar-practical-ai-solutions-every-business-leader-needs-to-know) **Published:** September 15, 2025 **Author:** Denys Yermakov **Content:** Artificial Intelligence has gone from being a niche experiment to a boardroom topic in just a few years. In fact, according to the World Economic Forum, **over 75% of executives now say AI is reshaping their industries** – a dramatic shift from only 40% just five years ago. Our own Sarah Walker-Leptick, Head of Partner Sales at Dedicated and former Google and Amazon Manager, introduces AI not as a trend, but as a practical tool for businesses of every size. With more than a decade of experience in AI, both as a profession and a passion, she frames the discussion in practical terms: not hype, not fear – just clarity on how leaders can make sense of the biggest technological wave of our time This business-focused session cuts through the technical jargon and shows how Canadian organizations are already leveraging GenAI to streamline operations, boost productivity, and stay competitive. ## What You’ll Learn **Brief History of AI** Learn about the surprisingly “long” history of AI, its evolution over time to where it is now and understand why this moment represents a unique opportunity for forward-thinking businesses. **See the Numbers That Matter** Review current adoption statistics specific to Canadian businesses and learn what’s driving successful implementations across industries. **Explore Proven Use Cases** Walk through real-world applications that deliver immediate value, from automating document processing to generating comprehensive reports that save hours of manual work. **Build Your GenAI Culture** Learn practical strategies to encourage employee adoption while addressing common concerns and resistance to new technology. **Make Smart Technology Choices** Understand why business-grade AI solutions matter and how to evaluate platforms that protect your data while delivering enterprise-level performance. **Prepare for What’s Next** Get insights into the future of GenAI and emerging Agentic AI technologies, so you can plan strategic investments that won’t become obsolete. ## The History of AI AI might feel like a new phenomenon, but its story goes back centuries: from Ada Lovelace writing the first algorithm in 1843 to Alan Turing cracking codes in World War II, and John McCarthy coining the term “artificial intelligence” in 1956. Even the ancient Greek Antikythera mechanism shows that humans have always dreamed of building “thinking machines.” Today, AI is moving faster than ever, with breakthroughs in generative AI, natural language processing, and computer vision transforming industries and creating real business opportunities. But to truly harness its power, you need to understand where it came from, how it got here, and how to apply it wisely. Watch this video to explore AI’s fascinating journey and see how its lessons can help businesses stay ahead. [https://vimeo.com/1118082316](https://vimeo.com/1118082316 "Play video "Brief history of AI"") ## Where AI is Today AI is now embedded in industries from healthcare to retail, but its growth comes with nuance. While 75% of surveyed executives report measurable ROI from AI pilots, they also name risks like hallucinations, deepfakes, and unsustainable compute demands as top concerns. This section will balance optimism with realism, because knowing both the promise and the pitfalls is the only way to lead responsibly. [https://vimeo.com/1118084930](https://vimeo.com/1118084930 "Play video "Where AI is Today"") ## Adoption Statistics of AI in Business AI is everywhere in conversation, but how deeply is it really embedded in businesses today? While many organizations are experimenting with pilots and isolated use cases, true transformation – where AI is aligned with strategy, operations, and decision-making – is still in its early stages. In fact, 74% of companies say they face challenges scaling AI, and only 16% feel prepared for AI-driven reinvention. This video breaks down where businesses actually stand in their adoption journey, why some industries are pulling ahead, and what steps leaders need to take to move from experimentation to enterprise-wide impact. While global discussions about AI often center on the U.S., Europe, and China, Canada has carved out its own position in the AI landscape. The government has invested over **$4.4 billion** into AI infrastructure, adoption programs, and a newly established AI Safety Institute, signaling a long-term commitment to leadership in this space. Yet, public trust tells a different story. Only **28% of Canadians believe companies will protect their personal data**, while **63% say AI products make them nervous**. This tension – investment at the national level versus hesitation at the individual level – creates both challenges and opportunities. For Canadian businesses, this means walking a fine line: adopting AI boldly while building trust carefully. As Sarah notes, this is not a uniquely Canadian dilemma, but Canada’s proactive funding combined with public skepticism makes it a test case for responsible innovation. [https://vimeo.com/1118087822](https://vimeo.com/1118087822 "Play video "Adoption Statistics of AI in Business"") ## Practical Business Use Cases of AI AI is not just for tech giants, it’s already embedded in the operations of everyday companies. Technology companies are pouring investments into AI chips, servers, and R&D to power innovation across all sectors. In finance, AI is being used for fraud detection, risk management, and customer service through chatbots and personalization. Consumer industries are leveraging AI for computer-simulated R&D, personalized engagement, and smarter business planning. Media, entertainment, and sports are enhancing creativity, audience experiences, and content production with hyper-personalized and immersive solutions. Telecommunications firms are moving beyond predictive AI to expand generative AI for automation, customer support, and network management. **Companies that scale AI beyond pilots achieve cost reductions of up to 30%**, proving these aren’t just nice-to-have tools ,but bottom-line drivers. As Sarah emphasizes, AI is not here to replace people – it’s here to **free human talent for strategy, innovation, and creativity**. Watch the video and get real-world applications show how AI is moving from experimentation to tangible business impact. [https://vimeo.com/1118090351](https://vimeo.com/1118090351 "Play video "Practical Business Use Cases of AI"") ## Beyond Gen AI – What’s next? The most disruptive changes are often those we cannot yet foresee. Nevertheless, certain market shifts are already apparent, providing valuable insights into potential future directions for AI. While generative AI has transformed how we work, it is only the beginning. Sarah introduces **Agentic AI** – a new phase where AI acts more like a colleague than a tool. Instead of waiting for instructions, it proactively manages tasks: filtering spam, scheduling meetings, or coordinating with other agents across organizations. This shift turns AI from a reactive assistant into an active problem-solver that saves significant time and mental energy. Imagine spending less time on routine admin and more on strategic projects, innovation, or professional growth. Though still early, the foundations for this technology are already being built, and leaders who understand it now will be better prepared to capture its value in the near future. The World Economic Forum reports that **executives expect agent-based AI to impact productivity at a scale comparable to the internet itself**. This isn’t speculative science fiction; the groundwork is already being laid in enterprise workflows. The organizations that begin preparing today will find themselves ahead of the curve when agentic AI becomes mainstream. [https://vimeo.com/1118091834](https://vimeo.com/1118091834 "Play video "Дизайн без названия, копия, копия (7)"") ## Will AI ever be Conscious? It’s the question every leader gets asked: will AI become sentient and replace humanity? Sarah’s answer is thoughtful but reassuring. True consciousness would require replicating the complexity of the human brain – a feat science is nowhere near achieving. The reality is that AI reflects the data and rules humans design; it doesn’t “want” or “decide” on its own. The real risks are not rogue robots, but **bias, misuse, and lack of governance**. So instead of worrying about AI “taking over,” the urgent question is how businesses and governments guide it responsibly. With trust, governance, and careful design, AI is more likely to serve as an enabler than a threat [https://vimeo.com/1118094115](https://vimeo.com/1118094115 "Play video "Will AI ever be Conscious?"") **Categories:** Blog **Services:** Agentic AI, Generative AI, Managed Services **Industry:** Automotive, Financial Services, Healthcare, Manufacturing, Retail --- ### [Cloud-Powered AI Consulting: Dedicatted`s Expert Assessment of Market Predictions](https://dedicatted.com/insights/cloud-powered-ai-consulting-dedicatteds-expert-assessment-of-market-predictions) **Published:** August 27, 2025 **Author:** Dmytro Petlichenko **Content:** C-suite executives face the critical challenge of navigating through many technology predictions and trends to identify truly transformative opportunities. To address this challenge, [**Dedicatted’s technology experts**](https://bit.ly/3HnzDD2) have conducted a comprehensive overview of forecasts from leading research and advisory firms. Our analysis cuts through the hype to provide strategic direction for technology investments that can drive meaningful business transformation. Rather than simply aggregating forecasts, we’ve applied our three decades of engineering expertise to evaluate each trend’s deployment risk and business value ## ******Multimodal AI: Unleash the power of context****** Multimodal AI mirrors human learning by integrating diverse data sources like images, video, and audio in addition to text-based commands.This unlocks AI’s ability to decipher and learn from a much broader range of contextual sources with unprecedented accuracy, producing outputs that are more precise, customized, and tailored, creating an experience that feels natural and intuitive. ### **The benefits of multimodal AI** **Greater grounding** One of the key advantages of multimodal LLMs is the ability to manage and process diverse forms of data – combining speech, text, images, audio, and video – to improve understanding and response to human commands. It can merge these inputs simultaneously to generate a wide-range of high-quality outputs that are grounded in enterprise truth and updated in real-time as information is exchanged and updated. **Enhanced decision-making** Multimodal AI enables organizations to unlock deeper insights and enhanced data analytics by combining unstructured and structured data. These insights can be used to improve backend efficiency and front-end user experience, especially in sectors like retail, healthcare, and customer service **More personalized customer interactions** Multimodal AI’s capability to generate personalized customer interactions is another core business benefit. By combining visual, audio, and text-based inputs, virtual assistants become more responsive and accurate – boosting customer satisfaction [![Architecture diagram of a multi-modal agent system. On the left, data storage includes tabular data, audio, unstructured text, and PDF files. This multi-modal data flows into agent tools for text processing, computation, sentiment analysis, audio processing, visuals, and customized intelligent search. In the center, the multi-modal agent connects with agent memory and a foundation model. On the right, users provide questions, and the multi-modal agent generates answers](https://dedicatted.com/wp-content/uploads/2025/08/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-16.png)](https://dedicatted.com/wp-content/uploads/2025/08/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-16.png)If your company makes tangible goods and your product development teams aren’t using AI for design, prototyping and testing, now is the time to start. Multimodal AI – capable of processing and generating diverse data types, from CAD files to simulations, is now revolutionizing product design and broader R&D processes. For example, GenAI tools can propose improved configurations for a car chassis, simulate performance under different conditions and even suggest designs that engineers might have overlooked. **[Dedicatted can help you create AI](#contact-form)[ ](#contact-form)**that is able to iterate designs in hours not weeks, test solutions virtually before building prototypes and troubleshoot more problems before you move to production. Based on our work with clients and analysis of technology and industry trends, we’re confident that adopting AI in R&D can reduce time-to-market 50% and lower costs 30% in industries like automotive and aerospace. In many pharmaceutical companies. ## ********AI agents: The evolution from chatbots to multi-agent systems******** With its deep intelligence, agentic AI marks a significant departure from traditional software programs. AI agents show reasoning, planning, and memory with a level of autonomy to make decisions, learn, and adapt. At Dedicatted, we’ve seen this in action with **[our work with Cassen Laboratories](https://dedicatted.com/insights/smarter-science-how-dedicatted-brought-agentic-ai-to-chemical-reporting-at-cassen-laboratories),** where a multi-agent AI platform, partially powered by Amazon Nova models, benefited the client by providing top-level reasoning, summarizing, and accuracy in a compliant environment. Today, many AI applications include multiple agents with human-in-the-loop (HITL) to address complex workflows. Workers are cultivating new skills to collaborate effectively with these AI agents, combining human creativity with AI’s analytical power. A study by Stanford, MIT, and NBER found that access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15% on average. The study also found that workers with less experience and skills improved both the speed and quality of their output when assisted by AI agents, while the most experienced and highest-skilled workers saw few gains in speed and a surprising decline in quality. The next phase of AI agent technology takes things one step further. Multi-agent systems (MAS) are composed of multiple independent agents that collaborate to achieve a goal or complex workflow beyond the ability of an individual agent. [![Comparison of Single Agent System vs Multi Agent System. On the left, a single agent connects a user, data, and action in a simple loop. On the right, a multi-agent system shows multiple agents interconnected, each linked to users, data, and actions, forming a network of interactions.](https://dedicatted.com/wp-content/uploads/2025/08/AI-2025-08-27T120751.767.png)](https://dedicatted.com/wp-content/uploads/2025/08/AI-2025-08-27T120751.767.png)## ****Types of Multi-Agent Systems that you can implement**** 1. **Competitive Multi-Agent Systems** Competitive MAS are driven by the principle of conflict. In these systems, agents have opposing goals and compete for resources. The interaction often involves strategies aimed at outmaneuvering the opponent, making them suitable for scenarios where competition is crucial. - *Opposing Goals:* Agents’ goals conflict with one another, leading to direct competition. - *Resource Competition:* Agents fight for limited resources, like, time, space, or assets. **Example:** In an online multiplayer strategy game, each player (acting as an agent) competes to control territories and defeat others. The agents must handle and counter the strategies of their competitors to succeed. 2. **Mixed-Agent Systems** Mixed-agent systems blend cooperation and competition. Agents in these systems collaborate in certain areas, while also competing in others. These systems mirror real-world environments where agents or entities might work together in some contexts but still vie for individual success or resources. - *Cooperation and Competition:* Agents might cooperate to achieve shared objectives but also compete when it benefits them. - *Dynamic Interactions*: Agents must navigate the balance between working together and pursuing personal goals. **Example:** In a business[ supply chain system](https://dedicatted.com/industries/retail), agents might cooperate in producing and distributing products but also compete in the market to maximize sales and customer base. Negotiation and shifting combinations create a dynamic environment for decision-making 3. **Hierarchical Multi-Agent Systems** Hierarchical MAS operates under a structured organization where agents are placed at different levels of authority and responsibility. Higher-level agents coordinate the actions of lower-level agents to ensure the system’s goals are achieved through task distribution. - *Organizational Structure:* Agents are divided into levels, with more powerful agents at the top. - *Delegation and Supervision:* Higher-level agents manage and delegate tasks to lower-level ones to ensure efficient execution. **Example**: In a large-scale automated factory, a high-level agent supervises the entire manufacturing process, delegating tasks to specialized agents like robots that handle assembly, quality control, and packaging, ensuring all parts function together. 4. **Cooperative Multi-Agent Systems** Cooperative MAS revolves around the principle of collaboration. In these systems, multiple agents work together toward a common goal, with success dependent on the collective efforts of all agents. Each agent brings its expertise to the table, and they share information and resources to maximize efficiency. - *Common Objectives:* Agents share a unified goal, such as solving a problem or completing a task. - *Collaboration:* These agents exchange information, offer resources, and synchronize their actions to achieve the goal. **Example**: In a disaster response scenario, a team of drones works together to locate and rescue individuals in affected areas. One drone identifies the survivors, another provides real-time health data, and a third coordinates with emergency responders, ensuring a smooth, efficient rescue operation [![Flowchart of an AI multi-agent system architecture. It starts with Environment as a shared operational space, leading to Perception that collects data. Communication & Sharing exchanges information among agents. Knowledge Base stores shared data and learned knowledge, feeding into Decision-Making that plans actions. Decision-Making connects to Coordination Layer, which manages task distribution, and to Action Module, which executes decisions in the environment. Feedback Loop updates the system after actions, linking back to Collaboration Layer for joint problem-solving and reinforcing the cycle.](https://dedicatted.com/wp-content/uploads/2025/08/AI-2025-08-27T121428.263.png)](https://dedicatted.com/wp-content/uploads/2025/08/AI-2025-08-27T121428.263.png)## **AI-powered customer experience: So seamless, it’s almost** **invisible** Just as text-based customer service chatbots were not the final frontier, today’s real-time conversational insights and speech-based customer support features that exist today are a stepping stone, not the final destination of AI-powered CX. This will be reached when companies can provide customers with precisely what they want: experiences so seamless, personalized, and efficient that issues are resolved without a customer even noticing they have interacted with a company’s customer service or support technology. [![Infographic titled 'AI solves common CX challenges' showing three key areas. 1) Customer support: 75% of customers use multiple channels, with AI-powered virtual agents providing consistent omnichannel experiences. 2) Customer sentiment: Higher customer loyalty scores increase shareholder returns; AI sentiment analysis helps brands gauge opinions in real time through emails, social media, and chat. 3) Personalization: 71% of consumers expect personalized interactions, and AI insights use behavior data to create tailored recommendations and predict customer needs.](https://dedicatted.com/wp-content/uploads/2025/08/AI-2025-08-27T121734.856.png)](https://dedicatted.com/wp-content/uploads/2025/08/AI-2025-08-27T121734.856.png)## Conclusion Drawing on over 10 years of software industry experience, our certified experts help you evaluate emerging technologies against your specific business context. Beyond technical assessment, we work with you to develop a strategic vision by mapping emerging technologies to your business roadmap. Our comprehensive approach includes: - Evaluating technical feasibility and business potential - Developing proof-of-concept solutions to validate assumptions - Creating detailed implementation roadmaps - Seamlessly transitioning to full-scale development As both advisors and implementers, we ensure that your investment in emerging technologies translates into real business value. [**Our end-to-end capabilities** ](#contact-form "Our end-to-end capabilities ")mean we don’t just help you identify promising trends-we help you turn them into competitive advantages. **Categories:** Blog **Services:** Agentic AI, Cloud and Infrastructure Security, Cloud Data Migration & Modernization, Generative AI **Industry:** Financial Services, High Tech Software, Manufacturing, Retail --- ### [Roadmap for your business: Accelerate AI innovation with AWS infrastructure](https://dedicatted.com/insights/roadmap-for-your-business-accelerate-ai-innovation-with-aws-infrastructure) **Published:** September 17, 2025 **Author:** Dedicatted **Content:** Generative artificial intelligence (gen AI) foundation models (FMs) can create new content and ideas, including conversations, stories, images, videos, music, and even software code, in response to a prompt. Gen AI is powered by large-scale FMs that can be trained with up to petabytes of data and supported by AWS infrastructure . As these FMs grow, their parameters also increase – upward of trillions of parameters. Even a smaller language model can be trained with a few billion parameters, and depending on the use case, that number can go up to 15 billion parameters. Organizations are leveraging gen AI to reshape industries, such as healthcare, entertainment, finance, and manufacturing. While most organizations aim to take advantage of gen AI through customized applications using large language models (LLMs) and FMs or fully managed services with industry-leading models, some still want to build and train their own models. As training and deploying these large-scale FMs continue to evolve, organizations need an unprecedented level of high-throughput, low-latency, and secure infrastructure to train these models in a reasonable time and deploy them for inference while working to lower costs and maintain the highest performance possible. This article takes you through the key challenges in training and inferencing models and how the right infrastructure can optimize cost, improve performance, and reduce time to market. [![Infographic with four metrics showing the impact of an AI assistant. Efficiency: 1 in 5 daily active users adopted the assistant in the first months of rollout. Productivity: 22% increase in conversion rate for AI-assisted sessions (2.3% → 2.8%). Revenue: 6% uplift in average order value through smarter bundling and cross-sell. Speed: 2 minutes saved per shopping session with faster product discovery.](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-09-17T135316.915-1.png)](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-09-17T135316.915-1.png)## Challenges across the AI workflow The range of infrastructure challenges organizations face is evolving as projects and technologies advance. I**ncreased interest in rapidly adopting new technologies** Gen AI integrations are being pursued at a fast pace, but organizations still need to thoughtfully address concerns over privacy, security, costs, performance, knowledge, training gaps, and other impacts to avoid risks. **Working with models at any scale** While some FMs have grown astronomically to include trillions of parameters, many organizations use smaller, more finely tuned models for their specific needs. Organizations want to flexibly scale compute, networking, and storage to meet diverse and changing requirements. **Balancing infrastructure costs while maintaining performance** Training, building, and deploying gen AI models requires an unprecedented level of performance and new technologies with budgets that remain similar year over year. This necessitates finding ways to lower costs while maintaining performance. You need a broad set of compute accelerators to meet the demand of any gen AI use case. **Data infrastructure modernization, integration, and scalability** Legacy systems inhibit advanced analytics and AI capabilities and bring substantial capacity constraints, requiring organizations to spearhead transformations that optimize value from the cloud. Plus, integrating gen AI systems into existing infrastructure and workflows can be complex and resource-intensive. While initial proofs of concept are easier to complete, scaling solutions and systems to handle increasing workloads and ensuring reliability and performance are not. Infrastructure should offer broad and flexible options to fit each scenario. **Data sovereignty, data residency, and regulatory considerations** Organizations in highly regulated industries are especially cautious about data security and privacy for gen AI applications, including concerns like exposure of intellectual property (IP) or code, data security and privacy, governance, and ensuring compliance. Organizations must navigate complex, uncertain, and ambiguous regulatory landscapes and ensure that their organizations comply with relevant laws and guidelines while exploring their cloud infrastructure solutions To navigate these complexities, it’s essential to separate hype from reality. [**Our expert assessment of market predictions**](https://dedicatted.com/insights/cloud-powered-ai-consulting-dedicatteds-expert-assessment-of-market-predictions) helps business leaders understand which AI trends deliver true value – and where to focus resources for long-term impact. [!["Banner with the text: 'Let our skilled team take care of your tech. Let's discuss how our vast tech expertise can serve your business needs in AWS Infrastructure Innovation' A button reads 'Speak to our expert.' On the right, twelve AWS certification badges are displayed, including Cloud Practitioner, Developer, Solutions Architect, SysOps Administrator, DevOps Engineer, Solutions Architect Professional, Advanced Networking, Data Analytics, Data Engineer, Machine Learning, Security, and Database.](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-09-17T135516.806-1.png)](https://dedicatted.com/wp-content/uploads/2025/09/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-09-17T135516.806-1.png)## Developing your generative AI solution on AWS Infrastructure 1. **Data collection and preparation** After you’ve identified a use case and set objectives, you will typically need to source large datasets, cleanse the data, and, in some cases, reprocess it. You will also need scalable tools to make data preparation efficient and manageable 2. **Selecting models and architecture** Pre-built models and solution templates can help data scientists and machine learning (ML) practitioners get started quickly. A wide range of publicly available and fine-tunable FMs for text and image generation are available from libraries such as Hugging Face. Choosing models that work with accelerated compute and tools, such as Amazon SageMaker AI, can help you innovate faster 3. **Model training** Data is typically split into sets for training, validation, and testing. The model is trained through multiple runs in which weights are adjusted, problems are identified, and tracking metrics, such as model accuracy, are refined. FMs are often trained on petabytes of data and may be too large to fit in a single GPU. You will need purpose-built ML silicon or GPUs in clusters with up to thousands of nodes. As a result, much of your training budget is likely to be spent on infrastructure. You will also need access to the latest ML frameworks and libraries and high-performing and secure technologies that speed up networking 4. **Fine-tuning and optimizing models** Your compute capacity and resource needs will vary depending on the type of fine-tuning or optimizations you choose – from full fine-tuning to parameter efficient fine-tuning. You will also need access to tools and software that help you maximize performance. 5. **[Deployment](https://dedicatted.com/services/glossary-of-terms#term-deployment)** As you prepare to deploy FMs for inference, your infrastructure needs will change. Inference can account for a large portion of the total cost of gen AI in production, so you will need to implement infrastructure that reduces the inference cost at scale. Compute needs are also different from the training stage because nodes can be distributed rather than clustered. You may find complexities in achieving the low latency needed for real-time inference – required by interactive use cases like chatbots – or the throughput needed for batch inference of large datasets ## **How Dedicatted Delivers Remittance Automation on AWS Infrastructure with GenAI** We start with process diagnostics to uncover workflow bottlenecks and ERP integration gaps. Then, we roll out in phases: 1. Foundation Setup – AWS-powered pipelines (S3, Lambda, Step Functions) replace manual uploads with secure automation. 2. AI Intelligence – Amazon Bedrock and Comprehend extract structured data from diverse documents with high accuracy. 3. ERP Integration – Parsed data flows seamlessly into Epicor Prophet, hardened for scalability and compliance. Our tailored approach ensures precision, compliance, and scalability **–** with confidence scoring, secure cloud-native tools, and workflows designed for growth. **The impact** - 40% cost reduction in financial operations - Faster, error-free remittance processing across 13 branches Teams freed to focus on strategic work, not manual tasks With proven AWS + GenAI expertise, Dedicatted transforms complex financial workflows into scalable, cost-efficient systems. When remittance workflows transform from manual drag to automated engine, you begin to see real leverage – not just efficiency. For the Hercules project, that meant 40 % less cost and smoother processing across branches. But it doesn’t stop there – in another recent engagement, we went even deeper by rearchitecting the client’s data foundation. Want to see that journey? **[Dive into our data-architecture case study next.](https://dedicatted.com/insights/automating-remittance-workflows-and-reducing-costs-with-genai-and-aws)** [![Infographic with three cost-saving benefits of AWS infrastructure EC2 instances. First: Up to 30%-40% better price performance for Gen AI training and inference using AWS Trainium2-powered EC2 Trn2 instances. Second: 40% lower inference costs with AWS Inferentia2-based EC2 Inf2 instances compared to similar EC2 instances. Third: Cost savings of up to 40% by using EC2 P5 instances instead of EC2 P4 instances.](https://dedicatted.com/wp-content/uploads/2025/09/Group-149-5.png)](https://dedicatted.com/wp-content/uploads/2025/09/Group-149-5.png)**Minimize latency with optimized networking** - Enable lightning-fast inter-node communication for high-performance AI applications with up to 3,200 gigabits per second (Gbps) of Elastic Fabric Adapter (EFA) networking, providing lowlatency, high-bandwidth networking throughput. - Reduce latency by 16 percent and support up to 20,000 GPUs with Amazon EC2 UltraClusters 2.0, a flatter and wider network fabric specifically optimized for ML accelerators. It offers up to 10 times more overall bandwidth than alternatives. - Increase network efficiency and optimize job scheduling with the Amazon EC2 Instance Topology API. - With insights into the proximity between your instances, it can help you strategically allocate each job to the instance type that best fits your requirements. **Optimize storage for throughput, low latency, and reduced costs** AWS offers a comprehensive choice of cloud storage options that meet every need in AI workflows, from delivering the performance to keep accelerators highly utilized to reducing the cost of long-term storage. - Amazon FSx for Lustre can help you accelerate ML with maximized throughput to compute resources and seamless access to training data stored in Amazon Simple Storage Service (Amazon S3). - Amazon S3 Express One Zone provides the lowest-latency cloud object storage available, with data access speed up to 10 times faster and request costs up to 50 percent lower than Amazon S3 Standard. - Amazon S3 is built to retrieve any amount of data from anywhere, offering industry-leading scalability, data availability, security, and performance. Use Amazon S3 to create a centralized repository or data lake that allows you to store all your structured and unstructured data at any scale. **Control data and AI infrastructure securely** Built on the foundation of the AWS Nitro System, AWS safeguards even your most sensitive data. The Nitro System is designed to enforce restrictions so that nobody, including anyone at AWS, can access your workloads or data running on your accelerated computing EC2 instances or any other Nitro-based EC2 instance. The level of security protection offered is so critical that we’ve added it to our AWS Service Terms to provide additional assurance to all of our customers, and it has been validated by the NCC Group, an independent cybersecurity firm **Categories:** Blog **Services:** Cloud and Infrastructure Security, Generative AI, Managed Services, Security Compliance **Industry:** Financial Services, Insurance, Manufacturing, Retail --- ### [How to Get the Most of Amazon Q for Business? We Did Our Research (PDF Inside)](https://dedicatted.com/insights/how-to-get-the-most-of-amazon-q-for-business-we-did-our-research-pdf-inside) **Published:** September 25, 2025 **Author:** Maksim Lukhanin **Content:** Today’s AI tools have moved beyond basic automation to predictive insights, sentiment analysis, and competitive intelligence that once took entire teams weeks to produce. Since Amazon Q and other AI business assistants launched in 2023, the new AWS agentic AI has drawn wide attention – but only few clear, practical examples showing which scenarios actually deliver business value. We analyzed a scope of partner pilots, customer deployments, and our own tests to identify the highest-return use cases and the real metrics behind them. ## **What is Amazon Q?** Amazon Q for Business is an AI assistant aimed at automating enterprise workflows: it indexes data sources (CRM, ERP, document stores, BI), and writes back structured results. The “index + conversational access” model is what turns static repositories into operational assistants, not just search tools. In short, Amazon Q is designed to make data and process automation directly usable by non-technical users. AWS **[officially describes Amazon Q Business](https://aws.amazon.com/blogs/machine-learning/amazon-q-business-and-amazon-q-in-quicksight-empowers-employees-to-be-more-data-driven-and-make-better-faster-decisions-using-company-knowledge/)** as “generative AI for business that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems” ### Q-Apps: No-code Micro-Apps for Teams One of the key extensions of Amazon Q are Q-Apps. They are lightweight, shareable automations and templates that let teams capture repeatable prompts and outputs – think account-brief generators for sales, pre-filled audit evidence packs for compliance, or pre-screening workflows for recruiters. Instead of ad-hoc usage, users can assemble and reuse these flows without coding skills, turning one-off prompts into repeatable scripts that run inside existing workflows. ### QuickSight + AWS Agentic AI Amazon QuickSight is a cloud-scale, serverless business intelligence service for building dashboards and visual reports. Paired with Amazon Q, QuickSight provides the visualization layer while the agentic AI tools turn dashboards into interactive decision tools: it can answer user queries, generate executive summaries, and build shareable “data stories” (visual gen AI solutions + plain-language narrative). In practice, this means leaders get concise, sourced insights without any manual research. ## **We Did the Research (Key Takeaways)** We spent a year running pilots with client companies and gathering structured evidence, using these 3 main inputs: (1) client pilots and surveys, (2) internal productivity measurements, (3) verified third-party case studies. The goal was practical: which Amazon Q features deliver measurable value fast and how to use them to get the most of it? The key finding sounds quite impressive: focused pilots on repeatable workflows commonly return **300-400% ROI within 4-8 weeks** when scoped correctly. What we observed in practice: We summarize the exact findings and the math behind them in our [**Executive Use-Case Guide (PDF):**](https://dedicatted.com/insights/how-to-get-the-most-of-amazon-q-for-business) prioritized use cases, pilot metrics, and ROI calculations. ## **How to Use Amazon Q for Business?** Below are the pragmatic steps we recommend for teams ready to run their first pilot. **1. Pick a Short, Repeatable Workflow.** Choose a task with frequent, measurable activity and clear ownership: meeting prep, monthly variance reporting, RFP customization, or candidate screening. These are high-value, low-risk pilots. **2. Index Only What You Need.** Start with the minimum set of sources that power the workflow (CRM records + slide libraries, or ledgers + reconciliation rules). Quality of indexing (granularity and metadata) matters more than volume. **3. Apply Security and Governance.** Define knowledge scopes (internal vs external), enable RBAC, and set guardrails for topics or sensitive fields. This keeps pilots auditable and avoids accidental exposure. **4. Build a Q-App or QuickSight Topic.** Turn the chosen workflow into a reusable asset: a Q-App template or QuickSight story. Include example prompts, expected outputs and a simple approval step for human review. **5. Measure with Simple KPIs.** Track time saved (before/after), error rates, revenue influence (e.g., uplift in upsell tests), and TCO. **6. Iterate and Scale.** If pilot KPIs meet targets, expand the index scope or replicate the Q-App across teams. Always maintain governance checkpoints as scale grows ## **The Executive Guide** If you want the practical use cases, pilot checklist and measurement templates, download our ***[Amazon Q for Business: An Executive Guide to 300% ROI with Generative BI (PDF).](https://dedicatted.com/insights/how-to-get-the-most-of-amazon-q-for-business "Amazon Q for Business: An Executive Guide to 300% ROI with Generative BI (PDF).")*** Inside you’ll learn how to use Amazon Q in 4 business areas: Along with practical examples; time-saving metrics from real pilots; setup recommendation and best practices. **Categories:** Blog **Services:** Agentic AI, Cloud and Infrastructure Security, Generative AI, Managed Services, Security Compliance **Industry:** Automotive, Financial Services, Healthcare, High Tech Software, Retail --- ### [Smarter Logistics for Travel and Hospitality: Kwiksave AWS Cloud Success](https://dedicatted.com/insights/smarter-logistics-for-travel-and-hospitality-kwiksave-aws-cloud-success) **Published:** October 10, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2025 – ongoing #### Industry: Travel and Hospitality, Logistics #### The service: Cloud Modernization, AI-Driven [Automation](https://dedicatted.com/services/glossary-of-terms#term-automation), Secure Architecture #### Overview **Kwiksave Logistics** is one of Canada’s largest and most experienced logistics operators, providing courier, trucking, e-commerce, and fulfillment services across the country. The company is known for its fast and reliable delivery services, offering same-day and rush fulfillment within key metropolitan areas like the Greater Toronto Area. With strong integrations across major e-commerce platforms such as Shopify and Amazon, Kwiksave plays a pivotal role in connecting retailers and consumers through efficient last-mile logistics. Dedicatted partnered with Kwiksave to design and implement a modern, AWS-powered architecture focused on automation, scalability, and data intelligence. The new solution leveraged AWS Lambda, EventBridge Scheduler, Bedrock and others to integrate systems, automate workflows, enrich logistics data with AI, and centralize insights. This modernization project enabled Kwiksave to **transform its operational backbone**, turning fragmented logistics workflows into an intelligent, event-driven ecosystem. By integrating automation, AI, and secure cloud services, the company not only improved its efficiency and employee productivity but also strengthened its position as a reliable logistics partner supporting the broader travel and hospitality ecosystems across Canada. ## The Challenge As customer expectations for faster and more transparent delivery grew, Kwiksave faced increasing operational complexity. Its internal Transportation Management System (TMS), e-commerce tools, and marketing platforms such as HubSpot and MailChimp were operating in silos, leading to fragmented workflows, manual updates, and inconsistent data. The company needed a **unified, automated, and secure cloud infrastructure** that could connect all its systems, reduce manual intervention, and enable real-time visibility into logistics performance. - **Manual and Time-Consuming Workflows:** Key processes, such as shipment updates, inventory synchronization, and reporting, required manual intervention, creating bottlenecks and slowing operations. - **Integration Complexity:** Kwiksave needed to connect multiple internal and external systems, including APIs for TMS, CRM, and e-commerce platforms, without disrupting operations. - **Security and Access Control:** Protecting sensitive transportation and customer data required stronger controls, including restricted database access and network segmentation. - **Limited Operational Visibility:** Lack of centralized monitoring and real-time analytics hindered the ability to identify bottlenecks, optimize resources, and measure performance. [![A diagram illustrating Kwiksave Logistics' technology ecosystem. On the left, icons represent various tools and services including AWS Lambda, machine learning, GitLab, data pipelines, cloud storage, and analytics. These connect to the Kwiksave Logistics logo in the center. On the right, the logo branches into four main categories - Architecture Design, Workflow Automation Development, AI-Driven Data Enrichment, CI/CD and Monitoring Setup, and Security & Compliance - showing the integration of cloud and AI technologies into logistics operations.](https://dedicatted.com/wp-content/uploads/2025/10/Group-129-7.png)](https://dedicatted.com/wp-content/uploads/2025/10/Group-129-7.png)## Technology & Approach Dedicatted designed and deployed a **modern, scalable AWS-powered platform** for Kwiksave, focused on automating logistics operations, enriching transportation data, and providing secure, scalable infrastructure to support travel, e-commerce, and fulfillment workflows. The solution emphasized operational efficiency, intelligent data processing, and team empowerment while aligning with best practices in travel and hospitality logistics. Key components of the solution: - **Event-Driven [Serverless](https://dedicatted.com/services/glossary-of-terms#term-serverless) Workflows:** AWS Lambda and Amazon EventBridge Scheduler automated recurring logistics tasks, such as shipment status updates and e-commerce inventory synchronization, reducing human error and improving operational responsiveness. - **[Orchestration](https://dedicatted.com/services/glossary-of-terms#term-orchestration) and Visibility with AWS Step Functions:** Complex multi-step logistics workflows were orchestrated to run efficiently while providing clear visibility into every stage of operations. This ensured reliability in high-volume transportation and fulfillment processes. - **AI-Powered Data Enrichment via AWS Bedrock:** AI agents automatically verified and enriched shipment and customer data, improving accuracy for operational decisions, route optimization, and customer-facing reporting in travel and hospitality supply chains. - **Centralized, Secure Data Management:** Amazon RDS served as a hub for raw, aggregated, and enriched operational data. Deploying the system within a private VPC with strict read/update permissions strengthened security and compliance across all logistics operations. - **Employee-Facing Operational Dashboard:** Amazon [API Gateway](https://dedicatted.com/services/glossary-of-terms#term-api-gateway) delivered a secure web interface, giving employees real-time access to shipments, workflows, and enriched data insights, increasing productivity and supporting operational agility. - **CI/CD and [Monitoring](https://dedicatted.com/services/glossary-of-terms#term-monitoring):** GitLab CI streamlined continuous integration and deployment, while AWS CloudWatch offered centralized monitoring and alerts for proactive management of travel and hospitality logistics processes. - **[Agile](https://dedicatted.com/services/glossary-of-terms#term-agile) Implementation & Knowledge Transfer:** Dedicatted collaborated closely with Kwiksave teams to iterate on workflow designs, integrate new data sources, and empower staff with the knowledge to maintain and extend the platform independently. By combining AI, serverless architecture, and **AWS Travel and Hospitality best practices**, Kwiksave now benefits from automated, data-driven workflows that enhance productivity, operational resilience, and service quality. #### If you find this case interesting, we recommend taking a closer look at [ Generative AI Solutions ](https://dedicatted.com/services/generative-ai/supply-chain) [ Our partner page ](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/) ## Our Solution Dedicatted approached Kwiksave’s project as an opportunity to **transform fragmented, manual logistics workflows into a dynamic, intelligent operations platform**. The goal was to automate repetitive processes, enrich transportation data with AI, and provide employees and managers with secure, real-time operational visibility – all without requiring constant developer intervention. To achieve this, the team focused on practical, high-impact interventions: - **Automated, Reliable Workflows:** Repetitive logistics processes, including shipment updates and inventory synchronization, were automated to reduce errors, increase efficiency, and allow staff to concentrate on mission-critical operations. - **Real-Time Insights:** Custom dashboards and reporting tools provide instant visibility into shipments, inventory, and performance metrics, giving managers the clarity they need to optimize routes, resources, and service delivery. - **AI That Works Behind the Scenes:** AWS Bedrock powers AI agents that enrich and verify data automatically, improving accuracy without requiring staff to spend time correcting errors. - **Flexible Employee Controls:** Operational teams gained intuitive interfaces to trigger workflows, monitor tasks, and access enriched data independently, enhancing productivity and minimizing dependency on IT or developer intervention. - **Scalable and Adaptable Platform:** The system was built to accommodate evolving business requirements, supporting growth in shipment volumes, e-commerce operations, and fulfillment complexity. - The private VPC and restricted access model significantly **reduced exposure to external threats**. Fine-tuned IAM policies ensured data integrity and compliance with industry regulations. Kwiksave’s operations have been transformed into a **living, intelligent system**. Automation reduces friction, AI ensures data accuracy, and real-time insights turn everyday operational decisions into **strategic opportunities**, improving efficiency while enhancing the end-customer experience. [![Infographic showing business results in purple boxes. Efficiency: Full return on investment achieved within 12 months. Cost Reduction: $3.2M net savings over 3 years, 30% lower operating expenses compared to manual operations. Automation: $700k saved per year through AWS tools, reducing manual workload by 9 FTEs (35% of total logistics effort). Additional Savings: $200k per year saved from improved process reliability and SLA compliance; $300k per year saved from AI-powered data enrichment via Bedrock, removing the need for external vendors.](https://dedicatted.com/wp-content/uploads/2025/10/Group-354.png)](https://dedicatted.com/wp-content/uploads/2025/10/Group-354.png) **Categories:** Case study **Services:** Cloud Modernization, Generative AI, Security Compliance **Industry:** Automotive --- ### [Cloud Optimization Strategy: How to Take Control of Your Cloud Spend](https://dedicatted.com/insights/cloud-optimization-strategy-how-to-take-control-of-your-cloud-spend) **Published:** October 1, 2025 **Author:** Andriy Kopachevskyy **Content:** Over the last decade, the cloud has moved from a competitive advantage to a necessity. Its agility, flexibility, and scalability are essential to keep up with today’s dynamic markets and increasingly remote and hybrid workforces. But as cloud adoption rates have soared, so has the complexity. There’s a public cloud and SaaS app to fill your every need, and according to Oracle, 98% of organizations today use or plan to use multiple public clouds (i.e., a multi-cloud architecture hybrid system architecture). But complexity has a price. A recent study found that 82% of organizations rank managing cloud optimization as their top business challenge. [![Horizontal bar chart showing how results compared to plans. 9% reported significantly higher than planned, 36% somewhat higher, 45% about the same, 9% somewhat lower, and 1% significantly lower](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-69-1.png)](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-69-1.png)We’re here to help you understand the ins and outs of cloud cost optimization. With the right cloud optimization strategy, you can reduce your editures and enhance performance at the same time. ## ****What is** [**cloud optimization**](https://dedicatted.com/what-we-do/devops/devops-as-a-service)**?**** [**Cloud cost optimization** ](https://dedicatted.com/what-we-do/devops "DevOps")means actively monitoring, managing, and controlling expenses to be more efficient and reduce waste. It involves analyzing usage and spending, setting budgets, monitoring expenditures, and modifying configurations and resource allocations accordingly. Cloud resource optimization is crucial for modern organizations due to the pay-as-you-go approach to pricing and resource allocation. As businesses adopt multi-cloud environments across public, private, and hybrid models, the complexity rises exponentially. Without optimization, costs can quickly spiral out of control. Recent studies suggest a typical organization wastes 30-35% of its public cloud spending due to inefficiency and waste. Proper optimization can eliminate this waste and enable more efficient use of budgets. Some key benefits of cloud cost optimization include: - Maximizing performance and capacity while minimizing expenses - Avoiding overprovisioning of resources that go unused - Right-sizing cloud workloads based on usage metrics - Leveraging discounts for reserved instances and spot pricing - Enforcing budgets, tracking costs, and allocating expenses As cloud computing adoption snowballs, expense optimization is essential for fiscal responsibility and sustainable adoption. The highly variable nature of the technology makes cost management an ongoing priority. [![A circular infographic titled 'Why is Cloud Cost Optimization Important?' with five benefits: Ensure Resource Efficiency (top), Operational Transparency (right), A Competitive Edge (bottom right), Budget Control & Predictability (bottom left), and Optimize Expenses (left). Each benefit is represented by a purple circular icon with white line illustrations related to finance, resources, operations, or cloud.](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-70.jpg)](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-70.jpg)### Understanding cloud costs Understanding what contributes to expenses in this domain provides a foundation for our discussion. Most service providers’ charges fall into three major categories: - **Compute costs** are based on the processing power consumed by your instances, VMs, and other components. These costs depend on the RAM, storage capacity, and processing cores your cloud applications consume. For example, machine learning tasks use more processing power, so they cost more. - **Networking costs** within cloud infrastructure optimization are influenced by bandwidth usage and data transfer within your cloud application environment. Most providers offer tiered pricing structures to cater to businesses with higher usage levels. - **Storage costs** are based on the type and quantity of storage used over a specific timeframe. Storage types include block, file, and object storage, which have different ease of access, performance, and redundancy levels. Other costs include maintenance and system upgrades, patches, and support. ### The evolution of cloud computing and its costs Cloud computing has come a long way over the past two decades. What started as a novel concept in the 1990s has become a cornerstone for modern companies’ operations. However, with its flexibility and convenience came new challenges around managing expenses and budgets. #### **In the Beginning** While the ideas behind cloud computing had been around for years, it truly entered the mainstream in the mid-2000s. In 2006, Amazon started EC2, a platform where companies can rent computing power instead of owning physical servers. This pay-as-you-go model was revolutionary and enabled far greater flexibility. Other tech giants like Microsoft and Google quickly followed suit with their own services. #### **Growing pains** As adoption grew through the late 2000s, many organizations encountered unforeseen challenges regarding expenses in the virtual environment. The ever-changing nature of this technology made it difficult to anticipate and budget for. Companies would overprovision resources, which led to paying for excess capacity. Cloud providers also charged for network egress bandwidth, which added up quickly with large data transfer volumes. Vendor lock-in was another concern, where customers could get stuck paying higher and higher rates after committing to a single provider. #### **Modern cloud cost management** Today, cost-related challenges persist, but new techniques have emerged to enhance expense optimization and control. Auto-scaling automatically adjusts resource capacity based on demand. Reserved instances allow upfront commitments to secure lower rates. Spot instances enable purchasing unused capacity at deep discounts. The workload optimization and rightsizing analyze usage and requirements to eliminate waste. However, cross-cloud cost management across multiple vendors and services continues to be difficult. As cloud services expand with AI, containers, edge computing, and more, businesses must continuously improve their cloud cost efficiency and reduce waste. Mastering expenses requires a combination of strategy, tools, and organizational culture. ## **The importance of cloud optimization strategy** Effective strategic planning is imperative for aligning business expenditures with its goals and ensuring investment efficiency. It enables cross-functional collaboration so technology best practices are implemented organization-wide. Planning can also help you identify skill gaps, training needs, and structural changes needed to optimize cloud usage. Strategic planning for cloud computing optimization is essential for several other reasons, too: - **Resource efficiency:** Cloud cost optimization helps identify underused or unused resources that you can downscale or eliminate to save costs. - **Budget forecasting:** With optimized spending, businesses can gain a clearer understanding of their expenditures, which is crucial for precise budget forecasting and allocation. - **Competitive advantage:** Cost savings from cloud optimization solutions can be reinvested in other areas, providing a competitive advantage. Lower operational costs might result in lower customer prices or more funds available for innovation. - **Improved customer experience:** Efficient resource allocation can lead to better performance, improving user experience and, potentially, the bottom line. This kind of strategic alignment is exactly what transformed one of our clients’ cloud environments. Faced with high storage costs, inefficient compute usage, and time-consuming manual data management, they partnered with us to rebuild their data architecture on AWS. The result: **An 80% reduction in storage costs**, **50% lower compute expenses**, and **three times faster data search**. Their newly automated pipelines now process data continuously – freeing their teams to focus on innovation instead of maintenance. See the full story:[ **Optimized Data Architecture – Transforming Data Costs into Growth**](https://dedicatted.com/insights/optimized-data-architecture-transforming-data-costs-into-growth) [![Diagram outlining four pillars of cloud financial management. See: Measurement & accountability with account tagging strategy, cost reporting, chargeback, and efficiency KPIs. Save: Cost optimization through architecture design, matching capacity with demand, pricing model selection, and waste reduction. Plan: Planning & forecasting with budgeting, POC-based estimation, business case articulation, and strategic fit. Run: Cloud financial operations with executive sponsorship, finance-technology partnerships, governance investments, and celebrating accomplishments.](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-71.png)](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-71.png)## **How cloud spending goes haywire** As important as optimization is, it’s not easy. There are a huge number of challenges that lead to business overspending, making optimization difficult. ### **Lack of planning and governance** Many organizations manage expenses reactively. With the average organization having hundreds of applications and utilizing multiple platforms, it’s almost certain that spending will spiral out of control without a documented optimization strategy and governance. Lack of visibility into usage and spending patterns across the organization leads to unexpected costs. It also leaves no way to reliably identify and shut down idle resources or unused services. And as data accumulates across cloud apps over time, unchecked storage costs can occur. Lack of proper governance and policies also allows users or departments to provision resources independently of IT, leading to uncontrolled growth in spending. ### **Overprovisioning** Organizations often overestimate resource needs and overprovision their infrastructure. With no strategic plan or visibility, they can’t take advantage of better pricing options like reserved instances or rightsize their instances when needed. [![Diagram explaining automatic transparent storage tiering. On the left, a disk capacity vs. disk usage chart leads to physical storage usage. On the right, storage is divided into hot data and cold data. Hot data is stored in Local Instance Storage (NVMe) and Amazon EBS Volumes (gp3, io2), while cold data goes to Amazon S3 Buckets. An arrow indicates performance increases toward NVMe and cost decreases toward S3](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-72.png)](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-72.png)### **Multicloud mishaps** Disjointed billing and dashboards across multiple cloud providers reduce visibility into overall usage and spending trends. For example, a company using AWS, Microsoft Azure, and GCP must manually compile reports to gain a complete view, leading to unexpected costs as needs change. Likewise, unplanned sprawl occurs when business units independently procure services without IT oversight. ### **Organizational challenges** Effective cloud optimization solutions require goal alignment between IT, Finance, and business units. Without shared objectives, suboptimal decisions occur. For example, IT seeks to maximize efficiency and reduce waste, while business units want flexibility and speed. Mismatched goals lead to overprovisioning, shadow IT, and poor cost visibility. An inability to accurately allocate expenses across business units also hinders optimization efforts. Often, organizations lack expertise in using native cloud tools and cloud optimization best practices. Finally, reliance on manual processes makes optimization and governance difficult to scale. Luckily, best practices and technology exist to help you overcome these challenges. We’ll discuss these soon. But in case it’s not already clear, let’s look at the benefits of optimizing your spending. ## ****Unlock Free AWS Cloud Resources: AWS Activate for Startups**** Beyond tools and best practices, startups often face a catch: they need advanced cloud services – but those services themselves can be costly to explore. That’s why AWS offers AWS Activate for Startups: a program that lets qualifying early-stage ventures access up to $100,000 in AWS credits, plus infrastructure templates, expert support, and exclusive offers. It’s an ideal way to test, iterate, and scale on AWS without the financial risk. [ ![AWS Activate for startups: a bonus program to save you up to 100K](https://media.dedicatted.com/wp-content/uploads/2024/01/AWS-Activate.-Cover-300x157.webp) BlogDenys Yermakov 5 min to read Also read ### AWS Activate for startups: a bonus program to save you up to 100K Financial Services Healthcare Insurance Retail Cloud and Infrastructure Security Cloud Data Migration & Modernization Cloud Migration Cloud Modernization Managed Services ](https://dedicatted.com/insights/aws-activate-for-startups-a-bonus-program-to-save-you-up-to-100k)## Cloud optimization best practices and tools Tackling cloud overspending requires a careful mix of tools and best practices. In our experience, the top four are: ### 1. Utilization tracking Cloud utilization tracking involves monitoring and analyzing the usage of resources like computing, storage, and networks. It provides visibility into how efficiently cloud environments are leveraging these resources. Key metrics tracked include CPU, memory, storage, network usage, and the number of virtual machine instances, usually tracked over time to identify trends and patterns. Tracking utilization over time helps make sure workloads are correctly sized, identifies opportunities to optimize costs, and highlights where additional resources may be required. Cloud platforms provide native tools like AWS CloudWatch, Azure Monitor, and GCP Stackdriver for tracking utilization. Analyzing utilization data helps avoid over-provisioning to lower costs and ensures there is enough capacity to meet workload demands. Efficient utilization tracking is essential for effectively managing environments. ### 2. Rightsizing resources Rightsizing involves provisioning an optimal amount of resources to run applications and workloads cost-effectively. It requires analyzing utilization data to determine the right mix of cloud services and the appropriate size of resources to support workloads and eliminate any overprovisioning. Rightsizing also ensures resources are not under-provisioned, which can lead to performance issues. Usage patterns should be studied to rightsize resources and scale them based on demand. Tools like Azure Cost Management and AWS Cost Explorer can help model and assess size options. As application usage changes, capacities must be re-evaluated and resized periodically for continued optimization. Rightsizing takes continuous effort but helps avoid wasting resources and reduces cloud costs significantly. [![Flowchart showing Dedicatted's cloud optimization process. AWS, Azure, GCP, and Multi-Cloud connect to Resource Collection, which sends usage data to a Usage Database. The database connects to an Analysis Engine, feeding into a Recommendation Engine. Outputs include Ticketing Systems, Cost Optimization, and a Rightsizing Dashboard.](https://dedicatted.com/wp-content/uploads/2025/10/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-01T143556.672.png)](https://dedicatted.com/wp-content/uploads/2025/10/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-01T143556.672.png)### 3. Reserved instances Cloud service providers offer prepaid reserved instances of discounted compute capacity. Committing to a cloud resource optimization for 1-3 years instead of paying as you go can save 10-30% – and sometimes significantly more. They are recommended for steady-state workloads that require a predictable amount of compute over time. With this model, you reserve capacity by instance type, region, tenancy, and term, and it’s always available to you when needed. If usage exceeds reserved capacity, you pay on-demand rates for the excess. The good news is you may be able to change your reservations even in flight – the bigger your company, the more flexible your agreement will likely be. Choosing optimal reserved instance terms and categories requires analyzing historical usage trends and optimizations to minimize costs. Reservations provide budget predictability and reduce overall computing costs substantially for stable workloads. But they require planning and commitment to realize the benefits. ### 4. Automation of resource provisioning and de-provisioning Manual provisioning and de-provisioning can be time-consuming and error-prone, especially for large environments. Automating these processes improves efficiency and agility in deploying and scaling cloud-based resources. That’s especially true for services that don’t need to be active all the time, like development environments. Say your company uses 5000 CPUs, of which your dev environment comprises 2%. By shutting down (i.e., de-provisioning) the dev environment at night, you’ll save 10% of your total costs! In a large company, that can equate to millions of dollars. [Automation](https://dedicatted.com/services/glossary-of-terms#term-automation) can be achieved through infrastructure-as-code tools like [Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform), CloudFormation, and [Ansible](https://dedicatted.com/services/glossary-of-terms#term-ansible), which allow declaratively defining and deploying resources. Scripts can also automate provisioning using CLI tools and APIs of the cloud platforms. And auto-scaling tools allow the automatic launching and termination of resources based on metrics like utilization, time, costs, and so on. Automated de-provisioning should include built-in approvals, alerts, and processes for governance. Automation makes cloud infrastructure agile and optimizes resource usage and costs by only keeping active resources running. ## If you find this article interesting, we recommend taking a closer look at [ Cloud optimization services ](https://dedicatted.com/what-we-do/devops/devops-as-a-service) [ AWS Partner page ](https://partners.amazonaws.com/partners/0010h00001kNkpfAAC/) ## **Bridging strategy with action: testing before investing** Even with the right strategies, many organizations hesitate to experiment with new services due to cost risks. This is where proof-of-concept (PoC) projects come in. With AWS, you can try new cloud services for free, validate their value, and see measurable impact before committing resources at scale. We’ve covered this approach in detail in our article: **[AWS Proof of Concept: How to Try New Cloud Services for Free](https://dedicatted.com/insights/aws-proof-of-concept-how-to-try-new-cloud-services-for-free)**[.](https://dedicatted.com/insights/aws-proof-of-concept-how-to-try-new-cloud-services-for-free) Leveraging PoCs not only reduces financial uncertainty but also accelerates innovation by helping your teams adopt, test, and optimize cloud solutions with confidence. After you implement these first four, there are several other ways to tackle budget overspending and efficiency. Here are a few of them: **Spot instances** Leveraging spot instances for non-critical workloads and short, batched jobs gives you access to a cloud provider’s unused compute capacity at massively reduced prices – up to 90% off on-demand pricing. Once you’ve done your strategic planning, you’ll know which workloads qualify. **Cost allocation** Cost allocation assigns cloud costs to departments and business units based on resource tags and usage. It enables chargeback and accountability by tracking spending per team. Allocated costs can integrate with billing and budgeting to optimize cloud usage and tie to business drivers. Well-defined allocation rules and automating the process are key for effective chargeback. Cost allocation provides visibility into spendings and drives optimization through accountability. **Governance policies** Governance policies establish a set of rules guiding resource allocation in your cloud environment while ensuring compliance with regulations and data security. These policies may also include setting spending limits, tagging for better resource management, and workflow for budget approvals. **Optimization tools** Optimization tools can help you monitor, analyze, and optimize your expenditures. Common features include cost reporting, right-sizing recommendations, usage analytics, budget alerts, and reserved instance planning. Your provider will have native cloud optimization solutions, and there are plenty of third-party options, too – CloudZero, DataDog, Cloud Checker, Granulate, and the list goes on! Look for one that tracks multiple cloud accounts and provides consolidated views across hybrid/multi-cloud environments. Integrations with billing, DevOps, monitoring, and financial systems will allow seamless workflows. Optimization tools are key for unlocking substantial cost savings and driving efficiency in dynamic environments. But remember, they’re not a replacement for everything else we’ve discussed up to this point! **Categories:** Blog **Services:** Cloud Modernization, DevOps Consulting, Managed Services **Industry:** Financial Services, Healthcare, High Tech Software, Retail --- ### [AI Supply Chain Management and Its Top Applications in the Era of Industry 4.0](https://dedicatted.com/insights/ai-supply-chain-management-and-its-top-applications-in-the-era-of-industry-4-0) **Published:** October 15, 2025 **Author:** Serhii Semenchenko **Content:** It’s certainly no longer “business as usual” for supply chains. A convergence of factors has placed significant pressure on organizations’ supply chains to address a wide range of new challenges and priorities that, in many cases, existing [supply chain capabilities ](https://dedicatted.com/industries/retail)aren’t capable of handling. In particular, companies’ are hampered by a lack of visibility. It’s been well documented, for instance, that COVID-19 caused large-scale supply chain disruptions, risks, and uncertainty that nearly paralyzed companies for a time, largely due to a combination of unforeseen demand spikes and insufficient capabilities to respond to them – and these challenges are still wreaking havoc in companies today. [![Infographic showing supply chain statistics. The top section illustrates that only around three in ten supply chain executives are confident in predicting and responding to issues like bottlenecks, failures, and demand changes. Below, two circular graphics highlight additional data: 75% of companies faced capacity problems due to transportation restrictions, and 94% of Fortune 1000 companies experienced supply chain disruptions during the pandemic.](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-75.jpg)](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-75.jpg)The compounding effect of these disruptions is an unprecedented rise in costs. For instance, the average price to ship a 40-foot sea container has quadrupled ; truck driver salaries are soaring due to labor shortages in major economies ; and commodity prices are the highest since 2014. And then there’s the ever-growing interest in and demand for responsible, sustainable business practices from consumers, governments, investors, and other critical stakeholders. ## Scaling AI in the supply chain: The next step toward intelligent, self-driving supply chains Investments in emerging technology are not immune to these challenges. Unsurprisingly, 45 percent of the largest barriers to implementation of generative AI center on its ability to deliver return on investment (ROI). From opportunity value and use case selection to investment cost and technical requirements, ROI for AI has never been more critical. Fortunately, supply chain organizations have experience in attaining value from digital and physical assets. Much can be gleaned from nearly a decade of investments made in machine learning, a form of AI used to predict a range of outcomes from supplier lead times to customer out-of-stocks. Leveraging the lessons learned from past AI and attuning them to the context of generative AI can help navigate a better path to healthier returns. [![A donut chart titled ](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-80.jpg)](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-80.jpg)## **Implications for the supply chain** The lessons learned from discriminative AI serve as guideposts for the supply chain. The principles of value, scalability, and adoption all continue as key success criteria for generative AI, where deep learning models are used to understand and create new content. AI-generated content can prove to be quite valuable depending on the use case. Generative pre-trained transformer (GPT) models can increase logistics productivity by formulating responses to shipment inquiries, helping to mediate between customers, carriers, and third-party logistics providers (3PLs). Generative adversarial network (GAN) models can increase warehouse productivity by creating synthetic data to train robotics in how to handle packages more accurately. While criteria for success remains the same, the tactical implications to generative AI are quite different. As organizations make progress on their generative AI journey, there are key considerations that help facilitate a better return. [![Comparison table showing differences between Discriminative AI and Generative AI. Discriminative AI: Improves service, cost, and inventory; spans products, assets, and network locations; relies on interpretability and explainability; finds patterns and predicts outcomes; requires model building and dashboards; trains on structured data. Generative AI: Improves employee productivity; spans employees, robotics, and network partners; relies on dependability and faces hallucination risks; understands and creates content; requires model tuning and application integration; pre-trains on large amounts of unstructured data.](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-76-1-1.jpg)](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-76-1-1.jpg)## Do the math to articulate productivity value Discriminative AI often provides a direct line-of-sight to benefits such as how higher demand prediction accuracy leads to reduced days of inventory. Generative AI, on the other hand, tends to center on employee productivity, which alone may not be enough to justify a supply chain investment. To justify a proposed investment, quantify how a proposed initiative increases employee productivity and articulate attributable impacts to key performance indicators (KPIs), be it service level, cost, or inventory. For example, a generative AI-powered chatbot that creates forecast reports might improve productivity for a demand planner. But would it improve order fill rate beyond the planner’s ability? Or would it merely provide the planner with more time to focus on other value-added activities? In this case, there is a clear difference in productivity value. Determining this upfront along with performing periodic validation helps cut investment losses sooner rather than later. [![A table showing how different types of business value connect to value drivers, operational KPIs, and productivity opportunities. It has four columns labeled ](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-77.png)](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-77.png)## From Proof of Concept to Proof of Scale At its core, generative AI is built on the premise of scalability. A large language model (LLM) for example, can be tuned to serve multiple cognitive use cases. The advantage for a supply chain is the ability to scale how its workforce interacts with multiple parties, products, and assets. Set scalability as a key objective for every project by setting AI targets to achieve high volumes of interaction among supply chain functions, network partners, operating equipment, or physical inventory across locations. A generative AI project that drives productivity at considerable scale is proof of success. [![A banner with a gradient blue-to-teal background featuring an AI chip icon and the text 'Explore our tech innovation with Generative AI.' On the right side, there is a green button labeled 'Discover.](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-78.png)](https://dedicatted.com/wp-content/uploads/2025/10/unnamed-78.png)## Deploy with – not for- operations Generative AI relies on deep learning models or “black box systems” with inputs and outputs that are known but with inner workings that are opaque. This makes it difficult for supply chain professionals to interpret AI reasoning and reliability. Compound this with a fear of job security stemming from tech-enabled automation, and AI adoption is at risk of becoming AI opposition. Recruit subject matter experts from the supply chain organization to validate and endorse data inputs and outputs. Given the lack of model explainability and potential for “AI hallucinations,” the quality of input used to tune AI and the output generated by AI becomes paramount, more so than is the case with discriminative models. To build competence in validation, the supply chain organization must develop a deeper understanding to help demystify generative AI and its technical impact on the workforce. Broadening the organization’s role and responsibilities in this way helps improve reliability and foster acceptance. ## Key use cases of generative AI in supply chain Generative AI has diverse applications in supply chain management, revolutionizing how companies plan, execute, and optimize their supply chain operations. Here’s a deep dive into some of the key applications: ### **Demand forecasting** Generative AI synthesizes historical sales data, market trends, consumer behavior, and external factors like economic indicators to predict future demand accurately. This dynamic forecasting enables companies to adjust their production schedules, manage inventory more effectively, and respond proactively to market changes, ensuring supply aligns with demand while minimizing waste and stockouts. Generative AI enables the analysis of real-time data streams, allowing supply chain managers to make immediate adjustments in response to changing conditions, thereby optimizing operations continuously. For example, Walmart employs Generative AI to tailor inventory management, accurately forecasting customer demand for different products and optimizing stock levels. The company tested a generative AI-powered negotiation bot from Pactum AI, achieving cost savings of about 3% on contracts. Interestingly, most suppliers preferred negotiating with the AI bot over human negotiators. ### **Inventory management** Generative AI identifies the best strategies for distribution and storage, factoring in delivery times, transport costs, and demand variability. The outcome is heightened operational efficiency and significant cost savings. The technology recommends reordering points and safety stock levels. This is crucial for Generative AI in manufacturing, where it aids in superior warehouse management, leading to fewer product shortages and lower storage costs. AI algorithms continuously assess sales data and demand patterns, recommending real-time adjustments to inventory levels for different products to align with market demand. Moreover, these models determine the optimal safety stock levels, considering demand variability, seasonal trends, and market dynamics to prevent stockouts of popular items. Generative AI simulates potential market scenarios, such as sudden demand spikes or supply chain disruptions, enabling companies to prepare and implement effective restocking strategies. By identifying slow-moving items that incur high holding costs, generative AI suggests actions like pricing strategies or targeted marketing to enhance product turnover. It devises efficient storage and distribution methods tailored to different product categories. A real-life example is Amazon’s use of Generative AI in its fulfillment centers showcases how the technology optimizes product placement, streamlining order processing processes, and reducing storage costs. ### **Procurement and supplier management** Generative AI enhances supply chain resilience by analyzing vast datasets to pinpoint optimal suppliers. It evaluates performance metrics, quality assessments, and cost factors, enabling effective supplier relationship management. Through analysis of historical interactions, contracts, and performance evaluations, generative AI identifies risks and opportunities for improvement, supporting proactive supplier management and fostering strong partnerships. ASOS uses Generative AI to handle returns, analyzing return data to identify common causes and implement strategies to decrease return rates, thereby enhancing customer satisfaction and operational efficiency. ### **Logistics and distribution** Generative AI for supply chains enables greater transportation efficiency by analyzing traffic flows, weather conditions, vehicle capacities, and customer demands to optimize delivery routes, ensuring fast and cost-effective paths. For example, logistics companies can use generative AI to manage delivery truck fleets, continuously gathering data from GPS traffic updates, weather forecasts, and current locations. AI models provide real-time monitoring and re-routing capabilities during transit to circumvent delays caused by traffic congestion, accidents, or other disruptions, enhancing on-time delivery rates. Generative AI streamlines reverse logistics by evaluating data related to product returns, repairs, and refurbishment, optimizing the pathways for returned items, and deciding on the most efficient and effective methods for repair, recycling, or disposal. It assists in managing the inventory of refurbished goods, ensuring efficient redistribution and reducing waste. ### **Warehouse layout optimization** Generative AI enhances warehouse layout optimization in supply chains by dynamically adjusting layouts based on real-time operational data and predictive analytics. An important advantage is its ability to adapt layouts on the fly to meet changing operational needs. Unlike traditional static layouts, which are based on historical data and assumptions, Generative AI continuously analyzes incoming data streams to identify opportunities for improvement. This dynamic approach ensures that warehouse layouts remain optimized in response to fluctuations in demand, inventory levels, and other factors. Furthermore, Generative AI can simulate various layout configurations and scenarios to identify the most efficient arrangement. For example, it can analyze historical order data to identify frequently accessed items and strategically place them closer to packing stations or shipping docks. Businesses can improve order fulfillment speed and customer satisfaction by reducing the distance traveled by warehouse workers and minimizing picking times. AI models can simulate various layout configurations to identify the most efficient arrangement. It considers product dimensions, order frequency, and picking paths to optimize the layout for maximum throughput and storage capacity. ## Real-World Example: Building AI Readiness through Cloud Data Modernization A great example of how data modernization enables AI-driven agility in the retail supply chain comes from one of Dedicatted’s clients: a premium regional grocery chain known for its curated selection of high-quality products and commitment to local sourcing. Facing siloed data systems, inconsistent reporting, and limited real-time visibility across store locations, the company partnered with us to migrate its operations to a cloud-native AWS data platform. The new architecture, built on Amazon S3, Redshift, and Kinesis, unified all sales, inventory, and supplier data into a single source of truth, enabling real-time analytics, automated reporting, and predictive insights. As a result, the retailer achieved a 50% faster insight cycle, a 65% reduction in manual data handling, and gained a fully AI-ready infrastructure – laying the foundation for smarter demand forecasting, dynamic inventory optimization, and data-driven decision-making across its supply chain. See how we helped them: **[Retail Data Modernization: Elevating In‑Store & Digital Operations with AWS](https://dedicatted.com/insights/retail-data-modernization-unlocking-agility-through-scalable-cloud-architecture)** ## Why choose Dedicatted for generative AI implementation When the time comes to implement[ **Generative AI within your supply chain operations**](https://dedicatted.com/industries/retail), selecting the right technology partner can spell the difference between success and failure. Dedicatted is a reliable partner with a proven track record in implementing generative AI in supply chain management. Here are a few reasons why you should entrust this task to our tech experts: - With over 10 years of experience in the tech industry, Dedicatted has delivered 20+ successful data science and AI projects. We have a team of skilled Generative AI developers, who are well-versed in various verticals. - We cover every process step, from the discovery phase, consulting, and end-to-end development to product release and post-production support. - We adhere strictly to security protocols and regulatory frameworks, including ISO 27001:2013, PCI DSS, ISO 9001:2015, and GDPR, to guarantee the protection and integrity of data. **Categories:** Blog **Services:** Cloud Data Migration & Modernization, Generative AI, Security Compliance **Industry:** High Tech Software, Retail --- ### [Intelligent AI Assistant for Centralized Corporate Knowledge with Amazon Q](https://dedicatted.com/insights/intelligent-ai-assistant-for-centralized-corporate-knowledge-with-amazon-q) **Published:** November 3, 2025 **Author:** Dedicatted **Content:** ## About project #### Working time: 2025- ongoing #### Industry: Civil Engineering #### The service: AI Consulting, AI Assistant #### Overview **A leading U.S.-based infrastructure and construction firm** specializing in innovative solutions for transportation, civil engineering, and environmental projects. With a history of delivering large-scale public and private infrastructure work, the company manages an extensive portfolio of technical documentation- from engineering drawings and regulatory submissions to project reports and safety standards. As the firm grew, so did the volume and diversity of its data. Project information was spread across legacy on-premises systems and departmental silos, making it increasingly difficult for employees to locate critical materials quickly. To overcome this, company partnered with Dedicatted to implement Amazon Q Business, an AI-powered assistant built on Retrieval-Augmented Generation (RAG) technology, to transform fragmented data into a centralized, intelligent knowledge platform. ## Want to get your copy of case study? Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## The Challenge As our client expanded its operations and project portfolio, the company’s knowledge landscape became increasingly fragmented. Critical information: from engineering blueprints to compliance reports was scattered across legacy on-premises systems, shared drives, and departmental silos. What once was manageable had grown into an operational bottleneck that slowed teams down and hindered collaboration. - **Slow, inconsistent information retrieval:** Employees often spent significant time searching through shared drives and outdated repositories to locate the right documents, leading to inefficiencies across project management, design, and compliance teams. - **Duplicate efforts and information silos:** Because documentation was stored in multiple systems, teams frequently recreated or revalidated existing materials, slowing project execution and increasing the risk of errors. - **Limited collaboration and visibility:** The lack of centralized access hindered cross-departmental coordination, particularly between engineering, sales, and regulatory groups. - **Need for modern AI-assisted access:** The company sought a secure, centralized knowledge platform capable of understanding natural language queries, providing intelligent summaries, and delivering context-aware answers across structured and unstructured data. Without a robust AI-driven solution, our customer risked slower project turnaround, higher operational costs, and potential errors in client deliverables. ## Technology & Approach Dedicatted implemented a secure, AI-powered knowledge management solution built on **Amazon Q Business** with Retrieval-Augmented Generation (RAG) technology. The platform was designed to centralize our client’s extensive documentation, streamline access through natural language queries, and empower teams to work smarter , without disrupting established workflows or requiring steep technical adaptation. The solution focused on intelligent information retrieval, simplified collaboration, and scalable knowledge governance, fully aligned with AWS best practices in enterprise AI and data security. Key components of the solution: - **AI-Powered Conversational Assistant (Amazon Q):** Powered by Retrieval-Augmented Generation (RAG) technology – a cutting-edge AI assistant designed to transform how teams interact with corporate knowledge. With Amazon Q, employees can simply “chat” with their data, asking questions in natural language and instantly receiving accurate, context-aware responses drawn from internal company materials rather than generic web sources. - **Centralized Knowledge Hub:** All technical, regulatory, and operational documentation was unified within a single, secure repository. This eliminated silos, reduced duplication, and enabled cross-departmental access to reliable, version-controlled information. - **Role-Based Access & Permissions:** Granular access controls were implemented to ensure security and compliance. Teams now have tailored permissions based on department and subscription level (e.g., Pro vs. Lite), allowing administrators to easily manage data governance. The implementation focused on **simplicity and accessibility**. Since customer teams weren’t ready for complex system changes, Dedicatted ensured that Amazon Q was seamlessly integrated into the existing corporate environment. The solution offered an intuitive interface and intelligent access controls, allowing administrators to easily manage permissions, oversee document organization, and grant role-based access across departments. [![Illustration explaining how a large language model retrieves and answers a user's question using a knowledge base. The process starts with the user asking, 'What was done at Greater Binghamton Airport?' The system fetches similar document chunks from the knowledge base, identifies related pieces of documents, and uses the large language model to generate an answer. The image includes labeled steps: 'Fetching similar Document Chunks,' 'Fetching chunks related to your question,' 'Knowledge Base,' 'Related documents from the Knowledge Base,' and 'Answer,' accompanied by an example of a chat interface displaying the airport project explanation.](https://dedicatted.com/wp-content/uploads/2025/10/Group-21-1-2-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/10/Group-21-1-2-scaled.png)## Outcome The successful implementation of the project has delivered measurable value across the organization. - **Enterprise AI Solution [Deployment](https://dedicatted.com/services/glossary-of-terms#term-deployment):** Amazon Q has been fully integrated with the corporate knowledge base, providing employees with intelligent, context-aware access to internal documentation. The platform enables natural language queries across previously shared information sources, significantly reducing time spent for information search. - **Comprehensive Knowledge Transfer & Team Enablement:** Two targeted workshops were conducted to ensure organizational readiness: - User Training Workshop – Equipping end-users with practical skills to leverage Amazon Q effectively in their daily workflows - Administrator Training Workshop – Enabling IT and admin teams to manage, monitor, and optimize the solution independently - **Managed Access for the teams:** Different levels of permissions were implemented – by subscription (Pro vs Lite) and for department access (Marketing, Divisions, or both) – giving the necessary level of access to the right employees - **Sustainable Foundation for Continuous Improvement:** The organization now possesses the infrastructure and expertise to continuously improve their knowledge management capabilities. Teams can immediately apply insights from historical projects to current initiatives, reducing redundant research and accelerating decision-making processes. What began as a struggle to manage vast amounts of project data has become a story of operational excellence and AI-powered transformation. Amazon Q has turned manual, repetitive tasks into automated, reliable workflows, freeing teams to focus on strategic thinking and creative problem-solving. Our customer now stands at the forefront of intelligent knowledge management, ready to harness AI as a true competitive advantage for years to come. [![Banner promoting Amazon Q. On the left, there's an image of a guide titled ](https://dedicatted.com/wp-content/uploads/2025/09/Group-158-1.png)](https://dedicatted.com/wp-content/uploads/2025/09/Group-158-1.png) **Categories:** Case study **Services:** Cloud and Infrastructure Security, Cloud Modernization, Generative AI **Industry:** Manufacturing --- ### [Amazon takes on Microsoft and Google in the workplace with new ‘Quick Suite’ business AI platform](https://dedicatted.com/insights/amazon-takes-on-microsoft-and-google-in-the-workplace-with-new-quick-suite-business-ai-platform) **Published:** November 7, 2025 **Author:** Serhii Semenchenko **Content:** AI has transformed our personal lives: smart assistants, recommendation engines, intuitive apps. Yet, in the workplace, many organizations still struggle to harness AI effectively. **[Amazon Quick Suite](https://aws.amazon.com/ru/quicksuite/)** changes that. It’s an AI-powered workspace , that allows users to interact with company systems using natural language while the platform executes actions on their behalf. The system can access structured and unstructured data from both internal and external sources, and users can query the system conversationally to find data, generate analyses. Beyond simple question-answering, Quick Suite is designed to perform end-to-end actions. For example, an agent can create or update opportunities in Salesforce, log support tickets in Jira or ServiceNow, or trigger financial operations like reconciling accounts or processing invoices. The platform supports integration with enterprise APIs, enabling organizations to define and manage their own workflows. [![A diagram titled ](https://dedicatted.com/wp-content/uploads/2025/10/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-31T165731.218.jpg)](https://dedicatted.com/wp-content/uploads/2025/10/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-31T165731.218.jpg)## **Spaces : The Playground Where Work Happens** Quick Suite introduces agentic AI workspaces, where interacting with your systems is as natural as chatting with a colleague. Ask questions, request reports, or trigger workflows – Quick Suite executes tasks on your behalf. It can parse structured databases, unstructured documents, and external sources, turning information into insights and actions through conversational prompts. Quick Suite organizes work into **Spaces**, which are like project pods for teams or initiatives. Each Space combines: - **Conversational AI chat** for instant insights. - **Document understanding** to make sense of contracts, reports, and manuals. - **Dashboards and BI tools** to visualize performance. - **Workflow automation** for repetitive tasks. With **[Dedicatted](https://dedicatted.com/our-partners/aws)** , you can take Spaces further: we customize agents for your teams, configure role-based access, and even build complex automations across multiple systems, turning your scattered data into a single actionable hub. ## **Features That Drive Real Business Outcomes** Forget jumping between tabs and dashboards. Quick Index brings all your data into one trusted environment, so every decision you make comes with full context and zero guesswork. **Quick Index** is your bridge to every data source and app you already use. With 50+ native connectors, from Adobe Analytics and Snowflake to SharePoint, Outlook, and ServiceNow – Quick Index turns silos into synergy. And thanks to OpenAPI and Model Context Protocol (MCP) integrations, you can securely link to over 1,000 additional platforms like Asana, Atlassian, Box, Canva, PagerDuty, and Zapier.. 1. Once you’ve connected your data to Quick, you can start interacting with the chat assistant. You can ask Quick to write and send communications for you, or if you want Quick to write in your style or for a particular task (like writing a case study), you can use natural language or point Quick at existing guides or documentation to create a custom agent able to communicate in your intended style. Complex research that once took weeks now takes minutes. Whether you’re analyzing legislation, evaluating markets, or exploring emerging tech, Quick Research gives you the context and credibility you need to make smart decisions fast. **[Quick Research](https://aws.amazon.com/quicksuite/research/)** is the most accurate and reliable research agent on the market, ready to answer your most in-depth questions. It’s like having your own personal Ph.D. to provide comprehensive answers and reports to questions that require extensive research. It uses sophisticated analysis capabilities and extended processing to dive into your company’s data, and the public internet, including real-time information from 200+ outlets. [![A screenshot of the Amazon QuickSuite ](https://dedicatted.com/wp-content/uploads/2025/10/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-31T183215.920.png)](https://dedicatted.com/wp-content/uploads/2025/10/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-31T183215.920.png)**Quick Flows: [Automation](https://dedicatted.com/services/glossary-of-terms#term-automation) for everyone**. With Quick Flows, any user can automate repetitive tasks by describing their workflow using natural language without requiring any technical knowledge. Quick Flows fetches information from internal and external sources, takes action in business applications, generates content, and handles process-specific requirements. Quick Sight takes the complexity out of analytics, helping every team make data-driven decisions. It’s BI that feels less like a dashboard and more like a dialogue. **[Quick Sight ](https://aws.amazon.com/ru/quicksuite/quicksight/)**provides AI-powered business intelligence capabilities that transform data into actionable insights through natural language queries and interactive visualizations, helping everyone make faster decisions and achieve better business outcomes. [![A screenshot of the Amazon QuickSuite ](https://dedicatted.com/wp-content/uploads/2025/10/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-31T183912.969.png)](https://dedicatted.com/wp-content/uploads/2025/10/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-31T183912.969.png)**[Quick Automate](https://aws.amazon.com/ru/quicksuite/automate/)** helps technical teams build and deploy sophisticated automation for complex, multistep processes that span departments, systems, and third-party integrations. Using AI-powered natural language processing, Quick Automate transforms complex business processes into multi-agent workflows that can be created merely by describing what you want to automate or uploading process documentation. Quick Automate includes enterprise-grade features such as user role management and human-in-the-loop capabilities that route specific tasks to users or groups for review and approval before continuing workflows. The service provides comprehensive observability with real-time monitoring, success rate tracking, and audit trails for compliance and governance. ## **Why choose Dedicatted for Amazon Quick Suite implementation** AWS Quick Suite is a powerhouse of AI-driven innovation , but like any powerful engine, it needs the right hands on the wheel to truly accelerate business performance. That’s where Dedicatted comes in. - With over 10 years of experience in the tech industry, Dedicatted has delivered 20+ successful data science and AI projects. We have a team of skilled Generative AI developers, who are well-versed in various verticals to help you identify where Quick Suite can drive the greatest value: whether it’s accelerating research, automating operations, or optimizing decision-making. - Quick Suite is most powerful when it connects everything. We handle the technical heavy lifting : integrating Quick Suite with your existing applications, databases, and APIs so that your workflows run smoothly from one platform to another. - We adhere strictly to security protocols and regulatory frameworks, including ISO 27001:2013, PCI DSS, ISO 9001:2015, and GDPR, to guarantee the protection and integrity of data. In short, **we make Quick Suite work for you, not the other way around.** Dedicatted turns enterprise AI from a promising concept into a daily productivity powerhouse, helping your teams work smarter, respond faster, and unlock the full potential of AWS innovation. [![The missing ingredient for automation success? An experienced team](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-29-1024x205.png)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-29-1024x205.png) **Categories:** Blog **Services:** Agentic AI, Cloud Modernization, Generative AI, Managed Services **Industry:** Financial Services, Healthcare, High Tech Software, Manufacturing --- ### [Agentic AI Development: Be-All and End-All Leader’s Guide](https://dedicatted.com/insights/agentic-ai-development-be-all-and-end-all-leaders-guide) **Published:** November 19, 2025 **Author:** Denys Yermakov **Content:** Technology evolution is continuous, rapid and unapologetic. The modern enterprise doesn’t just innovate. It executes. Agentic AI adoption serves as a pivotal force that propels companies to bridge the gap between technology investments and measurable business impact. Now, the next catalyst for broader AI innovation is here: Agentic AI – the latest development under the intelligent automation umbrella that promises to shape customer interactions and redraw the map of enterprise value creation. While many companies are still grappling with a holistic approach to generative AI (GenAI) at scale, they’re now facing the need to factor the next phase of AI disruption from agentic AI into their enterprise strategies. In the year ahead, companies need to move at a high speed while grounding their AI investments in tangible, pragmatic business goals and use cases. As we advance from prompts that serve answers to agents that “think” and act in radically new workflows, companies must build an ecosystem where AI agents collaborate with humans to drive business outcomes. For enterprises, establishing enablement programs that support adoption and governance to ensure safety and empower transparency will become paramount. [![“Diagram illustrating types of AI arranged in nested circles showing increasing value from basic AI to Analytical AI, Conversational AI, Generative AI (GenAI), and Agentic AI. Each tier includes examples: Analytical AI flags suspicious payments; Conversational AI enables chatting with a bot or paying bills with a voice assistant; Generative AI provides personalized content and generates reports; Agentic AI selects and makes purchases and resolves payment disputes. A vertical arrow labeled ‘Value’ rises beside the circles.](http://dedicatted.com/wp-content/uploads/2025/11/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-31T194438.336.jpg)](https://dedicatted.com/wp-content/uploads/2025/11/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-31T194438.336.jpg)## **What is Agentic AI & What’s in Store for the AI-Enabled Enterprise?** As the “last mile” in scaling AI, automation represents the critical phase where ideas and deployment efforts coalesce into tangible results. Within the realm of automation lies an emerging subset that’s reshaping the narrative – Agentic AI. While traditional robotic process automation (RPA) tools have served enterprises well , handling repetitive workflows and bringing efficiency, automation systems need to go beyond the basics. Agentic AI can optimize and refine processes, ensuring that companies achieve results from technological undertakings. And sometimes, with the right use case, agentic AI can reinvent the entire end-to-end value chain – connecting disparate processes and making new decision trees possible with and without human actors. [![A gradient purple-blue infographic titled ](http://dedicatted.com/wp-content/uploads/2025/11/unnamed-82.jpg)](https://dedicatted.com/wp-content/uploads/2025/11/unnamed-82.jpg)## **How Can Enterprises Gain Long-Term Competitive Advantage?** Adopting agentic AI only works when you focus on fulfilling a business need, ground your approach in human-centric principles and apply sound engineering practices integrated across data, security, cloud and technology ecosystems, supported by a skilled talent pool and effective process governance. Otherwise, your investment will have been all for naught. On this journey, executives run into large, complex challenges that require enterprise-wide alignment. These highly interconnected focus areas are make or break when it comes to scaling agentic AI. ### ADOPTION & ORGANIZATIONAL BUY-IN Developing agentic AI solutions at scale requires robust infrastructure, significant investment and coordination across departments. This demands that you balance pilot programs with scaling up enterprise-wide solutions effectively. ### RETURN ON INVESTMENT Demonstrating tangible ROI from agentic AI can be daunting. Many organizations face delays in seeing measurable outcomes, causing hesitation in further investment. You must focus on both immediate wins and long-term payoffs when building your strategy. ### SCALABILITY & INFRASTRUCTURE Scaling AI initiatives enterprise-wide while maintaining performance and reliability is critical, but difficult. You need to build a robust cloud, data and security infrastructure capable of supporting large-scale deployments, often under budget constraints and while coordinating across departments. ### TECHNOLOGY INTEGRATION Integrating agentic AI into existing IT ecosystems can be complex. Legacy systems and siloed architectures often create compatibility issues, increasing deployment time and costs. Modernizing systems that provide a solid AI foundation on which to build use cases while maintaining operational continuity is an essential balancing act. ### DATA QUALITY, ACCESSIBILITY & MANAGEMENT Agentic AI thrives on quality data, but ensuring data accessibility, accuracy and privacy is a significant hurdle. You must effectively manage vast and diverse datasets, giving your organizations access to clean, organized, protected and industry-specific information to train AI systems effectively. ### SECURITY RISKS Implementing agentic AI introduces new cybersecurity vulnerabilities, such as model hacking or data breaches. Building a new security model that covers attack vectors and fortifies defenses is a top priority to ensure operational resilience against evolving cybersecurity threats. Each part is a vital element to becoming a true AI-native enterprise. AI is a top-down initiative; without leadership at the helm, organizations cannot harness agentic AI. Enterprises that balance technology with enablement, vision with execution and automation with intelligence will be well-positioned to lead in the fast-evolving world of AI-driven transformation. To capture the business value you seek, each focus area must work in harmony , this is what we do here at **[Dedicatted.](https://dedicatted.com/contact "Contact")** [![A two-part diagram comparing today's customer service desk with an agentic AI-based customer service desk. The top section (](http://dedicatted.com/wp-content/uploads/2025/11/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-31T194948.829.jpg)](https://dedicatted.com/wp-content/uploads/2025/11/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-10-31T194948.829.jpg)## **Smarter Science: How Dedicatted Brought Agentic AI to Chemical Reporting at Cassen Laboratories** Cassen Testing Laboratories is a Canadian, enterprise-level, ISO 17025-accredited chemical and environmental health laboratory based in Toronto, Ontario. Active in the ecological services, inspection, and testing industry, Cassen provides accredited testing for VOC profiling, indoor air quality, environmental forensics, and customizable analytical packages. Its customers include government agencies, healthcare providers, industrial hygiene firms, environmental consultants, and private sector companies based in Canada and around the world. These clients rely on Cassen’s reports to meet strict compliance standards, guide safety and remediation decisions, and protect public health. This project, **[led by Dedicatted](https://dedicatted.com/contact "Contact")**, is among Canada’s first deployments of agentic AI in the environmental testing field. It introduces a multi-agent AI platform, partially powered by Amazon Nova models, into a highly regulated, precision-focused environment. Combining domain expertise with advanced AI, Cassen has not only improved its back-end processes but also opened new opportunities for services in both B2B and B2C markets. - With over 10 years of experience in the tech industry, Dedicatted has delivered 20+ successful data science and AI projects. We have a team of skilled AI developers, who are well-versed in various verticals to help you identify where it can drive the greatest value: whether it’s accelerating research, automating operations, or optimizing decision-making. - We adhere strictly to security protocols and regulatory frameworks, including ISO 27001:2013, PCI DSS, ISO 9001:2015, and GDPR, to guarantee the protection and integrity of data. [![The missing ingredient for automation success? An experienced team](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-29-1024x205.png)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-29-1024x205.png) **Categories:** Blog **Services:** Agentic AI, Cloud Migration, Generative AI, Managed Services **Industry:** Financial Services, Healthcare, High Tech Software, Manufacturing --- ### [Summary of all the biggest updates from AWS’ big tech show re:Invent 2025](https://dedicatted.com/insights/summary-of-all-the-biggest-updates-from-aws-big-tech-show-reinvent-2025) **Published:** December 11, 2025 **Author:** Dedicatted **Content:** Amazon Web Services’ annual tech conference AWS re:Invent has wrapped. And the singular message, amid a deluge of product news and keynotes, was AI for the enterprise. This year it was all about upgrades that give customers greater control to customize AI agents, including one that AWS claims can learn from you and then work independently for days. Amazon CTO Dr. Werner Vogels capped off the final night with a keynote aimed at lifting up developers and assuaging any fears that AI is coming for engineering jobs. AWS re:Invent 2025, which runs through December 5, started with a keynote from AWS CEO Matt Garman, who leaned into the idea that AI agents can unlock the “true value” of AI. The next day, Swami Sivasubramanian, AWS VP of Agentic AI, doubled down on this narrative. His message was: we’re entering a new era where natural language is all you need to describe an outcome – and agents simply build the solution. While AI agents were the headline story, AWS delivered a wave of additional announcements throughout the week. Here’s a concise recap of the highlights. ## **AI & ML: The New Agentic Era at AWS** AWS just redrew the AI landscape – again. What used to be “models and assistants” has evolved into a fully-fledged ecosystem of autonomous agents, custom silicon, and enterprise-ready AI infrastructure. This year’s announcements weren’t incremental, they marked a turning point. ### Amazon Nova 2: The Agent-Ready Model Family AWS expanded its flagship Nova model suite with four powerful additions, each engineered for real-world production use. - Nova 2 Sonic – Human-Level Voice Interactions: Speech-to-speech conversations with multilingual support, natural prosody, and contextual memory. Ideal for customer service, live assistance, and global voice experiences. - Nova 2 Lite – Fast, Efficient, and Scalable: Optimized for cost and speed, with a huge context window for long documents, logs, or multi-hour conversations. Designed for workloads where latency and cost per token matter. - Nova 2 Omni – Multimodal Intelligence in One Model . Accepts text, images, video, and audio as input and outputs text or images. One model, many modalities , removing the need for separate pipelines. - Nova Act – The Breakthrough: Autonomous Browser [Automation](https://dedicatted.com/services/glossary-of-terms#term-automation) . It is an AI agent built for web workflow automation and it’s now GA. (Surpassed 90% automation reliability in testing. Hertz achieved 5× faster development cycles using Nova Act in production) Pricing remains the standout factor. Nova 2.0 Pro is listed at 1.25 dollars per million input tokens and 10 dollars for output. According to Artificial Analysis, a benchmark run cost about 662 dollars with Nova 2.0 Pro, compared to 817 dollars for Claude 4.5 Sonnet and 1,201 dollars for Google Gemini 3 Pro. [![“A comparison chart of four Nova 2 AI model tiers—Lite, Pro, Omni, and Sonic—showing supported input and output modalities. Lite accepts text, code, images, video, and documents, producing text and code. Pro and Omni accept the same inputs plus audio; Pro outputs text and code, while Omni outputs text, code, and images. Sonic accepts text and speech and outputs text and speech. All models indicate support for 1M input tokens.”](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-10T134113.417-1.png)](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-10T134113.417-1.png)Beyond Nova Act, AWS is also introducing three frontier agents designed to work as persistent teammates across the software lifecycle. **Kiro** acts as a virtual developer that keeps long term project context, proposes changes across repositories and picks up engineering tasks like bug triage and code cleanup inside tools such as GitHub and Jira. Early users say it automates **20-30% of routine dev work**. The **AWS Security Agent** focuses on secure design and implementation. It scans code, reviews architecture documents and can run targeted penetration style checks, then suggests fixes so security review becomes continuous rather than a late stage gate. The **AWS DevOps Agent** targets incidents and operations, tying into observability tools and CI or CD systems to help isolate root causes, propose remediation steps and automate routine runbook work. Already being tested by Commonwealth Bank of Australia and Western Governors University. Together, the three agents embody Amazon’s “agents are the new cloud” narrative. ### ****Nova Forge: Custom Frontier Models Without Starting From Scratch**** With Nova Forge, Amazon is trying to move beyond shallow fine tuning and into shared frontier training. Organizations get access to pre trained, mid trained and post trained checkpoints, then mix in proprietary data at each stage. Those custom systems are called “Novellas**“** – private frontier models that keep Nova’s general reasoning while absorbing domain specific knowledge, policies and tone. Early users include Booking.com, Sony, Reddit and several financial and life sciences firms. Forge also adds reinforcement learning “gyms” where models practice inside simulated environments, plus tools for distilling smaller, faster variants and a safety toolkit for guardrails and policy enforcement. Otherwise, your investment will have been all for naught. On this journey, executives run into large, complex challenges that require enterprise-wide alignment. These highly interconnected focus areas are make or break when it comes to scaling agentic AI. ### **Bedrock AgentCore: Production-Ready Autonomous Systems** Amazon Bedrock AgentCore is an agentic platform to build, deploy, and operate highly capable agents securely at scale . As part of Amazon Bedrock, AWS’s fully managed service for building and scaling generative AI applications without managing infrastructure, Agent Core extends these capabilities to agentic systems. While experimentation and proofs of concept are valuable, they only deliver real business impact once agents are operational in production. It is this transition into production that introduces significant hurdles, including security, scalability, interoperability across heterogeneous agents, handling large payloads, and supporting long-running processes. Agent Core directly addresses these challenges by providing an enterprise-grade environment that unifies runtime execution, communication, state management, identity, and observability. It is framework- and model-agnostic, allowing organizations to use their preferred tools such as CrewAI, Strands, or LangGraph, and foundation models ranging from open source to providers like OpenAI, Google Gemini, or Anthropic (even those not available in Amazon Bedrock’s base model API). AWS strengthened Bedrock’s agent framework with several enterprise must-haves: - Policy Engine (Preview): Natural language rules that constrain agent behavior (“never delete customer data”). - Evaluations: 13 built-in quality and safety monitors. - Memory: Persistent learning from past interactions. Companies like MongoDB, Swisscom, and the PGA Tour are already scaling agents with these tools. [![A diagram titled 'Amazon Bedrock AgentCore' showing how an application interacts with the AgentCore Runtime. The runtime includes agent instructions, local tools, and agent context, connected to underlying AI models. AgentCore Memory and CloudWatch GenAI Observability integrate with the runtime. On the right, AgentCore Gateway, AgentCore Browser, and AgentCore Code Interpreter connect through AgentCore Identity. Arrows illustrate data flow between components within the agent framework.](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-10T135812.206.jpg)](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-12-10T135812.206.jpg)### **Accelerated ML Development** MLflow Integration with SageMaker in serverless mode means data scientists can track experiments and manage models with zero setup, seamlessly alongside SageMaker’s pipelines. HyperPod Checkpoint-Free Training enables elastic training on distributed clusters. Large model training jobs can recover from hardware failures in minutes without manual checkpoints and auto-scale across thousands of accelerators, boosting utilization and cutting training costs by up to 40%. ## **Compute & Infrastructure: The Silicon Advantage** Amazon is stepping deeper into custom silicon with the debut of its Graviton5-powered EC2 M9g instances, a major architectural leap that could reshape how enterprises run large-scale cloud workloads. Performance gains are meaningful: up to 25% higher compute versus the previous generation, powered by 192 CPU cores and a 5x larger L3 cache. With Graviton now responsible for more than half of all new CPU capacity added to AWS for three consecutive years, adoption is becoming widespread. Ninety-eight percent of the top 1,000 EC2 customers, including Airbnb, Adobe, Atlassian, Epic Games, Formula 1, Pinterest, SAP, Siemens, Snowflake, and Synopsysare already benefiting from Graviton’s price-performance profile. The architecture also incorporates faster memory speeds, higher bandwidth on both networking and storage, and improved energy efficiency, offering a very compelling mix of performance scaling and lower infrastructure cost. ### **Trainium3 UltraServers** AWS introduced a new version of its AI training chip called Trainium3 along with an AI system called UltraServer that runs it. The TL;DR: This upgraded chip comes with some impressive specs. It’s got 4x more performance over Trainium2, which is fantastic. It’s also going to be 40% more performance per watt, which is obviously very, very important as we think about how much compute we can get out of every watt of power that we put into the device. And that makes it about 40% better energy efficient as well when we go from Trainium2. We’ve also increased the memory bandwidth by 50%. #### David Brown, VP of AWS Compute and Machine Learning Services AWS also announced it is actively developing Trainium4, designed to bring performance improvements including at least six times the processing performance in FP4 precision, three times the FP8 performance and four times more memory bandwidth ### **AWS Lambda Managed Instances: Serverless Flexibility Meets EC2 Cost Models** Under the hood, AWS Lambda Managed Instances utilize capacity providers to organize EC2 instances based on specific compute characteristics such as instance type and scaling parameters, configured via the Lambda console or IaC tools. With this setup, efficient resource utilization is ensured by routing requests to pre-provisioned execution environments, thereby minimizing cold starts and enabling multiconcurrency. Moreover, during traffic spikes, AWS automatically scales by launching new instances. At the same time, built-in safeguards prevent resources from being overwhelmed, maintaining a serverless operational model with no manual configuration or instance management. ## **AWS AI Factories – On-Premises, Fully Managed AI Clouds** Amazon Web Services (AWS) is introducing its own on-premises AI factory, bringing cloud innovation and control directly into customer facilities and reshaping how organizations run AI alongside their ERP systems. [The new AWS AI factories](https://www.aboutamazon.com/news/aws/aws-data-centers-ai-factories), announced at the 2025 AWS re:Invent, install Nvidia GPUs, AWS Trainium chips, high-speed networking, storage, and security into customers’ existing data centers. The factories also are wrapped with managed services like Amazon Bedrock and SageMaker so organizations can develop and deploy AI applications at scale without building their own GPU data center from scratch. [![A complex architecture diagram showing a machine-learning data pipeline and platform. On the far left is a column labeled ](https://dedicatted.com/wp-content/uploads/2025/12/Fig2-FactoryArch-1024x548-1.png)](https://dedicatted.com/wp-content/uploads/2025/12/Fig2-FactoryArch-1024x548-1.png)AWS described the offering as a dedicated AI infrastructure that “operates like a private AWS Region” inside the customer facility, giving low-latency access to compute, storage, and AI services while helping meet security, sovereignty, and regulatory requirements. Saudi Arabia’s HUMAIN initiative, for instance, is deploying an AI Zone with **150,000 AI chips** using this model. For ERP-heavy organizations in regulated sectors, AWS AI factories effectively move the AI development and inference plant into the same physical footprint as the ERP core. Sensitive data can stay in existing data centers and jurisdictions, while AI workloads still use AWS services and Nvidia’s full-stack software without bespoke integration of GPUs, storage, and networking. Per Network World, the factories combine the on-premises control of AWS Outposts with the broader service catalog of AWS Local Zones, promising both low-latency access to ERP data and a wider palette of AI and agentic services. That positions AWS as one of several competing “factory as a service” providers, but with the benefit of its two decades of cloud operations and native integration into the broader AWS ecosystem. ## **Data & Analytics: Cheaper, Faster, More Collaborative** AWS continues to push the boundaries of cost efficiency, data privacy, and performance across its cloud stack. One of the most exciting updates is the expansion of Clean Rooms ML, which now allows multiple organizations to collaborate on machine learning using privacy-preserving synthetic datasets – a huge win for teams operating in regulated environments. Cost savings also took center stage with the introduction of Database Savings Plans, offering up to 35% reductions across services like RDS and DynamoDB, already proving valuable for companies such as Vanguard and SmugMug. RDS itself received major upgrades, including support for up to 256 TiB of storage, CPU optimizations that can cut licensing costs by more than half, and the addition of a free SQL Server Developer Edition for development and testing. Even deployment workflows get smoother thanks to a new one-click Vercel integration. On the search and analytics front, OpenSearch now delivers a significant GPU-powered boost, enabling vector search performance up to ten times faster at a fraction of the previous cost. Altogether, it’s a strong wave of updates focused on flexibility, performance, and real-world savings. - With over 10 years of experience in the tech industry, Dedicatted has delivered 20+ successful data science and AI projects. We have a team of skilled AI developers, who are well-versed in various verticals to help you identify where it can drive the greatest value: whether it’s accelerating research, automating operations, or optimizing decision-making. - We adhere strictly to security protocols and regulatory frameworks, including ISO 27001:2013, PCI DSS, ISO 9001:2015, and GDPR, to guarantee the protection and integrity of data. Whether you want to experiment with Nova agents, optimize infrastructure with Graviton or Trainium, or build a secure agent platform with Bedrock – we’ll help you make the right moves, fast. Let’s explore how AWS’s new AI ecosystem can accelerate your roadmap. **[Book a consultation with Dedicatted](https://dedicatted.com/contact "Contact")** and start building the next phase of your business today. [![The missing ingredient for automation success? An experienced team](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-29-1024x205.png)](https://dedicatted.com/wp-content/uploads/2025/08/unnamed-29-1024x205.png) **Categories:** Blog **Services:** Agentic AI, Cloud Data Migration & Modernization, Cloud Modernization, Generative AI, Security Compliance **Industry:** Financial Services, Healthcare, High Tech Software, Manufacturing --- ### [Agentic RAG: Architecture Patterns That Actually Work for Enterprise AI](https://dedicatted.com/insights/agentic-rag-architecture-patterns-that-actually-work-for-enterprise-ai) **Published:** December 29, 2025 **Author:** Dedicatted **Content:** Large language models are great at synthesizing and less great at knowing. Ask “How did we do on revenue yesterday?” and a base LLM hits its knowledge cutoff, then confidently guesses. Retrieval Augmented Generation (RAG) fixed part of this by accessing relevant information to produce more accurate responses. Yet, baseline RAG still struggles when queries are ambiguous, multi-step, or spread across systems. Agentic RAG closes the gap by layering AI agents on top of RAG so the system can plan, decide what to retrieve, where to retrieve it from, how to validate it, and when to try again. In short, it graduates from “search + summarize” to “reason + act.” [**Dedicatted’ AI engineers** ](https://dedicatted.com/services/artificial-intelligence)break it down and give hands-on advice on implementing Agentic RAG architectures. [![Diagram illustrating RAG architecture, showing documents split into chunks, embedded into a vector database, retrieved as context, and used by an LLM to generate a response.](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-21T060447.738.jpg)](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-21T060447.738.jpg)## Quick refresher: what RAG is and where it breaks RAG is an architecture that lets a language model pull in the information it needs from external knowledge sources. Instead of answering from its own parametric memory, the model with RAG on board guides the prompt straight to the information retrieval component, or retriever. The relevant data, fetched from documents, internal company data, or specialized datasets are then passed to the generator, the second RAG component, which combines it with the model’s own memory to formulate the answer. In the typical RAG setup for a single app, say, a customer support chatbot, you park all your info in one vector database. Both retrieval and generation operate exclusively within that repository. In such cases, where your knowledge is already under one roof, a simple retrieve-then-generate pipeline is the shortest, cheapest path to production. #### Alex Rozdolskyi, Director of GTM Engineering ## **What is Agentic RAG and how does it work?** When the standard retrieval framework is enriched with different types of AI agents, it takes on the shape of Agentic RAG. The agents’ memory, reasoning and planning capabilities, and context-driven decision-making elevate a RAG pipeline, so that actions and external tool calls (except those that are pre-programmed or rule-based) are guided by explicit reasoning steps. That way, instead of simply pulling in documents and passing them to the model without much judgment, once the system is fed a query, the flow takes on several distinct turns: ### **1. Query pre-processing** Before retrieval, thanks to natural language processing capabilities, query planning agents, clarify vague or multi-meaning queries, expand them with synonyms, related terms, or context, segment complex queries into smaller, manageable sub-queries, and inject session or metadata context for more precise retrieval. ### **2. Routing and retrieval** Routing agents determine which knowledge sources and external tools (vector stores, SQL databases, calculators, APIs, web search, etc.) are used to address a user query. From here, information retrieval agents rank documents or chunks based on relevance, deduplicate and cluster similar content, and synthesize evidence across multiple sources for coherent context. ### **3. Multi-step reasoning over retrieved contex**t Reasoning agents perform higher-order operations on retrieved chunks, such as ranking, clustering, or synthesizing evidence across multiple documents rather than passing raw context directly to the model. It reduces noise and contradictions, so generated answers are better grounded and easier to trust. ### **4. Validation and control** Validation agents apply consistency checks, source verification, confidence scoring, or other evaluation mechanisms to filter and refine retrieved context before it informs generation. This lowers the risk of hallucinations and reinforces factual correctness in the generated output. ### **5. Orchestration of output generation** To ensure that the final response is not just a raw aggregation of retrieved content but a cohesive, context-aware answer that leverages multiple sources while minimizing contradictions or hallucinations, agents guide how the LLM produces the final output, structure answers (summaries, step-by-step, bullet points), select which evidence to emphasize, and trigger follow-up retrieval if gaps are detected. So, with RAG agents folded into retrieval and generation processes, the constraints we talked about earlier lose much of their grip. ## Agentic RAG architecture It’s worth noting that the division of labor across intelligent agents is an architectural choice. Some Agentic RAG setups rely on a single agent that plans, retrieves, reasons, and validates in sequence. This is called a single-agent RAG system. It keeps the pipeline simple and easier to maintain, though it lacks the modularity and parallelism of multi-agent systems, those with a team of specialized agents, each dedicated to a particular function in the pipeline. It’s usually a task complexity that dictates the breadth of agent involvement. [![Diagram illustrating Agentic RAG architecture, where a retrieval agent uses tools like vector search engines, calculator, and web search to query multiple collections and generate a response via an LLM.](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-21T060049.864.jpg)](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-21T060049.864.jpg)For example, in customer support, for FAQs like “How do I reset my password if I’ve lost access to my email?” which can be answered straight from one knowledge base, a single-agent setup does the job just fine. But once a request gets messy, touches multiple systems, or has more than one ask, like: “I was double charged for my subscription last month, and I also need to update my billing address. Can you fix this and tell me when my refund will arrive?” – that’s where you need more than one brain at work. A multi-agent setup can split the load, tackle each piece, and give the customer a cleaner, more accurate answer. [![Diagram of a single-agent RAG (Retrieval-Augmented Generation) system showing how a user query is routed to a retrieval agent, which selects tools such as vector search engines, a calculator, or web search, retrieves data from multiple vector collections, and passes the results to an LLM to generate the final response.](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-21T060310.566.jpg)](https://dedicatted.com/wp-content/uploads/2025/12/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-21T060310.566.jpg)## What Agentic RAG brings to the enterprise table The ultimate payoff of agentic RAG is response accuracy so high it raises the ceiling for enterprise AI, moving from surface-level questions to nuanced, high-stakes queries. This goes beyond what traditional RAG or RAG-free LLMs can deliver. It comes from agentic-powered iterative, self-directed retrieval, on-the-fly fusion of structured data and unstructured text, autonomous tool usage, and built-in verification. Besides, agentic RAG is easy to scale. Without overhauling the infrastructure, agents can be brought in for tougher, more complex work requiring extra parallelism or specialized skills and pulled back when tasks lighten. Building on the customer support example we mentioned above: suppose the current multi-agent RAG system has two agents – one handling FAQs (password resets, account setup) and the other managing billing issues (simple refunds, payment verification). Now, the company launches a loyalty program. Customers soon start asking questions like “How do I redeem my points?” or “Can I combine coupons with loyalty rewards?” This is where a specialized agent can be added quickly, thanks to the system’s modular design. Each additional agent increases token usage and tool calls. Costs will scale roughly linearly and you’ll eventually run into context-window limits. So it’s ‘easy to scale’ operationally (compute can expand), but not costless or limitless. #### Alex Rozdolskyi, Director of GTM Engineering ## **Where Agentic RAG is already paying off** #### Customer support automation Agentic RAG is arguably the real breakthrough in hyper-personalized customer support. While reading a client’s intent, mood, and the context behind their issue, agents simultaneously pull in every record from the CRM and unstructured data like emails, PDFs, etc. to build a complete picture of the customer. This context-rich background allows them to craft responses that don’t just tick off a request, but wow the client with the level of service and lock in their loyalty. #### Employee support optimization To level up IT support, enterprises plug a RAG helper into the helpdesk so tickets get answered quicker and employees can get back to work. As soon as IT support bot hears “VPN drops every afternoon,” it decides whether to pull VPN logs, DHCP lease tables, or the user’s laptop event history, then pre-assembles a ticket with the likeliest fix and any sibling issues. #### Clinical decision support systems Retrieval agents help healthcare professionals synthesize vast amounts of medical information, research papers, patient records, and drug databases, to produce more reliable, context-aware recommendations when needed. Simple LLM searches or traditional RAG would struggle with multi-step reasoning, cross-referencing symptoms, treatments, and contraindications. #### Legal research support With Agentic RAG, days-long legal drudge-work shrinks into a ten-minute chat. The agentic-powered LLM dives through statutes, rulings, and filings, surfaces the cases that matter, maps how they hang together, and hands the lawyer a ready-made argument trail. #### Investment analysis Multiple agents pull Form 10-K, the latest Fed minutes, and internal risk models, cross-check trends, and synthesize a one-page brief explaining why spreads are widening. Analysts skim, click “agree,” and move on. [![Banner promoting Dedicatted's agentic RAG development services, featuring a call to book a free consultation alongside a photo of experts discussing solutions at a tech event.](https://dedicatted.com/wp-content/uploads/2025/12/Group-149-7-scaled.png)](https://dedicatted.com/wp-content/uploads/2025/12/Group-149-7-scaled.png)## **Pro tips from the field for implementing an Agentic RAG system (so you don’t learn the hard way)** To lock in better results from your LLM-based enterprise solutions, consider these field-tested guidelines for building Agentic RAG architectures. The key challenge of any RAG implementation is ensuring a robust data pipeline and secure data storage. Always ensure that databases are protected and access to them is tightly controlled. - No matter how solid your agentic RAG setup is, hallucinations can still pop up. Agents can step on each other’s toes and compete for resources, and the more of them you throw in, the harder it is to keep things running cleanly. As a rule of thumb, keep the agent team as lean as possible for the task at hand. - Take the time to provide agents with a full picture of each tool’s capabilities. Explain how it works and what it’s best suited for, enabling agents to choose the right tool for the job. - Regularly review a subset of agent decisions to ensure reasoning aligns with expected business logic. If the agent’s confidence in a tool choice or document relevance is low, trigger either a human-in-the-loop review or fallback logic. - Remember GIGO: if external data don’t provide clear, detailed context, even the smartest agent will churn out poor results. To enhance response accuracy, look after your data quality and make sure your knowledge base documents pack enough relevant context, so agents pull the accurate information instead of garbage. - With more autonomy comes the need for oversight. Set up detailed logging, monitoring, and alerting in your RAG model so you can track agent actions, detect issues, and continuously improve system performance. ## **Where to take it next** Agentic RAG can already push quality and speed up a noticeable notch, but it still slams into the same ceiling every enterprise AI hits: garbage data, brittle tools, compliance walls, and cost caps. **[Our team can map an Agentic RAG architecture](https://dedicatted.com/contact)** to your stack (connectors, security, KPIs) and prototype a path to production in weeks, not quarters. **Categories:** Blog **Services:** Agentic AI, Generative AI **Industry:** Automotive, Financial Services, High Tech Software, Insurance --- ### [Developer Platform That Unblocks Delivery: Self-Service, Dynamic Environments, and an Embedded AI Assistant](https://dedicatted.com/insights/developer-platform-that-unblocks-delivery-self-service-dynamic-environments-and-an-embedded-ai-assistant) **Published:** January 19, 2026 **Author:** Dedicatted **Content:** ## About project #### Working time: 2024 – 2025 #### Industry: Retail #### The service: Internal Developer Platform (IDP) and AI knowledge base #### Overview **A large retail company** had a strong engineering team, but delivery slowed down as more teams joined and the organization grew. As the organization grew, developers increasingly depended on DevOps for routine tasks, while documentation and ownership were fragmented. The result was predictable: long onboarding, inconsistent delivery flows, and slow test environment provisioning. Dedicatted partnered with the customer to design and implement an Internal Developer Platform built on AWS that made everyday workflows self-service, standardized, and easy to navigate. The platform combined a Backstage-based portal, reusable templates and blueprints, automated test environment provisioning, and an embedded AI assistant connected to internal systems ## The Challenge The customer faced three types of friction that kept repeating across teams and projects. ### **Communication and collaboration** - **Information silos Changes were not consistently communicated across teams. - **Lack of clear escalation and support flow People did not know whom to contact for what, creating bottlenecks. - **Insufficient collaboration between Dev and DevOps** DevOps is often brought in too late, leading to rework and architectural mismatches. ### **Process and delivery** **Long onboarding** slowed teams down because critical documentation was scattered across multiple tools, poorly structured, and difficult to search, forcing developers to rely on tribal knowledge instead of clear guidance. **Unclear rules and ownership** across environments created constant hesitation, as teams were unsure what they were allowed to deploy, which environments they could use, and who was responsible for approvals or support. **Unpredictable timelines** for task execution further impacted delivery speed, with progress often blocked by DevOps dependencies, competing priorities, and a lack of visibility into when requests would actually be completed. ### **Tooling and automation** - **Manual or inconsistent deployment flows Too many workflows depended on tribal knowledge. - **Fragmented access management RBAC expectations were not unified, creating ticket noise. - **Abandoned and forgotten manual resources** Limited FinOps visibility increased the risk of orphaned infrastructure and uncontrolled spend. ## Want to get your copy of the case study? Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## Technology & Approach Dedicatted built a modular Developer Platform centered around a portal-first experience and “automation by default”. Key elements included: **Developer self-service portal (Backstage UI)**. A single entry point where engineers can: - access documentation and platform guidance - create services and components - provision infrastructure using standardized blueprints - launch pipelines [![Internal developer portal built with Backstage, featuring a searchable catalog of microservices, APIs, and system components.](https://dedicatted.com/wp-content/uploads/2026/01/unnamed-83.png)](https://dedicatted.com/wp-content/uploads/2026/01/unnamed-83.png)**Blueprints inventory (templates that teams actually use)**. We introduced a curated catalog of reusable templates to reduce variation and speed up delivery, including: - analytics and data templates (jobs, notebooks, pipelines, refresh patterns) aligned with AWS services such as AWS Glue and Amazon Athena - service templates (standard scaffolding and golden paths, including deployment patterns for AWS Lambda and containerized services on Amazon EKS) - infrastructure templates ([Terraform](https://dedicatted.com/services/glossary-of-terms#term-terraform) modules and platform patterns for provisioning AWS resources such as Amazon VPC, Amazon EKS clusters, Amazon RDS, and Amazon S3) [![Screenshot of the Backstage 'Create a New Component' page showing available templates. The interface includes a sidebar with navigation items like Home, APIs, Docs, and Create, and a main area displaying template cards for 'Documentation Template,' 'React SSR Template,' and 'Spring Boot gRPC Service,' each with descriptions, tags, and a 'Choose' button. A 'Register Existing Component' button appears in the top right.](http://dedicatted.com/wp-content/uploads/2026/01/unnamed-84-1.png)](https://dedicatted.com/wp-content/uploads/2026/01/unnamed-84-1.png)**Dynamic environments and lifecycle automation**. Automated creation and teardown of development and test environments with lifecycle rules. Environments can be linked to work items (for example Jira tasks), so they follow the workflow instead of living forever. **Embedded AI assistant and AI knowledge base**. A built-in assistant integrated into the developer experience (web UI first, designed to extend to IDE and plugins). The assistant uses controlled connectors to internal tools and documentation, enabling: - quick answers to platform questions without context switching - clearer onboarding guidance based on internal standards - workflow support for common tasks (for example environment requests and task-driven actions) **Integrations through controlled connectors (MCP-style)**. The platform was designed to connect to enterprise systems such as: - other internal tooling and operational platforms - All with clear boundaries, permissions, and auditability. - Jira and Confluence - HR or identity systems - Amazon Q Developer integration, enabling contextual assistance and workflow triggers ## **Implementation plan** We delivered the platform as an incremental rollout with fast feedback loops. 1. **Pilot and adoption rollout**. Start with a small group, then expand based on usage and feedback. 2. **Discovery and platform mappin**g. Identify highest-friction workflows and define golden paths. 3. **Portal foundation and catalog setup**. Backstage UI, component catalog structure, ownership model. 4. **Blueprints and standard template**s. Initial set of templates for services, infra, and data workflows. 5. **Dynamic environment provisioning**. Self-service flow, lifecycle rules, optional task linking. 6. **AI assistant and knowledge onboarding**. Documentation onboarding, connector setup, guardrails, and evaluation ## User experience and adoption The experience was intentionally simple and developer-friendly: - **One place to start Instead of searching across multiple systems, developers use the portal to find templates, docs, and actions. [![Web-based developer assistant interface showing a chat conversation with options for infrastructure management, Jira task management, and development support.](http://dedicatted.com/wp-content/uploads/2026/01/unnamed-85-2.png)](https://dedicatted.com/wp-content/uploads/2026/01/unnamed-85-2.png)- **Self-service without losing control Teams can provision what they need through standardized blueprints and policies. [![Internal developer portal built with Backstage, featuring a searchable catalog of microservices, APIs, and system components.](http://dedicatted.com/wp-content/uploads/2026/01/unnamed-83.png)](https://dedicatted.com/wp-content/uploads/2026/01/unnamed-83.png)- **Faster answers, less ticket noise The embedded assistant helps engineers find the right guidance and complete routine workflows with fewer handoffs. [![Developer assistant dashboard demonstrating automated testing environment creation, Jira integration, and status transitions.](http://dedicatted.com/wp-content/uploads/2026/01/unnamed-88-1.png)](https://dedicatted.com/wp-content/uploads/2026/01/unnamed-88-1.png)- **More consistent delivery** Standard templates reduce variability and make outcomes easier to support. [![Developer assistant dashboard demonstrating automated testing environment creation, Jira integration, and status transitions](http://dedicatted.com/wp-content/uploads/2026/01/unnamed-88-1.png)](https://dedicatted.com/wp-content/uploads/2026/01/unnamed-88-1.png)## **Our solution** The result was a scalable AWS-based Internal Developer Platform, built on AWS, that reduced operational friction and improved delivery consistency across teams. It combined: - Backstage-based portal for developer self-service - a blueprints inventory to standardize service and infrastructure creation - automated test environment provisioning with lifecycle management - an embedded AI assistant and knowledge base connected to internal systems ## **Business impact** The platform helped the organization shift from “ask DevOps for everything” to a model where teams can move faster with clear guardrails and minimal friction. By separating responsibilities between developers and DevOps, both groups can operate independently – without constant handoffs, communication bottlenecks, or delivery slowdowns caused by cross-team dependency. Key outcomes included: - faster onboarding through a single entry point for standards, templates, and workflows - reduced dependency on DevOps for routine actions and environment setup - clearer separation of responsibilities, eliminating inefficiencies caused by escalations and unclear support paths - more consistent delivery via reusable blueprints and golden paths - improved operational clarity through explicit ownership and environment rules - stronger cost hygiene by reducing the risk of long-lived, orphaned environments **Categories:** Case study **Services:** DevOps Consulting, Generative AI, Managed Services, Security Compliance **Industry:** Retail --- ### [GenAI-Powered Video Intelligence Platform for a Global Television Network](https://dedicatted.com/insights/genai-powered-video-intelligence-platform-for-a-global-television-network) **Published:** February 11, 2026 **Author:** Dedicatted **Content:** ## About project #### Working time: 2025-ongoing #### Industry: Media and Entertainment #### The service: AI/GenAI solution development on AWS #### Overview **A global television network** broadcasting in more than 150 countries and reaching over 450 million viewers needed to transform how it manages and discovers video content across production systems and archival libraries. With continuously expanding media volumes and increasing demand for faster production workflows, the organization required a scalable solution capable of understanding video context, improving content discoverability, and enabling teams to retrieve relevant footage using natural language. The objective was not only to improve search accuracy, but to create an intelligent foundation for content reuse, operational efficiency, and long-term monetization of media assets. ## The Challenge Our client managed extensive video libraries across production systems, archives, and internal repositories. While the content held significant editorial and commercial value, it was difficult to access in practice. Traditional metadata and keyword-based search methods could not capture the full meaning of video content. They relied on manual annotations and static tags, which were often incomplete, inconsistent, or outdated. As a result: - **Slow and inefficient content discovery** across archives and production libraries - **Heavy reliance on manual workflows and inconsistent metadata** - **Limited ability** to reuse and monetize existing content - **Lost productivity** for editors, producers, and content teams searching for relevant footage - **Lack of a scalable approach** to support future content growth The organization needed a platform that could automatically interpret video content, enrich it with contextual understanding, and make it searchable through natural language, while integrating with existing production environments. ## Want to get your copy of case study? Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). ## Technology & Approach Dedicatted designed and implemented a GenAI-powered video intelligence platform that combines automated ingestion, AI-driven analysis, semantic indexing, and conversational search within a unified cloud-native architecture. The solution was built to treat video not as static files, but as structured, searchable data. ### **Scalable ingestion and processing pipeline** Video assets from production and archives were stored and managed in **AWS S3**, providing durable, scalable storage as the foundation for processing workflows. Event-driven processing pipelines powered by **AWS Lambda** enabled automated ingestion and analysis of new and existing video content. This allowed the platform to process media continuously without manual intervention. Videos were segmented into meaningful units using time-based, scene-based, and camera-based segmentation strategies. Breaking long-form footage into structured segments enabled more accurate indexing and retrieval. [![“Four-step process diagram labeled: Content ingestion → Segmentation and metadata extraction → AI understanding and indexing → Natural language retrieval.](http://dedicatted.com/wp-content/uploads/2026/02/Group-24.png)](https://dedicatted.com/wp-content/uploads/2026/02/Group-24.png)### **Semantic enrichment and vector indexing** Generated captions, transcripts, and existing metadata were combined and enriched into a unified content representation. This information was converted into **vector embeddings**, enabling semantic understanding of content rather than keyword matching. Embeddings were indexed within a semantic search index powered by **OpenSearch / vector database**, allowing the system to identify relationships between scenes, themes, and concepts across the entire media library. This approach enabled users to search based on intent and meaning, even when exact keywords were not present in metadata ### **Natural-language search and API-driven access** A semantic search layer was exposed through **[API Gateway](https://dedicatted.com/services/glossary-of-terms#term-api-gateway)**, enabling applications and internal tools to query the video library using natural language. Editors, producers, and researchers could describe scenes, topics, or actions conversationally, and retrieve relevant video segments ranked by contextual relevance. This significantly improved the usability of archives and integrated discovery into existing production workflows. ### **Cloud-native orchestration and microservices architecture** The entire platform operated within a cloud-native microservices architecture, supported by AI orchestration pipelines managing ingestion, analysis, indexing, and retrieval workflows. This architecture ensured: scalability for growing video volumes, reliable processing performance and modular expansion for future GenAI capabilities. It was designed to operate at enterprise scale while maintaining flexibility ### Gen**AI-powered content understanding** AI models orchestrated through **Amazon Bedrock** were applied to interpret video content and generate structured descriptions. These models produced captions and contextual summaries describing: - Scene-level context - Subjects and participants - Objects and environments - Actions and events A core design principle of the platform is **cost-aware GenAI usage**. By applying GenAI only after deterministic validation steps, the system minimizes unnecessary inference calls and prevents low-confidence data from entering business systems. **Amazon Transcribe** was used to convert speech into text, adding an additional layer of searchable content through subtitles and dialogue analysis. This process transformed raw video into machine-readable knowledge, significantly reducing reliance on manual tagging. [![Here are three alt text options with different levels of detail: Version 1 - Concise: ](http://dedicatted.com/wp-content/uploads/2026/02/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2026-02-11T131742.343.png)](https://dedicatted.com/wp-content/uploads/2026/02/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2026-02-11T131742.343.png)## Business impact The GenAI-powered video intelligence platform delivered measurable improvements across content discovery, archive utilization, and operational efficiency: - **Faster content discovery:** Teams locate relevant footage across production libraries and archives significantly faster using semantic search. - **Reduced retrieval time:** Editors and producers spend less time navigating fragmented systems and manual metadata, accelerating production workflows. - **Improved archive utilization:** Previously underused footage became more accessible, increasing opportunities for content reuse and repurposing. - **Operational efficiency at scale:** Automated ingestion, transcription, captioning, and indexing reduced manual workloads while supporting continuous content growth. ### Long-Term Value - **Foundation for intelligent media operations:** Semantic understanding and natural-language search enable long-term transformation of how the network manages and leverages its content ecosystem. - **Enhanced monetization potential:** Improved discoverability allows the network to identify valuable assets for licensing, redistribution, and new content creation. - **Scalable AI-driven workflows:** The platform supports future content growth and can integrate additional AI capabilities over time. - **Enterprise-grade reliability:** Production-ready deployment ensures performance, scalability, and seamless integration with existing systems. This initiative marked a shift from treating video as static media to managing it as a dynamic, intelligent asset. Content teams can now navigate vast libraries with clarity and speed, uncovering relevant footage in ways that were previously impractical or impossible. What used to depend on manual effort and institutional knowledge is now supported by a system that understands context, intent, and relationships across content. Beyond improving day-to-day operations, the platform introduces a new way of thinking about media value. Archives become a strategic resource rather than a storage challenge, and discovery evolves from a technical task into a creative and commercial enabler. With a scalable AI foundation in place, the organization is positioned to continuously expand how it produces, reuses, distributes, and monetizes content – while adapting to the next generation of media workflows. **Categories:** Case study **Services:** Data Architecture, Generative AI **Industry:** High Tech Software --- ### [Migration of Canadian retail operations platform from IBM to cloud architecture on AWS](https://dedicatted.com/insights/migration-of-canadian-retail-operations-platform-from-ibm-to-cloud-architecture-on-aws) **Published:** April 27, 2026 **Author:** Dedicatted **Content:** ## About project #### Working time: 2025 – 2026 #### Industry: Retail, Logistics #### The service: Legacy App modernization, [Cloud Migration](https://dedicatted.com/services/glossary-of-terms#term-cloud-migration) #### Overview A major Canada-based retail corporation operating over 300 locations nationwide needed to modernize the core platform powering its supply chain, inventory management, point-of-sale (POS), and financial systems. For years, the business relied on a legacy IBM i (AS/400) environment that had grown into a mission-critical backbone for daily operations. While stable, the platform increasingly limited the company’s ability to scale, innovate, and compete in a rapidly evolving retail landscape shaped by e-commerce and real-time customer expectations. The organization partnered with Dedicatted to execute a large-scale modernization initiative, transforming millions of lines of legacy code into a modern, cloud-ready architecture. The goal was not only to migrate systems, but to unlock faster delivery cycles, enable digital commerce, and future-proof the retail technology stack. [![Group 351 1](https://dedicatted.com/wp-content/uploads/2026/04/Group-351-1-1024x278.png)](https://dedicatted.com/wp-content/uploads/2026/04/Group-351-1-1024x278.png)## The Challenge At the heart of the retailer’s operations was a deeply embedded legacy system: - 4.5 million lines of RPG IV code - 300,000 lines of CL scripts - 2,000 DSPF terminal screens - 1,800 DB2/400 tables This platform powered everything from **inventory and logistics to POS transactions and financial reporting** across all store locations. Despite its reliability, the system created growing constraints for the business. - **Limited talent availability**: Finding experienced RPG developers had become increasingly difficult, making it harder to maintain and evolve the platform over time. - **Slow release cycles**: Even minor changes required navigating tightly coupled codebases, resulting in release cycles stretching from 3 to 6 months – far too slow for modern retail demands. - **Lack of e-commerce readiness**: The architecture was not designed for API-based integrations, preventing seamless connectivity with modern e-commerce platforms and digital services. - **Operational rigidity**: Any transformation carried significant risk. The platform supported daily operations across 300+ stores, meaning downtime or disruption was not an option. They required a modernization strategy that could ensure uninterrupted business operations while systematically transforming both the application landscape and the underlying infrastructure. ## Technology & Approach Dedicatted delivered the transformation as a **factory-scale modernization program**, combining automation, phased delivery, and deep system analysis to ensure both speed and reliability. ### **Step 1: Deep system assessment and optimization** The engagement began with a four-month discovery phase, leveraging CAST Imaging and custom static analysis tools to fully map the application landscape. This analysis provided visibility into: - Cross-module dependencies across **six core business domains** - Code complexity and coupling - Redundant and unused logic As a result, the team identified 18% dead code, eliminating approximately 810,000 lines of unnecessary logic before migration began. This significantly reduced project scope and accelerated delivery timelines. ### **Step 2: Automated code transformation at scale** To handle millions of lines of legacy code, Dedicatted developed a custom automated conversion pipeline, transforming RPG IV → Java (Spring Boot). The pipeline achieved 82% automated code conversion, dramatically reducing manual effort while maintaining consistency and quality. Manual remediation was applied selectively to business-critical components, such as the **inventory pricing engine**, where precision and performance were essential. ### **Step 3: Modernizing the user experience** The legacy platform relied on 2,000 DSPF terminal-based screens, used daily by more than 20,000 employees across stores and operational centers. Dedicatted converted these interfaces into modern Angular-based web applications using custom-built tooling designed to: - Preserve existing workflows and user logic - Minimize retraining requirements - Improve usability and performance ### **Step 4: Phased migration with zero disruption** Given the scale of operations, the migration was executed through a carefully orchestrated, multi-phase rollout. The transformation was delivered across **five waves**, aligned with regional operations: - Western Canada - Central Canada - Eastern Canada To ensure data consistency throughout the transition, Dedicatted implemented Qlik Replicate CDC (Change Data Capture) to synchronize data between: Legacy DB2/400 systems and Target PostgreSQL database. his enabled a parallel-run strategy, allowing both systems to operate simultaneously until validation was complete, eliminating risk and ensuring a smooth transition. ### **Step 5: Building a modern, scalable architecture** The target platform was built using modern technologies designed for scalability and integration: **Java (Spring Boot)** for backend services , **Angular** for user interfaces, **PostgreSQL** for data management This new architecture introduced API-driven capabilities, allowing seamless integration with external systems, including e-commerce platforms and digital services. ## Business impact The modernization delivered immediate and measurable improvements across performance, efficiency, and business agility. - **Zero operational disruption**: All 300+ retail locations remained fully operational throughout the entire 20-month migration, ensuring uninterrupted customer service and business continuity. - **Significantly improved POS performance**: Average transaction times were reduced from 2.4 seconds to 1.1 seconds, delivering a 50% improvement in POS performance and enhancing the in-store customer experience. - **Reduced project scope and faster delivery**: By eliminating 18% of dead code, the team reduced complexity and saved approximately three months of development effort. - **E-commerce enablement**: The new API-driven architecture enabled seamless integration with a modern e-commerce platform, which was fully operational within 60 days of go-live. [![core service card 4](https://dedicatted.com/wp-content/uploads/2026/04/core-service-card-4-1024x262.png)](https://dedicatted.com/wp-content/uploads/2026/04/core-service-card-4-1024x262.png) **Categories:** Case study **Services:** Cloud Data Migration & Modernization, Cloud Migration **Industry:** Retail --- ### [Agentic RAG: what it is and its role in truly usable Enterprise AI](https://dedicatted.com/insights/agentic-rag-what-it-is-and-its-role-in-truly-usable-enterprise-ai) **Published:** February 10, 2026 **Author:** Denys Yermakov **Content:** Large language models are great at synthesizing and less great at knowing. Ask “How did we do on revenue yesterday?” and a base LLM hits its knowledge cutoff, then confidently guesses. Retrieval Augmented Generation (RAG) fixed part of this by accessing relevant information to produce more accurate responses. Yet, baseline RAG still struggles when queries are ambiguous, multi-step, or spread across systems. Agentic RAG closes the gap by layering AI agents on top of RAG so the system can plan, decide what to retrieve, where to retrieve it from, how to validate it, and when to try again. In short, it graduates from “search + summarize” to “reason + act.” [**Dedicatted’ AI engineers** ](https://dedicatted.com/services/artificial-intelligence)break it down and give hands-on advice on implementing Agentic RAG architectures. ## **Quick refresher: what RAG is and where it breaks** RAG is an architecture that lets a language model pull in the information it needs from external knowledge sources. Instead of answering from its own parametric memory, the model with RAG on board guides the prompt straight to the information retrieval component, or retriever. The relevant data, fetched from documents, internal company data, or specialized datasets are then passed to the generator, the second RAG component, which combines it with the model’s own memory to formulate the answer. [![Diagram illustrating a Retrieval-Augmented Generation (RAG) architecture, showing documents split into chunks, embedded into a vector database, retrieved as context, combined with a user query in a prompt template, and passed to an LLM to generate a response through three stages: retrieval, augmentation, and generation.](http://dedicatted.com/wp-content/uploads/2026/02/unnamed-89.jpg)](https://dedicatted.com/wp-content/uploads/2026/02/unnamed-89.jpg)## **What is Agentic RAG and how does it work?** When the standard retrieval framework is enriched with different types of AI agents, it takes on the shape of Agentic RAG. The agents’ memory, reasoning and planning capabilities, and context-driven decision-making elevate a RAG pipeline, so that actions and external tool calls (except those that are pre-programmed or rule-based) are guided by explicit reasoning steps. That way, instead of simply pulling in documents and passing them to the model without much judgment, once the system is fed a query, the flow takes on several distinct turns: - **[Query](https://dedicatted.com/services/glossary-of-terms#term-query) pre-processing** Before retrieval, thanks to natural language processing capabilities, query planning agents, clarify vague or multi-meaning queries, expand them with synonyms, related terms, or context, segment complex queries into smaller, manageable sub-queries, and inject session or metadata context for more precise retrieval. - ****Routing and retrieval**** Routing agents determine which knowledge sources and external tools (vector stores, SQL databases, calculators, APIs, web search, etc.) are used to address a user query. From here, information retrieval agents rank documents or chunks based on relevance, deduplicate and cluster similar content, and synthesize evidence across multiple sources for coherent context. - **Multi-step reasoning over retrieved context** Reasoning agents perform higher-order operations on retrieved chunks, such as ranking, clustering, or synthesizing evidence across multiple documents rather than passing raw context directly to the model. It reduces noise and contradictions, so generated answers are better grounded and easier to trust. - ******Validation and control****** Validation agents apply consistency checks, source verification, confidence scoring, or other evaluation mechanisms to filter and refine retrieved context before it informs generation. This lowers the risk of hallucinations and reinforces factual correctness in the generated output. - ********[Orchestration](https://dedicatted.com/services/glossary-of-terms#term-orchestration) of output generation******** To ensure that the final response is not just a raw aggregation of retrieved content but a cohesive, context-aware answer that leverages multiple sources while minimizing contradictions or hallucinations, agents guide how the LLM produces the final output, structure answers (summaries, step-by-step, bullet points), select which evidence to emphasize, and trigger follow-up retrieval if gaps are detected. So, with RAG agents folded into retrieval and generation processes, the constraints we talked about earlier lose much of their grip. ## ****Agentic RAG architecture**** It’s worth noting that the division of labor across intelligent agents is an architectural choice. Some Agentic RAG setups rely on a single agent that plans, retrieves, reasons, and validates in sequence. This is called a single-agent RAG system. It keeps the pipeline simple and easier to maintain, though it lacks the modularity and parallelism of multi-agent systems, those with a team of specialized agents, each dedicated to a particular function in the pipeline. It’s usually a task complexity that dictates the breadth of agent involvement. [![Diagram explaining Agentic RAG, where a user query is handled by a retrieval agent that selects tools such as multiple vector search engines, a calculator, or web search to retrieve data from different collections, passes results to an LLM, and generates a final response.](http://dedicatted.com/wp-content/uploads/2026/02/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-21T060049.864.jpg)](https://dedicatted.com/wp-content/uploads/2026/02/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-21T060049.864.jpg)For example, in customer support, for FAQs like “How do I reset my password if I’ve lost access to my email?” which can be answered straight from one knowledge base, a single-agent setup does the job just fine. But once a request gets messy, touches multiple systems, or has more than one ask, like: “I was double charged for my subscription last month, and I also need to update my billing address. Can you fix this and tell me when my refund will arrive?” – that’s where you need more than one brain at work. A multi-agent setup can split the load, tackle each piece, and give the customer a cleaner, more accurate answer. [![Diagram of a single-agent RAG system acting as a router, where a user query is handled by one retrieval agent that selects appropriate tools - such as multiple vector search engines, a calculator, or web search - to retrieve data from different collections, sends context to an LLM, and produces a final response.](https://dedicatted.com/wp-content/uploads/2026/02/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-21T060310.566.jpg)](https://dedicatted.com/wp-content/uploads/2026/02/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-21T060310.566.jpg)## What Agentic RAG brings to the enterprise table The ultimate payoff of agentic RAG is response accuracy so high it raises the ceiling for enterprise AI, moving from surface-level questions to nuanced, high-stakes queries. This goes beyond what traditional RAG or RAG-free LLMs can deliver. It comes from agentic-powered iterative, self-directed retrieval, on-the-fly fusion of structured data and unstructured text, autonomous tool usage, and built-in verification. Besides, agentic RAG is easy to scale. Without overhauling the infrastructure, agents can be brought in for tougher, more complex work requiring extra parallelism or specialized skills and pulled back when tasks lighten. Building on the customer support example we mentioned above: suppose the current multi-agent RAG system has two agents – one handling FAQs (password resets, account setup) and the other managing billing issues (simple refunds, payment verification). Now, the company launches a loyalty program. Customers soon start asking questions like “How do I redeem my points?” or “Can I combine coupons with loyalty rewards?” This is where a specialized agent can be added quickly, thanks to the system’s modular design. Each additional agent increases token usage and tool calls. Costs will scale roughly linearly and you’ll eventually run into context-window limits. So it’s ‘easy to scale’ operationally (compute can expand), but not costless or limitless #### Alex Rozdolskyi, Director of GTM Engineering [![Diagram of a multi-agent RAG system where a user query is routed through multiple retrieval agents (A, B, and C), each selecting different tools such as vector search engines, web search, Slack, or Gmail, retrieving information from multiple collections, and sending consolidated context to an LLM to generate the final response.](https://dedicatted.com/wp-content/uploads/2026/02/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-21T060655.429.jpg)](https://dedicatted.com/wp-content/uploads/2026/02/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2025-11-21T060655.429.jpg)## Where Agentic RAG is already paying off ### ****Customer support automation**** Agentic RAG is arguably the real breakthrough in hyper-personalized customer support. While reading a client’s intent, mood, and the context behind their issue, agents simultaneously pull in every record from the CRM and unstructured data like emails, PDFs, etc. to build a complete picture of the customer. This context-rich background allows them to craft responses that don’t just tick off a request, but wow the client with the level of service and lock in their loyalty. ### **Employee support optimization** To level up IT support, enterprises plug a RAG helper into the helpdesk so tickets get answered quicker and employees can get back to work. As soon as IT support bot hears “VPN drops every afternoon,” it decides whether to pull VPN logs, DHCP lease tables, or the user’s laptop event history, then pre-assembles a ticket with the likeliest fix and any sibling issues. ### **Clinical decision support systems** Retrieval agents help healthcare professionals synthesize vast amounts of medical information, research papers, patient records, and drug databases, to produce more reliable, context-aware recommendations when needed. Simple LLM searches or traditional RAG would struggle with multi-step reasoning, cross-referencing symptoms, treatments, and contraindications. ### **Legal research support** With Agentic RAG, days-long legal drudge-work shrinks into a ten-minute chat. The agentic-powered LLM dives through statutes, rulings, and filings, surfaces the cases that matter, maps how they hang together, and hands the lawyer a ready-made argument trail. ### **Investment analysis** Multiple agents pull Form 10-K, the latest Fed minutes, and internal risk models, cross-check trends, and synthesize a one-page brief explaining why spreads are widening. Analysts skim, click “agree,” and move on. [![Promotional banner for Dedicatted featuring the headline ](http://dedicatted.com/wp-content/uploads/2026/02/unnamed-98.png)](https://dedicatted.com/wp-content/uploads/2026/02/unnamed-98.png)## **Pro tips from the field for implementing an Agentic RAG system (so you don’t learn the hard way)** To lock in better results from your LLM-based enterprise solutions, consider these field-tested guidelines for building Agentic RAG architectures. The key challenge of any RAG implementation is ensuring a robust data pipeline and secure data storage. Always ensure that databases are protected and access to them is tightly controlled. - **Take the time to provide agents with a full picture of each tool’s capabilitie**s. Explain how it works and what it’s best suited for, enabling agents to choose the right tool for the job. - **Regularly review a subset of agent decisions to ensure reasoning aligns with expected business logic.** If the agent’s confidence in a tool choice or document relevance is low, trigger either a human-in-the-loop review or fallback logic. - **Remember GIGO:** if external data don’t provide clear, detailed context, even the smartest agent will churn out poor results. To enhance response accuracy, look after your data quality and make sure your knowledge base documents pack enough relevant context, so agents pull the accurate information instead of garbage. - **With more autonomy comes the need for oversight.** Set up detailed logging, monitoring, and alerting in your RAG model so you can track agent actions, detect issues, and continuously improve system performance. - **No matter how solid your agentic RAG setup is, hallucinations can still pop up.** Agents can step on each other’s toes and compete for resources, and the more of them you throw in, the harder it is to keep things running cleanly. As a rule of thumb, keep the agent team as lean as possible for the task at hand. ## Where to take it next Agentic RAG can already push quality and speed up a noticeable notch, but it still slams into the same ceiling every enterprise AI hits: garbage data, brittle tools, compliance walls, and cost caps. [**Our team can map an Agentic RAG architecture**](https://dedicatted.com/contact) to your stack (connectors, security, KPIs) and prototype a path to production in weeks, not quarters. **Categories:** Blog **Services:** Agentic AI **Industry:** Financial Services, Insurance, Manufacturing, Retail --- ### [Legacy Data Platform Modernization for a European Pet Insurance Provider](https://dedicatted.com/insights/legacy-data-platform-modernization-for-a-european-pet-insurance-provider) **Published:** March 31, 2026 **Author:** Dedicatted **Content:** ## About project #### Working time: 2025-ongoing #### Industry: Insurance #### The service: Legacy App modernization #### Overview A leading European pet insurance provider operating in a highly regulated financial environment needed to rethink how its data infrastructure supported the business. Over the years, the organization had built a complex ecosystem of databases powering policy administration, claims processing, actuarial modeling, and regulatory reporting. While these systems successfully handled day-to-day operations, they were increasingly difficult to maintain and scale.The organization partnered with Dedicatted to transform its legacy data platform into a unified, cloud-based architecture. The goal was not simply to migrate systems, but to create a modern, resilient data foundation capable of supporting faster analytics, lower operational costs, and future growth. ## The Challenge The insurer’s data landscape had evolved into a fragmented environment spread across multiple legacy technologies. Core operational data was stored in 12 zDB2 mainframe databases responsible for policy and claims transactions. Alongside them, six DB2 LUW databases supported actuarial calculations, financial modeling, and regulatory reporting. Altogether, the environment contained over 2,000 tables, hundreds of stored procedures, and complex interdependencies between systems. To process the growing volume of insurance data, the company also operated an on-premises Apache Spark cluster that analyzed nearly 2.8 million policy events every night to generate risk scores and pricing insights. - Data was spread across multiple platforms, making it challenging to maintain a consistent and unified view of operations. Dependencies between systems created complexity whenever new features or analytics capabilities were introduced. At the same time, mainframe licensing **costs were rising by more than 12-15% each year**, placing pressure on long-term infrastructure budgets. - **Regulatory expectations were also evolving.** Financial authorities required stronger guarantees around data resilience, traceability, and operational continuity. Demonstrating these capabilities with a fragmented legacy environment was becoming progressively harder. - **Operational risk during modernization.** Any migration had to occur without disrupting active insurance operations, including policy issuance, claims processing, and regulatory reporting workflows. - **Limited scalability for analytics workloads**. The on-premises Spark cluster handled millions of policy events every night but lacked the elasticity needed to process growing data volumes efficiently. The organization needed a modernization strategy capable of transforming its infrastructure while maintaining complete operational continuity. ## Technology & Approach Dedicatted approached the project as a **large-scale data modernization initiative**, designed to consolidate legacy systems while preserving operational stability and business logic. ### **Deep system discovery and dependency mapping** The first step was gaining full visibility into the existing ecosystem. Dedicatted conducted a comprehensive analysis of the entire database environment, cataloging all **18 database instances**, including: - 2,040 tables - 375 stored procedures - 180 triggers - 340 cross-database joins This discovery phase revealed complex dependencies between transactional systems, actuarial models, and regulatory reporting workflows. Mapping these relationships allowed the team to design a target architecture that preserved business functionality while simplifying the overall system design. [![Visual pipeline illustrating migration from legacy systems to an AWS-based cloud data platform, including data discovery, schema transformation, and resulting analytics insights.](http://dedicatted.com/wp-content/uploads/2026/03/Group-27-1.png)](https://dedicatted.com/wp-content/uploads/2026/03/Group-27-1.png)### **Consolidating legacy databases into a unified cloud platform** The core of the modernization effort focused on consolidating the fragmented database landscape into a **single managed platform built on Amazon Aurora PostgreSQL**.Using **AWS Schema Conversion Tool (SCT)** together with custom-developed transformation scripts, Dedicatted converted schemas and migrated data from both zDB2 mainframe databases and DB2 LUW systems. The migration process was executed in parallel across multiple environments to minimize risk. In total, 2,040 tables from 18 databases were consolidated into one unified cloud architecture, significantly simplifying operations and improving data governance. ### **Replatforming large-scale analytics workloads** Alongside database consolidation, the company’s analytics infrastructure also needed to evolve. The existing on-premises **Apache Spark** cluster, responsible for processing millions of nightly policy events, was migrated to **Amazon EMR**, enabling elastic scaling and simplified infrastructure management. Analytics pipelines supporting actuarial risk modeling, claims analysis, and premium calculations were redesigned to run on top of an Amazon S3-based data lake, allowing the organization to process growing data volumes far more efficiently. A key requirement throughout this process was maintaining exact numerical equivalence for the company’s pricing and risk calculations. More than 340 premium calculation formulas were carefully validated to ensure the new system produced identical results to the legacy platform. ### **Ensuring data integrity throughout migration** To guarantee complete accuracy during the migration process, Dedicatted developed a custom data reconciliation framework designed specifically for large-scale database transitions. This system performed continuous validation using: - row-level data checksums - table-level record count comparisons - financial total reconciliation across reporting systems The migration itself was carried out in **four carefully planned phases over 14 months**, using **change data capture (CDC) replication pipelines** to synchronize legacy and cloud systems during parallel operation. This approach ensured that the new platform could be validated continuously while the existing systems continued running without interruption. [![Architecture diagram of an event-tracking and analytics pipeline running in the AWS Cloud. Web and mobile tracking SDKs send data to an AWS Elastic Beanstalk collection endpoint, which stores session data in DynamoDB and pushes raw clickstream events to Amazon SQS. The data is enriched through another Elastic Beanstalk service using DynamoDB metadata and streamed via Amazon Kinesis. AWS Lambda processes the stream for multiple outputs, including an in-memory EC2 database, Elasticsearch and Kibana for search and visualization, external system integrations through SNS and Lambda, and storage in Amazon S3. Data in S3 is loaded into Amazon Redshift for dashboards and BI tools, with optional querying through Amazon Athena. Backup is handled in Amazon S3.](http://dedicatted.com/wp-content/uploads/2026/03/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2026-03-16T145924.963.jpg)](https://dedicatted.com/wp-content/uploads/2026/03/We-have-achieved-the-AWS-Retail-Services-Competency-Innovate-faster-and-improve-customer-experiences-with-HCLTech-and-AWS-2026-03-16T145924.963.jpg)## Business impact The modernization delivered substantial improvements across performance, cost efficiency, and operational resilience. - **Uninterrupted operations**: All 18 database systems were migrated with zero downtime, ensuring that policy issuance, claims processing, and regulatory reporting continued seamlessly throughout the project. - **Simplified and unified infrastructure**; The previously fragmented environment was consolidated into a single Aurora PostgreSQL platform, dramatically simplifying database management and governance. - **Dramatically faster analytics**: Actuarial processing pipelines that once required 4.5 hours overnight now complete in just 38 minutes, enabling faster insights and improved responsiveness for pricing and risk analysis. - **Significant cost savings**: By eliminating mainframe DB2 licensing, legacy server maintenance, and the on-premises analytics cluster, the organization achieved a **45% reduction in overall data infrastructure costs.** - **Successful regulatory validation**: Within three months of launch, the new platform successfully passed an **FCA and PRA regulatory audit**, supported by stronger data lineage, validation capabilities, and infrastructure resilience. ### Long-Term Value Beyond the immediate operational gains, the modernization created a foundation for the organization’s future data strategy. The unified architecture now enables the company to scale analytics workloads, integrate new data sources, and support evolving insurance products without the constraints of legacy infrastructure. What was once a complex collection of aging databases has been transformed into a modern, cloud-native data platform capable of supporting advanced analytics, regulatory compliance, and long-term innovation. With a resilient AWS-based architecture in place, the insurer is now positioned to continue expanding its data capabilities, delivering faster insights, greater operational efficiency, and a stronger foundation for the next generation of insurance services. **Categories:** Case study **Services:** Cloud Data Migration & Modernization, Cloud Migration, Cloud Modernization **Industry:** Financial Services, Insurance --- ### [The State of AI: How Far GenAI Has Come in 2026?](https://dedicatted.com/insights/the-state-of-ai-how-far-genai-has-gone-in-2026) **Published:** February 26, 2026 **Author:** Maksim Lukhanin **Categories:** Whitepaper **Services:** Generative AI, Managed Services **Industry:** Financial Services, Healthcare, High Tech Software, Retail --- ### [AI, Cloud, and the Future of Finance: The Engineering Behind Cloud-Native Financial Systems](https://dedicatted.com/insights/ai-cloud-and-the-future-of-finance-the-engineering-behind-cloud-native-financial-systems) **Published:** February 12, 2026 **Author:** Maksim Lukhanin **Categories:** Whitepaper **Services:** Agentic AI, Cloud Modernization, Generative AI **Industry:** Financial Services --- ### [Amazon QuickSight BI Review (2025): Pricing, Features, Performance](https://dedicatted.com/insights/amazon-quicksight-bi-review-2025-pricing-features-performance) **Published:** January 14, 2026 **Author:** Maksim Lukhanin **Categories:** Whitepaper **Services:** Agentic AI, Cloud Modernization, Generative AI **Industry:** Financial Services, Healthcare, Insurance, Manufacturing, Retail --- ### [RLHF for RAG Chatbots: a Guide for Advanced Machine Learning](https://dedicatted.com/insights/rlhf-for-rag-chatbots-a-guide-for-advanced-machine-learning) **Published:** December 12, 2025 **Author:** Maksim Lukhanin **Categories:** Whitepaper **Services:** Agentic AI, Generative AI **Industry:** Financial Services, High Tech Software, Manufacturing, Retail --- ### [How Dedicatted Brought Agentic AI to Chemical Reporting at Cassen Laboratories](https://dedicatted.com/insights/how-dedicatted-brought-agentic-ai-to-chemical-reporting-at-cassen-laboratories) **Published:** August 13, 2025 **Author:** Dedicatted **Categories:** Whitepaper **Services:** Agentic AI, Cloud Modernization **Industry:** Healthcare --- ### [AI Multi-Agent Systems for Data Modeling: Workflows, Metrics, Impact](https://dedicatted.com/insights/ai-multi-agent-systems-for-data-modeling-workflows-metrics-impact) **Published:** October 8, 2025 **Author:** Maksim Lukhanin **Categories:** Whitepaper **Services:** Agentic AI, Data Architecture **Industry:** Automotive, Financial Services, High Tech Software, Manufacturing --- ### [GenAI for retail: The secret weapon of customer-first brands](https://dedicatted.com/insights/genai-for-retail-the-secret-weapon-of-customer-first-brands) **Published:** June 19, 2025 **Author:** Dedicatted **Categories:** Whitepaper **Services:** Generative AI **Industry:** Retail --- ### [How to Get the Most of Amazon Q for Business?](https://dedicatted.com/insights/how-to-get-the-most-of-amazon-q-for-business) **Published:** September 10, 2025 **Author:** Maksim Lukhanin **Categories:** Whitepaper **Services:** Agentic AI, Cloud Modernization, Generative AI, Managed Services **Industry:** Automotive, Healthcare, High Tech Software, Manufacturing --- ### [Why Financial Institutions need DevOps now: Insights & FAQ](https://dedicatted.com/insights/why-financial-institutions-need-devops-now-insights-faq) **Published:** June 24, 2025 **Author:** Dedicatted **Categories:** Whitepaper **Services:** DevOps Consulting **Industry:** Financial Services --- ### [Powering Modern Finance Inside a Cloud-Native Success Story](https://dedicatted.com/insights/powering-modern-finance-inside-a-cloud-native-success-story) **Published:** June 19, 2025 **Author:** Dedicatted **Categories:** Whitepaper **Services:** Cloud and Infrastructure Security, DevOps Consulting, Security Compliance **Industry:** Financial Services --- ### [Transform Your Infrastructure with Dedicatted’s AWS Accelerator](https://dedicatted.com/insights/transform-your-infrastructure-with-dedicatteds-aws-accelerator) **Published:** June 19, 2025 **Author:** Dedicatted **Categories:** Whitepaper **Services:** DevOps Consulting, Managed Services **Industry:** Automotive, Financial Services, Healthcare, High Tech Software, Insurance, Manufacturing, Retail --- ### [MAP to the Cloud: Navigating Migration on AWS](https://dedicatted.com/insights/map-to-the-cloud-navigating-migration-on-aws) **Published:** June 25, 2025 **Author:** Dedicatted **Categories:** Whitepaper **Services:** Cloud Migration, DevOps Consulting, Managed Services **Industry:** Automotive, Financial Services, Healthcare, Manufacturing --- ### [Smarter Shopping Assistants: Retrieval-Augmented AI and AR for Retail](https://dedicatted.com/insights/smarter-shopping-assistants-retrieval-augmented-ai-and-ar-for-retail) **Published:** October 20, 2025 **Author:** Maksim Lukhanin **Categories:** Whitepaper **Services:** Data Architecture, Generative AI **Industry:** Retail --- ## Pages ### [Home Page](https://dedicatted.com) **Published:** December 11, 2024 **Author:** Dedicatted **Content:** # Cloud, AI & DevOps for Modern Businesses Build secure, scalable cloud and DevOps foundations for real AI impact. [Book a free meeting](/contact) [Explore our services](/services/cloud-devops) - 10+ years in business - [ 20+ enterprise clients ](/insights) - [ AI + MSP Partner The only AWS partner in Canada with both ](/our-partners/aws) - [ SOC 2 Type 2 + ISO 27001 Independently audited ](/services/security) ## Our services Six practices, one method. Every new page lives under one of these. [ Artificial Intelligence GenAI, agentic systems and computer vision, evaluated before they ship. → ](/services/artificial-intelligence) [ Data & Analytics Architecture, migration and governed platforms your analysts can trust. → ](/services/data/architecture) [ Cloud & DevOps Platforms, pipelines and operations on AWS, with named engineers running them afterwards. → ](/services/cloud-devops) [ Engineering Product engineering and modernization, written with AI and signed by an engineer. → ](/services/engineering) [ Security & Compliance Policy as code on every change, evidence collected continuously. → ](/services/security) [ Internet of Things Device to decision at fleet scale, with sub-second control. → ](/services/iot/platforms) [Everything we do](/services) ## Featured technology partners ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only GenAI & MSP Partner in Canada As the only AWS GenAI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) - ![Vanta](https://dedicatted.com/wp-content/uploads/2025/01/vanta.webp) - ![Datadog](https://dedicatted.com/wp-content/uploads/2025/01/datadog.webp) ## Solving industry challenges Technology alone does not solve industry challenges, so we lead with the domain: engineers who know the sector's systems, rules and data, and one outcome per industry you can read about. ![retail 1](https://dedicatted.com/wp-content/uploads/2026/08/retail-1-1024x768.jpg)![Home Page]()![Home Page]()![Home Page]()![Home Page]()![Home Page]() - Retail Hold the seams between store, stock and customer with one trusted view of inventory and demand, and platforms that stay up through peak season. A 20-year SAP landscape moved to AWS with zero downtime and a 31% lower run-rate. [Modernize retail operations→](/industries/retail) - Healthcare & Life Sciences Modernize clinical and research workflows with governed data platforms and AI on regulated infrastructure, so patient and study data become usable without loosening a single control. A clinical cohort question answered in 4 minutes, down from 6 hours. [Modernize clinical workflows→](/industries/healthcare-industry) - Manufacturing Connect the floor to the systems that plan, buy and ship, with edge-to-cloud data, quality inspection and predictive maintenance on one platform. Idea to proof of concept in days, down from a month. [Connect the floor to the plan→](/industries/manufacturing) - Financial services Modernize regulated platforms with document AI, fraud scoring and audit-ready reporting, built with the evidence trail the regulator asks for. 18 databases unified on AWS at 45% lower cost, analytics 7 times faster. [Modernize regulated platforms→](/industries/financial) - Automotive Run the vehicle as a software product, with telemetry at fleet scale, over-the-air delivery and governed analytics for the systems around it. [Run the vehicle as software→](/industries/automotive) - High-tech software Build the product, not the platform underneath it: pipelines, environments, reliability and product data, run by us or handed over documented. Product recognition in 0.73 seconds, down from 5, at 61% lower cost per scan. [Build the product, not the platform→](/industries/high-tech-software) [## Insights ](https://dedicatted.com/insights) [Blog ### How to Get the Most of Amazon Q for Business? We Did Our Research (PDF Inside) Automotive Financial Services Healthcare High Tech Software Retail Agentic AI Cloud and Infrastructure Security Generative AI Managed Services Security Compliance ![‘Amazon Q’ and the new era of AI for business: A picture of AWS VP Matt Wood](https://dedicatted.com/wp-content/uploads/2025/09/Designing-Platform-for-Instant-Market-Penetration-70.png) Learn how to maximize Amazon Q for Business. Our research shows 300% ROI with real use cases, pilot steps, and a free executive guide (PDF). ](https://dedicatted.com/insights/how-to-get-the-most-of-amazon-q-for-business-we-did-our-research-pdf-inside) [Blog ### Webinar | Practical AI Solutions Every Business Leader Needs to Know Automotive Financial Services Healthcare Manufacturing Retail Agentic AI Generative AI Managed Services ![Close-up on AI Solution, which determines anomalies and failures during production at the earliest stage](https://dedicatted.com/wp-content/uploads/2025/08/Designing-Platform-for-Instant-Market-Penetration-48.png) Practical AI solutions for businesses: boost efficiency, reduce costs, and drive growth with expert insights and real cases. ](https://dedicatted.com/insights/webinar-practical-ai-solutions-every-business-leader-needs-to-know) [Whitepaper ### GenAI for retail: The secret weapon of customer-first brands Retail Generative AI ![A beautiful client using the digital interface of augmented reality in a modern shopping center.](https://dedicatted.com/wp-content/uploads/2025/06/Designing-Platform-for-Instant-Market-Penetration-94.png) See how GenAI on AWS helps customer-first retailers boost margins, enable conversational search, AI stylists, and smarter personalization – unlocking up to $390 billion in value. ](https://dedicatted.com/insights/genai-for-retail-the-secret-weapon-of-customer-first-brands) [Case study ### Launching a Flexible, SEO-Optimized Website for Adomik’s Marketing Teams High Tech Software ![Two young contemporary elegant businessmen discussing terms of new deal , enhanced by Adomik logo on the front](https://dedicatted.com/wp-content/uploads/2025/09/Designing-Platform-for-Instant-Market-Penetration-61-1024x537.png) See how Dedicatted modernized Adomik's platform into a flexible, SEO-Optimized Website that boosts marketing efficiency and user engagement. ](https://dedicatted.com/insights/launching-a-flexible-seo-optimized-website-for-adomiks-marketing-teams) [Case study ### Building a Compliance-Ready Cloud: Zivian Health AWS to Azure Migration Healthcare Cloud Data Migration & Modernization Cloud Migration Cloud Modernization ![Healthcare professional conducting a virtual patient consultation via laptop with stethoscope and 'Doctor Online' signage – telehealth compliance setup](https://dedicatted.com/wp-content/uploads/2025/09/Designing-Platform-for-Instant-Market-Penetration-98.png) Learn how Dedicatted enabled a smooth AWS to Azure migration for a healthcare platform, boosting performance, compliance, and cloud efficiency. ](https://dedicatted.com/insights/building-a-compliance-ready-cloud-zivian-health-aws-to-azure-migration) ## Talk to a Dedicatted consultant Tell us what you are building or where delivery is stuck. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Healthcare & Life Sciences](https://dedicatted.com/industries/healthcare-industry) **Published:** June 12, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Industries](https://dedicatted.com/industries) 3. Healthcare & Life Sciences # Cloud, data and AI delivery for healthcare and life sciences Clinical, lab and trial data is the most governed data your organisation holds and the least joined up. We build the platforms that make it usable, from the clinic to the research bench, without loosening a single control. [Book a free meeting](#contact-form) [See how we start](#offerings) ![Old computer workstation on a wooden desk featuring a CRT monitor, keyboard, mouse, and tangled cables.](https://dedicatted.com/wp-content/uploads/2026/08/healthcare-1024x572.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner - AWS Premier Tier Services Partner, the top tier of the AWS network - Generative AI competency The only GenAI and MSP partner in Canada - Agentic AI Specialization Selected Canadian partner - ![soc2 badge](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/svg/soc2-badge.svg) SOC 2 Type 2 + ISO 27001 Independently audited ## What is changing in healthcare - 81% of physicians now use health AI professionally, more than double the 2023 rate American Medical Association, March 2026 - 49% of healthcare organisations are still only experimenting with generative and agentic AI Deloitte, December 2025 - 1 billion health records exchanged over TEFCA, up from 10 million in under a year ASTP/ONC, June 2026 ## Who we work with in healthcare The healthcare and life science organisations we build for, and the problem each brings us. - ### Providers Clinicians who need the record to be complete before the appointment, not after it. - ### Payers Claims, prior authorisation and the evidence that both were handled correctly. - ### Health tech Product teams shipping into a regulated environment on someone else’s timeline. - ### Pharma and biotech Research data that has to be reproducible years after the study closes. - ### Medical devices Fleets in the field reporting back under quality and safety obligations. - ### Clinical networks Many sites, one view, and interoperability that has to hold across all of them. ## From encounter to evidence in five stages One chain from the moment data is created to the moment it is defensible. Each stage names the services that serve it, so the page routes into the work rather than describing it. 1. Capture Intake · Interoperability 2. 3. Exchange FHIR · Integration 4. 5. Govern Access · Audit 6. 7. Assist Clinical AI · Automation 8. 9. Evidence Analytics · Reporting What happens here Notes, forms and device output arrive structured, with the standard they came in on recorded rather than assumed. What you get An intake pipeline and the mapping that keeps it standards-clean. The failure it prevents A record that is complete only after someone retypes it. [Agentic voice automation in a clinic revenue cycle →](https://dedicatted.com/insights/agentic-voice-automation-for-the-clinic-revenue-cycle) Services behind this stage [Generative AI for healthcare](https://dedicatted.com/services/generative-ai/healthcare) [Engineering](https://dedicatted.com/services/engineering) What happens here Systems that were never designed to talk share a record, through interfaces that survive an upgrade at either end. What you get Working interfaces, with the tests that prove they still work. The failure it prevents A referral that arrives as a fax because the interface broke. [Self-service clinical analytics on a FHIR data store →](https://dedicatted.com/insights/self-service-clinical-analytics-on-a-fhir-data-store-for-a-cardiology-network) Services behind this stage [Data architecture](https://dedicatted.com/services/data/architecture) [Cloud data migration](https://dedicatted.com/services/data/cloud-data-migration) What happens here Access is granted by role and purpose, and every read and write leaves a trail that an auditor can follow. What you get Controls as code, with the evidence collected as they run. The failure it prevents Discovering during the audit that nobody can say who saw what. Services behind this stage [Security](https://dedicatted.com/services/security) [Managed services](https://dedicatted.com/what-we-do/devops/devops-as-a-service) What happens here Documentation, coding and prior authorisation are drafted by models and approved by staff, with the human in the loop where regulation puts them. What you get An assistant in your workflow, with its accuracy measured. The failure it prevents Automation that a clinician cannot override and will not trust. Services behind this stage [Generative AI](https://dedicatted.com/services/generative-ai) [Agentic AI](https://dedicatted.com/services/artificial-intelligence/agentic-ai) What happens here Quality, cost and outcome measures come off governed data with lineage, so a number can be traced back to its source. What you get A governed model and the reporting your quality team signs. The failure it prevents A quarter spent reconstructing how a reported figure was produced. Services behind this stage [Data architecture](https://dedicatted.com/services/data/architecture) [Security](https://dedicatted.com/services/security) ## Our Focus Area 01 Intelligent Medical Mobility Routing and asset tracking for transfers, home care and deliveries, connected to hospital and EMS systems. 02 AI-Driven Medical Equipment Uptime Monitoring and anomaly detection on clinical equipment, so maintenance is planned before a diagnosis is delayed. 03 Smart Medical Manufacturing Real-time quality control, predictive maintenance and supply chain integration, with traceability from lab to shelf. 04 Fragmented Patient Data & Lack of Interoperability Records joined across systems, labs and devices over HL7 and FHIR, into one reliable source. 05 Low Patient Engagement & Adherence Reminders, mobile journeys and assistants that keep contact between appointments rather than only at them. 06 High R&D Costs Platforms that speed discovery and trial design, from screening to prediction, with the analysis automated. ### Intelligent Medical Mobility ### Intelligent Medical Mobility Routing and asset tracking for transfers, home care and deliveries, connected to hospital and EMS systems. Vehicle position, job status and clinical schedules run in one stream, so dispatch works from the same picture as the ward. Transfers stop being coordinated by phone call and the fleet stops idling between jobs. #### Benefits - ✓ Routes optimised in real time - ✓ Transfers and fleets coordinated - ✓ Connected to hospital records ### AI-Driven Medical Equipment Uptime ### AI-Driven Medical Equipment Uptime Monitoring and anomaly detection on clinical equipment, so maintenance is planned before a diagnosis is delayed. Device telemetry and service history are joined per asset, and every alert names the signal behind it. Biomedical teams work from a ranked list instead of a queue of faults already affecting patients. #### Benefits - ✓ Failures predicted before they delay care - ✓ Maintenance planned, not reactive - ✓ Longer asset life ### Smart Medical Manufacturing ### Smart Medical Manufacturing Real-time quality control, predictive maintenance and supply chain integration, with traceability from lab to shelf. Batch, equipment and environmental data are captured as they are produced, which makes an audit a query rather than a search. Deviations surface while the batch can still be corrected. #### Benefits - ✓ Traceability across production - ✓ Quality issues caught early - ✓ Supply chain visible end to end ### Fragmented Patient Data & Lack of Interoperability ### Fragmented Patient Data & Lack of Interoperability Records joined across systems, labs and devices over HL7 and FHIR, into one reliable source. Most of the work is mapping and terminology: deciding what a field means in each system before deciding where it lands. Where a system predates FHIR we bridge it rather than replacing it, so the change stays reversible. #### Benefits - ✓ HL7 and FHIR pipelines - ✓ One patient record across sources - ✓ Less manual re-entry ### Low Patient Engagement & Adherence ### Low Patient Engagement & Adherence Reminders, mobile journeys and assistants that keep contact between appointments rather than only at them. Messaging runs off the same record the clinic uses, so a cancelled appointment does not trigger a reminder for it. Teams can see which prompts actually change attendance instead of guessing. #### Benefits - ✓ Reminders for medication and follow-ups - ✓ Assistants answering around the clock - ✓ Fewer missed appointments ### High R&D Costs ### High R&D Costs Platforms that speed discovery and trial design, from screening to prediction, with the analysis automated. Data from instruments, public sources and prior studies is prepared once and reused, which removes most of the manual assembly. Researchers spend their time on the question rather than on the spreadsheet. #### Benefits - ✓ Screening that narrows the field - ✓ Predictive modelling before physical trials - ✓ Analysis pipelines instead of spreadsheets ## How we start Three scoped entry points, each ending in something your team keeps. - ### Clinical data readiness Two weeks, fixed scope - ✓ Systems, standards and access paths mapped - ✓ The gaps that block interoperability, ranked - ✓ A costed plan your team keeps whether or not we build it [ Get offer → ](#contact-form) - Most popular### Documentation assistant pilot Four to six weeks - ✓ One workflow, your own documents - ✓ Accuracy and handling time measured against today - ✓ A production path if it lands - ✓ Nothing to maintain if it does not [ Get offer → ](#contact-form) - ### Managed clinical platform Monthly - ✓ Platform run, monitored and patched - ✓ Evidence collected continuously for audit - ✓ Named engineers and agreed response times [ Get offer → ](#contact-form) ## Work in this industry [ All case studies ↗ ](https://dedicatted.com/insights) [ ![Three glowing translucent spheres of networked nodes linked by threads of light on a dark teal ground](https://dedicatted.com/wp-content/uploads/2026/08/agentic-hero.jpg) Case study ↗ ### Agentic voice automation for the clinic revenue cycle Calls handled end to end, with the human in the loop where it counts. ](https://dedicatted.com/insights/agentic-voice-automation-for-the-clinic-revenue-cycle) [ ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet.jpg) Case study ↗ ### Self-service clinical analytics on a FHIR data store A cardiology network querying its own data without a ticket. ](https://dedicatted.com/insights/self-service-clinical-analytics-on-a-fhir-data-store-for-a-cardiology-network) Want to see this working in your workflows? Tell us which clinical or research workflow you want to modernize. An engineer replies within one business day with what we would build first. [ Book a free meeting ↗ ](#contact-form) ## Questions we get ### How do you handle protected health information? It stays in your accounts, under your controls, with access granted by role and purpose and every read logged. Our own practice is SOC 2 Type 2 and ISO 27001 audited, which is relevant because your auditors will ask about the supplier as well as the system. ### Can you work with our EHR? Yes, through the interfaces it publishes. Where the EHR is the constraint we say so early rather than building around it and calling the result integration. ### Does a clinician have to accept what the model drafts? No, and that is the design. The assistant drafts, a person approves, and the approval is recorded. Where regulation requires a human in the loop, the workflow enforces it rather than assuming it. ### What happens to the pilot if accuracy is not good enough? You keep the measurement and we stop. Scoping it so the answer is useful either way is the point of a fixed-scope pilot. ### Who owns the models and the code? You do. Everything is built in your accounts, in your repositories, under your licences. ## Cloud, data and AI for healthcare and life sciences Healthcare data sits in electronic health records, laboratory systems, imaging archives and devices in the field. Most of it was never designed to be read together. That is the work: joining those sources over HL7 and FHIR, governing who can see what, and putting the result somewhere clinical and research teams can query. Dedicatted has delivered cloud, data and AI work since 2016. We are an AWS Premier Tier Services Partner with the Generative AI and Machine Learning competencies, and we operate under SOC 2 Type 2 and ISO 27001. Engineering, data and security sit in one team, so a pipeline ships with its evidence rather than after it. ## What we build for healthcare organisations - Interoperability pipelines over HL7 v2 and FHIR, including the mapping and terminology work that decides whether records actually join. - Governed clinical and research data platforms, with access, lineage and retention set per data class rather than per request. - Clinical and back office AI, from documentation assistants to prior authorisation triage, measured against how the work is done today. ## How the work starts Most engagements open with a two week readiness assessment. We map the systems, the standards in use and the access paths, rank the gaps that block interoperability, and hand back a costed plan your team keeps whether or not we build it. A pilot follows on one workflow, with accuracy and handling time measured against your current baseline. - Compliance handled as engineering: audit evidence collected continuously, not assembled the week before a review. - Named engineers and agreed response times where we run the platform after go-live. - DevOps and platform work included, because the release process is usually what limits how fast clinical software can change. Where a system predates FHIR, we bridge it with an integration layer rather than replacing it. That keeps the change reversible and the clinical risk contained. Show more ## Get started with a healthcare technology consultant Outline your clinical, data or compliance challenge. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Industry:** Healthcare --- ### [Everything we do](https://dedicatted.com/services) **Published:** September 10, 2026 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. Services # Everything we do Six practices, one method. Our engineers use AI at every stage of delivery and an engineer owns every line that ships, on a two-week assessment and on a three-year managed contract alike. [Book a free meeting](#contact-form) [See the AI Readiness Assessment](/services/ai-readiness-assessment) ![Two developers reviewing code on a monitor](https://dedicatted.com/wp-content/uploads/2026/09/eng-hero-code-review-1024x682.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner [ 01Artificial Intelligence ](#ai) [ 02Data & Analytics ](#data) [ 03Cloud & DevOps ](#cloud) [ 04Engineering ](#engineering) [ 05Security & Compliance ](#security) [ 06Internet of Things ](#iot) ## [Artificial Intelligence→](/services/artificial-intelligence) Production AI on your own data. Every model ships with its evaluation set written first, and the same review and sign-off as any other change: AI reads it, an engineer signs it. - [Generative AI→](/services/generative-ai) - [Agentic AI→](/services/artificial-intelligence/agentic-ai) - [Computer Vision→](/services/artificial-intelligence/computer-vision) - [AI Readiness Assessment→](/services/ai-readiness-assessment) - [AI-Augmented Development→](/services/ai-augmented-development) ## [Data & Analytics→](/services/data) The estate profiled, the target model set by engineers, every pipeline tested against a baseline before it runs on your data. Databricks and the AWS data stack, governed from day one. - [Data Architecture→](/services/data/architecture) - [Cloud Data Migration→](/services/data/cloud-data-migration) ## [Cloud & DevOps→](/services/cloud-devops) Landing zones, CI/CD, Kubernetes and cost control on AWS, delivered by engineers who write with AI and reviewed before it ships. Managed operations on published monthly tiers. - [DevOps as a Service→](/what-we-do/devops/devops-as-a-service) - [DevOps Consulting→](/what-we-do/consulting/devops-consulting) - [Cloud Migration→](/services/cloud-devops/cloud-migration) - [Cloud Optimization→](/what-we-do/cloud-services/cloud-optimization) - [Kubernetes Consulting→](/solutions/key-technologies/kubernetes) ## [Engineering→](/services/engineering) Product engineering and the modernization of what you already run, mainframe included. AI drafts, tests and reviews; an engineer owns every line that ships. - [AI-Driven Modernization→](/what-we-do/cloud-services/app-modernisation) - [Mainframe Modernization→](/services/engineering/mainframe-modernization) ## [Security & Compliance→](/services/security) Security runs alongside every practice, not after it: policy as code on every change, evidence collected continuously, and the standards we hold ourselves to, SOC 2 Type 2 and ISO 27001. - [Security & Compliance services→](/services/security) ## [Internet of Things→](/services/iot) Device to cloud to decision, with the simulation and load tests generated before a fleet is connected, and sub-second control loops the engineers own end to end. - [IoT Platforms→](/services/iot/platforms) Not sure where to start? The AI Readiness Assessment is two weeks, fixed price, and yours whether or not we build what it finds. [ See the assessment ↗ ](/services/ai-readiness-assessment) ## Talk to a Dedicatted engineer Tell us what you are building or where delivery is stuck. An engineer replies within one business day with relevant experience and what we would build first. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Financial](https://dedicatted.com/industries/financial) **Published:** June 25, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Industries](https://dedicatted.com/industries) 3. Financial # Cloud, data and AI delivery for financial services Every change has to satisfy a regulator as well as a customer. We build the platform and the evidence trail together, so shipping faster does not mean explaining more. [Book a free meeting](#contact-form) [See how we start](#offerings) ![Team of engineers working together at a monitor](https://dedicatted.com/wp-content/uploads/2026/08/data-ecosystem-implementation-team-1024x512.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner - AWS Premier Tier Services Partner, the top tier of the AWS network - Generative AI competency The only GenAI and MSP partner in Canada - Agentic AI Specialization Selected Canadian partner - ![soc2 badge](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/svg/soc2-badge.svg) SOC 2 Type 2 + ISO 27001 Independently audited ## What is changing in financial - 90%+ of bank data users say the data they need is unavailable or hard to use Deloitte, October 2025 - 236.6bn US noncash payments in 2024, up 31.9 billion since 2021 Federal Reserve, July 2026 - $12.5bn reported lost to fraud in the US in 2024, a quarter more than the year before Federal Trade Commission, March 2025 ## Who we work with in financial services The financial institutions we build for, and what each of them has to prove. - ### Retail and commercial banks Core systems that cannot stop while everything around them changes. - ### Capital markets Latency, lineage and a regulator who asks how a number was produced. - ### Insurance Claims and underwriting where the document is the process. - ### Payments and fintech Volume, fraud and a release cadence the incumbents cannot match. - ### Wealth and asset management Client data spread across custodians, platforms and spreadsheets. - ### Lenders Origination and servicing that has to be fast and provable at once. ## From onboarding to reporting in five stages One chain from the first customer document to the regulator’s report. Each stage names the services that serve it, so the page routes into the work rather than describing it. 1. Onboarding Document AI · Workflow 2. 3. Core Modernization · Integration 4. 5. Risk Streaming · ML 6. 7. Servicing Data platform · APIs 8. 9. Reporting Governance · Evidence What happens here Documents are read, checked and routed, and every decision is recorded as it is made rather than reconstructed later. What you get A document service in your account, with its accuracy measured. The failure it prevents A five-day onboarding that loses the customer on day two. Services behind this stage [Generative AI for financial services](https://dedicatted.com/services/generative-ai/financial-services) [Agentic AI](https://dedicatted.com/services/artificial-intelligence/agentic-ai) What happens here Core functions move off the monolith in slices, each one reversible and separately testable, so a risk committee can actually approve it. What you get A working slice in production, and the pattern for the next one. The failure it prevents A big-bang migration nobody is willing to schedule. [Legacy data platform modernization for a European lender →](https://dedicatted.com/insights/legacy-data-platform-modernization-for-a-european-pet-insurance-provider) Services behind this stage [Cloud migration](https://dedicatted.com/services/cloud-devops/cloud-migration) [Engineering](https://dedicatted.com/services/engineering) What happens here Fraud and risk signals are scored in motion, with the reasons kept beside the score so a decision can be explained. What you get A scoring service with its features and reasons logged. The failure it prevents A model that flags a transaction and cannot say why. Services behind this stage [Data architecture](https://dedicatted.com/services/data/architecture) [Security](https://dedicatted.com/services/security) What happens here One customer record serves the app, the branch and the call centre, without waiting for a nightly batch. What you get A customer data service your channels call directly. The failure it prevents Three channels giving one customer three different balances. Services behind this stage [Data architecture](https://dedicatted.com/services/data/architecture) [Cloud and DevOps](https://dedicatted.com/services/cloud-devops) What happens here Regulatory reporting runs off governed data with lineage, so an audit is a query rather than a project. What you get Lineage, controls as code, and evidence collected as they run. The failure it prevents A quarter spent reconstructing how a number was produced. Services behind this stage [Security](https://dedicatted.com/services/security) [Managed services](https://dedicatted.com/what-we-do/devops/devops-as-a-service) ## our Core focus area 01 Core Banking Modernization Core functions move off the monolith in slices, each one reversible and separately testable. 02 AI-Powered Fraud Detection Behavioural models score risk in motion, with the reasons kept beside the score. 03 Digital Onboarding & KYC Automation Identity checked, watchlists screened and decisions recorded, in a flow a customer will finish. 04 Open Banking & API Enablement Secure API ecosystems for open finance, with consent, governance and third-party access handled properly. 05 Cloud-Ready Regulatory Reporting Reporting runs off governed data with lineage, so an audit is a query. ### Core Banking Modernization ### Core Banking Modernization Core functions move off the monolith in slices, each one reversible and separately testable. The integration boundary stays explicit, so the existing system remains authoritative until the new path has proved itself. That turns a multi-year programme into a series of changes the bank can stop at any point. #### Benefits - ✓ API-first, cloud-native architecture - ✓ New products released faster - ✓ Transactions and balances in real time ### AI-Powered Fraud Detection ### AI-Powered Fraud Detection Behavioural models score risk in motion, with the reasons kept beside the score. Features are computed once and served to both training and production, which stops a model behaving differently on the day it goes live. Analysts can see why a transaction was flagged, so a decision can be explained to a regulator. #### Benefits - ✓ Fraud scored in real time - ✓ Models that adapt to new patterns - ✓ Fewer false positives to investigate ### Digital Onboarding & KYC Automation ### Digital Onboarding & KYC Automation Identity checked, watchlists screened and decisions recorded, in a flow a customer will finish. Document extraction and screening run in the same pipeline as the audit trail, so every step is evidenced as it happens. Onboarding is measured in minutes and the file is ready for review without assembly. #### Benefits - ✓ Documents verified in the flow - ✓ AML and PEP screening automated - ✓ Onboarding measured in minutes ### Open Banking & API Enablement ### Open Banking & API Enablement Secure API ecosystems for open finance, with consent, governance and third-party access handled properly. Gateways, scopes and consent records are part of the platform rather than a layer added afterwards. Partners integrate against a stable contract and compliance can see who accessed what. #### Benefits - ✓ Gateway and management layer - ✓ Consent handled and recorded - ✓ Standards met across regions ### Cloud-Ready Regulatory Reporting ### Cloud-Ready Regulatory Reporting Reporting runs off governed data with lineage, so an audit is a query. Every figure traces to its source, the version of the rule that produced it and the run that published it. A new requirement becomes a change to the model instead of a quarter of manual reconciliation. #### Benefits - ✓ Aggregation and reports automated - ✓ Full audit trail and versioning - ✓ Faster response to a new rule ## How we start Three scoped entry points, each ending in something your team keeps. - ### Regulatory readiness assessment Two weeks, fixed scope - ✓ Data lineage, controls and access mapped - ✓ Gaps against the framework you are held to, ranked - ✓ A costed plan your team keeps whether or not we build it [ Get offer → ](#contact-form) - Most popular### Document AI pilot Four to six weeks - ✓ One document type, your own files - ✓ Accuracy and handling time measured against today - ✓ A production path if it lands - ✓ Nothing to maintain if it does not [ Get offer → ](#contact-form) - ### Managed platform and evidence Monthly - ✓ Platform run, monitored and patched - ✓ Evidence collected continuously for audit - ✓ Named engineers and agreed response times [ Get offer → ](#contact-form) ## Work in this industry [ All case studies ↗ ](https://dedicatted.com/insights) [ ![A tower server in a blue-lit server room](https://dedicatted.com/wp-content/uploads/2026/09/da-hero-tower-server.jpg) Case study ↗ ### Legacy data platform modernization for a European insurer Reporting moved onto governed data, with lineage kept. ](https://dedicatted.com/insights/legacy-data-platform-modernization-for-a-european-pet-insurance-provider) [ ![Team collaboration on financial data analysis, with one person pointing at a monitor displaying real-time stock market trends](https://dedicatted.com/wp-content/uploads/2025/03/KOR.-Cover.webp) Case study ↗ ### Scaling platform stability and operational maturity Release cadence up, incident load down, controls intact. ](https://dedicatted.com/insights/scaling-platform-stability-and-operational-maturity-with-managed-support-services) ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) Want to see this working in your institution? Tell us which regulated workflow you want to modernize. An engineer replies within one business day with what we would build first. [ Book a free meeting ↗ ](#contact-form) ## Questions we get ### How do you handle data residency for a regulated workload? Residency is set in the platform rather than in each application, so it is one decision and one control rather than a promise repeated in six places. Which markets constrain what comes out of the readiness assessment. ### Can you work inside our existing change process? Yes, and it usually improves the evidence it produces rather than bypassing it. Where the process is the bottleneck we say which step and why, and leave the decision with you. ### What evidence do you produce for an audit? Controls expressed as code, lineage from source to report, and the collection running continuously rather than assembled at audit time. The point is that the answer to “show me” is a query. ### How small can the first slice of a core migration be? Small enough to be reversible in an afternoon. If a slice cannot be undone, it is too big, and that is the test we apply before scheduling one. ### Who owns the models and the code? You do. Everything is built in your accounts, in your repositories, under your licences. ## Cloud, data and AI for financial services In financial services the constraint is rarely the model. It is whether a number can be explained, traced to its source and reproduced months later for a regulator. That shapes how the platform is built long before anything is scored. Dedicatted builds governed data platforms, core modernization in slices and the AI that runs against them. We have delivered this work since 2016 as an AWS Premier Tier Services Partner with the Generative AI and Machine Learning competencies, under SOC 2 Type 2 and ISO 27001. ## What we build for banks, insurers and fintechs - Core modernization in slices, each one reversible and separately testable, instead of a single migration event. - Fraud and risk scoring in motion, with the reasons kept beside the score so a decision can be defended. - Regulatory reporting off governed data with lineage, so an audit request is a query rather than a project. ## How the work starts A two week assessment maps the data behind one reporting or decision process, what it costs to govern, and the gaps that would fail a review. You get a costed plan your team keeps. The first build is normally that one process, taken end to end with evidence, before the pattern is applied more widely. - Document AI for onboarding, claims and know your customer packs, measured against handling time today. - Open banking and API access with consent recorded and third party access governed. - Platform and DevOps work included, because a control that is not automated is a control that drifts. Where a core system is not moving this year, we build around it and keep the integration boundary explicit. That keeps the modernization reversible. Show more ## Get started with a financial services consultant Outline your challenge, whether it is compliance, data platforms or core modernisation. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Industry:** Financial Services --- ### [High-Tech Software](https://dedicatted.com/industries/high-tech-software) **Published:** June 12, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Industries](https://dedicatted.com/industries) 3. High-Tech Software # Cloud, data and AI delivery for software companies Your product team should be building the product, not the platform underneath it. We build and run that platform, and we hand it over documented. [Book a free meeting](#contact-form) [See how we start](#offerings) ![Engineer at a workstation reviewing dense circuit-board design software](https://dedicatted.com/wp-content/uploads/2026/08/card1-ai-product-expert-1024x431.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner - AWS Premier Tier Services Partner, the top tier of the AWS network - Generative AI competency The only GenAI and MSP partner in Canada - Agentic AI Specialization Selected Canadian partner - ![soc2 badge](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/svg/soc2-badge.svg) SOC 2 Type 2 + ISO 27001 Independently audited ## What is changing in high tech software - 90% of technology professionals now report using AI at work Google Cloud DORA, September 2025 - 56% of tech executives use generative AI to write and test software Deloitte, February 2025 - 46% of developers distrust the accuracy of AI output Stack Overflow, July 2025 ## Who we work with in software The software companies we build for, and what each of them is trying to fix. - ### B2B SaaS Multi-tenant from day one, and a cost per tenant somebody will ask about. - ### Platform and infrastructure Products whose customers are engineers, so the bar is the API. - ### AI-native products Inference cost, evaluation and a model that changes under you. - ### Media and streaming Volume, latency and content pipelines that never stop. - ### Marketplaces Two sides to keep honest and data quality that decides trust. - ### Scale-ups A stack built to prove the idea, now carrying the company. ## From commit to customer in five stages One chain from a developer’s commit to a customer’s experience. Each stage names the services that serve it, so the page routes into the work rather than describing it. 1. Build SDLC · AI assistance 2. 3. Ship CI/CD · Environments 4. 5. Run Reliability · Observability 6. 7. Secure DevSecOps · Compliance 8. 9. Learn Product data · ML What happens here AI sits in the delivery pipeline rather than beside it: generation, review and tests, with the metrics to say whether it helped. What you get A measured pipeline, with the before and after on the record. The failure it prevents Adopting AI tooling and having no way to tell if it worked. Services behind this stage [AI-augmented development](https://dedicatted.com/services/ai-augmented-development) [Engineering](https://dedicatted.com/services/engineering) What happens here Environments come on demand and releases are routine, so a deploy is not an event that needs a meeting. What you get Self-service environments and a pipeline your team owns. The failure it prevents A release process that only two people can run. [A developer platform that unblocks delivery →](https://dedicatted.com/insights/developer-platform-that-unblocks-delivery-self-service-dynamic-environments-and-an-embedded-ai-assistant) Services behind this stage [Cloud and DevOps](https://dedicatted.com/services/cloud-devops) [Managed services](https://dedicatted.com/what-we-do/devops/devops-as-a-service) What happens here Production is observable by the people on call, and the platform scales without a rewrite each time you double. What you get Observability wired in, with the runbooks your on-call uses. The failure it prevents Finding out about the outage from a customer. Services behind this stage [Managed services](https://dedicatted.com/what-we-do/devops/devops-as-a-service) [Cloud and DevOps](https://dedicatted.com/services/cloud-devops) What happens here Scanning and policy sit in the pipeline, and the evidence collects itself, which is what a customer security review actually wants. What you get Controls in the pipeline and the evidence pack behind them. The failure it prevents A security questionnaire that stalls a deal for a month. Services behind this stage [Security](https://dedicatted.com/services/security) [Managed services](https://dedicatted.com/what-we-do/devops/devops-as-a-service) What happens here Usage and cost data reach the product team as a model they can query, not as a monthly export. What you get A product data model and the dashboards behind the roadmap. The failure it prevents Roadmap decisions made on anecdote because the data is a quarter old. Services behind this stage [Data architecture](https://dedicatted.com/services/data/architecture) [Generative AI](https://dedicatted.com/services/generative-ai) 01 Autonomous System Coordination A coordination layer for distributed components, managing data, events and actions across the system. 02 AI-Powered System Reliability Observability and predictive resilience for platforms where uptime is the product. 03 Intelligent Product Engineering Architecture and delivery for AI-native products, from idea to something that scales. 04 Cloud-Native DevOps Enablement Delivery pipelines modernised and infrastructure automated, on containers and managed services. 05 Observability & Incident Intelligence Full-stack observability across infrastructure, applications and user flows, with incidents correlated. ### Autonomous System Coordination ### Autonomous System Coordination A coordination layer for distributed components, managing data, events and actions across the system. Agents share state through one contract instead of calling each other directly, so a new component joins without a rewrite. Failures stay contained and visible rather than propagating through the system. #### Benefits - ✓ Faster system response - ✓ Agents that work together - ✓ Observability across the whole flow ### AI-Powered System Reliability ### AI-Powered System Reliability Observability and predictive resilience for platforms where uptime is the product. Traces, metrics and logs are correlated per request, and error budgets decide what ships next. Issues are found from telemetry rather than from the support queue. #### Benefits - ✓ Failures predicted before impact - ✓ Issues classified automatically - ✓ Telemetry and tracing at scale ### Intelligent Product Engineering ### Intelligent Product Engineering Architecture and delivery for AI-native products, from idea to something that scales. Evaluation is built alongside the feature, so an assistant is measured rather than demonstrated. Infrastructure ships as code and the second product starts from the first one paved path. #### Benefits - ✓ Prototypes that reach production - ✓ LLM and agentic features from MVP - ✓ Infrastructure as code, secure by default ### Cloud-Native DevOps Enablement ### Cloud-Native DevOps Enablement Delivery pipelines modernised and infrastructure automated, on containers and managed services. Environments are created and destroyed on demand, which removes most of the waiting between a change and its test. Teams release on their own schedule instead of on the platform team schedule. #### Benefits - ✓ Shorter time to market - ✓ Infrastructure that scales without a rewrite - ✓ Less operational overhead ### Observability & Incident Intelligence ### Observability & Incident Intelligence Full-stack observability across infrastructure, applications and user flows, with incidents correlated. Alerts carry the trace that raised them and the owner who acts on them. Time to resolution drops because the first question, what changed, already has an answer. #### Benefits - ✓ Issues detected in real time - ✓ Anomalies surfaced automatically - ✓ Shorter time to resolution ## How we start Three scoped entry points, each ending in something your team keeps. - ### Delivery assessment Two weeks, fixed scope - ✓ Pipeline, environments and release path mapped - ✓ The constraints that slow delivery, ranked - ✓ A costed plan your team keeps whether or not we build it [ Get offer → ](#contact-form) - Most popular### Platform pilot Four to six weeks - ✓ One service end to end on the new pipeline - ✓ Delivery metrics measured before and after - ✓ A production path if it lands - ✓ Nothing to maintain if it does not [ Get offer → ](#contact-form) - ### Managed platform Monthly - ✓ Platform run, monitored and patched - ✓ On-call support with agreed response times - ✓ Named engineers who know your stack [ Get offer → ](#contact-form) ## Work in this industry [ All case studies ↗ ](https://dedicatted.com/insights) [ ![card 3](https://dedicatted.com/wp-content/uploads/2026/08/card-3.png) Case study ↗ ### Custom computer vision on AWS, from 5 to 0.73 seconds Recognition latency cut to under a second in production. ](https://dedicatted.com/insights/custom-computer-vision-aws-case-study) [ ![Designing Platform for Instant Market Penetration – 2026-02-11T133414.933](https://dedicatted.com/wp-content/uploads/2026/02/Designing-Platform-for-Instant-Market-Penetration-2026-02-11T133414.933.png) Case study ↗ ### GenAI-powered video intelligence for a television network A model pipeline against a broadcast-scale library. ](https://dedicatted.com/insights/genai-powered-video-intelligence-platform-for-a-global-television-network) ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) Want to see how this would work on your product? Tell us what you are building and where delivery slows down. An engineer replies within one business day with relevant experience and a first read. [ Book a free meeting ↗ ](#contact-form) ## Questions we get ### Do you replace our platform team or work with it? Work with it, in nearly every case. The handover is the deliverable: documented, in your accounts, with your team able to run it. Where a team does not exist yet, we run it until one does. ### How do you measure whether AI in the pipeline helped? Against the delivery metrics you already have, before and after, on the same codebase. If they do not move, that is the finding and we say so. ### Can you work in our repositories and accounts? Yes, and that is the default. Nothing important is built in ours; there is no runtime of ours in the middle and nothing to unpick later. ### What does a security review look like with you involved? Shorter, because the evidence exists already. Controls are in the pipeline and the evidence collects continuously, so the questionnaire is a retrieval rather than a project. ### Who owns the models and the code? You do. Everything is built in your accounts, in your repositories, under your licences. ## Cloud, data and AI for software companies Software companies rarely have a technology problem. They have a delivery one: environments that take days, a release process someone has to watch, and a product data model that answers the question after the meeting that needed it. Dedicatted builds the platforms product teams ship on, and the data and AI features that go into the product itself. We have delivered this work since 2016 as an AWS Premier Tier Services Partner with the Generative AI and Machine Learning competencies, under SOC 2 Type 2 and ISO 27001. ## What we build for software teams - Delivery pipelines and environments as code, so a new service starts from the same paved path as the last one. - Reliability work on real telemetry: tracing, error budgets and the alerting that follows from them. - Product data and AI features, from search and assistants to agentic flows, built to be evaluated rather than demonstrated. ## How the work starts A two week assessment looks at how a change reaches production today, where it waits and what that costs. You get a costed plan and a ranked list of what to fix first. From there we build alongside your team or run the platform under agreed response times, and that choice can change later. - DevSecOps evidence produced by the pipeline, which is what makes a customer security review short. - Cost work on the same platform, so growth does not arrive as a surprise on the cloud bill. - Handover treated as a deliverable: runbooks, ownership and the access to run it without us. We work inside your stack. Where a tool is already in place and doing its job, replacing it is not part of the proposal. Show more ## Get started with a software delivery consultant Outline where your platform or release process is under strain. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Industry:** High Tech Software --- ### [Retail](https://dedicatted.com/industries/retail) **Published:** June 12, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Industries](https://dedicatted.com/industries) 3. Retail # Cloud, data and AI delivery for retail Retail systems fail at the seams: inventory that disagrees with the shelf, a promotion the store app has not heard of, a customer record split across four tools. We build the platform underneath so the seams hold. [Book a free meeting](#contact-form) [See how we start](#offerings) ![Grocery store aisle with a wooden central display and shelves stocked with items on both sides under bright lights.](https://dedicatted.com/wp-content/uploads/2026/08/retail-1-1024x768.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner - AWS Premier Tier Services Partner, the top tier of the AWS network - Generative AI competency The only GenAI and MSP partner in Canada - Agentic AI Specialization Selected Canadian partner - ![soc2 badge](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/svg/soc2-badge.svg) SOC 2 Type 2 + ISO 27001 Independently audited ## What is changing in retail - 68% of retail executives expect to deploy agentic AI within 12 to 24 months Deloitte, January 2026 - 44% say their legacy systems are slowing innovation down Deloitte, January 2026 - 17.1% of US retail sales were online in the second quarter of 2026 US Census Bureau, August 2026 ## Who we work with in retail The retailers we build for, and the problem each of them brings us. - ### Grocery and convenience High volume, thin margin, and a supply chain that cannot wait for a nightly batch. - ### Specialty and apparel Seasonal ranges, heavy returns, and personalization that has to work in store as well as online. - ### Home improvement and trade Mixed retail and trade accounts, deep catalogues and quoting that spans both. - ### Marketplaces Third-party sellers, data quality at scale and pricing that moves hourly. - ### Direct-to-consumer brands One system of record, small teams, and growth that outruns the first stack. - ### Wholesale and distribution Orders from every channel landing in one warehouse and one plan. ## From shelf to doorstep in five stages Every retail platform we build follows the same chain. Each stage names the services that serve it, so the page routes into the work rather than describing it. 1. Demand Forecasting · Planning 2. 3. Inventory Data platform · Integration 4. 5. Storefront Commerce · Personalization 6. 7. Fulfilment Orchestration · Logistics 8. 9. Insight Analytics · ML What happens here Demand signals from sales, seasonality and promotions become one forecast, with the assumptions visible and the overrides recorded. What you get A forecast service in your account, with the accuracy report against your current process. The failure it prevents Buying to a spreadsheet that was last right a quarter ago. Services behind this stage [Data architecture](https://dedicatted.com/services/data/architecture) [Generative AI](https://dedicatted.com/services/generative-ai) What happens here Stock positions reconcile across stores, warehouses and the site, so one number answers where an item is and when it moves. What you get One governed stock model, with the integrations that feed it. The failure it prevents A promise on the website that the shelf cannot keep. [How we moved a Canadian retail operations platform →](https://dedicatted.com/insights/migration-of-canadian-retail-operations-platform-from-ibm-to-cloud-architecture-on-aws) Services behind this stage [Data architecture](https://dedicatted.com/services/data/architecture) [Cloud data migration](https://dedicatted.com/services/data/cloud-data-migration) What happens here Search, recommendations and pricing run on the same customer record, online and in store, so the two channels stop contradicting each other. What you get A customer profile service your commerce stack can call. The failure it prevents Personalization that treats a loyal customer as a stranger. Services behind this stage [Generative AI for retail](https://dedicatted.com/services/generative-ai/retail) [Engineering](https://dedicatted.com/services/engineering) What happens here Orders route by cost, distance and available stock, and the customer is told the truth about timing rather than a default. What you get Routing rules you can change without a release. The failure it prevents Split shipments and a delivery date nobody believes. Services behind this stage [Cloud and DevOps](https://dedicatted.com/services/cloud-devops) [Managed services](https://dedicatted.com/what-we-do/devops/devops-as-a-service) What happens here Margin, sell-through and returns land in one governed model that the merchant team queries themselves, without a ticket. What you get A semantic layer and the dashboards your merchants asked for. The failure it prevents A dashboard that reports last week to a business that moves daily. Services behind this stage [Data architecture](https://dedicatted.com/services/data/architecture) [AI-augmented development](https://dedicatted.com/services/ai-augmented-development) ## Our Core Focus Area 01 Smart Inventory Optimization Demand forecasting and stock balancing across locations, adjusted for season, promotion and local behaviour. 02 Omnichannel Experience Engine One customer journey across the site, the store app and everything after the purchase. 03 Dynamic Pricing Intelligence Prices that move with competitor data, demand signals and your own rules, without manual work. 04 Customer 360 & Personalization Every touchpoint feeds one customer record, so segmentation and recommendations run on the same truth. 05 AI-Powered Retail Analytics Sales, stock and behaviour in one model, so merchants can answer their own questions. ### Smart Inventory Optimization ### Smart Inventory Optimization Demand forecasting and stock balancing across locations, adjusted for season, promotion and local behaviour. We join point of sale, warehouse and supplier data into one model, then run replenishment against it rather than against last year average. Buyers see the same availability the website shows, and a store stops holding stock the region next door needs. #### Benefits - ✓ Stock visible across stores and warehouses - ✓ Fewer stockouts, lower carrying cost - ✓ Replenishment that follows demand ### Omnichannel Experience Engine ### Omnichannel Experience Engine One customer journey across the site, the store app and everything after the purchase. Cart, checkout, fulfilment and returns read the same order record, so a change in one channel is visible in the others within seconds. Staff stop reconciling by hand and customers stop hearing two different answers. #### Benefits - ✓ One customer profile across channels - ✓ Checkout, cart and returns in step - ✓ Personalization that works in store too ### Dynamic Pricing Intelligence ### Dynamic Pricing Intelligence Prices that move with competitor data, demand signals and your own rules, without manual work. We build the ingestion and the rule engine together, so every price change is traceable and stays inside the margin floors your team sets. A promotion can be tested on one category before it runs across the estate. #### Benefits - ✓ Automated pricing on your own rules - ✓ Competitor prices monitored - ✓ Margin protected on discount ### Customer 360 & Personalization ### Customer 360 & Personalization Every touchpoint feeds one customer record, so segmentation and recommendations run on the same truth. Identity resolution, consent and preference data are handled in the pipeline rather than separately in each channel. Campaign teams get segments they can trust and the product gets recommendations that reflect the last visit. #### Benefits - ✓ One profile across every system - ✓ Recommendations that use it - ✓ Campaigns aimed at real segments ### AI-Powered Retail Analytics ### AI-Powered Retail Analytics Sales, stock and behaviour in one model, so merchants can answer their own questions. Definitions are agreed once and reused, which is what stops two dashboards reporting different numbers for the same week. Anomalies and trends surface without a ticket to the data team. #### Benefits - ✓ Dashboards on your own KPIs - ✓ Trends and anomalies surfaced - ✓ Answers without a ticket ## How we start Three scoped entry points, each ending in something your team keeps. - ### Retail data readiness Two weeks, fixed scope - ✓ Inventory, order and customer data mapped end to end - ✓ The gaps that block forecasting, ranked by what they cost - ✓ A costed plan your team keeps whether or not we build it [ Get offer → ](#contact-form) - Most popular### Demand forecasting pilot Four to six weeks - ✓ One category, your own history - ✓ Accuracy measured against your current process - ✓ A production path if it lands - ✓ Nothing to maintain if it does not [ Get offer → ](#contact-form) - ### Managed retail platform Monthly - ✓ Data platform run, monitored and patched - ✓ Peak-season readiness reviews before you need them - ✓ Named engineers and agreed response times [ Get offer → ](#contact-form) ## Work in this industry [ All case studies ↗ ](https://dedicatted.com/insights) [ ![Two warehouse workers in blue uniforms load cardboard boxes from a delivery van onto a pallet inside a warehouse.](https://dedicatted.com/wp-content/uploads/2026/04/Designing-Platform-for-Instant-Market-Penetration-2026-04-27T210007.521.png) Case study ↗ ### Migration of a Canadian retail operations platform An operations platform moved off IBM onto AWS without stopping trade. ](https://dedicatted.com/insights/migration-of-canadian-retail-operations-platform-from-ibm-to-cloud-architecture-on-aws) [ ![Woman interacting with a digital touchscreen displaying clothing items in a smart retail environment.](https://dedicatted.com/wp-content/uploads/2026/01/Designing-Platform-for-Instant-Market-Penetration-2026-01-19T163109.872.png) Case study ↗ ### A developer platform that unblocks delivery Self-service environments, so delivery teams stop waiting on a central queue. ](https://dedicatted.com/insights/developer-platform-that-unblocks-delivery-self-service-dynamic-environments-and-an-embedded-ai-assistant) ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) Want to see this working in your stores? Tell us where the shopping or fulfilment journey breaks down. An engineer replies within one business day with what we would build first. [ Book a free meeting ↗ ](#contact-form) ## Questions we get ### What does a retail data platform cost to run? It depends on volume rather than on headcount: the storage and query cost of your own transaction and inventory data, plus the pipelines that keep it current. We size it against your actual volumes in the readiness assessment, and the number you get is a run rate, not a licence. ### Can you work with the commerce stack we already have? Yes. The work sits behind your commerce platform rather than replacing it, and we integrate through the APIs it already exposes. Replatforming commerce is a separate decision, and not one this work forces. ### How do you forecast demand for a product with no history? By attribute rather than by item: a new product inherits the pattern of comparable ones until it has enough of its own history to stand alone. The model says which it is doing, so a planner can tell the difference. ### What happens if the pilot does not beat our current process? You keep the measurement and we stop. The pilot is scoped so that the answer is useful either way, and there is nothing left running that you have to maintain. ### Who owns the models and the code? You do. Everything is built in your accounts, in your repositories, under your licences. There is no runtime of ours in the middle and nothing to unpick if you take it in house. ## Cloud, data and AI for retail Retail runs on numbers that disagree. The website, the store system and the warehouse each hold their own version of what is in stock, and the gap between them is what a customer experiences as a cancelled order. That is a data problem before it is an AI problem. Dedicatted builds the cloud and data platforms retailers plan on, then the forecasting and personalization that run on them. We have delivered this work since 2016 as an AWS Premier Tier Services Partner with the Generative AI and Machine Learning competencies, under SOC 2 Type 2 and ISO 27001. ## What we build for retailers - One stock model across stores, warehouses and channels, so availability means the same thing everywhere it is shown. - Demand forecasting and replenishment that account for season, promotion and local behaviour instead of last year’s average. - Customer data platforms and personalization that read one profile rather than a different one per channel. ## How the work starts A two week assessment maps the systems holding stock, price and customer data, ranks what blocks a single view, and returns a costed plan. Most retailers then pick one category or one region for the first build, so the model is proven on real trading before it is rolled out. - Analytics your merchants can query themselves, with the definitions agreed once and reused. - Pricing and promotion rules automated against competitor and demand signals, inside margin floors your team sets. - Platform and DevOps work included, so a peak trading period is a capacity plan rather than an incident. Store systems and existing ERP stay where they are until there is a reason to move them. We integrate first and migrate only what earns it. Show more ## Get started with a retail technology consultant Outline your commerce, inventory or customer data challenge. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Industry:** Retail --- ### [Manufacturing](https://dedicatted.com/industries/manufacturing) **Published:** June 12, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Industries](https://dedicatted.com/industries) 3. Manufacturing # Cloud, data and AI delivery for manufacturing The line produces data all day and the business sees it a shift later. We connect the floor to the systems that plan, buy and ship, and we keep the connection running. [Book a free meeting](#contact-form) [See how we start](#offerings) ![Rows of identical machined metal parts moving along a conveyor belt](https://dedicatted.com/wp-content/uploads/2026/08/card2-computer-vision-1024x431.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner - AWS Premier Tier Services Partner, the top tier of the AWS network - Generative AI competency The only GenAI and MSP partner in Canada - Agentic AI Specialization Selected Canadian partner - ![soc2 badge](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/svg/soc2-badge.svg) SOC 2 Type 2 + ISO 27001 Independently audited ## What is changing in manufacturing - 80% of manufacturers plan to put a fifth or more of improvement budgets into smart manufacturing Deloitte, November 2025 - 22% plan to use physical AI within two years, against 9% today Manufacturing Leadership Council, November 2025 - 30% of AI-using EU manufacturers apply it to marketing and sales, more than to production Eurostat, December 2025 ## Who we work with in manufacturing The manufacturers we build for, and what each of them needs from a data platform. - ### Discrete manufacturing Mixed lines, frequent changeovers and traceability down to the part. - ### Process manufacturing Continuous runs where a sensor drift shows up as scrap hours later. - ### Industrial equipment Machines in the field that have to report back without a technician. - ### Automotive suppliers Tier demands on quality evidence and delivery windows measured in hours. - ### Electronics and high-tech Short product lives, tight tolerances and yield that has to be explained. - ### Contract manufacturers Many customers, many systems, one plant floor to satisfy them all. ## From the floor to the customer in five stages One chain from a machine signal to a shipped order. Each stage names the services that serve it, so the page routes into the work rather than describing it. 1. Floor Edge · Connectivity 2. 3. Quality Vision · ML 4. 5. Maintenance Telemetry · Analytics 6. 7. Supply Integration · Planning 8. 9. Insight Data platform · BI What happens here Machine and sensor data leaves the line with an identity and a timestamp, buffered locally so a network gap costs nothing. What you get An edge pipeline in your plant, with the buffer and the certificates. The failure it prevents A line that goes dark whenever the link drops. Services behind this stage [IoT platforms](https://dedicatted.com/services/iot/platforms) [Security](https://dedicatted.com/services/security) What happens here Defects are caught at the station by inspection models your quality team can retrain, not at the customer. What you get An inspection model and the loop that keeps it current. The failure it prevents Finding the batch problem in a complaint rather than on the line. Services behind this stage [Computer vision](https://dedicatted.com/services/artificial-intelligence/computer-vision) [Generative AI](https://dedicatted.com/services/generative-ai) What happens here Wear signals become scheduled work, planned around production rather than around failure. What you get A prediction service, and the maintenance calendar it feeds. The failure it prevents Unplanned downtime priced in shifts rather than minutes. Services behind this stage [IoT platforms](https://dedicatted.com/services/iot/platforms) [Data architecture](https://dedicatted.com/services/data/architecture) What happens here Orders, inventory and supplier commitments reconcile into one plan that survives a late delivery. What you get One planning model, with the integrations that keep it honest. The failure it prevents A schedule that assumes every part arrives on time. Services behind this stage [Data architecture](https://dedicatted.com/services/data/architecture) [Cloud data migration](https://dedicatted.com/services/data/cloud-data-migration) What happens here Utilisation, scrap and cost per unit land in one governed model, queryable by plant and by line. What you get A governed model and the dashboards your plant managers asked for. The failure it prevents Six plants reporting six different truths. [An AI assistant over centralized corporate knowledge →](https://dedicatted.com/insights/intelligent-ai-assistant-for-centralized-corporate-knowledge-with-amazon-q) Services behind this stage [Data architecture](https://dedicatted.com/services/data/architecture) [Engineering](https://dedicatted.com/services/engineering) ## Our Core Focus Area 01 Smart Transportation Vehicles, infrastructure and control centres coordinated in real time, so a disruption is rerouted rather than absorbed. 02 AI-Powered System Reliability Monitoring, anomaly detection and failure prediction, so systems move from reactive repair to planned work. 03 Intelligent Product Engineering Modular, AI-ready architecture for R&D teams, from prototype to a product that scales. 04 Smart Factory Automation Software connected to your machines, controllers and back office, from inspection to robotic process control. 05 Predictive Maintenance Machine data and pattern recognition catch wear early, so maintenance is scheduled rather than urgent. ### Smart Transportation ### Smart Transportation Vehicles, infrastructure and control centres coordinated in real time, so a disruption is rerouted rather than absorbed. Telemetry from fleets and roadside systems lands in one stream, and the routing logic runs against it continuously. Control room staff see the same picture the vehicles report, which makes a reroute a decision rather than a guess. #### Benefits - ✓ Traffic and conditions in real time - ✓ Vehicles and centres in one view - ✓ Routing that reacts to disruption ### AI-Powered System Reliability ### AI-Powered System Reliability Monitoring, anomaly detection and failure prediction, so systems move from reactive repair to planned work. Models train on your own event history rather than a vendor reference set, and every alert carries the signal that raised it. Maintenance is scheduled around production instead of around a breakdown. #### Benefits - ✓ Failures predicted, not discovered - ✓ Root cause found automatically - ✓ Less unplanned downtime ### Intelligent Product Engineering ### Intelligent Product Engineering Modular, AI-ready architecture for R&D teams, from prototype to a product that scales. Components are reused across programmes and the infrastructure ships as code, so a second product does not start from an empty repository. Compliance and observability are part of the build rather than a later project. #### Benefits - ✓ Prototypes from reusable components - ✓ GenAI features from day one - ✓ Compliance and observability built in ### Smart Factory Automation ### Smart Factory Automation Software connected to your machines, controllers and back office, from inspection to robotic process control. We read from the line first and write to it only where an owner signs for the change, which keeps the plant safe while the data starts moving. Cycle times and defect rates become numbers the plant can act on the same day. #### Benefits - ✓ Automated workflows across assembly and QA - ✓ Visual inspection that catches defects - ✓ Shorter cycle times ### Predictive Maintenance ### Predictive Maintenance Machine data and pattern recognition catch wear early, so maintenance is scheduled rather than urgent. Sensor streams and maintenance records are joined per asset, and the model is measured against what your engineers already know. The result is fewer emergency repairs and parts ordered before the line stops. #### Benefits - ✓ Anomalies detected from sensors and logs - ✓ Models trained on your asset history - ✓ Fewer emergency repairs ## How we start Three scoped entry points, each ending in something your team keeps. - ### Industrial data assessment Two weeks, fixed scope - ✓ Machines, protocols and historians mapped - ✓ What is reachable today and what needs a gateway - ✓ A costed plan your team keeps whether or not we build it [ Get offer → ](#contact-form) - Most popular### Predictive maintenance pilot Six weeks - ✓ One asset class, your own telemetry - ✓ Predictions measured against your maintenance log - ✓ A production path if it lands - ✓ Nothing to maintain if it does not [ Get offer → ](#contact-form) - ### Managed industrial platform Monthly - ✓ Edge and cloud pipeline run and monitored - ✓ Models retrained on your data as the line changes - ✓ Named engineers and agreed response times [ Get offer → ](#contact-form) ## Work in this industry [ All case studies ↗ ](https://dedicatted.com/insights) [ ![Two engineers review data on a tablet inside a large aircraft hangar with a plane in the background.](https://dedicatted.com/wp-content/uploads/2026/08/airline-2.png) Case study ↗ ### How an aviation tech company cut proof-of-concept time From a month of environment setup to days, without adding staff. ](https://dedicatted.com/insights/how-an-aviation-tech-company-cut-poc-time-from-a-month-to-days) [ ![Two engineers wearing helmets and safety vests review construction plans at a site with a crane, featuring engineers at a construction site symbolizing the integration of AI and real-world industry work](https://dedicatted.com/wp-content/uploads/2025/10/Designing-Platform-for-Instant-Market-Penetration-89.png) Case study ↗ ### An AI assistant over centralized corporate knowledge Engineers find the right procedure without asking three people. ](https://dedicatted.com/insights/intelligent-ai-assistant-for-centralized-corporate-knowledge-with-amazon-q) ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS GenAI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) Want to see this working on your line? Tell us which part of production or planning you want to modernize. An engineer replies within one business day with what we would build first. [ Book a free meeting ↗ ](#contact-form) ## Questions we get ### Will this work with machines that predate the internet? Usually yes, through a gateway that speaks the protocol the machine already speaks and handles identity on its behalf. The assessment says which machines are reachable as they are, which need a gateway and which are not worth connecting. ### Do we have to move the historian to the cloud? No. Plenty of plants keep the historian where it is and replicate what the business needs. Moving it is a decision about cost and access, not a prerequisite for anything here. ### How much telemetry do you need before a prediction is useful? Enough history to have seen the failure you want to predict, which usually means months rather than weeks. Where that history does not exist, we start by collecting it properly and say so rather than shipping a model with nothing behind it. ### What happens on the line if the cloud link drops? Nothing that matters. Latency-critical logic runs at the edge and data is buffered locally, so a link outage is a gap in reporting rather than a stop in production. ### Who owns the models and the code? You do. Everything is built in your accounts, in your repositories, under your licences, and there is no runtime of ours in the middle. ## Cloud, data and AI for manufacturing Plant data is usually trapped in the plant. Machine controllers, historians and quality systems each hold part of the picture, and none of them was built to share it with a central team. The work is moving that data out safely, without touching what keeps the line running. Dedicatted builds edge to cloud pipelines, plant data models and the AI that runs on them. We have delivered this work since 2016 as an AWS Premier Tier Services Partner with the Generative AI and Machine Learning competencies, under SOC 2 Type 2 and ISO 27001. ## What we build for manufacturers - Edge to cloud pipelines from controllers, historians and sensors, with buffering for sites that lose connectivity. - One plant model across sites, so output, downtime and scrap are counted the same way in every report. - Predictive maintenance and visual quality inspection trained on your own asset history rather than a vendor demonstration. ## How the work starts A two week assessment covers one line or one site: what data exists, what it costs to move, and which failure is worth predicting first. You get a costed plan and a ranked list, not a platform proposal. The first build is normally one use case on one line, measured against the current baseline. - Operational technology boundaries respected: read paths first, write paths only where an owner signs for them. - Dashboards for plant managers and an API for the systems that need the same numbers. - Platform and DevOps work included, so a model in production is monitored like the rest of the estate. Where a site runs equipment that cannot be modified, we collect around it. That constraint is normal and it does not stop the rest of the programme. Show more ## Get started with a manufacturing technology consultant Outline your plant data, OT integration or automation challenge. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Industry:** Manufacturing --- ### [Automotive](https://dedicatted.com/industries/automotive) **Published:** June 15, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Industries](https://dedicatted.com/industries) 3. Automotive # Cloud, data and AI delivery for automotive The vehicle is a software product now, and the systems around it were built for a hardware release cycle. We build the platforms that let both run at their own speed. [Book a free meeting](#contact-form) [See how we start](#offerings) ![Six robotic arms with yellow and black stripes surround a car chassis on a high-tech production line, performing assembly tasks.](https://dedicatted.com/wp-content/uploads/2026/08/automotive-1024x683.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner - AWS Premier Tier Services Partner, the top tier of the AWS network - Generative AI competency The only GenAI and MSP partner in Canada - Agentic AI Specialization Selected Canadian partner - ![soc2 badge](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/svg/soc2-badge.svg) SOC 2 Type 2 + ISO 27001 Independently audited ## What is changing in automotive - 52% of US consumers would keep a vehicle longer if it received regular software updates Deloitte, January 2026 - 20%+ of new cars sold worldwide in 2024 were electric, more than 17 million vehicles IEA, 2025 - 55% of large EU enterprises now use AI technologies, against 20% of enterprises overall Eurostat, 2025 ## Who we work with in automotive The automotive organisations we build for, and what each of them needs to move faster. - ### OEMs Software release cycles that have to coexist with a vehicle programme. - ### Tier suppliers Components that ship with firmware and an obligation to keep it current. - ### Mobility operators Fleets whose economics depend on uptime and utilisation. - ### Dealer and aftersales networks Service data spread across sites that head office cannot see. - ### EV and charging Infrastructure that reports, bills and fails in public. - ### Logistics fleets Vehicles as the moving part of a supply chain, not as the product. ## From vehicle to decision in five stages One chain from a signal in the car to a decision at head office. Each stage names the services that serve it, so the page routes into the work rather than describing it. 1. Vehicle Edge · Telematics 2. 3. Connectivity Ingestion · Fleet scale 4. 5. Software OTA · Delivery 6. 7. Data Platform · Governance 8. 9. Insight Analytics · ML What happens here Signals leave the vehicle with an identity, and the logic that must work offline stays on board. What you get An edge and telematics pipeline with its buffering proven. The failure it prevents A connected feature that stops working out of coverage. Services behind this stage [IoT platforms](https://dedicatted.com/services/iot/platforms) [Security](https://dedicatted.com/services/security) What happens here Fleets authenticate individually and publish at scale, with permissions per vehicle rather than per fleet. What you get An ingestion tier sized to your fleet, with per-device policy. The failure it prevents One compromised unit with a view of the whole fleet. Services behind this stage [IoT platforms](https://dedicatted.com/services/iot/platforms) [Cloud and DevOps](https://dedicatted.com/services/cloud-devops) What happens here Updates reach the vehicle in stages, with a way back, so a release is a decision rather than a gamble. What you get A delivery pipeline with staged rollout and rollback. The failure it prevents A rollout that cannot be stopped once it starts. Services behind this stage [Engineering](https://dedicatted.com/services/engineering) [AI-augmented development](https://dedicatted.com/services/ai-augmented-development) What happens here Vehicle, service and customer data land in one governed model, with retention rules that survive a regulator’s question. What you get A governed data platform, with lineage and retention set. The failure it prevents Years of telemetry nobody can query and nobody dares delete. [Platform and data migration with security enhancement →](https://dedicatted.com/insights/platform-data-migration-with-security-enhancement) Services behind this stage [Data architecture](https://dedicatted.com/services/data/architecture) [Cloud data migration](https://dedicatted.com/services/data/cloud-data-migration) What happens here Warranty, uptime and usage models feed the teams that plan the next programme, not just the ones fixing this one. What you get Models and dashboards in the hands of engineering, not only IT. The failure it prevents Learning about a fault from the warranty claims. Services behind this stage [Generative AI](https://dedicatted.com/services/generative-ai) [Computer vision](https://dedicatted.com/services/artificial-intelligence/computer-vision) ## Expertise That We Provide 01 Connected Mobility Cloud and edge systems for vehicle communication, navigation and routing, for OEMs and mobility operators. 02 Predictive Vehicle Insights Telemetry and diagnostics that find a fault before the driver does, across the vehicle lifecycle. 03 Smart Automotive Manufacturing Production systems built on AI, IoT and cloud, from component tracking to real-time quality checks. 04 Vehicle Data and Analytics Raw vehicle data turned into models the service and engineering teams can act on. 05 Digital Twin for Automotive Systems Real-time twins of vehicle and production behaviour, so a change is simulated before it ships. 06 In-Vehicle Experience & Infotainment Infotainment, voice and adaptive interfaces built as software, with updates delivered over the air. ### Connected Mobility ### Connected Mobility Cloud and edge systems for vehicle communication, navigation and routing, for OEMs and mobility operators. Messages from vehicles and infrastructure are normalised once and consumed by every service that needs them. Routing reacts to congestion within seconds instead of after the trip. #### Benefits - ✓ Vehicles and infrastructure exchanging data - ✓ Routing that reacts to congestion - ✓ Telemetry feeding vehicle awareness ### Predictive Vehicle Insights ### Predictive Vehicle Insights Telemetry and diagnostics that find a fault before the driver does, across the vehicle lifecycle. Signals are joined with warranty and service history per vehicle, so a pattern in the field is visible while the fleet is still under warranty. Service networks get a ranked list rather than a stream of codes. #### Benefits - ✓ Faults detected early - ✓ Health monitored across the fleet - ✓ Lower warranty and repair cost ### Smart Automotive Manufacturing ### Smart Automotive Manufacturing Production systems built on AI, IoT and cloud, from component tracking to real-time quality checks. Line data lands centrally without changing what runs on the floor, and forecasting works from the same numbers the plant reports. Quality issues are caught while the batch is still on site. #### Benefits - ✓ Production visible in real time - ✓ Forecasting that plans the line - ✓ Sensors feeding maintenance and QA ### Vehicle Data and Analytics ### Vehicle Data and Analytics Raw vehicle data turned into models the service and engineering teams can act on. Schema, retention and consent are decided before volume becomes the constraint, which is what keeps the platform affordable at fleet scale. Usage and wear trends reach the people who need them without a new integration. #### Benefits - ✓ Maintenance recommended, not guessed - ✓ Usage and wear trends visible - ✓ Insight shared with service partners ### Digital Twin for Automotive Systems ### Digital Twin for Automotive Systems Real-time twins of vehicle and production behaviour, so a change is simulated before it ships. The twin runs on live telemetry rather than a static model, and it is versioned with the software it represents. Engineering tests an update against the conditions the fleet actually sees. #### Benefits - ✓ Physical assets mirrored live - ✓ Updates tested before deployment - ✓ Root cause found faster ### In-Vehicle Experience & Infotainment ### In-Vehicle Experience & Infotainment Infotainment, voice and adaptive interfaces built as software, with updates delivered over the air. Releases are staged by cohort with rollback, and every vehicle reports what it is running. Features can be improved after the vehicle is sold instead of waiting for the next model year. #### Benefits - ✓ Content personalised per driver - ✓ Voice and navigation that work together - ✓ Features updated over the air ## How we start Three scoped entry points, each ending in something your team keeps. - ### Connected vehicle assessment Two weeks, fixed scope - ✓ Signals, gateways and cloud path mapped - ✓ What is reachable today and what needs work - ✓ A costed plan your team keeps whether or not we build it [ Get offer → ](#contact-form) - Most popular### Fleet telemetry pilot Four to six weeks - ✓ One vehicle class, your own signals - ✓ A working pipeline to a dashboard your team uses - ✓ A production path if it lands - ✓ Nothing to maintain if it does not [ Get offer → ](#contact-form) - ### Managed vehicle data platform Monthly - ✓ Ingestion and analytics run and monitored - ✓ Release support for over-the-air updates - ✓ Named engineers and agreed response times [ Get offer → ](#contact-form) ## Work in this industry [ All case studies ↗ ](https://dedicatted.com/insights) [ ![Tecnic Driving School case study cover featuring an instructor assessing a driving student.](https://dedicatted.com/wp-content/uploads/2025/03/Tecnic.-Cover-2.webp) Case study ↗ ### Platform and data migration with security enhancement A platform moved and hardened in the same programme. ](https://dedicatted.com/insights/platform-data-migration-with-security-enhancement) [ ![Warehouse loading dock scene showing a pallet jack moving a shrink-wrapped pallet of cardboard boxes into the back of an open delivery truck. The pallet sits on a lift gate, and the boxes feature a logo combining a maple leaf and French flag colors, suggesting shipping or logistics operations.](https://dedicatted.com/wp-content/uploads/2026/02/Designing-Platform-for-Instant-Market-Penetration-2026-02-10T155621.133.png) Case study ↗ ### Production-grade GenAI lead enrichment on AWS A model in production against a real operational workflow. ](https://dedicatted.com/insights/production-grade-genai-lead-enrichment-on-aws-for-kwiksave-logistics-operations) ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) Want to see this working in your plant? Tell us where connected vehicles or the factory floor need the software. An engineer replies within one business day with what we would build first. [ Book a free meeting ↗ ](#contact-form) ## Questions we get ### Can you work with our existing telematics supplier? Yes. In most programmes the supplier keeps the vehicle link and we build what happens after it, which avoids reopening a contract to make progress. ### How do you handle vehicle data privacy across markets? Residency and retention are set per market in the platform rather than in each application, so a rule changes in one place. The assessment names which markets constrain what. ### What does over-the-air support actually involve? A staged rollout, a way back, and the evidence of both. We build the pipeline and the controls; the vehicle-side agent stays with whoever owns the ECU. ### How small can a first project be? A four to six week pilot on one vehicle class, ending in a working pipeline and a decision. That is deliberately smaller than a programme. ### Who owns the models and the code? You do. Everything is built in your accounts, in your repositories, under your licences. ## Cloud, data and AI for automotive A modern vehicle produces more data in a day than the systems behind it were designed to hold. Telemetry, diagnostics and usage records arrive continuously from a fleet that is still driving, and the value is in what engineering, quality and service can do with them within hours. Dedicatted builds vehicle data platforms, over-the-air delivery and the analytics on top of them. We have delivered this work since 2016 as an AWS Premier Tier Services Partner with the Generative AI and Machine Learning competencies, under SOC 2 Type 2 and ISO 27001. ## What we build for automotive teams - Vehicle telemetry pipelines at fleet scale, with schema and retention decided before the volume becomes the problem. - Over-the-air update delivery staged by cohort, with rollback and evidence of what each vehicle is running. - Predictive diagnostics and warranty analytics trained on your own fleet history. ## How the work starts A two week assessment maps the signals you already collect, what they cost to keep, and which question the business needs answered first. You get a costed plan and a ranked list. The first build is normally one signal set and one consumer, proven end to end before the rest of the fleet is onboarded. - Consent and regional data rules handled in the pipeline, not in a spreadsheet beside it. - Dealer and service networks given the same view as engineering, without a second integration project. - Platform and DevOps work included, so a release to vehicles follows the same discipline as a release to the cloud. Where a programme is already committed to a platform, we build inside it. Replacing what works is rarely the fastest route to the answer. Show more ## Get started with an automotive technology consultant Outline your connected-vehicle, manufacturing or dealer-systems challenge. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Industry:** Automotive --- ### [AI-Driven Application Modernization](https://dedicatted.com/what-we-do/cloud-services/app-modernisation) **Published:** July 15, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. AI-Driven Modernization AI-Driven Application Modernization # Legacy out. Measured, not promised. Our AI-augmented delivery model pairs modernization engineers with AI at every stage: refactoring legacy code, re-architecting monoliths and migrating outdated systems to cloud-native platforms. Engineers use AI where it measurably accelerates delivery and proven practice where it does not, with every change verified in production before it ships. [Start with a modernization assessment](#contact-form) ![Engineer working on application code on a laptop](https://dedicatted.com/wp-content/uploads/2026/08/appmod-hero-code-editor-1024x678.jpg) AWS Premier Tier The only GenAI + MSP partner in Canada Pilot before rollout AI workflows scale only after measured gains [ 01Why modernize ](#why) [ 02What we offer ](#services) [ 03Where AI helps ](#ai) [ 04How it works ](#process) [ 05Benefits ](#benefits) [ 06FAQ ](#faq) ## Why Legacy Systems Are a Growing Liability The cost of standing still compounds, in maintenance budget, in security exposure, and in everything your team cannot ship. - 60-80% of enterprise IT budgets go to maintaining existing systems rather than building new capabilities - 2x the market for modernization services is set to double by 2027 — your competitors are already moving - \#1 unsupported legacy software is a leading source of security vulnerabilities and compliance findings Industry figures, cited for context. The numbers that decide your business case come out of the assessment, measured on your estate. ## What is AI-driven application modernization? - ### What is AI-driven application modernization? AI-augmented application modernization upgrades legacy software, from outdated code and monolithic architectures to aging databases, to modern cloud-native platforms, with engineers using AI to accelerate code analysis, refactoring, test generation and documentation. A disciplined approach pilots each AI workflow on your actual codebase first, measures the gain, and scales only what demonstrably works. - ### What are application modernization services? Application modernization services include legacy system assessment, replatforming, refactoring, cloud migration, microservices adoption, and performance optimization. - ### What does an application modernization company do? An application modernization company analyzes existing systems, defines a modernization strategy, upgrades application architecture, and ensures seamless transition to modern cloud environments. ## What We Offer Five modernization tracks. Most engagements combine two or three — the assessment tells you which. 01 Modernization Consulting Before anything is rebuilt, we audit what you have: architecture, code quality, dependencies, security posture, and… 02 Architecture Modernization We break monoliths into microservices and serverless architectures where it pays off — and consolidate where it does… 03 Code & Engineering Modernization AI-accelerated refactoring and technical debt remediation on your actual codebase — legacy language migration, version… 04 Infrastructure & Delivery The platform underneath the modernized application: containerization, CI/CD, Infrastructure as Code, and SecDevOps —… 05 Sustenance & Managed Operations Legacy systems that cannot be modernized yet still need to run — and modernized ones need operating. ### Modernization Consulting ### Modernization Consulting Before anything is rebuilt, we audit what you have: architecture, code quality, dependencies, security posture, and total cost of ownership — producing a prioritized roadmap with effort-versus-impact ranking. #### What's included - ✓ Current-state audit and technical debt map - ✓ Modernization strategy and roadmap - ✓ 6 R’s mapping per application (rehost to refactor) - ✓ TCO calculation and business case ### Architecture Modernization ### Architecture Modernization We break monoliths into microservices and serverless architectures where it pays off — and consolidate where it does not. Cloud-native by design, not by fashion. #### What's included - ✓ Monolith to microservices migration - ✓ Serverless and event-driven re-architecture - ✓ Database replatforming to managed services - ✓ API design and integration architecture ### Code & Engineering Modernization ### Code & Engineering Modernization AI-accelerated refactoring and technical debt remediation on your actual codebase — legacy language migration, version roll-ups, and test coverage where none existed. #### What's included - ✓ AI-assisted refactoring and code translation - ✓ Automated test generation for legacy code - ✓ Version roll-up and dependency upgrades - ✓ Documentation recovered from code analysis ### Infrastructure & Delivery ### Infrastructure & Delivery The platform underneath the modernized application: containerization, CI/CD, Infrastructure as Code, and SecDevOps — the practices that keep it modern after we leave. #### What's included - ✓ Containerization with Kubernetes - ✓ CI/CD pipeline discovery, build-out, and optimization - ✓ Infrastructure as Code (Terraform) - ✓ SecDevOps and compliance controls ### Sustenance & Managed Operations ### Sustenance & Managed Operations Legacy systems that cannot be modernized yet still need to run — and modernized ones need operating. We do both, as an ongoing service with SLOs. #### What's included - ✓ Legacy system sustenance engineering - ✓ 24/7 monitoring and incident response - ✓ Cost and performance optimization - ✓ DevOps as a Service ## Where AI Actually Accelerates Modernization Generic AI coding tools learn from clean, documented code — legacy systems are the opposite. We pilot AI on your codebase first, measure the gain, and scale only what works. - ### Code Analysis u0026 Discovery AI maps undocumented dependencies, dead code, and hidden business logic across millions of lines — weeks of archaeology compressed to days. - ### Refactoring u0026 Translation AI-assisted conversion of legacy languages and frameworks, with every change reviewed by engineers who own the outcome. - ### Test Generation Legacy code rarely has tests. AI generates characterization tests that lock in current behavior before anything is touched — the safety net refactoring needs. - ### Documentation Recovery Generated from the code itself: architecture diagrams, API docs, and runbooks for systems whose original authors are long gone. - ### Migration Acceleration AI drafts the boilerplate of replatforming — IaC templates, service scaffolding, config translation — engineers make the decisions. - ### Quality Gates AI-driven code review and anomaly detection in CI, catching regressions the moment they appear rather than in production. The rule: measured before scaled Every AI workflow is piloted on your production code with before/after metrics. If it does not show a real gain, it does not go into the delivery process. [See it on your codebase](#contact-form) ## How Modernization Works With Us ### Assess Weeks 1–3 Architecture and code quality review Security and compliance assessment TCO calculation and risk map Prioritized modernization backlog ### Pilot Weeks 4–8 Proof of concept on a selected workload AI workflows measured before and after Budget and scope validated Go/no-go evidence for the full program ### Modernize From week 9 Incremental delivery, wave by wave Change management and documentation Testing at every promotion No big-bang cutover ### Support Ongoing User feedback collection Solution monitoring and SLOs Continuous improvements On-demand engineering support ## What Modernization Returns ### For your engineering team - Technical debt repaid systematically, not deferred - Outdated code and frameworks eliminated - Security hardened — unsupported components retired - Modern tooling and practices adopted with governance - Integration capabilities your legacy stack could not offer - Delivery velocity measured before and after — on your code ### For your business - Maintenance spend redirected to new capabilities - Faster releases — features ship in days, not release windows - Compliance with industry regulations by design - Scalability that follows demand instead of hardware - Better customer experience on every channel - A codebase new hires can actually work in Engineering Guide ![Cover of the Dedicatted guide Modernization without the three-year program](https://dedicatted.com/wp-content/uploads/2026/08/modguide-cover.jpg) - 16 pages Written by our CTO - Free Instant PDF access ## Modernization budgets go to archaeology. This guide shows where. Written for CTOs and heads of engineering carrying an inherited estate: how agentic AI changes the sequence, the risk, and the business case for legacy modernization on AWS. - ✓ Why the three-year program is no longer the only option - ✓ Where AI changes the sequence — and where it changes nothing - ✓ How to build the business case that was turned down twice Please leave this field empty. Full nameBusiness email \* By submitting my details I accept the [Terms & Conditions](https://dedicatted.com/privacy-policy) and agree to receive relevant news from Dedicatted. I can unsubscribe at any time – see our [Privacy Notice](https://dedicatted.com/privacy-policy). Published by Dedicatted, AWS Premier Tier Services Partner. Instant PDF access. Your download is starting. [Click here if the download hasn't started automatically](https://dedicatted.com/wp-content/uploads/2026/08/Dedicatted-Modernization-Guide.pdf) ## How is application modernization implemented? - ### How do companies modernize applications? Companies modernize applications by evaluating legacy systems, selecting the right approach such as rehost, replatform, or refactor, and implementing cloud-native and DevOps solutions. - ### What is the application modernization process? The process includes system audit, modernization roadmap creation, architecture redesign, code refactoring, cloud integration, testing, and continuous optimization. - ### How long does application modernization take? Application modernization timelines vary from several weeks to months depending on system complexity, technical debt, and required level of transformation. ## What are the benefits of application modernization? - ### Why invest in application modernization services? Application modernization services help reduce maintenance costs, improve system performance, increase scalability, and enable faster feature delivery. - ### What are the key benefits of application modernization? Key benefits include improved user experience, enhanced security, better integration capabilities, reduced downtime, and support for cloud-native development. - ### How does application modernization improve business outcomes? Application modernization improves outcomes by accelerating time-to-market, increasing operational efficiency, and enabling businesses to respond faster to changing demands. ## Insights [Blog ### Data Lake Migration to the Cloud: What Our Experts at Dedicatted Are Telling Clients Right Now Automotive Healthcare Retail Cloud Data Migration & Modernization Cloud Modernization Data Architecture ![Rows of server racks in a data center with purple-lit screens displaying code, extending into the distance.](https://dedicatted.com/wp-content/uploads/2026/07/Lake-Migration.png) Data lake migration to the cloud, explained by Dedicatted's experts: real cost savings, risk management, and a proven 5-phase process. ](https://dedicatted.com/insights/data-lake-migration-to-the-cloud-what-our-experts-at-dedicatted-are-telling-clients-right-now) [Case study ### Enabling Security Compliance and DevOps Agility in a Regulated Healthcare Environment Healthcare Cloud and Infrastructure Security Cloud Migration DevOps Consulting Security Compliance ![Healthcare professional conducting a virtual patient consultation via laptop with stethoscope and 'Doctor Online' signage – telehealth compliance setup](https://dedicatted.com/wp-content/uploads/2025/04/Zivian-Cover-2-1024x537.webp) Seamless AWS to Azure cloud migration with HIPAA compliance and GitOps automation. Enabling secure, scalable DevOps for digital healthcare delivery. ](https://dedicatted.com/insights/enabling-security-compliance-and-devops-agility-in-a-regulated-healthcare-environment) [Blog ### Top 5 DevOps Pitfalls – And How Dedicatted Expert Consulting Helps You Avoid Them Automotive Healthcare High Tech Software Insurance Retail DevOps Consulting Managed Services ![Cloud Computing Artificial Intelligence Data Center Multi Cloud Hybrid Cloud Information Storage Cyber Security Encryption Edge Computing Data Lake](https://dedicatted.com/wp-content/uploads/2025/09/Designing-Platform-for-Instant-Market-Penetration-53.png) Discover the top 5 DevOps pitfalls and how Dedicatted's expert consulting helps you avoid mistakes, boost agility, and achieve sustainable growth. ](https://dedicatted.com/insights/top-5-devops-pitfalls-and-how-dedicatted-expert-consulting-helps-you-avoid-them) [Blog ### Inside a WAF Challenge Rule: How We Stop DDoS Attacks Without AWS Shield Alone Financial Services Healthcare Retail Cloud Operations Managed Services Security Compliance ![Hooded figure typing at a laptop in a blue-toned data center, screen displaying 'DDoS ATTACK'.](https://dedicatted.com/wp-content/uploads/2026/09/Ddos.png) A real DDoS attack, from first alert to full resolution in under 5 minutes. See how Dedicatted's incident response actually works, step by step. ](https://dedicatted.com/insights/inside-a-waf-challenge-rule-how-we-stop-ddos-attacks-without-aws-shield-alone) ## FAQ ### What are application modernization services? They cover the full path from legacy to modern: assessment of existing systems, strategy and roadmap, re-architecting monoliths, refactoring code, replatforming databases, migrating to cloud, and operating the result — with the scope tailored to what your systems actually need. ### How does AI change the modernization process? Our engineers use AI for the mechanical parts: code analysis, test generation, documentation recovery and boilerplate translation, which typically consume most of a modernization budget. Architectural decisions and production changes stay with engineers. Every AI workflow is piloted on your code and its gain measured before it is scaled. ### Do we have to rewrite everything? Almost never. The 6 R’s framework maps each application to the lightest strategy that meets the goal — many workloads rehost or replatform as-is, and only the systems where business logic must change get refactored or rebuilt. ### How long does application modernization take? The assessment takes two to three weeks and gives you a per-application answer. A first pilot lands in four to eight weeks; full programs run in incremental waves — you see working modernized systems from the first months, not at the end. ### How do you avoid breaking production during modernization? Characterization tests lock in current behavior before changes begin, delivery is incremental with rollback paths at every wave, and no big-bang cutover is ever required. The old system keeps running until the new one has proven itself. ### What happens after modernization is done? Either your team takes over — with documentation, runbooks, and training — or we keep operating the platform as a managed service with 24/7 monitoring and SLOs. Most clients choose a transition period between the two. ### Which cloud platforms do you modernize to? AWS most often — we are an AWS Premier Tier Services Partner and the only AWS Generative AI and MSP partner in Canada — as well as Azure and Google Cloud. The target is chosen on your constraints: existing licensing, data residency, team skills, and the services each workload actually needs. ### How much does application modernization cost? The assessment is fixed-scope and fixed-price, and it produces the number for everything after it: per-application effort, sequencing, and the TCO comparison against running the estate as-is. Cost drivers are estate size, integration count, test coverage, and how much business logic has to change. Question not answered? [Ask a modernization engineer directly](#contact-form) — same-day reply on feasibility questions. ## AI-Driven Application Modernization Services Modernize legacy applications and data with enterprise-grade modernization services from Dedicatted. We help businesses refactor outdated code, re-architect monolithic applications into microservices and serverless architectures, replatform databases, and migrate workloads to AWS, Azure, and Google Cloud using the 6 R’s framework and SecDevOps practices. Our engineers combine AI-assisted code analysis, refactoring, and test generation with proven cloud-native engineering — CI/CD pipelines, Infrastructure as Code, containerization, and 24/7 managed operations — so modernization delivers measurable velocity, security, and cost improvements, not just newer technology. ## Cloud-Native Modernization Services We help organizations migrate, refactor, and re-architect workloads across all major cloud platforms. Our cloud-native modernization services combine application replatforming with container orchestration, serverless adoption, and [DevOps automation](https://dedicatted.com/what-we-do/devops/devops-as-a-service), so legacy systems become flexible, observable, and ready for continuous delivery. **Cloud providers we work with:** - Amazon Web Services (AWS) - Microsoft Azure - Google Cloud Platform (GCP) - Oracle Cloud Infrastructure - Hybrid and multi-cloud environments **Services we deliver:** - Legacy application assessment and modernization roadmap - Replatforming, refactoring, and rebuilding monolithic systems - Microservices design and container orchestration with [Kubernetes](https://dedicatted.com/solutions/key-technologies/kubernetes) - Serverless workloads and event-driven architectures - Cloud migration, observability, and FinOps optimization ## Kubernetes, DevOps & Automation Modernization is more than a lift-and-shift. We pair [Kubernetes](https://dedicatted.com/solutions/key-technologies/kubernetes) orchestration with automated infrastructure, CI/CD pipelines, and GitOps workflows so deployment, scaling, and observability stop being bottlenecks. Our engineers package workloads into containers, wire them into hardened pipelines, and codify everything with infrastructure as code and policy guardrails. **Benefits of our Kubernetes and DevOps approach:** - faster, safer releases through automated CI/CD pipelines - elastic scaling and self-healing for production workloads - repeatable environments with infrastructure as code - strong observability, SRE practices, and incident automation - tighter security and compliance through policy as code ## Reduce Technical Debt & Improve Scalability Legacy applications quietly tax every roadmap. Slow releases, brittle integrations, ballooning maintenance costs, and security exposure all become bottlenecks until the platform itself blocks the business. Application modernization tackles that debt head-on by sequencing the work into pragmatic waves — rehost, replatform, refactor, or rebuild — while keeping services running and protecting data integrity. **Outcomes our clients see:** - faster release cadence and shorter time to market - lower run-rate infrastructure and maintenance costs - improved resilience and uptime during peak load - cleaner foundation for AI, analytics, and new product features - measurable reduction in long-term technical debt ### Why Choose Dedicatted Dedicatted is an AWS Premier Tier Partner with proven [DevOps expertise](https://dedicatted.com/insights) and deep cloud-native engineering experience. We have modernized workloads across finance, healthcare, SaaS, and data-intensive platforms, and we work hand-in-hand with internal engineering leaders rather than as a black-box vendor. - end-to-end modernization strategy, assessment, and delivery - hardened reference architectures for Kubernetes and serverless - mature DevOps, SRE, and FinOps practices - security, compliance, and governance baked into every pipeline - cross-cloud expertise across AWS, Azure, GCP, and hybrid - focus on measurable business outcomes, not just technology change Show more ## featured technology partners ![The Only GenAI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only GenAI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only GenAI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) ## Get started with a modernization assessment Tell us which legacy system is holding you back. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Managed services](https://dedicatted.com/what-we-do/devops/devops-as-a-service) **Published:** March 11, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [DevOps](https://dedicatted.com/what-we-do/devops) 3. Managed Services DevOps as a Service · cloud and on-prem # Managed Services Your platform on AWS, Azure, Google Cloud, private cloud or on-premises, run to an SLA you can read before you sign DevOps as a Service is a managed service model that helps businesses automate software delivery, manage cloud infrastructure, optimize CI/CD pipelines, and improve application reliability through continuous monitoring, DevOps automation, and cloud operations support. [See packages & pricing](#managed-service-packages) [Read the SLA terms](#sla) ![IT technician at a console in a blue-lit data center server room](https://dedicatted.com/wp-content/uploads/2026/09/daas-hero-data-center-technician-1024x684.jpg) P1 response: up to 15 min Response and resolution targets for every tier are published below. - Published SLA Response and resolution times on this page, per tier - From $4,000/mo Predictable subscription, no long-term lock-in, 14-day exit - 24×7 coverage On-call L2 and L3 and major incident management on Gold and Platinum - SOC 2 + ISO 27001 AWS Premier Tier, the only GenAI + MSP partner in Canada ## What is DevOps as a Service? - ### What is DevOps as a Service? DevOps as a Service (DaaS) is a delivery model where DevOps practices, tools, and workflows are provided as a managed service to automate software development, deployment, and infrastructure management. - ### What are DevOps services? DevOps services include CI/CD pipeline setup, infrastructure automation, cloud management, monitoring, containerization, and continuous delivery optimization. - ### What does a DevOps company do? A DevOps company helps businesses improve software delivery speed, automate infrastructure, reduce deployment risks, and ensure system reliability through modern DevOps practices. ## What are managed DevOps services? - ### What are managed DevOps services? Managed DevOps services are ongoing services where an external DevOps team manages and improves your cloud infrastructure, CI/CD pipelines, automation, monitoring, security, and deployments. Dedicatted helps businesses reduce operational workload, deploy faster, improve reliability, and scale infrastructure using AWS, Kubernetes, Terraform, Docker, and modern DevOps practices. - ### What are managed services for DevOps? Managed services for DevOps provide ongoing expert support for cloud infrastructure, CI/CD, automation, monitoring, security, and deployments. Dedicatted helps companies improve reliability, speed up software delivery, reduce manual operations, and scale efficiently using AWS, Kubernetes, Terraform, Docker, and modern DevOps practices. - ### Managed services vs DevOps: what’s the difference? Managed services focus on operating and maintaining IT infrastructure, while DevOps focuses on improving collaboration, automation, and software delivery processes. Dedicatted combines both approaches by managing cloud environments while also optimizing CI/CD, infrastructure automation, monitoring, security, and deployment workflows. ## Service benefits A managed cloud and DevOps delivery model from an AWS Premier Tier Partner specializing in enterprise cloud, DevOps and infrastructure operations. ![Document with clock icon in gradient purple-teal circle representing scheduling or time-sensitive documentation](https://dedicatted.com/wp-content/uploads/2025/03/3.png) ### Flexible terms #### Use our services without long-term commitments. If needed, you can cancel within 14 days and we handle the handoff. ![Rocket icon in gradient purple-teal circle representing launch, startup, or acceleration](https://dedicatted.com/wp-content/uploads/2025/03/Group-309.png) ### Tailored and scalable #### Our services grow with you, customized to your business and easily scalable as demand changes. ![Calendar with checkmark icon in gradient purple-teal circle representing scheduled events or completed tasks](https://dedicatted.com/wp-content/uploads/2025/03/1.png) ### Monthly insights #### Clear, data-driven reports to track progress, unlock insights and plan ahead. ## Managed Support Service Packages Published because managed operations is the last place a buyer should have to guess. Every tier includes The full 3-level service structure, monthly reporting and a 14-day exit. The tiers differ in coverage depth and ownership. - ![SOC 2 Type 2](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/svg/soc2-badge.svg) - ![ISO 27001](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/svg/iso27001-badge.svg) SOC 2 Type 2 and ISO 27001, held and renewed for multiple consecutive years. ### SILVER From $4,000/mo\* Essential 3-level support for: - ✓Client application - ✓Cloud - ✓Data platform - ✓Infrastructure [Get offer](#contact-form) Most popular### Gold From $8,500/mo\* Everything in Silver, plus: - ✓On-call support for P1/P2 incidents, 24×7 - ✓L2 support - ✓L3 support [Get offer](#contact-form) ### Platinum From $20,000/mo\* Everything in Gold, plus end-to-end ownership of: - ✓Client application - ✓Cloud - ✓Data platform - ✓Infrastructure [Get offer](#contact-form) SILVER From $4,000/mo\* Gold From $8,500/mo\* Platinum From $20,000/mo\* Built-in ITSM processes Incident, Request, Knowledge and Change Management Everything in Silver, plus Release and Problem Management All agreed ITSM processes On-call 24×7 Support for L2 and L3 ✖ ✓ ✓ Major Incident Management (MIM) – P1/P2 incidents Only within service hours 24×7 24×7 Reporting Escalation + Regular Monthly Escalation + Regular Monthly Escalation + Regular Monthly/Weekly Customer Satisfaction Survey Regular Monthly Regular Monthly Regular Monthly/Weekly Proactive Problem Management Best Effort Up to 10% of ticket capacity Up to 15% of ticket capacity Continuous Service Improvement Best Effort Up to 10% of ticket capacity Up to 20% of ticket capacity Exclusive Cloud Billing Discount* 4% 5% 5% Included Services SILVER **Annually:** - Well-Architected Review - Infrastructure Vulnerability Scanning - Platform Security State Report **Every 6 Months:** - Cost Optimization Check **Other:** - Account Manager Gold Includes **all** **Silver** benefits **&** **Annually:** - Disaster Recovery Simulation **Every 6 Months:** - Well-Architected Remediations - Cost Optimization Implementation - Infrastructure Vulnerability Patch Management - Platform Security Enhancements **Other:** - Technical Account Manager Platinum Includes **all** **Gold** benefits **&** **Annually:** - Compliance Reports & Support (ISO, SOC, etc.) **Monthly:** - 10h/month with Solution Architect (Data, AI, Cloud) **Other:** - Dedicated Technical Account Manager - Technical Online Workshops Add-ons SILVER - 24/7 On-Call Support & MIM - Dedicated Experts - Vulnerability Patch Management - Well-Architected Remediation - Implementation - Security Enhancements Gold - Proactive Problem Management - Extended MIM Services - Expanded Continuous Improvement - Dedicated Experts - Compliance Reports & Support (ISO, SOC, etc.) Platinum - Proactive Problem Management - Extended MIM Services - Expanded Continuous Improvement - Customized Services with Dedicated Experts - On-Site/Online Workshop \*Monthly starting points; the exact quote follows a scoping call. Not sure which tier fits? Let the estimator recommend one Four questions about your team, support needs, service level and infrastructure · A package with a monthly price range [ Estimate my package ↗ ](https://msp.dedicatted.com/?utm_source=dedicatted.com&utm_medium=referral&utm_campaign=daas-page&utm_content=packages) ## Business SLA Terms Guaranteed response, targeted resolution and communication frequency, per severity and per tier. This is the table your contract references. Incidents SILVER response · resolution · communication gold response · resolution · communication platinum response · resolution · communication Critical (P1) **up to 1 h** · 6 h · 1 h **up to 30 m** · 4 h · 30 m **up to 15 m** · 3 h · 30 m High (P2) **4 h** · 8 h · 2 h **1 h** · 6 h · 1 h **30 m** · 5 h · 1 h Medium (P3) **8 h** · 32 h · 4 h **3 h** · 24 h · 4 h **1 h** · 24 h · 3 h Low (P4) **16 h** · 80 h · 8 h **8 h** · 40 h · 8 h **4 h** · 32 h · 6 h Service requests (non-incident): response · resolution High priority **1 biz day** · 4 biz days **4 biz hours** · 3 biz days **1 biz hour** · 2 biz days Medium priority **4 biz days** · 6 biz days **6 biz hours** · 5 biz days **2 biz hours** · 4 biz days Low priority **8 biz days** · 10 biz days **8 biz hours** · 7 biz days **4 biz hours** · 6 biz days [Get an offer against these SLAs](#contact-form) ## How is DevOps implemented? - ### How do companies implement DevOps? Companies implement DevOps by adopting automation tools, building CI/CD pipelines, integrating development and operations teams, and using cloud-native technologies. - ### What is the DevOps implementation process? The process includes assessment, architecture design, tool selection, pipeline setup, automation, testing, deployment, and continuous monitoring. - ### How long does it take to implement DevOps? Implementation typically takes 2–12 weeks depending on infrastructure complexity, team readiness, and the level of automation required. ## What are the benefits of DevOps as a Service? - ### Why use DevOps as a Service? DevOps as a Service reduces time-to-market, improves deployment frequency, enhances scalability, and minimizes operational overhead. - ### What are the key benefits of DevOps? Key benefits include faster releases, improved collaboration, automated workflows, better system stability, and reduced downtime. - ### How does DevOps improve business performance? DevOps improves performance by accelerating delivery cycles, reducing errors, increasing system uptime, and enabling faster innovation. ## Our managed delivery platform Three support levels under one service governance: every ticket enters at the service desk and escalates on a defined path. ### Service structure: the escalation path Service Delivery Management- United service governance - Service level management - Client communication 1. Ticket or alert 2. Level 1 · Service Desk Every ticket enters here - Monitoring - Accepting - Tracking - Triaging 3. 4. Level 2 · Run Escalates if L1 can't resolve - Operation - Troubleshooting - Updates - Configuration 5. 6. Level 3 · Change Engineering fixes the root cause - Engineering - Maintenance - Bug fixing - Minor enhancements Most tickets resolve at L1 to L2; only root-cause work reaches engineering. ### Tools & processes - Unified transition approach - Common toolset usage or integration with client tools - Metrics and reporting - Continuous improvement and automation ### Supported technologies Application platforms - ![java](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/java.svg) Java - ![net](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/net.svg) .NET - ![node js](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/node-js.svg) Node.js - ![python](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/python.svg) Python - ![Managed services](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/go.svg) Go - ![php](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/php.svg) PHP - ![react](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/react.svg) React - 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![on premises](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/on-premises.svg) On-premises - ![kubernetes](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kubernetes.svg) Kubernetes - ![openshift](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/openshift.svg) OpenShift - ![vmware](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/vmware.svg) VMware Data and analytics - ![postgresql](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/postgresql.svg) PostgreSQL - ![mysql](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mysql.svg) MySQL - ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) MS SQL Server - ![mongodb](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mongodb.svg) MongoDB - ![redis](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/redis.svg) Redis - ![snowflake](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/snowflake.svg) Snowflake - ![databricks](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/databricks.svg) Databricks - ![bigquery](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/bigquery.svg) BigQuery - ![aws](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws.svg) Amazon Redshift - ![kafka](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kafka.svg) Kafka - ![power bi](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/power-bi.svg) Power BI - ![tableau](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/tableau.svg) Tableau - ![aws](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws.svg) Amazon Athena - ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) Azure Data Factory AI and ML platforms - ![aws](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws.svg) Amazon Bedrock - ![aws sagemaker](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws-sagemaker.svg) Amazon SageMaker - ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) Azure OpenAI - ![gcp](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/gcp.svg) Vertex AI - ![mlflow](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mlflow.svg) MLflow - ![hugging face](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/hugging-face.svg) Hugging Face \*The list of supported technologies is not exhaustive and can be expanded based on client demand. ## Our implementation cycle ### Discovery Week 1 Assess applications, dependencies, infrastructure & business needs Define key goals and performance metrics Identify potential risks and mitigation strategies Plan the transition strategy ### Setup and transition Weeks 2 to 3 Configure processes and tools Train the team and ensure readiness Execute a seamless service transition Run a pilot for a smooth launch ### Ongoing operation From week 4 Proactive monitoring and system management Incident resolution aligned with SLAs Continuous optimization of IT environments Regular performance and security reporting ## What tools uses for DevOps? - ### What tools are used in DevOps? Common DevOps tools include Jenkins, GitHub Actions, Docker, Kubernetes, Terraform, Ansible, and cloud platforms like AWS, Azure, and Google Cloud. - ### Is DevOps only for large companies? No, DevOps is suitable for businesses of all sizes, including startups, as it helps scale operations efficiently from early stages. - ### What is CI/CD in DevOps? CI/CD stands for Continuous Integration and Continuous Delivery, a practice that automates code integration, testing, and deployment. ## Managed services or professional services? Two ways to work with the same team. Pick by who runs the platform afterwards. This pageManaged services We run your platform as an ongoing service. You get outcomes against an SLA, not billable hours. - Continuous, proactive management We watch, patch and improve; you do not open tickets to keep the lights on - Predictable subscription pricing A monthly fee you can budget: from $4,000/mo, 14-day exit - Broad, ongoing scope Scales with demand across application, cloud, data and infrastructure [See managed packages →](#managed-service-packages) Professional services We build something specific and hand over the keys. Your team runs it afterwards. - Project-based, specialized expertise A defined engagement with a start, an end and a deliverable - Priced by project scope A fixed proposal after scoping; you know the number before we start - Specific, focused outcomes CI/CD build-out, migration, IaC: one problem solved well [Explore DevOps Consulting →](https://dedicatted.com/what-we-do/consulting/devops-consulting) Not sure? Most clients start with a project, then hand us the keys. The same team carries over. ## Insights [Blog ### Managed services vs professional services: Differences and applications Automotive Financial Services Healthcare High Tech Software Insurance Manufacturing Retail Cloud and Infrastructure Security Managed Services Security Compliance ![Team collaboration on business strategy, with one person holding a tablet while discussing insights in a corporate environment.](https://dedicatted.com/wp-content/uploads/2025/03/Managed-Services.-Cover-1024x537.webp) Compare Managed vs Professional Services. Learn the key differences, benefits, and how to choose the right IT service model for your business needs. ](https://dedicatted.com/insights/managed-services-vs-professional-services-differences-and-applications) [Case study ### Scaling Platform Stability and Operational Maturity with Managed Support Services Financial Services Cloud and Infrastructure Security DevOps Consulting Managed Services Security Compliance ![Team collaboration on financial data analysis, with one person pointing at a monitor displaying real-time stock market trends](https://dedicatted.com/wp-content/uploads/2025/03/KOR.-Cover-1024x537.webp) Ensure platform stability with 24/7 Managed Support Services. Proactive monitoring, incident resolution, and compliance to optimize performance and reliability. ](https://dedicatted.com/insights/scaling-platform-stability-and-operational-maturity-with-managed-support-services) [Case study ### Strengthening Platform Reliability and Operational Excellence through Managed Support Services High Tech Software Cloud and Infrastructure Security Cloud Operations Managed Services Security Compliance ![Adomik case study cover featuring a presenter discussing performance metrics with an engaged audience in a conference room.](https://dedicatted.com/wp-content/uploads/2025/03/Adomik.-Cover-2-1024x537.webp) See how Adomik improved platform stability, security, and compliance with 24/7 Managed Support Services, ensuring seamless operations and growth. ](https://dedicatted.com/insights/strengthening-platform-reliability-and-operational-excellence-through-managed-support-services) [Case study ### Strengthening Platform Reliability and Service Continuity Through Managed Support Services Retail Cloud and Infrastructure Security Managed Services Security Compliance ![Case study: Strengthening Platform Reliability and Service Continuity Through Managed Support Services | Dedicatted](https://dedicatted.com/wp-content/uploads/2025/03/Tawseel.-Cover-1024x537.webp) Discover how our Managed Support Services enhanced platform reliability, ensuring 24/7 support, incident management, and business continuity for e-commerce. ](https://dedicatted.com/insights/strengthening-platform-reliability-and-service-continuity-through-managed-support-services) ## FAQ ### Which platforms do you run? AWS, Azure, Google Cloud, private cloud and on-premises estates, under the same tiers and the same SLA. Hybrid estates are the normal case: the service desk, run and change levels cover every environment in scope, and the runbooks are written per environment during onboarding. ### Which package is right for us? Silver covers essential 3-level support within service hours. Gold adds 24×7 on-call for P1 and P2 incidents with L2 and L3 support. Platinum takes end-to-end ownership of the application, cloud, data platform and infrastructure. If you would rather not guess, [the MSP estimator](https://msp.dedicatted.com/?utm_source=dedicatted.com&utm_medium=referral&utm_campaign=daas-page&utm_content=packages) asks four questions and recommends a package with a monthly price range. ### What is not included in a tier? Each tier lists what it covers; anything beyond it is either an add-on (24/7 on-call on Silver, dedicated experts, patch management, Well-Architected remediation, compliance support) or a separate project such as a migration, a CI/CD build-out or an infrastructure-as-code rewrite, which our DevOps Consulting team scopes and prices on its own. ### How is this different from AWS or hyperscaler support? Hyperscaler support covers the platform’s own services. We run your workloads on top of it: monitoring, patching, releases, incident and problem management to a published SLA, with an engineer who knows your environment, and across AWS, Azure, Google Cloud, private cloud and on-premises rather than one vendor. ### What is Infrastructure as Code (IaC)? IaC means defining servers, networks and services in versioned configuration files (for example Terraform), so environments are repeatable, auditable and recoverable with a single command, with no configuration drift across dev, staging and production. ### How does DevOps enhance security? Security is embedded in the pipeline: vulnerability scanning, secrets management, patch management and policy-as-code run continuously rather than as an afterthought, backed by our SOC 2 Type 2 and ISO 27001 certified practices. ### Can DevOps work with legacy systems? Yes. Managed services routinely cover hybrid estates: legacy applications, private cloud and on-premises systems are supported alongside cloud-native workloads, with modernization available as a separate track when you are ready. Question not answered? [Ask a managed services engineer directly](#contact-form) ## What is DevOps? What is DevOps all about? At its core, DevOps as a managed service is all about cooperation between your development and operations teams. Plus, DevOps and managed services can help automate tasks, help security teams fix problems quicker, make the software better, and speed up approval times, which makes the whole process more efficient. Sometimes, you just need a dedicated DevOps or a good managed DevOps service to keep everything running smoothly and help you easily make all your deadlines. Deciding on the right services plan is often the first step for companies looking at DevOps services in Canada. If you want to improve your software development process, you should think about getting help from a DevOps as a service company such as Dedicatted. We provide services like DevOps consulting and managed cloud services that can seriously improve your workflow and increase efficiency by uniting development and operations. As a DevOps managed service provider, we believe that automation with version control can help your DevOps Company avoid errors and get better results. Choosing fully managed DevOps makes everything simpler and easier to get work done on time. You’ll find many different managed service plans available, sometimes including related things like analytics managed services. Here at Dedicatted, we’re always trying to ensure customer satisfaction with our managed services for DevOps, including managed services in Canada, while keeping our rates competitive. ## DevOps Consulting Thinking about DevOps consulting? Then you’re on the right track because if you want to implement DevOps, you need to get it right the first time. That’s where Dedicatted comes in. For businesses looking for a DevOps company in Toronto, Dedicatted brings a fresh perspective that will help you find the right path forward. We’ll dive deep into your business plan and current setup to make certain that all of our recommendations are in line with your business goals. ## Managed Services for Private Cloud Private cloud managed services should be your go to option if you want to have more control over the infrastructure of your company without placing all the burden of the extra work on your internal team. When you’re using private cloud services, it becomes much easier to enjoy the benefits of having a private environment while being able to receive professional help with updates, maintenance, monitoring, and other vital tasks, making managed services for cloud essential for companies that care about reliability of their infrastructure but do not wish to expand their IT teams. ### AWS Cloud Managed Services If you’re looking for DevOps as a service or a reliable managed services AWS you should definitely check out the options within AWS. Now you might be wondering, what is AWS managed services? Basically, with AWS managed services, you can enjoy all the benefits of a DevOps setup without having to manage the infrastructure. Sounds good right? These services are scalable and reliable and since AWS cloud managed services offer features like auto-scaling and self-healing, they are the perfect fit for any DevOps Company looking to make their workflow better. For those of you looking for flexibility and a dedicated DevOps approach, then you should check out managed services for AWS from an AWS managed service provider or AWS MSP partner, as it’s a really good solution for getting everything to run smoothly and efficiently, which allows you to meet all those deadlines. ### Azure Managed Services Looking to make things easier for your developers in Azure? With Azure DevOps, a cloud-based service, your developers will have access to a toolset that will allow them to plan, track and manage software development services in an efficient manner. Your developers can then use these tools, with help from Azure DevOps managed services providers to build, test, and later deploy applications to the cloud. The fact that it’s cloud-based also brings the benefits of an Azure managed service to you, meaning that you won’t have to worry about infrastructure anymore. This approach is a key part of Azure cloud managed services. Companies that prefer to keep their finger on the pulse can also use managed Azure cloud services, which makes daily management substantially easier. This is a really good solution for any DevOps Company looking for tooling on a managed basis. In addition to this, if you are looking for managed services for Azure or just wish to work with an Azure managed service provider, you’ll quickly see that this is pretty much the foundation of their strategy. With Azure forming a solid foundation, the goal of DevOps as a service will be reached easily, with the benefit of smooth and efficient operation. ### Google Cloud Managed Services If you need assistance with implementing DevOps in your company, then a DevOps engineer is the right person to call. For a successful DevOps company, the right people on your team are the key to success. A great engineer is someone who has extensive knowledge about your software development and the operations you do. Having dedicated DevOps focus can be really helpful if you want to get things done right. The best engineers are those who know the right tools, or even how they interact with Google managed services. If your infrastructure uses the cloud, knowledge about Google cloud platform managed services would be a huge plus for them. A talented engineer working alongside a Google cloud managed service provider can greatly improve your software development as well as your company’s efficiency and profits. So no matter if you’re planning on hiring internal teams or getting assistance with Google cloud managed services, hiring a skilled DevOps engineer will be extremely helpful to your company. ### Cloud Security Managed Services Naturally, security is vital to keeping all your cloud environments running smoothly. With the right managed services cloud option, you’ll be able to quickly identify potential problems and respond to them in a timely manner. Managed security services cloud solutions also allow for easier monitoring, while keeping your infrastructure protected, allowing your developers to focus on building better applications. #### Service Provider Company Choosing the right partner for your business, especially when comparing DevOps managed services providers, is more than going with a seller who has a cool presentation. You have to pick a team that’s obsessed with making sure everything goes smoothly and according to plan. After completing dozens of digital transformation projects, we’ve realized that even though no two projects are the same, many issues are. These include things like release bottlenecks, fragile code, snowflake servers, and problems with keeping track of what’s happening. Our fix for this is a practical and strong DevOps-as-a-service model that’s run by certified experts who work closely with your teams. Instead of having high upfront costs, this model gives you steady and predictable operating expenses. That way, decision-makers can easily see the total cost while making sure they get top-notch service. As service providers working with businesses in Toronto and across Canada, we make our success dependent on yours with outcome-based contracts. It doesn’t matter whether your goal is making e-commerce rollouts quicker or keeping regulated workloads compliant. From day one we map value streams, baseline your DORA metrics, and create a plan that ends up paying for itself through quicker lead times and lower failure rates. The variety of our services is what makes us stand out from the crowd. While many cloud companies only specialize in a single vendor, the new architectures have to work on AWS, Azure, and Google Cloud. Our expertise in managed DevOps services for cloud native apps allows us to pick the best services for you such as EKS for container orchestration, Azure DevOps Boards for planning, or GCP’s Artifact Registry for binary management. Many growing and established companies trust us when it comes to re-architecting old systems, enabling blue/green releases, and making GitOps a part of their day-to-day activities. Finally, we believe that just providing awesome resources is not enough. This is why we mentor your developers, SREs, and security champions to make sure those new habits will stay with them for a long time. At the end of the day, you’ll have a delivery engine that will keep up with all of your needs. We’re a true service provider, and we’ll always put your interests first. As a service company that always looks into the future, Dedicatted delivers value and improvements that align with all of your objectives and strategies. Show more ## Get started with managed DevOps Tell us what you need run and to what standard. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [DevOps Consulting](https://dedicatted.com/what-we-do/consulting/devops-consulting) **Published:** June 27, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. DevOps Consulting DevOps Consulting & Automation # DevOps Consulting Ship faster without breaking what works DevOps consulting helps businesses improve software delivery, automate infrastructure, and streamline development and operations workflows — combining CI/CD, Infrastructure as Code, monitoring, and security to accelerate releases and improve operational efficiency. [Talk to a DevOps consultant](#contact-form) ![Server racks in the computer network security of the server room](https://dedicatted.com/wp-content/uploads/2025/07/devops-consalting-1024x363.jpg) AWS Premier Tier Partner SOC 2 and ISO 27001 certified - Premier Tier AWS Services Partner — top 2% globally - GenAI + MSP Partner The only AWS partner in Canada holding both - SOC 2 + ISO 27001 Audited security and delivery practices - 1 business day Response with relevant experience included ## What is DevOps consulting? - ### What is DevOps consulting? DevOps consulting is a service that helps organizations adopt DevOps practices, automate software delivery, and improve collaboration between development and operations teams. - ### What are DevOps consulting services? DevOps consulting services include CI/CD implementation, infrastructure automation, cloud integration, containerization, monitoring setup, and DevOps strategy development. - ### What does a DevOps consulting company do? A DevOps consulting company assesses existing workflows, designs DevOps architecture, implements automation tools, and provides ongoing support to improve software delivery performance. ## Our Core Focus Areas Seven disciplines, one delivery practice. Every engagement scopes only the areas your pipeline actually needs. 01 Cloud-Native Engineering We design and build distributed systems on AWS, Azure, and GCP, from microservices to event-driven architecture. 02 Infrastructure as Code (IaC) We write your infrastructure as code using Terraform, CloudFormation, or Pulumi, so it is version-controlled… 03 CI/CD We set up your delivery pipeline from commit to production using GitHub Actions, GitLab CI, Jenkins, or ArgoCD. 04 DevSecOps We add security to every stage of your DevOps process, including secure pipelines, compliance as code, and secrets… 05 Observability & SRE We set up full-stack observability with logs, metrics, and traces, so your team can see how systems perform and how… 06 Cost Optimization We help you reduce cloud costs without losing performance. 07 GenAI & MLOps Readiness We prepare your infrastructure for AI workloads, including GPU clusters and model pipelines. ### Cloud-Native Engineering ### Cloud-Native Engineering We design and build distributed systems on AWS, Azure, and GCP, from microservices to event-driven architecture. Your workloads are scalable, fault-tolerant, and ready for production, so you can release faster and recover quickly when something fails. #### Benefits - ✓ Accelerated time-to-market with microservices and containers - ✓ Built-in high availability and auto-recovery - ✓ Cost-efficient use of compute with autoscaling and spot instances - ✓ Seamless cloud migration and modernization support ### Infrastructure as Code (IaC) ### Infrastructure as Code (IaC) We write your infrastructure as code using Terraform, CloudFormation, or Pulumi, so it is version-controlled, repeatable, and testable. This removes manual configuration drift and gives your teams self-service environments. #### Benefits - ✓ Infrastructure environments reproducible in minutes - ✓ GitOps-friendly workflows for changes and rollbacks - ✓ Reduced human error and manual misconfiguration - ✓ Streamlined cloud resource provisioning across environments ### CI/CD & Release Automation ### CI/CD & Release Automation We set up your delivery pipeline from commit to production using GitHub Actions, GitLab CI, Jenkins, or ArgoCD. Automated quality gates reduce deployment friction, so your team can release more often and with more confidence. #### Benefits - ✓ Shortened release cycles with higher velocity - ✓ End-to-end automation of build, test, and deploy - ✓ Rollback and canary deployments with zero downtime - ✓ Improved developer productivity and collaboration ### DevSecOps & Compliance Enablement ### DevSecOps & Compliance Enablement We add security to every stage of your DevOps process, including secure pipelines, compliance as code, and secrets management with Snyk, Aqua, or HashiCorp Vault. You stay audit-ready without slowing down releases. #### Benefits - ✓ Continuous compliance for SOC2, HIPAA, GDPR, ISO27001 - ✓ Secure image scanning, secrets management, and access control - ✓ Governance automation and audit traceability - ✓ Shift-left security posture for reduced vulnerability windows ### Observability & SRE Enablement ### Observability & SRE Enablement We set up full-stack observability with logs, metrics, and traces, so your team can see how systems perform and how users are affected. Our SRE practices help you track SLAs and SLOs, spot problems early, and automate incident response. #### Benefits - ✓ Reduced mean time to resolution (MTTR) - ✓ Actionable alerts and visualization dashboards - ✓ SLO-based prioritization and capacity planning - ✓ Improved reliability through automated remediations ### Cost Optimization & FinOps ### Cost Optimization & FinOps We help you reduce cloud costs without losing performance. We find idle resources, rightsize workloads, and rework expensive patterns, using FinOps practices to keep spending tied to business value. #### Benefits - ✓ Up to 30% cost savings via reserved instances and autoscaling - ✓ Real-time cost visibility and forecasting - ✓ Elimination of cloud waste and zombie resources - ✓ Engineering and finance teams aligned on usage and ROI ### GenAI & MLOps Infrastructure Readiness ### GenAI & MLOps Infrastructure Readiness We prepare your infrastructure for AI workloads, including GPU clusters and model pipelines. Whether you are fine-tuning LLMs or deploying your own models, you get a setup that is fast, repeatable, and under your control. #### Benefits - ✓ Accelerated AI/ML experimentation and deployments - ✓ Cost-efficient GPU utilization and scaling strategies - ✓ ML pipeline automation (training, testing, deployment) - ✓ Observability and governance for AI models in production ## What Changes After the Engagement We measure DevOps the way the industry does — the four DORA metrics. This is what a mature pipeline looks like against a manual one. - Deployment frequency On demand, not on a calendar From monthly release windows to shipping whenever the build is green — 10× more frequent releases is a normal first quarter. - Lead time for changes Days, not months Commit-to-production time drops by up to 70% once the pipeline does the promotion. - Time to restore Minutes, not all-nighters Observability plus one-click rollback keeps typical recovery under 30 minutes. - Change failure rate Caught in CI, not in production Automated tests and security gates hold failed changes below 5% of deployments. ## our process ![Gradient of dynamic purple background llines](https://dedicatted.com/wp-content/uploads/2025/06/image-168-1.jpg) ### Explore Weeks 1–2Start with a deep dive into your business and tech stack to align DevOps strategy with your goals. Identify drivers, risks, and KPIs Audit current systems and workflows Define priorities and delivery roadmap ![Gradient of dynamic purple background llines](https://dedicatted.com/wp-content/uploads/2025/06/image-168-1.jpg) ### Implement Weeks 3–10Certified cloud engineers turn the roadmap into code — building secure, scalable platforms. Spin up CI/CD with security gates Automate infra with IaC & GitOps Containerize apps and deploy to the cloud ![Gradient of dynamic purple background llines](https://dedicatted.com/wp-content/uploads/2025/06/image-168-1.jpg) ### Maintain OngoingEnsure ongoing performance, cost-efficiency, and security with proactive platform support. 24/7 monitoring, alerts, and SLOs FinOps tuning & performance tweaks Proactive patching and seamless upgrades [Start with a free discovery call](#contact-form) ## Consulting or Managed Services? Two ways to work with the same team — pick by who runs the platform afterwards. DevOps Consulting We design and build the pipeline, train your team, and hand over the keys. Your engineers run it; you own everything. Best when you have an engineering team ready to operate the platform. Managed Cloud Services We build it and keep running it — 24/7 monitoring, patching, cost tuning, and SLOs as an ongoing service. [Explore Managed Services →](https://dedicatted.com/what-we-do/devops/devops-as-a-service) ## Technologies We Work With - ![aws](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws.svg) AWS - ![gcp](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/gcp.svg) GCP - ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) MS Azure - ![docker](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/docker.svg) Docker - ![kubernetes](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kubernetes.svg) Kubernetes - ![helm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/helm.svg) Helm - ![ansible](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ansible.svg) Ansible - ![terraform](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/terraform.svg) Terraform - ![vault](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/vault.svg) Vault - ![consul](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/consul.svg) Consul - ![jenkins](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/jenkins.svg) Jenkins - ![grafana](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/grafana.svg) Grafana - ![prometheus](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/prometheus.svg) Prometheus - ![datadog](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/datadog.svg) Datadog - ![elastic search](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/elastic-search.svg) Elastic Search ## Related Services ### INFRASTRUCTURE MODERNIZATION [](#contact-form)### INFRASTRUCTURE MODERNIZATION Migrate from legacy bottlenecks to scalable, cloud-native environments. We help you modernize infrastructure with containerization, Kubernetes adoption, and Infrastructure as Code – unlocking faster deployments, higher resilience, and future-ready performance. [ Get a Consultation→ ](#contact-form) ![Row of dark server racks with red indicator lights and blue cables in a data center corridor](https://dedicatted.com/wp-content/uploads/2026/08/devops-infrastructure-modernization-racks-1024x683.jpg) ### Pipeline Automation [](#contact-form)### Pipeline Automation Accelerate software delivery with end-to-end CI/CD automation. We implement secure, repeatable pipelines integrated with your source control, testing, and deployment tools to reduce lead time and human error. [ Get a Consultation→ ](#contact-form) ![Software developers coding at shared desks with multiple monitors in a bright open office.](https://dedicatted.com/wp-content/uploads/2026/08/devops-pipeline-automation-engineers-1024x683.jpg) ### Cost Optimization & FinOps [](#contact-form)### Cost Optimization & FinOps Gain full visibility into your cloud usage and control spend with proactive monitoring, resource right-sizing, and FinOps best practices. Optimize environments without sacrificing performance or availability. [ Get a Consultation→ ](#contact-form) ![Top-down view of a hand on a silver laptop keyboard with a printed chart sheet showing various graphs and donuts nearby](https://dedicatted.com/wp-content/uploads/2026/08/devops-finops-cost-dashboard-1024x683.jpg) ### Observability & Uptime [](#contact-form)### Observability & Uptime Turn noise into signal with centralized logs, distributed tracing, and real-time alerting. Our observability solutions empower teams to detect, diagnose, and respond to incidents before they impact users. [ Get a Consultation→ ](#contact-form) ![Laptop on a desk displaying an analytics dashboard with charts and a list of top pages (slightly blurred).](https://dedicatted.com/wp-content/uploads/2026/08/devops-observability-monitoring-dashboard-1024x683.jpg) ### Platform Engineering [](#contact-form)### Platform Engineering Build internal platforms that empower your teams. We help you design reusable developer portals, golden paths, and self-service environments that enable product teams to move fast without compromising security or governance. [ Get a Consultation→ ](#contact-form) ![Two coworkers sit at a desk, reviewing code on a computer monitor in a modern office setting.](https://dedicatted.com/wp-content/uploads/2026/08/devops-platform-engineering-pairing-1024x683.jpg) [](#contact-form)### INFRASTRUCTURE MODERNIZATION Migrate from legacy bottlenecks to scalable, cloud-native environments. We help you modernize infrastructure with containerization, Kubernetes adoption, and Infrastructure as Code – unlocking faster deployments, higher resilience, and future-ready performance. [ Get a Consultation→ ](#contact-form) ![Row of dark server racks with red indicator lights and blue cables in a data center corridor](https://dedicatted.com/wp-content/uploads/2026/08/devops-infrastructure-modernization-racks-1024x683.jpg) [](#contact-form)### Pipeline Automation Accelerate software delivery with end-to-end CI/CD automation. We implement secure, repeatable pipelines integrated with your source control, testing, and deployment tools to reduce lead time and human error. [ Get a Consultation→ ](#contact-form) ![Software developers coding at shared desks with multiple monitors in a bright open office.](https://dedicatted.com/wp-content/uploads/2026/08/devops-pipeline-automation-engineers-1024x683.jpg) [](#contact-form)### Cost Optimization & FinOps Gain full visibility into your cloud usage and control spend with proactive monitoring, resource right-sizing, and FinOps best practices. Optimize environments without sacrificing performance or availability. [ Get a Consultation→ ](#contact-form) ![Top-down view of a hand on a silver laptop keyboard with a printed chart sheet showing various graphs and donuts nearby](https://dedicatted.com/wp-content/uploads/2026/08/devops-finops-cost-dashboard-1024x683.jpg) [](#contact-form)### Observability & Uptime Turn noise into signal with centralized logs, distributed tracing, and real-time alerting. Our observability solutions empower teams to detect, diagnose, and respond to incidents before they impact users. [ Get a Consultation→ ](#contact-form) ![Laptop on a desk displaying an analytics dashboard with charts and a list of top pages (slightly blurred).](https://dedicatted.com/wp-content/uploads/2026/08/devops-observability-monitoring-dashboard-1024x683.jpg) [](#contact-form)### Platform Engineering Build internal platforms that empower your teams. We help you design reusable developer portals, golden paths, and self-service environments that enable product teams to move fast without compromising security or governance. [ Get a Consultation→ ](#contact-form) ![Two coworkers sit at a desk, reviewing code on a computer monitor in a modern office setting.](https://dedicatted.com/wp-content/uploads/2026/08/devops-platform-engineering-pairing-1024x683.jpg) ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) ## Insights [Case study ### Enabling Security Compliance and DevOps Agility in a Regulated Healthcare Environment Healthcare Cloud and Infrastructure Security Cloud Migration DevOps Consulting Security Compliance ![Healthcare professional conducting a virtual patient consultation via laptop with stethoscope and 'Doctor Online' signage – telehealth compliance setup](https://dedicatted.com/wp-content/uploads/2025/04/Zivian-Cover-2-1024x537.webp) Seamless AWS to Azure cloud migration with HIPAA compliance and GitOps automation. Enabling secure, scalable DevOps for digital healthcare delivery. ](https://dedicatted.com/insights/enabling-security-compliance-and-devops-agility-in-a-regulated-healthcare-environment) [Blog ### Top 5 DevOps Pitfalls – And How Dedicatted Expert Consulting Helps You Avoid Them Automotive Healthcare High Tech Software Insurance Retail DevOps Consulting Managed Services ![Cloud Computing Artificial Intelligence Data Center Multi Cloud Hybrid Cloud Information Storage Cyber Security Encryption Edge Computing Data Lake](https://dedicatted.com/wp-content/uploads/2025/09/Designing-Platform-for-Instant-Market-Penetration-53.png) Discover the top 5 DevOps pitfalls and how Dedicatted's expert consulting helps you avoid mistakes, boost agility, and achieve sustainable growth. ](https://dedicatted.com/insights/top-5-devops-pitfalls-and-how-dedicatted-expert-consulting-helps-you-avoid-them) [Blog ### How can DevOps help ML train models cheaper DevOps Consulting ![Business analytics workspace with laptop displaying bar chart data visualization, overlaid with purple filter](https://dedicatted.com/wp-content/uploads/2023/02/Designing-Platform-for-Instant-Market-Penetration-92.png) Discover how DevOps optimizes infrastructure, automation, and CI/CD to cut costs in machine learning model training and deployment. Learn best practices today! ](https://dedicatted.com/insights/how-can-devops-help-ml-to-train-models-cheaper) [Whitepaper ### Powering Modern Finance Inside a Cloud-Native Success Story Financial Services Cloud and Infrastructure Security DevOps Consulting Security Compliance ![Close up hand of businesswoman using digital pencil hologram for drawing business forex chart interface on dark blue background Digital transformation and trading concept](https://dedicatted.com/wp-content/uploads/2025/06/Designing-Platform-for-Instant-Market-Penetration-2025-11-14T123815.170.png) Explore how a cloud-native overhaul delivered field-level encryption, sub-second trade repository, and $2M AWS support – driving secure, ultra-fast finance modernization. ](https://dedicatted.com/insights/powering-modern-finance-inside-a-cloud-native-success-story) ## FAQ ### What tools are used in DevOps consulting? Typical engagements use Terraform, Kubernetes, Docker, Helm, GitHub Actions or GitLab CI, ArgoCD, Prometheus, Grafana, and cloud-native services from AWS, Azure, or GCP — selected to fit your existing stack rather than replace it. ### Is DevOps consulting suitable for small teams? Yes. Small teams often see the largest relative gains — automation replaces the manual work that would otherwise require additional hires. ### What is Infrastructure as Code in DevOps? Infrastructure as Code (IaC) means defining servers, networks, and services in versioned configuration files — so environments are repeatable, auditable, and recoverable with a single command. ### How does DevOps consulting support cloud adoption? Consultants design the landing zone, migration path, and automation needed to move workloads safely — avoiding the cost and security pitfalls of ad-hoc cloud adoption. ### Can DevOps consulting improve security? Yes. DevSecOps practices embed scanning, secrets management, and policy-as-code directly into the pipeline, catching vulnerabilities before they reach production. ## DevOps Consulting Services Modern software delivery requires more than a few automated scripts or isolated deployment pipelines. Companies need scalable infrastructure, reliable automation, and a DevOps strategy that supports long-term business growth. At Dedicatted, we provide enterprise-grade DevOps consulting and cloud engineering solutions that help businesses streamline operations, accelerate releases, and improve infrastructure reliability. Our engineers help organizations modernize development workflows, automate infrastructure, optimize cloud environments, and implement scalable DevOps practices across the entire software delivery lifecycle. From CI/CD automation and Kubernetes management to cloud migration and AI-driven operations, we build systems designed for performance, security, and scalability. As a provider of DevOps consulting and managed cloud services, Dedicatted combines deep cloud expertise with hands-on engineering support to help businesses reduce operational complexity and accelerate digital transformation. ## Cloud DevOps Consulting Many businesses in Toronto still rely on outdated infrastructure, manual deployments, and disconnected development processes. Our cloud DevOps consulting services, including cloud consulting in Toronto, help organizations transition to modern cloud-native environments while improving speed, reliability, and operational efficiency. ### We support businesses at every stage of cloud adoption, including: - Cloud migration planning - Infrastructure modernization - Multi-cloud optimization - CI/CD pipeline automation - Kubernetes deployment - Infrastructure as Code (IaC) - Monitoring and observability - DevSecOps implementation Our goal is to create scalable delivery pipelines and cloud architectures that improve developer productivity while reducing operational risk. ## AWS Cloud Consulting Services As an experienced AWS consulting partner, Dedicatted provides AWS DevOps services by helping companies to design, migrate, and optimize workloads on Amazon Web Services. ### Our AWS cloud consulting services include: - AWS migration strategy - Cloud architecture design - Infrastructure optimization - Kubernetes on AWS - AWS cost optimization - CI/CD automation - Disaster recovery planning - Security and compliance implementation Using tools such as CloudFormation, AWS CDK, CodePipeline, CodeBuild, and CodeDeploy, our engineers automate infrastructure delivery and improve deployment consistency across environments. Organizations looking for reliable DevOps consulting and managed cloud services choose Dedicatted for end-to-end cloud operations, infrastructure automation, and continuous optimization. ### DevOps Consulting Solutions Our DevOps consulting services cover the entire software delivery lifecycle and help organizations scale engineering operations efficiently. ### CI/CD Automation ### We design and optimize CI/CD pipelines using technologies such as: - GitHub Actions - Azure DevOps - GitLab CI/CD - Jenkins - AWS CodePipeline Automated pipelines reduce deployment time, minimize manual errors, and improve software release consistency. ### Infrastructure as Code (IaC) Infrastructure as Code allows businesses to manage cloud environments through automated and repeatable configurations. We work with: - Terraform - CloudFormation - Pulumi - Ansible IaC improves infrastructure scalability, security, disaster recovery, and deployment reliability. ### Kubernetes & Cloud-Native Engineering Containerized environments require advanced orchestration and observability strategies. ### Our Kubernetes services include: - Cluster architecture - Autoscaling optimization - Container security - Resource management - Observability implementation - Kubernetes cost optimization We help organizations deploy resilient cloud-native applications that support rapid scaling and high availability. ### DevSecOps & Compliance Security must be integrated directly into the development lifecycle. ### Our DevSecOps services include: - Security automation - Compliance frameworks - Infrastructure hardening - Secrets management - Vulnerability scanning - Access control policies We support compliance requirements including ISO 27001, SOC 2, and enterprise security standards. ## DevOps Implementation Consulting Successful DevOps transformation requires more than technology alone. It also involves processes, team alignment, and operational culture. ### Our DevOps implementation consulting services help organizations: - Standardize engineering workflows - Improve collaboration between development and operations - Automate repetitive operational tasks - Implement monitoring and alerting systems - Align KPIs with business objectives We automate critical workflows such as deployments, infrastructure provisioning, scaling policies, and incident response to improve operational efficiency and reduce downtime. ### DevOps Consulting and Managed Cloud Services Dedicatted provides both strategic consulting and long-term operational support. ### Our DevOps consulting and managed cloud services include: - 24/7 infrastructure monitoring - Cloud environment management - Incident response - Continuous optimization - Infrastructure maintenance - Performance monitoring - Cost governance As both a cloud consulting company and managed service provider, we help clients plan, build, optimize, and continuously improve modern cloud infrastructures from a single partnership. ### Enterprise DevOps Consulting Enterprise organizations require secure, scalable, and policy-driven DevOps environments. ### Our enterprise DevOps consulting frameworks are built around: - ITIL best practices - Infrastructure governance - Automated compliance - Multi-region scalability - Cloud cost control - Security enforcement We help global organizations manage complex infrastructures, large-scale Kubernetes environments, and multi-cloud architectures while maintaining operational stability and predictable cloud spending. ### Business Outcomes We Deliver Our DevOps consulting services help organizations achieve measurable business improvements, including: - Faster software delivery cycles - Reduced infrastructure costs - Higher deployment reliability - Improved cloud scalability - Better operational visibility - Increased developer productivity - Reduced incident recovery times Businesses that implement modern DevOps practices often achieve faster release cadences, stronger infrastructure resilience, and improved customer experience through automation and cloud optimization. Dedicatted combines cloud engineering expertise, automation, and enterprise DevOps practices to help companies modernize infrastructure and scale with confidence. We help businesses build reliable, scalable, and future-ready cloud operations that support long-term growth and innovation. Show more ## Get started with a DevOps consultant Outline where your delivery pipeline is slowing down. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Kubernetes Consulting](https://dedicatted.com/solutions/key-technologies/kubernetes) **Published:** July 14, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Solutions](https://dedicatted.com/solutions) 3. [Key Technologies](https://dedicatted.com/solutions/key-technologies) 4. Kubernetes Consulting Kubernetes Consulting & Platform Engineering # Kubernetes Consulting Services Clusters you don't have to babysit Kubernetes consulting services help businesses deploy, manage, optimize, and scale containerized applications using Kubernetes for cloud-native infrastructure, automated orchestration, high availability, and efficient multi-cloud operations. [Start deploying to Kubernetes](#contact-form) ![Server racks in a blue-lit data center aisle](https://dedicatted.com/wp-content/uploads/2026/08/k8s-hero-server-racks-blue-1024x684.jpg) EKS at production scale Run by an AWS Premier Tier Partner — SOC 2 and ISO 27001 audited - Certified K8s engineers CKA/CKS-certified, running production clusters daily - EKS, GKE, AKS, on-prem Managed or self-hosted — we run both - 24/7 cluster operations Available as ongoing managed service with SLOs - 1 business day Response with relevant experience included ## What is Kubernetes? - ### What is Kubernetes? Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of cloud-native applications in modern infrastructure environments. - ### What are Kubernetes services? Kubernetes services include Kubernetes deployment services, cluster management, container orchestration, monitoring, and managed Kubernetes services for scalable application infrastructure. - ### What does a Kubernetes company do? A Kubernetes consulting company provides Kubernetes consulting services, cluster setup, workload optimization, and ongoing Kubernetes support to ensure high availability and performance. ## Why Should You Use Kubernetes? Kubernetes consulting services help businesses deploy, scale, and optimize containerized applications — delivered by Dedicatted, an AWS Premier Tier Partner. 01 High Availability Your application will always be up and running, whether you have a new update to roll out or some unexpected pods… 02 Scalability Kubernetes scales your applications up and down automatically based on real demand — no capacity guessing, no overnight… 03 Flexibility Run the same workloads on any cloud or on-premises — Kubernetes abstracts the infrastructure so your applications stay… 04 Observability Kubernetes exposes rich metrics, logs, and events for every workload — the foundation for monitoring that tells you… 05 Disaster Recovery Declarative configuration means your entire platform can be rebuilt from code — recovery becomes a procedure, not a… ### High Availability ### High Availability Your application will always be up and running, whether you have a new update to roll out or some unexpected pods crashing. - ✓ Self-healing workloads restart failed containers automatically - ✓ Rolling updates ship releases with zero downtime - ✓ Multi-zone clusters survive infrastructure failures - ✓ Load balancing keeps traffic on healthy pods only ### Scalability ### Scalability Kubernetes scales your applications up and down automatically based on real demand — no capacity guessing, no overnight pager duty. - ✓ Horizontal pod autoscaling driven by real metrics - ✓ Cluster autoscaling adds nodes only when needed - ✓ Handles traffic spikes without manual intervention - ✓ Scale to zero for batch and event-driven workloads ### Flexibility ### Flexibility Run the same workloads on any cloud or on-premises — Kubernetes abstracts the infrastructure so your applications stay portable. - ✓ Identical deployments on EKS, GKE, AKS, or bare metal - ✓ No cloud vendor lock-in for your workloads - ✓ Hybrid and multi-cloud strategies become practical - ✓ Consistent tooling across every environment ### Observability ### Observability Kubernetes exposes rich metrics, logs, and events for every workload — the foundation for monitoring that tells you what’s wrong before customers do. - ✓ Native metrics for every pod, node, and service - ✓ Prometheus and Grafana integrate out of the box - ✓ Distributed tracing across microservices - ✓ Alerting on SLOs, not just server CPU ### Disaster Recovery ### Disaster Recovery Declarative configuration means your entire platform can be rebuilt from code — recovery becomes a procedure, not a scramble. - ✓ Whole-cluster state versioned in Git - ✓ Automated backups of workloads and volumes - ✓ Cross-region failover strategies - ✓ Tested recovery runbooks, not hopeful guesses - 65% Faster deployments reported after migrating to Kubernetes - 96% Of enterprises now run Kubernetes in production - 54% Lower infra costs via right-sizing and autoscaling ## Key Kubernetes Benefits - Automated Operations Deployment and scaling of day-to-day automation to facilitate DevOps speed to market and application elasticity. - Infrastructure Abstraction Networking, compute, and storage management on behalf of workloads allows developers to focus on applications. - Service Health Monitoring Continuous health checks restart containers that fail or have stalled, keeping services available to users while running. ## How is Kubernetes implemented? - ### How do companies implement Kubernetes? Companies implement Kubernetes by containerizing applications, setting up Kubernetes clusters, integrating CI/CD pipelines, and deploying workloads in cloud-native environments. - ### What is the Kubernetes implementation process? The process includes infrastructure assessment, Kubernetes architecture design, cluster provisioning, application containerization, deployment automation, and continuous monitoring. - ### How long does it take to implement Kubernetes? Kubernetes implementation typically takes 2–10 weeks depending on application complexity, multi-cloud setup, and integration with existing DevOps workflows. ## What are the benefits of Kubernetes? - ### Why use Kubernetes? Kubernetes enables automated scaling, high availability, efficient resource utilization, and container orchestration, making it essential for modern DevOps environments. - ### What are the key benefits of Kubernetes? Key benefits include managed Kubernetes scalability, self-healing infrastructure, workload portability, and optimized cloud resource management. - ### How does Kubernetes improve performance? Kubernetes improves performance through auto-scaling, load balancing, and optimized cluster resource allocation, ensuring reliable application delivery. ## Our Kubernetes Services End to end or any stage alone — from first cluster to a platform your team runs confidently. - 01### Adoption & Cluster Design Architecture tailored to your workloads and compliance needs, with a migration path that doesn't disrupt delivery. - ✓ EKS, GKE, AKS, or on-prem distributions - ✓ Multi-zone, multi-region topologies - ✓ Landing zone and network design [ Plan your cluster → ](#contact-form) - 02### Containerization & Migration We containerize existing applications and move them incrementally — without a big-bang cutover. - ✓ Stateless services first, stateful after - ✓ Persistent volumes and data migration - ✓ Rollback path at every step [ Assess your workloads → ](#contact-form) - 03### CI/CD & GitOps Automated pipelines where every change is tracked in Git, automatically applied, and easily rolled back. - ✓ ArgoCD or Flux GitOps workflows - ✓ Helm-packaged, versioned deployments - ✓ Security gates in every pipeline [ Automate your releases → ](#contact-form) - 04### Security Hardening Cluster security aligned with your compliance frameworks — enforced by policy, not by memo. - ✓ RBAC and network policies - ✓ Vault-backed secret management - ✓ Pod security standards and audits [ Harden your cluster → ](#contact-form) - 05### Cost Optimization Right-sizing and autoscaling tuned to real usage — so the cluster bill tracks demand, not peak guesses. - ✓ Per-team and per-service cost visibility - ✓ Spot and reserved capacity where safe - ✓ Continuous FinOps reviews [ Cut your cluster bill → ](#contact-form) - 06### 24/7 Managed Operations We keep running it — monitoring, upgrades, and incident response as an ongoing service with SLOs. - ✓ 24/7 monitoring and alerting - ✓ Proactive patching and upgrades - ✓ Incident response with defined SLOs [ Explore managed ops → ](#contact-form) ## Build It, or Have Us Run It? Two ways to work with the same team — pick by who operates the cluster afterwards. Kubernetes Consulting We design and build the platform, train your team, and hand over the keys. Your engineers run it; you own everything. Best when you have an engineering team ready to operate the cluster. Managed Kubernetes (MSP) We build it and keep running it — 24/7 monitoring, upgrades, incident response, and cost tuning as an ongoing service with SLOs. [Explore Managed Services →](https://dedicatted.com/what-we-do/devops/devops-as-a-service) Also related: [DevOps Consulting](https://dedicatted.com/what-we-do/consulting/devops-consulting) · [App Modernization](https://dedicatted.com/what-we-do/cloud-services/app-modernisation) ## Solving Industry Challenges Six industries, six recurring problems — and what each one looks like once the cluster handles it. - ### Manufacturing $ kubectl get challenges -n factory edge-cluster ONLINE runs offline, syncs when connectivity returns Edge clusters on the factory floor keep IoT and MES workloads running when the network doesn't. - ### Healthcare $ kubectl get challenges -n clinical hipaa-audit PASSED workload isolation · full audit trail HIPAA-ready isolation and audit trails — compliant workloads without release freezes. - ### Retail $ kubectl get challenges -n storefront black-friday-peak SCALED 4 → 64 pods in 90 seconds, back down by morning Autoscaling that survives peak season — pay for capacity only while peaks happen. - ### Automotive $ kubectl get challenges -n plants site-rollout SYNCED one deployment model · 12 sites Consistent platforms from plant to cloud — the same release lands everywhere at once. - ### Financial Sector $ kubectl get challenges -n payments friday-release SHIPPED zero downtime · rollback in one command Zero-downtime releases under regulatory scrutiny — every change tracked and reversible. - ### High Tech Software $ kubectl get challenges -n saas tenant-isolation ENFORCED one cluster · hundreds of customers Multi-tenant SaaS on shared clusters — isolation without per-customer infrastructure. Want to know how we’ll solve your challenge? [Book a free meeting](#contact-form) [## Insights ](https://dedicatted.com/insights) [Blog ### Top 6 Solutions for Kubernetes Intrusion Protection with IDS and IPS Cloud and Infrastructure Security Security Compliance ![Developer working on code with laptop screen showing programming interface.](https://dedicatted.com/wp-content/uploads/2023/04/Designing-Platform-for-Instant-Market-Penetration-3-1024x537.png) Learn how IDS and IPS for Kubernetes help detect threats, prevent attacks, and keep your containerized environment secure. Explore the best tools today! ](https://dedicatted.com/insights/top-6-solutions-for-kubernetes-intrusion-protection-with-ids-and-ips) [Blog ### How can DevOps help ML train models cheaper DevOps Consulting ![Business analytics workspace with laptop displaying bar chart data visualization, overlaid with purple filter](https://dedicatted.com/wp-content/uploads/2023/02/Designing-Platform-for-Instant-Market-Penetration-92.png) Discover how DevOps optimizes infrastructure, automation, and CI/CD to cut costs in machine learning model training and deployment. Learn best practices today! ](https://dedicatted.com/insights/how-can-devops-help-ml-to-train-models-cheaper) [Case study ### Enabling Security Compliance and DevOps Agility in a Regulated Healthcare Environment Healthcare Cloud and Infrastructure Security Cloud Migration DevOps Consulting Security Compliance ![Healthcare professional conducting a virtual patient consultation via laptop with stethoscope and 'Doctor Online' signage – telehealth compliance setup](https://dedicatted.com/wp-content/uploads/2025/04/Zivian-Cover-2-1024x537.webp) Seamless AWS to Azure cloud migration with HIPAA compliance and GitOps automation. Enabling secure, scalable DevOps for digital healthcare delivery. ](https://dedicatted.com/insights/enabling-security-compliance-and-devops-agility-in-a-regulated-healthcare-environment) [Blog ### Top 5 DevOps Pitfalls – And How Dedicatted Expert Consulting Helps You Avoid Them Automotive Healthcare High Tech Software Insurance Retail DevOps Consulting Managed Services ![Cloud Computing Artificial Intelligence Data Center Multi Cloud Hybrid Cloud Information Storage Cyber Security Encryption Edge Computing Data Lake](https://dedicatted.com/wp-content/uploads/2025/09/Designing-Platform-for-Instant-Market-Penetration-53.png) Discover the top 5 DevOps pitfalls and how Dedicatted's expert consulting helps you avoid mistakes, boost agility, and achieve sustainable growth. ](https://dedicatted.com/insights/top-5-devops-pitfalls-and-how-dedicatted-expert-consulting-helps-you-avoid-them) ## FAQ ### What is container orchestration in Kubernetes? Container orchestration is the automated management of container lifecycles — scheduling them onto servers, restarting failures, scaling with demand, and routing traffic — so applications run reliably without manual operations. ### What tools are used with Kubernetes? Typical stacks pair Kubernetes with Helm for packaging, ArgoCD or Flux for GitOps deployment, Prometheus and Grafana for monitoring, Istio or Linkerd for service mesh, and Terraform for provisioning the underlying infrastructure. ### Is Kubernetes suitable for small businesses? Yes, with the right scope. Managed services like EKS remove most operational burden, and a well-designed small cluster costs less than the VM sprawl it replaces. We size the platform to the team — not the other way around. ### What is a Kubernetes cluster? A cluster is a set of machines (nodes) that run containerized applications under a shared control plane. The control plane schedules workloads, maintains desired state, and handles failures across all nodes. ### How does Kubernetes handle scaling? Three ways: horizontal pod autoscaling adds application replicas under load, vertical autoscaling adjusts resource allocations, and cluster autoscaling adds or removes nodes — all driven by real usage metrics. ### Can Kubernetes work with existing applications? Most applications can be containerized and migrated incrementally — stateless services first, then stateful workloads with persistent volumes. A migration assessment identifies what moves as-is and what needs refactoring. ### If we use managed EKS or GKE, do we still need consultants? Managed services run the control plane — the infrastructure Kubernetes itself runs on. They don’t design your application architecture, CI/CD pipelines, observability stack, or security configuration. That’s the layer where most complexity lives, and where consulting pays off. Question not answered? [Ask a Kubernetes engineer directly](#contact-form) — same-day reply on architecture questions. ## Kubernetes Consulting Services Kubernetes consulting services help businesses deploy, manage, scale, and optimize containerized applications using cloud-native infrastructure, automated orchestration, and DevOps best practices, delivered by Dedicatted, an AWS Premier Tier Partner with expertise in Kubernetes, cloud operations, and enterprise infrastructure modernization. ## Kubernetes Infrastructure and Container Management Kubernetes is an open-source container orchestration platform designed to automate application deployment, scaling, workload management, and infrastructure operations across cloud and on-premise environments. Widely adopted in modern DevOps ecosystems, Kubernetes enables organizations to build scalable, resilient, and highly available cloud-native applications. Dedicatted helps companies implement and optimize Kubernetes environments using infrastructure as code, CI/CD automation, observability platforms, and cloud-native DevOps workflows. Our Kubernetes engineers support organizations across AWS, Azure, and hybrid cloud infrastructures. ## Kubernetes Support and Managed Operations Managing Kubernetes at scale requires expertise in infrastructure automation, monitoring, security, networking, and performance optimization. Dedicatted provides Kubernetes support services that help businesses maintain stable, secure, and high-performing container environments. ### Our Kubernetes services include: - Kubernetes cluster deployment and optimization - managed Kubernetes operations and monitoring - CI/CD integration and DevOps automation - autoscaling and performance tuning - Kubernetes security and compliance - backup, disaster recovery, and observability - multi-cloud and hybrid Kubernetes support We help organizations reduce operational complexity while improving deployment reliability, scalability, and infrastructure efficiency. ## Kubernetes Migration Services Kubernetes migration services help organizations modernize legacy infrastructure and transition workloads to scalable cloud-native environments. Dedicatted supports Kubernetes migrations across AWS, Azure, Google Cloud, and hybrid infrastructures with minimal downtime and operational disruption. ### Our migration expertise includes: - VM and legacy application modernization - Docker and container adoption - cloud-native architecture transformation - AWS to Azure Kubernetes migration - on-premise to Kubernetes migration - PCF to Kubernetes migration - OpenShift to Kubernetes migration We design migration strategies focused on workload portability, infrastructure optimization, operational continuity, and long-term scalability. ## AWS and Azure Kubernetes Expertise Dedicatted helps businesses build and optimize Kubernetes environments across AWS and Azure cloud platforms. Our engineers implement secure and scalable Kubernetes architectures aligned with cloud-native best practices and enterprise operational requirements. ### We support: - Amazon EKS deployments and optimization - Azure Kubernetes Service (AKS) implementation - Kubernetes networking and security - DevOps workflows for Kubernetes environments - infrastructure automation with Terraform and GitOps - monitoring and observability integration By combining Kubernetes with DevOps automation and cloud infrastructure expertise, we help organizations accelerate software delivery, improve system reliability, and optimize cloud operations. ### Amazon EKS Migration An Amazon EKS migration can make Kubernetes easier to manage on AWS, but despite that it still requires a considerable amount of planning around security, workloads, networking, and deployment pipelines. Dedicatted provides businesses with EKS consulting services allowing them to move Kubernetes workloads to Amazon EKS, properly set up their clusters, and connect monitoring and automation while avoiding unnecessary disruption. ### OpenShift to Kubernetes Migration Organizations migrate from OpenShift to Kubernetes to improve infrastructure flexibility, reduce operational complexity, and adopt standardized cloud-native technologies. Kubernetes provides broader ecosystem support, stronger community adoption, and greater integration capabilities across cloud providers and DevOps platforms. Dedicatted helps businesses migrate OpenShift workloads to Kubernetes environments while preserving application stability, operational continuity, and deployment efficiency. ### Enterprise Kubernetes Consulting Dedicatted provides Kubernetes consulting services for organizations looking to modernize infrastructure, optimize cloud operations, and improve application scalability. Our Kubernetes consultants help businesses design secure architectures, automate deployment workflows, implement observability, and optimize containerized environments for enterprise workloads. Whether you are adopting Kubernetes for the first time, scaling existing clusters, or modernizing legacy infrastructure, Dedicatted delivers enterprise Kubernetes solutions designed for performance, scalability, security, and long-term operational efficiency. Show more ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) ## Get started with a Kubernetes consultant Outline your cluster, scaling or platform challenge. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Dedicatted & AWS](https://dedicatted.com/our-partners/aws) **Published:** April 23, 2025 **Author:** Dedicatted **Content:** # Dedicatted & AWS As an AWS Premier Tier Partner with the Generative AI Competency and Managed Service Provider, we help organizations modernize applications, unlock data, and innovate faster – all while staying aligned to the AWS Well-Architected Framework. [Let’s Talk](#contact-form) ![AWS white logo](https://dedicatted.com/wp-content/uploads/2025/04/amazon-logo-white-aws-1-1024x612.png) ### AWS partner Dedicatted modernizes applications, data platforms, analytics – and now Generative AI – on AWS, delivering cost-efficient, resilient, and infinitely scalable solutions. As the first Canadian partner to earn the AWS Generative AI Competency – in addition to our Premier Tier status – we combine deep GenAI expertise with vendor-agnostic agility. From strategy and migration to Bedrock-powered GenAI pilots and 24 × 7 operations, we handle the most complex enterprise workloads end-to-end, ensuring you unlock the full value of AWS on time, on budget, and without friction. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ## OUR DIFFERENTIATOR We simplify the path to AWS cloud adoption, application modernization, and Generative AI innovation. Our approach helps organizations optimize time, resources, and investments while delivering faster outcomes, stronger security, and real business value to customers and stakeholders. ### 100+ AWS Certified Resources ### 40+ AWS Professional Certifications ### 20+ AWS Competencies & Counting ### 81 Net Promoter Score ## Why Dedicatted & aws? ![A lightning bolt icon inside an orange circular background.](https://dedicatted.com/wp-content/uploads/2025/04/icon3-1-1.svg) ### Reduce Reduce time-to-market for new features and updates, improve SLAs, and decrease RTO and RPO through AWS-native automation and cloud optimization ![A shield icon with a cloud inside, set on an orange circular background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-1-2.svg) ### Improve Improve infrastructure efficiency and lower cloud costs by adopting AWS Well-Architected best practices across all layers of your environment ![A group of three person icons inside an orange circular background.](https://dedicatted.com/wp-content/uploads/2025/04/icon2-1-1.svg) ### Enhance Enhance scalability and availability by designing flexible AWS architectures that can ‘scale up’ and ‘scale out’ with business demands ![A gear icon inside an orange circular background.](https://dedicatted.com/wp-content/uploads/2025/04/icon4-1-1.svg) ### Ensure Ensure data integrity and privacy with security-first cloud solutions, continuous compliance monitoring, and encrypted workloads ![A star icon inside an orange circular background.](https://dedicatted.com/wp-content/uploads/2025/04/icon5-1-1.svg) ### optimize Dedicatted adheres to the pillars of the AWS Well-Architected Framework and is authorized to deliver Well-Architected Reviews, helping clients optimize performance, security, and operational excellence [Discuss your project with Dedicatted technical experts ](https://dedicatted.com/contact) ## AWS solution areas ### Generative AI [](https://dedicatted.com/services/generative-ai)### Generative AI Accelerate innovation with secure, scalable Generative AI solutions powered by Amazon Bedrock and SageMaker. Dedicatted helps you build retrieval-augmented generation (RAG) pipelines, fine-tune foundation models, and orchestrate agentic AI workflows – all while maintaining full control over data privacy, compliance, and intellectual property. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### EKS ### EKS Deploy and manage containerized applications effortlessly with Amazon EKS. Build a scalable, resilient, and cost-optimized Kubernetes platform that accelerates modern application development and simplifies operations. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### MSK ### MSK Amazon MSK offers the lowest total cost of ownership (TCO) in the industry, saving up to 40% compared to self-managed Kafka clusters. Simplify Kafka deployment, scaling, and maintenance while leveraging full compatibility with Kafka-native tools like Kafka Connect, KSQLDB, REST Proxy, Kafka Streams, and Schema Registry. Dedicatted helps you build resilient, scalable real-time data pipelines powered by AWS-native integrations. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Machine Learning ### Machine Learning Unlock new business opportunities by automating tasks with computer vision, natural language processing, and speech recognition. AI and ML solutions streamline system operations, enhance business efficiency, accelerate fraud detection, and drive smarter decision-making. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Migration ### Migration Accelerate business agility, optimize total cost of ownership (TCO), and solve challenges around scalability, security, and availability with AWS Cloud. Choose the right distribution model – whether IaaS, PaaS, SaaS, or FaaS – and leverage AWS managed services to build solutions tailored to your business needs. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### SaaS ### SaaS AWS provides the ideal platform for building reliable, scalable, and cost-effective SaaS solutions. Dedicatted helps clients leverage modern AWS technologies to streamline customer onboarding, implement secure identity and access management, enable tenant isolation, and ensure robust data partitioning – accelerating SaaS product delivery and growth. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Resiliency ### Resiliency Ensure your systems stay available, responsive, and secure – even during failures or unexpected spikes. Dedicatted designs highly resilient architectures on AWS using multi-AZ deployments, automated failover, backup and recovery strategies, and disaster recovery planning – minimizing downtime and protecting critical business operations. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) [](https://dedicatted.com/services/generative-ai)Accelerate innovation with secure, scalable Generative AI solutions powered by Amazon Bedrock and SageMaker. Dedicatted helps you build retrieval-augmented generation (RAG) pipelines, fine-tune foundation models, and orchestrate agentic AI workflows – all while maintaining full control over data privacy, compliance, and intellectual property. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) Deploy and manage containerized applications effortlessly with Amazon EKS. Build a scalable, resilient, and cost-optimized Kubernetes platform that accelerates modern application development and simplifies operations. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) Amazon MSK offers the lowest total cost of ownership (TCO) in the industry, saving up to 40% compared to self-managed Kafka clusters. Simplify Kafka deployment, scaling, and maintenance while leveraging full compatibility with Kafka-native tools like Kafka Connect, KSQLDB, REST Proxy, Kafka Streams, and Schema Registry. Dedicatted helps you build resilient, scalable real-time data pipelines powered by AWS-native integrations. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) Unlock new business opportunities by automating tasks with computer vision, natural language processing, and speech recognition. AI and ML solutions streamline system operations, enhance business efficiency, accelerate fraud detection, and drive smarter decision-making. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) Accelerate business agility, optimize total cost of ownership (TCO), and solve challenges around scalability, security, and availability with AWS Cloud. Choose the right distribution model – whether IaaS, PaaS, SaaS, or FaaS – and leverage AWS managed services to build solutions tailored to your business needs. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) AWS provides the ideal platform for building reliable, scalable, and cost-effective SaaS solutions. Dedicatted helps clients leverage modern AWS technologies to streamline customer onboarding, implement secure identity and access management, enable tenant isolation, and ensure robust data partitioning – accelerating SaaS product delivery and growth. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) Ensure your systems stay available, responsive, and secure – even during failures or unexpected spikes. Dedicatted designs highly resilient architectures on AWS using multi-AZ deployments, automated failover, backup and recovery strategies, and disaster recovery planning – minimizing downtime and protecting critical business operations. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ## Industry Expertise ![White factory icon on dark circular background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-3.svg) ### Financial Services To meet evolving industry demands, financial services firms on AWS accelerate fraud detection, risk modelling, and customer personalization using Amazon Bedrock, SageMaker, and the AWS Financial Services Competency framework. Dedicatted helps banks and insurers modernize core systems while meeting strict regulatory, resilience, and security requirements. [Read More](https://dedicatted.com/services/generative-ai/financial-services) ![A white shield with a cross in the center, on a transparent background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-2.svg) ### Retail AWS-powered retailers personalize shopping experiences at scale with Amazon Personalize, Bedrock, and real-time inventory analytics on Redshift. Dedicatted designs cloud-native architectures that absorb peak demand, optimize fulfillment, and unify customer data across digital and physical channels. [Read More](https://dedicatted.com/services/generative-ai/retail) ![A white circular icon on a transparent background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-1-1.svg) ### Energy, Oil & Gas Leading energy companies are becoming more agile, resilient, and efficient by leveraging AWS cloud technologies. Dedicatted partners with energy and utilities organizations to deliver tailored cloud strategies, streamline operations, and deploy cutting-edge AWS integrations that drive innovation and business growth. ![A white shopping cart icon on a transparent background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-4.svg) ### Healthcare Healthcare and life sciences organizations are transforming patient care, research, and operations with AWS cloud solutions. Dedicatted helps providers design secure, HIPAA-compliant, and scalable systems aligned with organizational goals, patient needs, and the latest technology capabilities. [Read More](https://dedicatted.com/services/generative-ai/healthcare) ![A white circle on a transparent background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-5.svg) ### Manufacturing & Industrial Manufacturers are optimizing operations, reinventing supply chains, and accelerating digital transformation with AWS. Dedicatted leverages expertise in digital twins, AI, robotics, and cloud automation to deliver smart, scalable solutions for complex manufacturing and industrial challenges. [Read More](https://dedicatted.com/services/generative-ai/manufacturing) ## Service Expertise - Generative AI / Agentic AI - Data - Software as a Service (SaaS) Solutions ### Generative AI / Agentic AI ### Generative AI Adoption [](https://dedicatted.com/services/generative-ai)### Generative AI Adoption Define the best adoption model – whether using pre-trained models, fine-tuning, or custom development. Align GenAI initiatives with your business priorities, security requirements, and data governance policies. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### RAG Implementation ### RAG Implementation Integrate Retrieval-Augmented Generation (RAG) pipelines to ground AI outputs with trusted enterprise data. Increase model accuracy, reduce hallucinations, and keep critical knowledge assets secure. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Agentic AI & AI Agent [](https://dedicatted.com/services/artificial-intelligence/agentic-ai)### Agentic AI & AI Agent Design and deploy multi-agent systems that plan, reason, and autonomously execute tasks across workflows. Enable complex process automation with fully governed agent coordination and task management. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Custom LLM Fine-Tuning ### Custom LLM Fine-Tuning Fine-tune foundation models on proprietary datasets for maximum relevance and performance. Deploy models securely using Amazon Bedrock, SageMaker, or open-source frameworks with full control over IP. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) [](https://dedicatted.com/services/generative-ai)### Generative AI Adoption Define the best adoption model – whether using pre-trained models, fine-tuning, or custom development. Align GenAI initiatives with your business priorities, security requirements, and data governance policies. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### RAG Implementation Integrate Retrieval-Augmented Generation (RAG) pipelines to ground AI outputs with trusted enterprise data. Increase model accuracy, reduce hallucinations, and keep critical knowledge assets secure. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) [](https://dedicatted.com/services/artificial-intelligence/agentic-ai)### Agentic AI & AI Agent Design and deploy multi-agent systems that plan, reason, and autonomously execute tasks across workflows. Enable complex process automation with fully governed agent coordination and task management. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Custom LLM Fine-Tuning Fine-tune foundation models on proprietary datasets for maximum relevance and performance. Deploy models securely using Amazon Bedrock, SageMaker, or open-source frameworks with full control over IP. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Data ### Machine Learning (ML) [](https://dedicatted.com/services/artificial-intelligence)### Machine Learning (ML) AI and ML automate system operations, monitor and forecast business performance, detect anomalies, and accelerate decision-making across structured and unstructured data sources. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Advanced Analytics [](https://dedicatted.com/services/data/architecture)### Advanced Analytics Break through traditional analytics limits by using AI to deliver faster, deeper, and more scalable predictive insights – unlocking real-time, actionable business intelligence. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Enterprise Data Platform [](https://dedicatted.com/services/data/architecture)### Enterprise Data Platform Design and implement robust, cloud-native data architectures using proven AWS best practices – facilitating adoption of new technologies and driving enterprise agility. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Big Data Cloud Migration [](https://dedicatted.com/services/data/cloud-data-migration)### Big Data Cloud Migration Accelerate time-to-market by migrating big data workloads to cloud platforms that simplify onboarding, enable faster innovation, and unlock new business use cases. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Data Strategy & Governance ### Data Strategy & Governance Establish a strong foundation for data quality, access, and governance. Democratize data usage across the enterprise while ensuring security, compliance, and trusted insights. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) [](https://dedicatted.com/services/artificial-intelligence)### Machine Learning (ML) AI and ML automate system operations, monitor and forecast business performance, detect anomalies, and accelerate decision-making across structured and unstructured data sources. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) [](https://dedicatted.com/services/data/architecture)### Advanced Analytics Break through traditional analytics limits by using AI to deliver faster, deeper, and more scalable predictive insights – unlocking real-time, actionable business intelligence. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) [](https://dedicatted.com/services/data/architecture)### Enterprise Data Platform Design and implement robust, cloud-native data architectures using proven AWS best practices – facilitating adoption of new technologies and driving enterprise agility. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) [](https://dedicatted.com/services/data/cloud-data-migration)### Big Data Cloud Migration Accelerate time-to-market by migrating big data workloads to cloud platforms that simplify onboarding, enable faster innovation, and unlock new business use cases. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Data Strategy & Governance Establish a strong foundation for data quality, access, and governance. Democratize data usage across the enterprise while ensuring security, compliance, and trusted insights. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Software as a Service (SaaS) Solutions ### Managed SaaS Services [](https://dedicatted.com/what-we-do/devops/devops-as-a-service)### Managed SaaS Services Deliver end-to-end managed services for SaaS environments, including 24×7 monitoring, automated scaling, incident management, patching, and cost optimization – allowing your teams to focus on innovation, not infrastructure. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Migrate ### Migrate Migrate SaaS solutions to AWS using cloud-native principles, proven migration frameworks, and fully managed services to accelerate time-to-value. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Security Assessment & Optimization ### Security Assessment & Optimization Identify security risks in existing SaaS environments, classify and protect sensitive data, properly isolate tenants and users, and implement AWS-aligned security best practices. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Cost Assessment & Optimization ### Cost Assessment & Optimization Develop and continuously refine a cloud cost optimization strategy – leveraging reserved instances, scaling models (scale out/up), smart tagging, and automation to maximize savings. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Re-architecture ### Re-architecture Modernize SaaS applications by integrating APIs and services for better interoperability, scalability, and future-proofing across cloud ecosystems. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) [](https://dedicatted.com/what-we-do/devops/devops-as-a-service)### Managed SaaS Services Deliver end-to-end managed services for SaaS environments, including 24×7 monitoring, automated scaling, incident management, patching, and cost optimization – allowing your teams to focus on innovation, not infrastructure. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Migrate Migrate SaaS solutions to AWS using cloud-native principles, proven migration frameworks, and fully managed services to accelerate time-to-value. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Security Assessment & Optimization Identify security risks in existing SaaS environments, classify and protect sensitive data, properly isolate tenants and users, and implement AWS-aligned security best practices. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Cost Assessment & Optimization Develop and continuously refine a cloud cost optimization strategy – leveraging reserved instances, scaling models (scale out/up), smart tagging, and automation to maximize savings. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ### Re-architecture Modernize SaaS applications by integrating APIs and services for better interoperability, scalability, and future-proofing across cloud ecosystems. ![AWS Partner Premier Tier Services badge on a purple-to-teal gradient background, with orange border outline.](https://dedicatted.com/wp-content/uploads/2026/07/Group-135.png) ## Services - AI & ML - AWS 24 / 7 Support - Big Data & Analytics - Cloud Adoption - DevOps ### AI & ML ##### Dedicatted's AI and ML specialists apply best practices in data science and advanced analytics to create tailored solutions that drive business success. ![Amazon SageMaker logo featuring a white stylized brain circuit design on a green gradient background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-4.webp) ##### Amazon SageMaker ![Amazon Transcribe logo featuring a white speech bubble with a waveform and a document icon on a green gradient background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-1.webp) ##### Amazon Transcribe ![Amazon Bedrock icon featuring a stylized white brain circuit design on a green gradient background.](https://dedicatted.com/wp-content/uploads/2025/04/icon_amazon_bedrock.webp) ##### Amazon Bedrock ![Amazon Rekognition icon featuring a magnifying glass icon with a square target on a green gradient background.](https://dedicatted.com/wp-content/uploads/2025/04/icon.webp) ##### AWS Rekognition ![AWS Panorama icon featuring a simplified robot or camera device icon in white on a green gradient background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-2.webp) ##### AWS DeepLens ![Amazon Lex icon representing machine learning or automated customer interaction, showing two overlapping speech bubbles—one with horizontal lines (text) and one with a gear symbol—on a green gradient background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-3.webp) ##### Amazon Lex ![white hexagon icon with letter K inside on orange gradient background representing Amazon EKS (Elastic Kubernetes Service)](https://dedicatted.com/wp-content/uploads/2025/04/icon-7.webp) ##### Amazon EKS ![AWS EC2 icon with two overlapping microchip icons in white on an orange background](https://dedicatted.com/wp-content/uploads/2025/04/icon-6.webp) ##### Amazon EC2 ![Amazon Redshift icon featuring a white cylinder with connected data points on a purple background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-5.webp) ##### Amazon Redshift ### AWS 24 / 7 Support ##### Dedicatted offers full-service AWS environment maintenance, 24/7 monitoring, support, and continuous improvement using industry-standard tools and processes. For businesses with an existing cloud environment, we deliver customized support solutions to ensure system uptime, fast recovery, and alignment with specific operational needs. ![white cloud icon with magnifying glass on pink gradient background representing AWS CloudWatch](https://dedicatted.com/wp-content/uploads/2025/04/aws-cloudwatch.png) ##### Amazon CloudWatch ![white cloud icon with horizontal lines on dark pink background representing AWS CloudTrail](https://dedicatted.com/wp-content/uploads/2025/04/aws-cloudtrail.jpg) ##### AWS CloudTrail ![white cloud icon with bar chart inside circle on blue gradient background representing AWS CloudWatch Metrics](https://dedicatted.com/wp-content/uploads/2025/04/aws-x-ray.jpg) ##### AWS X-Ray ![white icon of control sliders and gear with sync arrows on pink gradient background representing AWS Application Auto Scaling](https://dedicatted.com/wp-content/uploads/2025/04/aws-config.jpg) ##### AWS Config ![White heart icon with a heartbeat line across a magenta background, symbolizing heart health.](https://dedicatted.com/wp-content/uploads/2026/05/Health-Dashboard.png) ##### AWS Health ![Cloud computing settings icon: a gear with code lines in front of a cloud outline.](https://dedicatted.com/wp-content/uploads/2026/05/Systems-Manager.png) ##### AWS Systems Manager ![White shield and network-node icon on a pink background, symbolizing cybersecurity.](https://dedicatted.com/wp-content/uploads/2026/05/Trusted-Advisor.png) ##### AWS Trusted Advisor ![White data funnel icon with circuit connections on a magenta background, symbolizing data filtering and integration.](https://dedicatted.com/wp-content/uploads/2026/05/Simple-Notification-Service.png) ##### Amazon SNS ### Big Data & Analytics ##### Dedicatted brings deep expertise in building big data strategies and enterprise-grade decision support systems. We tailor solutions to your organization's goals, priorities, user needs, and technology capabilities – driving smarter insights and better outcomes. ![white interconnected nodes with a plus symbol on purple background representing Amazon EMR (Elastic MapReduce)](https://dedicatted.com/wp-content/uploads/2025/04/amazon-emr.png) ##### Amazon EMR ![white cylindrical database icon with line graph and data points on purple background representing Amazon Redshift](https://dedicatted.com/wp-content/uploads/2025/04/amazon-redshift.png) ##### Amazon Redshift ![white curved and straight streaming lines on purple background representing Amazon Kinesis](https://dedicatted.com/wp-content/uploads/2025/04/amazon-kinesis.png) ##### Amazon Kinesis ![white bucket icon on green background representing Amazon S3 (Simple Storage Service)](https://dedicatted.com/wp-content/uploads/2025/04/amazon-s3.png) ##### Amazon S3 ![white stacked database icon with four outward-pointing arrows on blue background representing Amazon RDS (Relational Database Service)](https://dedicatted.com/wp-content/uploads/2025/04/amazon-rds.png) ##### Amazon RDS ![white stacked database icon with lightning bolt on blue background representing Amazon DynamoDB Accelerator (DAX)](https://dedicatted.com/wp-content/uploads/2025/04/amazon-dynamodb-aws.jpg) ##### Amazon DynamoDB ![white magnifying glass over S3 bucket icon with circular scan lines on purple background representing Amazon Athena](https://dedicatted.com/wp-content/uploads/2025/04/Amazon-Athena_light-bg-01.png) ##### Amazon Athena ![white funnel icon filtering data blocks with downward arrow on purple background representing AWS Glue](https://dedicatted.com/wp-content/uploads/2025/04/AWS-Glue_light-bg-01.png) ##### AWS Glue ![white funnel filtering data into a circular lake with wave lines on purple background representing AWS Lake Formation](https://dedicatted.com/wp-content/uploads/2025/04/AWS-Lake-Formation_light-bg-01.png) ##### AWS Lake Formation ![white stacked database icon with downward motion lines on blue background representing AWS Database Migration Service (DMS)](https://dedicatted.com/wp-content/uploads/2025/04/AWS-Database-Migration-Service_light-bg-01.png) ##### AWS Database Migration Service ### Cloud Adoption ##### Dedicatted's cloud experts deliver a clear, streamlined process for lift-and-shift migrations, re-platforming, and re-architecting operations. ![three connected white hexagon icons on pink gradient background representing AWS Service Catalog](https://dedicatted.com/wp-content/uploads/2025/04/aws-well-architected-tool.jpg) ##### AWS Well-Architected Tool ![white cloud icon with shield in front on purple gradient background representing AWS Cloud Security or AWS Identity and Access Management (IAM)](https://dedicatted.com/wp-content/uploads/2025/04/amazon-vpc.jpg) ##### Amazon VPC ![white microchip icon overlapping another on orange gradient background representing Amazon EC2 (Elastic Compute Cloud)](https://dedicatted.com/wp-content/uploads/2025/04/amazon-ec2.jpg) ##### Amazon EC2 ![white cylindrical database icon with four outward-pointing arrows on green gradient background representing Amazon DynamoDB](https://dedicatted.com/wp-content/uploads/2025/04/aws-ebs.jpg) ##### AWS EBS ![white hexagon icon with abstract container design on orange gradient background representing Amazon ECS (Elastic Container Service)](https://dedicatted.com/wp-content/uploads/2025/04/amazon-ecs-aws.png) ##### Amazon ECS ![white hexagon icon with letter K inside on orange gradient background representing Amazon EKS (Elastic Kubernetes Service)](https://dedicatted.com/wp-content/uploads/2025/04/amazon-eks-aws.jpg) ##### Amazon EKS ![white Greek letter lambda icon on orange gradient background representing AWS Lambda](https://dedicatted.com/wp-content/uploads/2025/04/aws-lambda.png) ##### AWS Lambda ![white magnifying glass over vertical bars icon on purple gradient background representing AWS OpenSearch Service (formerly Elasticsearch Service)](https://dedicatted.com/wp-content/uploads/2025/04/aws-elasticsearch.jpg) ##### AWS Elasticsearch ### DevOps ##### Dedicatted's proven DevOps methodology accelerates and optimizes application deployment at scale. We assess your needs, build a tailored improvement plan, and collaborate closely with your teams to deliver continuous innovation and efficiency. ![white construction crane lifting code symbol box on blue gradient background representing AWS CodeBuild](https://dedicatted.com/wp-content/uploads/2025/04/aws-codebuild.jpg) ##### AWS CodeBuild ![white branching version control diagram with code symbol on blue gradient background representing AWS CodeCommit](https://dedicatted.com/wp-content/uploads/2025/04/aws-codecommit.png) ##### AWS CodeCommit ![white parachute icon carrying a box with code symbol on blue gradient background representing AWS CodeDeploy](https://dedicatted.com/wp-content/uploads/2025/04/aws-codedeploy.jpg) ##### AWS CodeDeploy ![white browser window with code symbol on blue gradient background representing AWS Cloud9](https://dedicatted.com/wp-content/uploads/2025/04/aws-pipeline.png) ##### AWS CodePipeline ![white cloud icon with bar chart inside circle on blue gradient background representing AWS CloudWatch Metrics](https://dedicatted.com/wp-content/uploads/2025/04/aws-x-ray.jpg) ##### AWS X-Ray ![white cloud icon with magnifying glass on pink gradient background representing AWS CloudWatch](https://dedicatted.com/wp-content/uploads/2025/04/aws-cloudwatch.png) ##### Amazon CloudWatch ![white cloud icon with horizontal lines on dark pink background representing AWS CloudTrail](https://dedicatted.com/wp-content/uploads/2025/04/aws-cloudtrail.jpg) ##### AWS CloudTrail ![white icon of control sliders and gear with sync arrows on pink gradient background representing AWS Application Auto Scaling](https://dedicatted.com/wp-content/uploads/2025/04/aws-config.jpg) ##### AWS Config ![white cloud and document icon on pink gradient background representing AWS CloudFormation](https://dedicatted.com/wp-content/uploads/2025/04/aws-cloudformation.png) ##### AWS CloudFormation ![white gear icon connected to three hierarchical nodes on pink gradient background representing AWS Systems Manager](https://dedicatted.com/wp-content/uploads/2025/04/aws-opsworks.jpg) ##### AWS OpsWorks ## AWS Consulting and Cloud Solutions Partner At Dedicatted, we help businesses in Toronto and across Canada design, migrate, optimize, and scale cloud infrastructure on Amazon Web Services. As a trusted AWS consulting and engineering partner, we deliver secure, high-performance, and cost-efficient cloud environments tailored to modern business needs. Our AWS experts support startups, SaaS companies, enterprises, and fast-growing digital platforms with end-to-end cloud transformation services — from cloud architecture design and migration to DevOps automation, Kubernetes, AI infrastructure, FinOps, and 24/7 cloud operations. We build AWS ecosystems that are optimized not only for performance and scalability, but also for long-term business growth, security, and operational efficiency. AWS Services We Provide ## AWS Cloud Consulting Our AWS consulting in Toronto helps companies create cloud strategies aligned with business objectives, technical requirements, and future scalability goals. ### We assist with: - AWS cloud adoption strategy - Infrastructure architecture design - Security and compliance planning - High availability and disaster recovery - Multi-account AWS environments - Cost optimization and governance - Infrastructure modernization Our engineering team analyzes your existing infrastructure and designs cloud-native solutions that reduce operational complexity and accelerate deployment cycles. ## AWS Cloud Migration Services We provide secure and seamless cloud migration services for businesses moving from on-premise infrastructure, private clouds, or other cloud providers to AWS. ### Our migration process includes: - Infrastructure assessment - Migration roadmap planning - Application and database migration - Zero-downtime migration strategies - Containerization and modernization - Performance optimization after migration - Security validation and compliance checks Whether you need lift-and-shift migration or complete cloud-native modernization, Dedicatted ensures stable and optimized AWS adoption. ## AWS DevOps and Automation DevOps on AWS allows businesses to deploy faster, improve reliability, and automate infrastructure management. ### Our AWS DevOps services include: - CI/CD pipeline implementation - Infrastructure as Code (IaC) - Kubernetes and Amazon EKS management - Docker containerization - GitOps workflows - Monitoring and observability - Automated scaling and self-healing infrastructure We use industry-leading technologies including Terraform, Kubernetes, Helm, ArgoCD, GitHub Actions, Jenkins, and AWS-native automation services. ## AWS Managed Services Dedicatted provides fully managed AWS operations to help companies reduce infrastructure overhead and maintain stable cloud environments. ### Our AWS managed services include: - 24/7 monitoring and incident response - Cloud infrastructure management - AWS security monitoring - Backup and disaster recovery - Performance optimization - Cost monitoring and FinOps - SLA-driven support Our cloud engineers proactively manage your AWS infrastructure to ensure reliability, uptime, and predictable cloud spending. ### AWS Security and Compliance Security is integrated into every AWS environment we build. ### We implement: - IAM and least-privilege access - AWS security best practices - Encryption and secrets management - Network segmentation - Compliance-ready cloud architectures - Security monitoring and logging - Vulnerability assessments Our team helps organizations meet compliance requirements including GDPR, HIPAA, SOC 2, PCI DSS, and ISO standards. ### AWS Solutions for AI and Data Platforms Modern AI workloads require scalable and optimized cloud infrastructure. Dedicatted helps companies build AI-ready AWS environments for machine learning, data engineering, and generative AI applications. ### We support: - AI infrastructure deployment - GPU-based cloud environments - Data lakes and analytics platforms - MLOps pipelines - Vector databases and RAG systems - Scalable storage architecture - High-performance Kubernetes clusters Our engineers help businesses accelerate AI adoption while maintaining infrastructure reliability and cloud cost efficiency. Why Businesses Choose Dedicatted for AWS Services ### Certified AWS Expertise Our AWS engineers and architects have deep experience designing and operating complex cloud environments across industries. If you’re choosing between an AWS Premier Tier Services Partner and an AWS Professional Services Partner, our team is ready to help with the cloud engineering needed to handle complex AWS environments. ### Cloud Cost Optimization We help businesses reduce unnecessary cloud spending through infrastructure optimization, automation, and FinOps best practices. ### Scalable Cloud Architecture We design AWS infrastructures that scale efficiently with business growth and application demand. ### DevOps and Kubernetes Focus Dedicatted specializes in cloud-native engineering, Kubernetes operations, CI/CD automation, and modern DevOps methodologies. ### Long-Term Technology Partnership That long-term model is especially valuable for businesses that want a consulting partner in Canada or a partner in Toronto to stay involved beyond the initial implementation. #### Industries We Support ### We provide AWS cloud services for: - SaaS platforms - FinTech companies - Healthcare organizations - AI and machine learning startups - eCommerce businesses - Enterprise software providers - Media and gaming companies Our industry experience allows us to deliver cloud infrastructures optimized for performance, compliance, and business continuity. ### Build a Scalable AWS Environment with Dedicatted At Dedicatted, we help businesses unlock the full potential of AWS through secure cloud architecture, DevOps automation, infrastructure optimization, and managed cloud operations. Whether you are planning a cloud migration, optimizing existing AWS infrastructure, or building AI-ready cloud platforms, our team delivers reliable and scalable AWS solutions tailored to your business goals. Contact Dedicatted AWS Experts to discuss your cloud infrastructure and AWS transformation strategy. Show more [## Insights ](https://dedicatted.com/insights) [Blog ### Dedicatted Achieves the AWS Agentic AI Specialization Automotive Financial Services High Tech Software Manufacturing Retail Agentic AI ![A person’s hand holds a glowing digital sphere with the letters “AI” at the center, surrounded by circuit-like lines symbolizing artificial intelligence and technology.](https://dedicatted.com/wp-content/uploads/2025/11/Designing-Platform-for-Instant-Market-Penetration-2025-12-01T155111.994.png) Dedicatted, a cloud and AI consulting company that specializes in building enterprise-ready autonomous AI systems on AWS, has achieved the AWS Agentic AI Specialization, a new category launched within the AWS AI Competency ](https://dedicatted.com/insights/dedicatted-achieves-the-aws-agentic-ai-specialization) [Blog ### Dedicatted Achieves Canadian AWS Premier Partner Status: What it Means Cloud Data Migration & Modernization Cloud Migration Cloud Modernization ![AWS Partner Premier Tier Services badge centered on a dark blue background.](https://dedicatted.com/wp-content/uploads/2026/07/look.png) Dedicatted is now AWS Premier Tier certified. See what that actually requires, how it differs from Advanced tier, and how to evaluate any partner's badge. ](https://dedicatted.com/insights/dedicatted-achieves-canadian-aws-premier-partner-status-what-it-means) [Blog ### Amazon Bedrock Best Practices for Building and Scaling Generative AI Automotive Financial Services High Tech Software Insurance Agentic AI Cloud Modernization Managed Services ![Amazon Bedrock Best Practices for Building and Scaling Generative AI](https://dedicatted.com/wp-content/uploads/2026/05/Designing-Platform-for-Instant-Market-Penetration.png) Explore how Amazon Bedrock and AgentCore help organizations build, deploy, and scale secure generative AI solutions faster on Amazon Web Services. ](https://dedicatted.com/insights/amazon-bedrock-best-practices-for-building-and-scaling-generative-ai) [Blog ### Inside a WAF Challenge Rule: How We Stop DDoS Attacks Without AWS Shield Alone Financial Services Healthcare Retail Cloud Operations Managed Services Security Compliance ![Hooded figure typing at a laptop in a blue-toned data center, screen displaying 'DDoS ATTACK'.](https://dedicatted.com/wp-content/uploads/2026/09/Ddos.png) A real DDoS attack, from first alert to full resolution in under 5 minutes. See how Dedicatted's incident response actually works, step by step. ](https://dedicatted.com/insights/inside-a-waf-challenge-rule-how-we-stop-ddos-attacks-without-aws-shield-alone) ## Get started with an AWS consultant Outline your AWS challenge, from migration and architecture to cost and security. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Cloud Optimization](https://dedicatted.com/what-we-do/cloud-services/cloud-optimization) **Published:** December 5, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [What We Do](https://dedicatted.com/what-we-do) 3. [Cloud Services](https://dedicatted.com/what-we-do/cloud-services) 4. Cloud Optimization Cloud Optimization & FinOps # Cloud Optimization Services Pay for the cloud you use, not the cloud you guessed Cloud optimization services help businesses improve the performance, scalability, security, and cost-efficiency of their cloud infrastructure. At Dedicatted, we optimize every layer of your cloud environment to ensure it operates efficiently, securely, and reliably. [Find out what you could save](#contact-form) ![Close-up of blade servers with status LEDs in a data center rack](https://dedicatted.com/wp-content/uploads/2026/08/cloudopt-hero-blade-servers-1024x682.jpg) Optimization that sticks Run by an AWS Premier Tier Partner — SOC 2 and ISO 27001 audited - Every layer optimized Compute, storage, network, pipelines, and observability - AWS, Azure, GCP Certified cloud engineers across all three platforms - Performance-safe savings Cost cuts never at the expense of reliability or speed - 1 business day Response with a first read on the savings available ## What is cloud optimization? - ### What is cloud optimization? Cloud optimization is the process of improving cloud infrastructure performance, cost efficiency, and resource utilization across cloud environments such as AWS, Azure, and Google Cloud. - ### What are cloud optimization services? Cloud optimization services include cost analysis, resource right-sizing, workload optimization, performance tuning, and continuous monitoring of cloud infrastructure. - ### What does a cloud optimization company do? A cloud optimization company provides cloud consulting, analyzes infrastructure usage, reduces cloud costs, and improves system performance through optimization strategies and tools ## How Can You Benefit Optimization isn’t only a smaller bill — it changes how fast you ship and how well you sleep. 01 Cost Optimization Flexible payment models, spot pricing, and demand-based provisioning — so spend tracks real usage instead of last… 02 Enhanced Automation Accelerated development and deployment processes that lower the error ratio and shorten time to market. 03 Exceptional User Experiences With optimized resource allocation, you raise customer satisfaction and boost customers’ trust and loyalty. 04 Visibility & Observability Clear view of your resource use — identifying optimization opportunities before they become budget problems. ### Cost Optimization ### Cost Optimization Flexible payment models, spot pricing, and demand-based provisioning — so spend tracks real usage instead of last year’s capacity guess. - ✓ Rightsizing driven by real utilization data - ✓ Spot and reserved capacity used where safe - ✓ Idle and orphaned resources eliminated - ✓ Commitment planning matched to workload stability ### Enhanced Automation ### Enhanced Automation Accelerated development and deployment processes that lower the error ratio and shorten time to market. - ✓ Autoscaling replaces manual capacity changes - ✓ Scheduled shutdown of non-production environments - ✓ Policy-driven provisioning guardrails - ✓ Automated anomaly alerts before bills surprise you ### Exceptional User Experiences ### Exceptional User Experiences With optimized resource allocation, you raise customer satisfaction and boost customers’ trust and loyalty. - ✓ Latency tuning for user-facing services - ✓ Right-sized databases that stop throttling - ✓ CDN and caching strategies that pay for themselves - ✓ Capacity headroom exactly where traffic peaks ### Visibility & Observability ### Visibility & Observability Clear view of your resource use — identifying optimization opportunities before they become budget problems. - ✓ Per-team and per-service cost allocation - ✓ Consistent tagging traced to owners - ✓ Dashboards finance and engineering both trust - ✓ Forecasts based on usage trends, not hope - 10+ Years of engineering experience - 48 Active clients across industries - 100+ Domain-focused engineers - 15–40% Typical savings from FinOps practices\* \*Industry range for organizations adopting rightsizing, commitment planning, and waste reduction ## Where Cloud Money Hides Six places we look first in every cost review — and what each one typically returns. - ### Oversized compute 20–40% of compute Instances provisioned for peak-plus-safety running at 10% utilization. Rightsizing from real metrics is the single biggest lever in most environments. - ### Idle & orphaned resources 5–15% of total bill Unattached volumes, forgotten snapshots, dev environments running weekends — waste that accumulates silently until someone looks. - ### On-demand everything up to 75% on steady loads Predictable workloads paying on-demand rates. Reserved instances and savings plans cut steady-state costs dramatically — when sized correctly. - ### Non-spot batch work 60–90% on interruptible jobs CI runners, batch jobs, and ML training that tolerate interruption but run on full-price capacity. Spot instances are made for exactly this. - ### Storage on wrong tiers 30–70% on storage Hot-tier storage full of data nobody has touched in a year. Lifecycle policies move it to infrequent-access and archive tiers automatically. - ### Untagged spend visibility, then everything Costs nobody can trace to a team or product can't be optimized by anyone. Tagging and allocation come first — every other lever depends on it. Where is your money hiding? One review finds out. [Request a cost review](#contact-form) ## Our Cloud Optimization Process ![Gradient of dynamic purple background llines](https://dedicatted.com/wp-content/uploads/2025/06/image-168-1.jpg) ### Assess Weeks 1–2We test your monitoring capabilities and map where every cloud dollar goes — before recommending anything. Audit monitoring tools and visibility gaps Analyze spend, utilization, and tagging Baseline performance so savings stay safe ![Gradient of dynamic purple background llines](https://dedicatted.com/wp-content/uploads/2025/06/image-168-1.jpg) ### Optimize Weeks 3–8A customized plan based on cost-benefit analysis — then we implement and automate the improvements. Prioritized savings plan with projected impact Rightsizing, tiering, and commitment changes Automation so fixes don’t regress ![Gradient of dynamic purple background llines](https://dedicatted.com/wp-content/uploads/2025/06/image-168-1.jpg) ### Sustain OngoingConsistent cloud optimization and high application performance — maintained, not just achieved once. Monthly cost reviews and anomaly alerts FinOps governance and team accountability Continuous tuning as workloads evolve [Start with a cost assessment](#contact-form) ## One-Time Cleanup, or Costs That Stay Down? Both are real engagements — pick by what happens after we leave. Optimization Sprint A focused engagement: we find the waste, fix the big items, and hand you the runbook. Fast payback, clear scope. Best for a first pass or a budget crunch. Continuous FinOps Optimization as an ongoing practice: monthly reviews, automated guardrails, and commitment planning — savings that don't erode as your environment grows. [Explore Managed Services →](https://dedicatted.com/what-we-do/devops/devops-as-a-service) ## Best Practices To Try 1. 01### Right-Sizing Computing Continually monitor instances through real-time data analysis — downscaling or switching off idle and underperforming instances as and when needed. 2. 02### Spot Instances A cost-effective way to run workloads that are not time-sensitive. Cloud providers offer spare compute capacity at significantly lower cost than on-demand pricing. 3. 03### Reserved Instances Investment Best suited for predictable and steady workloads. While this involves a long-term commitment, it can provide cost savings of up to 75% compared to on-demand. 4. 04### Monitoring and Management Regularly review and remove unused or underused resources — idle instances, unattached storage volumes, and outdated snapshots. ## Insights [Blog ### Cloud Optimization Strategy: How to Take Control of Your Cloud Spend Financial Services Healthcare High Tech Software Retail Cloud Modernization DevOps Consulting Managed Services ![Futuristic cloud computing illustration shows glowing cloud icon. Rests on circuit board with digital data points, implying global network connection data storage. Modern tech design.](https://dedicatted.com/wp-content/uploads/2025/10/Designing-Platform-for-Instant-Market-Penetration-2025-11-14T123410.776.png) Take control of cloud spend with smart optimization strategies. Learn to cut costs, eliminate waste, and boost performance across multi-cloud environments. ](https://dedicatted.com/insights/cloud-optimization-strategy-how-to-take-control-of-your-cloud-spend) [Blog ### How to build your FinOps strategy for 2026 Automotive Healthcare Insurance Manufacturing Cloud Data Migration & Modernization Generative AI Security Compliance ![Digital visualization of data streams represented as glowing blue tubes filled with binary code, symbolizing data transfer, cybersecurity, or digital communication.](https://dedicatted.com/wp-content/uploads/2025/11/Designing-Platform-for-Instant-Market-Penetration-95.png) Learn how to build a future-ready FinOps strategy for 2026 with cost optimization tactics, better governance, and sustainability best practices. ](https://dedicatted.com/insights/how-to-build-your-finops-strategy-for-2026) [Blog ### ARM vs x86: How AWS Graviton Can Save up to 40% on Cloud Costs Financial Services Healthcare High Tech Software Manufacturing Cloud Data Migration & Modernization Cloud Migration Cloud Modernization Cloud Security ![Close-up view of a computer processor with visible pins placed on a circuit board, highlighted in cool blue lighting](https://dedicatted.com/wp-content/uploads/2025/12/Designing-Platform-for-Instant-Market-Penetration-2025-12-05T164251.927.png) Discover how AWS Graviton4 compares to Intel Xeon and AMD EPYC in real benchmarks. See cost savings, and guidance on choosing the right RDS instance in 2025. ](https://dedicatted.com/insights/arm-vs-x86-how-aws-graviton-can-save-up-to-40-on-cloud-costs) [Blog ### How to Manage AWS SageMaker and Bedrock Costs: A FinOps Playbook for AI Teams Financial Services Healthcare Insurance Cloud Modernization ![Team of professionals collaborating around a table, one person showing charts on a tablet on the table.](https://dedicatted.com/wp-content/uploads/2026/06/foinop.png) Tokens, GPUs, Spot instances, chargeback models – the complete AWS FinOps guide for AI/ML workloads in financial services, insurance, and SaaS. ](https://dedicatted.com/insights/how-to-manage-aws-sagemaker-and-bedrock-costs-a-finops-playbook-for-ai-teams) ## FAQ ### What tools are used for cloud optimization? Native cost tools (AWS Cost Explorer, Azure Cost Management, GCP Billing), plus specialized platforms like CloudHealth or Kubecost for Kubernetes, and infrastructure automation with Terraform — selected to fit your stack rather than replace it. ### What is cloud cost optimization? The practice of reducing cloud spend without hurting performance — eliminating waste, right-sizing resources, choosing better pricing models, and building the visibility to keep costs aligned with usage over time. ### Is cloud optimization a one-time process? No. Environments drift: teams launch new services, traffic patterns change, providers release new instance types and pricing. A one-time cleanup delivers a spike of savings; continuous FinOps keeps them from eroding. ### How does automation help in cloud optimization? Automation makes savings persist — autoscaling matches capacity to demand, scheduled policies shut down idle environments, lifecycle rules tier storage, and anomaly alerts catch overspending in hours instead of at month-end. ### Will cost optimization hurt our performance? Not when done from data. We baseline performance before changing anything and cut only where utilization shows genuine headroom — cost and reliability are optimized together, never traded against each other. ### How much can we realistically save? Industry benchmarks put typical savings at 15–40% for organizations adopting FinOps practices — more where spot instances and storage tiering apply. The assessment gives you the number for your environment, with projected impact per change. Question not answered? [Ask a cloud engineer directly](#contact-form) — same-day reply on cost questions. ## Cloud Optimization Services Turn your cloud infrastructure into a high-performance, cost-efficient growth engine with Dedicatted cloud optimization services. Many companies overspend on cloud resources while still struggling with slow deployments, scaling issues, and poor visibility into infrastructure performance. Dedicatted Cloud Optimization Services help businesses eliminate waste, improve reliability, and maximize cloud ROI through continuous infrastructure analysis, automation, and performance tuning. Whether you operate on AWS, Azure, or Google Cloud, our certified cloud engineers optimize every layer of your environment — from compute and storage to Kubernetes, CI/CD pipelines, observability, and FinOps governance. ## Why Cloud Optimization Matters Cloud environments become increasingly complex as businesses scale. Without continuous optimization, companies often face: - Rising monthly cloud bills - Idle or underutilized resources - Performance bottlenecks - Poor workload visibility - Unpredictable scaling behavior - Slow deployment cycles - Increased operational risk A modern cloud optimization strategy helps organizations align infrastructure performance, scalability, and cloud spending with real business goals. At Dedicatted, we combine cloud engineering, automation, and FinOps practices to ensure your infrastructure stays resilient, scalable, and cost-efficient at every stage of growth. ## Cloud Cost Optimization Reduce unnecessary cloud spending without compromising performance. Our engineers analyze your infrastructure usage patterns, identify waste, and implement optimization strategies that improve resource efficiency across your environment. Additionally, our AWS cost optimization services provide outstanding cost optimization, helping teams to identify waste, manage resources, and improve how well cloud capacity is purchased and used. ## What we optimize - Idle and unused resources - Overprovisioned compute instances - Kubernetes cluster inefficiencies - Storage utilization - Reserved and spot instance usage - Autoscaling policies - Multi-cloud resource allocation ### Benefits - Lower operational costs - Better resource utilization - Predictable cloud spending - Improved budgeting and forecasting - Higher infrastructure efficiency Reserved instance strategies alone can reduce cloud costs significantly compared to standard on-demand pricing models. ## Infrastructure Performance Optimization Improve application speed, stability, and scalability across cloud workloads. We continuously analyze infrastructure metrics, traffic patterns, and application behavior to eliminate bottlenecks and improve system responsiveness. ### Our optimization approach - Compute right-sizing - Load balancing improvements - Database performance tuning - Network latency reduction - Container optimization - Kubernetes workload balancing - Resource scheduling optimization Optimized cloud architectures improve application responsiveness, customer experience, and operational stability. ### Kubernetes & Container Optimization Kubernetes environments often become one of the largest sources of cloud waste. Our cloud engineers optimize Kubernetes clusters to improve workload efficiency, autoscaling behavior, and infrastructure utilization. ### Kubernetes optimization includes - Cluster rightsizing - Pod resource optimization - Node autoscaling - Spot instance integration - Cost-aware scheduling - Observability configuration - Resource governance policies Modern AI-driven optimization platforms increasingly focus on Kubernetes automation and dynamic scaling to reduce infrastructure waste. ### FinOps & Cloud Governance Cloud optimization is not only about reducing costs – it is about building long-term operational discipline. Our FinOps specialists help organizations implement governance frameworks that improve visibility, accountability, and spending control across teams and cloud environments. ### FinOps services include - Cloud spend visibility - Usage reporting dashboards - Cost allocation by team or project - Budget controls - Forecasting and anomaly detection - Resource tagging policies - Governance automation With proper observability and governance, organizations gain better control over infrastructure growth and operational efficiency. ### Cloud Observability & Monitoring You cannot optimize what you cannot measure. We implement end-to-end observability systems that provide real-time visibility into infrastructure health, application performance, and cloud spending. ### Monitoring capabilities - Infrastructure monitoring - Application performance monitoring - Log aggregation - Distributed tracing - SLA and SLO tracking - Automated alerting - Incident response workflows Our engineers work with leading observability platforms including Datadog, Grafana, CloudWatch, and Prometheus to create actionable monitoring ecosystems. ## Multi-Cloud Optimization Organizations operating across AWS, Azure, and Google Cloud often struggle with fragmented visibility and inconsistent resource management. Dedicatted helps businesses standardize cloud operations and optimize workloads across multi-cloud and hybrid infrastructures. ### Multi-cloud optimization services - Cross-platform cost visibility - Centralized governance - Workload distribution optimization - Unified monitoring - Infrastructure as Code standardization - Security and compliance alignment Our team includes AWS, Azure, and Google Cloud certified engineers with deep experience in enterprise cloud modernization. #### Industries We Support We provide cloud optimization solutions for businesses operating in: - SaaS - Retail - Healthcare - Manufacturing - Automotive - AI & machine learning - Big data platforms - Enterprise software Different industries require different optimization strategies. For example, AI and machine-learning workloads require specialized optimization for compute-intensive operations and scalable resource allocation. ## Our Cloud Optimization Process ### 1. Infrastructure Assessment We audit your cloud environment, architecture, workloads, and operational workflows to identify inefficiencies and optimization opportunities. ### 2. Optimization Strategy Our cloud specialists create a tailored roadmap focused on performance, scalability, reliability, and cost reduction. ### 3. Implementation We implement infrastructure improvements, automation workflows, observability systems, and governance policies. ### 4. Continuous Optimization Cloud environments constantly evolve. We continuously monitor and optimize workloads to maintain long-term efficiency and performance. ### Why Choose Dedicatted - Our engineers are certified across AWS, Azure, and Google Cloud platforms. - We combine cloud optimization with DevOps engineering, Infrastructure as Code, CI/CD automation, and SRE practices. - We align cloud infrastructure decisions with measurable business outcomes and operational efficiency. - We support compliance requirements including GDPR, ISO standards, and enterprise security practices. - Cloud optimization is an ongoing process. Our team continuously monitors infrastructure performance, cost efficiency, and scalability opportunities. ### Frequently Asked Questions - Cloud optimization services improve the performance, scalability, reliability, and cost-efficiency of cloud infrastructure through monitoring, automation, and infrastructure tuning. - Dedicatted supports AWS, Microsoft Azure, Google Cloud Platform, hybrid cloud, and multi-cloud environments. - We optimize Kubernetes clusters, container workloads, autoscaling policies, observability, and infrastructure efficiency to improve both performance and cloud cost management. #### Why is observability important for cloud optimization? Observability provides visibility into system performance, usage patterns, and infrastructure bottlenecks, allowing organizations to make data-driven optimization decisions. Cloud infrastructure should accelerate innovation – not create operational complexity and uncontrolled costs. With advanced cloud optimization services, FinOps expertise, and enterprise-grade DevOps engineering, Dedicatted helps organizations improve performance, strengthen scalability, and maximize the value of every cloud investment. Show more ## Find out what you could save on cloud Tell us where your cloud spend is going. Our team responds within one business day with relevant experience and a first read on the savings available. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Services:** Cloud and Infrastructure Security, Cloud Migration, Cloud Modernization --- ### [Dedicatted & Google Cloud](https://dedicatted.com/our-partners/google-cloud) **Published:** April 29, 2025 **Author:** Dedicatted **Content:** # Dedicatted & Google Cloud Dedicatted is a trusted Google Cloud Partner, helping companies architect, modernize, and scale cloud-native applications using cutting-edge technologies like AI, machine learning, and data analytics. From fast-moving startups to Fortune 100 enterprises, we deliver high-impact solutions with speed, security, and engineering excellence. [Let’s Talk](#contact-form) ![GCP cloud](https://dedicatted.com/wp-content/uploads/2025/06/image-62-1024x823.png) ### google cloud partner As a trusted Google Cloud Partner, Dedicatted helps businesses migrate, modernize, and scale on Google Cloud Platform (GCP) – a powerful, flexible cloud solution known for its advanced AI, analytics, and multi-cloud capabilities. Whether you’re moving from legacy infrastructure or expanding globally, our team ensures a smooth transition to GCP with cost-effective architecture, robust security, and high availability. By partnering with Dedicatted, you gain a future-ready cloud foundation built to optimize performance, reduce overhead, and support rapid innovation as your business grows. ![white Google Cloud icon with gauge symbol inside on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2026/01/img-4-1024x663.webp) ## OUR DIFFERENTIATOR We accelerate success on Google Cloud through expert-led migrations, cloud-native modernization, and intelligent AI/ML solutions. Our approach empowers businesses to reduce complexity, scale with confidence, and unlock long-term value through automation, data-driven insights, and reliable cloud operations ### 30+ GCP-CERTIFIED ENGINEERS ### 50+ GCP PROJECTS DELIVERED ### 10+ YEARS EXPERIENCE ### 81 NET PROMOTER SCORE ## WHY DEDICATTED & Google Cloud? ![four white circular dots arranged in a square pattern on purple circular background representing app grid or menu icon](https://dedicatted.com/wp-content/uploads/2025/04/1.svg) ### MODERNIZE Modernize legacy systems and apps with Google Cloud’s scalable infrastructure. Dedicatted ensures seamless migration with minimal downtime and future-ready design. ![white gear icon partially combined with square grid blocks on purple circular background representing system configuration or data management features](https://dedicatted.com/wp-content/uploads/2025/04/2.svg) ### SECURE Secure your cloud environment with built-in zero-trust architecture, continuous compliance, and end-to-end data protection – powered by GCP and delivered by Dedicatted. ![three white sparkle or star icons on purple circular background representing enhancement or magic features icon](https://dedicatted.com/wp-content/uploads/2025/04/3.svg) ### INNOVATE Unlock next-gen innovation using Google Cloud’s AI, ML, and BigQuery tools. Build intelligent, data-driven solutions that drive smarter decisions and better outcomes. ## Google Cloud SOLUTION AREAS - Cloud Adoption - Google Big Data Solution Offerings - Cloud Optimization ### Cloud Adoption ##### Google Cloud based Big Data Solutions for Enterprises to Capture, Process, Store, and Analyze data in real-time and develop Cloud Native Capabilities. ![white grouped square blocks icon on blue hexagon background representing Google Cloud Datastore](https://dedicatted.com/wp-content/uploads/2025/04/cloud-adoption-1.webp) ##### Cloud Datastore ![white connected nodes icon on blue hexagon background representing Google Cloud AI Platform](https://dedicatted.com/wp-content/uploads/2025/04/cloud-adoption-2.webp) ##### Cloud Dataproc ![white abstract pipeline icon on blue hexagon background representing Google Cloud Dataflow](https://dedicatted.com/wp-content/uploads/2025/04/cloud-adoption-3.webp) ##### Cloud Dataflow ### Google Big Data Solution Offerings ##### Google Cloud based Big Data Solutions for Enterprises to Capture, Process, Store, and Analyze data in real-time and develop Cloud Native Capabilities. ![white magnifying glass with bar chart inside on blue hexagon background representing Google BigQuery](https://dedicatted.com/wp-content/uploads/2025/04/google-solution-1.webp) ##### Google BigQuery ![white central circle surrounded by six smaller dots on blue hexagon background representing Google Cloud Pub/Sub](https://dedicatted.com/wp-content/uploads/2025/04/google-solution-2.webp) ##### Cloud Pub/Sub ![white stacked hexagonal shapes icon inside blue hexagon representing Google Cloud Dataproc](https://dedicatted.com/wp-content/uploads/2025/04/google-solution-3.webp) ##### Google Bigtable ### Cloud Optimization ##### Google Cloud based Big Data Solutions for Enterprises to Capture, Process, Store, and Analyze data in real-time and develop Cloud Native Capabilities. ![white angular bracket icon inside blue hexagon representing Google Cloud Endpoints](https://dedicatted.com/wp-content/uploads/2025/04/cloud-optimization-1.webp) ##### Google AutoML ![white propeller-like icon inside blue hexagon representing Google App Engine](https://dedicatted.com/wp-content/uploads/2025/04/cloud-optimization-2.webp) ##### Google App Engine ![white connected nodes with upward arrow icon inside blue hexagon representing Google Cloud Machine Learning Engine](https://dedicatted.com/wp-content/uploads/2025/04/cloud-optimization-3.webp) ##### Prediction API ## Google CLoud SOLUTION AREAS ### Cloud & DevOps [](https://dedicatted.com/what-we-do/devops/devops-as-a-service)### Cloud & DevOps With decades of engineering experience, Dedicatted helps businesses design, build, and operate scalable cloud infrastructure and applications with confidence. We lay the groundwork for successful cloud transformation through modern DevOps practices – combining infrastructure as code (IaC), CI/CD automation, cloud-native architecture, proactive monitoring, and security-by-design. Our approach ensures agility, reliability, and operational excellence across every stage of your cloud journey. ![white cloud icon with gauge symbol inside representing Google Cloud on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-1.webp) ### Application Modernization ### Application Modernization Modernizing legacy applications is key to unlocking agility, reducing technical debt, and turning siloed systems into connected, data-driven platforms. At Dedicatted, we combine deep cloud expertise with FinOps practices to manage modernization effectively – ensuring performance, scalability, and cost control from day one. Our engineers refactor legacy stacks into cloud-native architectures while optimizing spend and avoiding budget overages through proactive cloud cost governance. Application modernization isn’t just a tech upgrade – it’s a business strategy. We help you build a future-ready ecosystem that accelerates innovation and drives measurable impact. ![white cloud icon with gauge symbol inside representing Google Cloud on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-1.webp) ### Data & Analytics [](https://dedicatted.com/services/data/architecture)### Data & Analytics Unlock the full value of your data with scalable analytics solutions powered by Google Cloud and guided by our Data Intelligence Framework. At Dedicatted, we help organizations turn complex, unstructured, and siloed data into actionable insights. Our approach begins with understanding your business goals, identifying available data assets, and determining what should be collected to support informed, real-time decision-making. From building data lakes to deploying advanced analytics and machine learning models, we ensure your data strategy is aligned with both your architecture and your outcomes. ![white cloud icon with gauge symbol inside representing Google Cloud on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-1.webp) ### AI & Machine Learning [](https://dedicatted.com/services/generative-ai)### AI & Machine Learning AI and ML are at the heart of today’s digital innovation – and at Dedicatted, we help you harness them to build intelligent, data-driven solutions on Google Cloud. From predictive modeling and natural language processing to real-time personalization and intelligent automation, our experts bring deep experience in AI/ML design and deployment. Backed by Google Cloud’s Vertex AI and pre-trained models, we deliver scalable, production-ready solutions that transform business outcomes. We’re not just following the AI wave – we’re helping lead it. ![white cloud icon with gauge symbol inside representing Google Cloud on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-1.webp) ### Internet of Things (IoT) [](https://dedicatted.com/services/iot)### Internet of Things (IoT) IoT unlocks new data streams that enhance products, optimize operations, and power intelligent user experiences across industries. At Dedicatted, we help companies securely connect devices, capture real-time insights, and integrate IoT data into cloud-native apps and services using Google Cloud. Our team delivers scalable architectures for edge-to-cloud data flow, device management, and analytics – all designed for resilience, security, and growth. With deep GCP expertise and hands-on IoT delivery, we help you turn connected devices into competitive advantage. ![white cloud icon with gauge symbol inside representing Google Cloud on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-1.webp) ### Multi/Hybrid Cloud Environments ### Multi & Hybrid Cloud Environments In today’s evolving tech landscape, combining on-prem, public, and private cloud environments is key to building flexible, future-ready infrastructure. At Dedicatted, we help businesses navigate hybrid and multi-cloud strategies using Google Cloud solutions like Anthos and Google Cloud VMware Engine (GCVE). Our experts design and implement unified environments that support workload portability, centralized management, and consistent security – across any platform. Whether modernizing legacy systems or optimizing hybrid deployments, we make multi-cloud work seamlessly for your business. ![white cloud icon with gauge symbol inside representing Google Cloud on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-1.webp) [](https://dedicatted.com/what-we-do/devops/devops-as-a-service)### Cloud & DevOps With decades of engineering experience, Dedicatted helps businesses design, build, and operate scalable cloud infrastructure and applications with confidence. We lay the groundwork for successful cloud transformation through modern DevOps practices – combining infrastructure as code (IaC), CI/CD automation, cloud-native architecture, proactive monitoring, and security-by-design. Our approach ensures agility, reliability, and operational excellence across every stage of your cloud journey. ![white cloud icon with gauge symbol inside representing Google Cloud on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-1.webp) ### Application Modernization Modernizing legacy applications is key to unlocking agility, reducing technical debt, and turning siloed systems into connected, data-driven platforms. At Dedicatted, we combine deep cloud expertise with FinOps practices to manage modernization effectively – ensuring performance, scalability, and cost control from day one. Our engineers refactor legacy stacks into cloud-native architectures while optimizing spend and avoiding budget overages through proactive cloud cost governance. Application modernization isn’t just a tech upgrade – it’s a business strategy. We help you build a future-ready ecosystem that accelerates innovation and drives measurable impact. ![white cloud icon with gauge symbol inside representing Google Cloud on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-1.webp) [](https://dedicatted.com/services/data/architecture)### Data & Analytics Unlock the full value of your data with scalable analytics solutions powered by Google Cloud and guided by our Data Intelligence Framework. At Dedicatted, we help organizations turn complex, unstructured, and siloed data into actionable insights. Our approach begins with understanding your business goals, identifying available data assets, and determining what should be collected to support informed, real-time decision-making. From building data lakes to deploying advanced analytics and machine learning models, we ensure your data strategy is aligned with both your architecture and your outcomes. ![white cloud icon with gauge symbol inside representing Google Cloud on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-1.webp) [](https://dedicatted.com/services/generative-ai)### AI & Machine Learning AI and ML are at the heart of today’s digital innovation – and at Dedicatted, we help you harness them to build intelligent, data-driven solutions on Google Cloud. From predictive modeling and natural language processing to real-time personalization and intelligent automation, our experts bring deep experience in AI/ML design and deployment. Backed by Google Cloud’s Vertex AI and pre-trained models, we deliver scalable, production-ready solutions that transform business outcomes. We’re not just following the AI wave – we’re helping lead it. ![white cloud icon with gauge symbol inside representing Google Cloud on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-1.webp) [](https://dedicatted.com/services/iot)### Internet of Things (IoT) IoT unlocks new data streams that enhance products, optimize operations, and power intelligent user experiences across industries. At Dedicatted, we help companies securely connect devices, capture real-time insights, and integrate IoT data into cloud-native apps and services using Google Cloud. Our team delivers scalable architectures for edge-to-cloud data flow, device management, and analytics – all designed for resilience, security, and growth. With deep GCP expertise and hands-on IoT delivery, we help you turn connected devices into competitive advantage. ![white cloud icon with gauge symbol inside representing Google Cloud on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-1.webp) ### Multi & Hybrid Cloud Environments In today’s evolving tech landscape, combining on-prem, public, and private cloud environments is key to building flexible, future-ready infrastructure. At Dedicatted, we help businesses navigate hybrid and multi-cloud strategies using Google Cloud solutions like Anthos and Google Cloud VMware Engine (GCVE). Our experts design and implement unified environments that support workload portability, centralized management, and consistent security – across any platform. Whether modernizing legacy systems or optimizing hybrid deployments, we make multi-cloud work seamlessly for your business. ![white cloud icon with gauge symbol inside representing Google Cloud on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-1.webp) ## Industry Expertise ![White factory icon on dark circular background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-3.svg) ### Financial Services To meet evolving industry demands, financial services firms on Google Cloud accelerate AI adoption with Vertex AI, BigQuery, and Looker — combining real-time risk analytics, fraud detection, and personalized customer experiences. Dedicatted helps regulated institutions modernize data platforms while meeting compliance, security, and resilience standards. [Read More](https://dedicatted.com/services/generative-ai/financial-services) ![A white shield with a cross in the center, on a transparent background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-2.svg) ### Retail Retailers on Google Cloud unify customer data, optimize merchandising, and personalize search with Vertex AI Search for Retail, BigQuery, and Looker. Dedicatted designs cloud-native architectures that scale with demand and deliver real-time, AI-powered omnichannel experiences. [Read More](https://dedicatted.com/services/generative-ai/retail) ![A white circular icon on a transparent background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-1-1.svg) ### Energy, Oil & Gas Leading energy companies are becoming more agile, resilient, and efficient by leveraging Google Cloud technologies. Dedicatted partners with energy and utilities organizations to deliver tailored cloud strategies, streamline operations, and deploy cutting-edge Google Cloud integrations that drive innovation and business growth. ![A white shopping cart icon on a transparent background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-4.svg) ### Healthcare Healthcare and life sciences organizations are transforming patient care, research, and operations with Google Cloud solutions. Dedicatted helps providers design secure, HIPAA-compliant, and scalable systems aligned with organizational goals, patient needs, and the latest technology capabilities. [Read More](https://dedicatted.com/services/generative-ai/healthcare) ![A white circle on a transparent background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-5.svg) ### Manufacturing & Industrial Manufacturers are optimizing operations, reinventing supply chains, and accelerating digital transformation with Google Cloud. Dedicatted leverages expertise in digital twins, AI, robotics, and cloud automation to deliver smart, scalable solutions for complex manufacturing and industrial challenges. [Read More](https://dedicatted.com/services/generative-ai/manufacturing) [## Insights ](https://dedicatted.com/insights) [Case study ### Platform & Data Migration with Security Enhancement Automotive Cloud and Infrastructure Security Cloud Data Migration & Modernization Cloud Migration Cloud Modernization Cloud Operations DevOps Consulting Security Compliance ![Tecnic Driving School case study cover featuring an instructor assessing a driving student.](https://dedicatted.com/wp-content/uploads/2025/03/Tecnic.-Cover-2-1024x537.webp) Discover how Tecnic improved security, reduced costs, and automated operations with AWS migration, GitOps, and infrastructure modernization. ](https://dedicatted.com/insights/platform-data-migration-with-security-enhancement) [Blog ### Managing cloud data migration: Tools, Tips, and Expert Help Financial Services Healthcare High Tech Software Manufacturing Cloud Data Migration & Modernization Cloud Migration Cloud Modernization ![Close-up to the real cloud with the shining around it as a reference to the cloud computing in the AWS environment](https://dedicatted.com/wp-content/uploads/2025/08/Designing-Platform-for-Instant-Market-Penetration-43.png) Master cloud data migration with expert tips, proven tools, and a clear roadmap to boost scalability, cost-efficiency, and performance ](https://dedicatted.com/insights/managing-cloud-data-migration-tools-tips-and-expert-help) [Blog ### Cloud-Powered AI Consulting: Dedicatted`s Expert Assessment of Market Predictions Financial Services High Tech Software Manufacturing Retail Agentic AI Cloud and Infrastructure Security Cloud Data Migration & Modernization Generative AI ![Cloud Computing Backup Cyber Security Fingerprint Identity Encryption Technology](https://dedicatted.com/wp-content/uploads/2025/08/Designing-Platform-for-Instant-Market-Penetration-49.png) Dedicatted's AI consulting decodes tech predictions into real business value with insights on multimodal AI, AI agents, and customer experience. ](https://dedicatted.com/insights/cloud-powered-ai-consulting-dedicatteds-expert-assessment-of-market-predictions) [Blog ### Unlocking the Power of Cloud Migration: Why enterprises are adopting it? Cloud Migration Cloud Modernization ![Corporate skyscrapers viewed from below against blue sky with clouds.](https://dedicatted.com/wp-content/uploads/2023/07/Designing-Platform-for-Instant-Market-Penetration-91.png) Discover why enterprises are using cloud migration for scalability, cost savings, security, and efficiency. Unlock the future of business transformation. ](https://dedicatted.com/insights/unlocking-the-power-of-cloud-migration-why-enterprises-are-embracing-the-future) ## Google Cloud Managed Services Keeping modern workloads online on Google Cloud requires you to be on top of everything 24/7, or you risk falling behind. Luckily, you can use Google Cloud managed services that allow platform specialists to take care of your operational work for you. For organisations that need tightly aligned DevOps services, letting experts handle the platform is the fastest route to stability and innovation. That way your engineers will have more time to come up with new ideas instead of fixing things all the time. Dedicatted provides full Google Cloud platform managed services including automated monitoring, architecture reviews, proactive optimization, and incident response. Our comprehensive DevOps services span CI/CD pipeline design, infrastructure as code, and continuous compliance, ensuring that everything from Kubernetes clusters to BigQuery data warehouses stays secure, cost efficient, and compliant with SLAs 24/7. We don’t just stick to basic upkeep. We combine the best parts of site reliability with DevOps automation and DevOps services, which changes how Google managed services work, making releases happen faster and greatly boosting overall business value. As a certified Google Cloud managed service provider, we offer transparent reporting and easy billing. By choosing Dedicatted, you’ll turn your Google Cloud into something resilient and with predictable costs. Show more ## Get started with a Google Cloud consultant Outline your Google Cloud challenge, from migration to data platforms and cost. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Dedicatted & Microsoft](https://dedicatted.com/our-partners/microsoft) **Published:** April 29, 2025 **Author:** Dedicatted **Content:** # Dedicatted & Microsoft At Dedicatted, we combine deep DevOps expertise with the power of Microsoft Azure to help organizations modernize infrastructure, streamline operations, and scale intelligently. Whether you’re migrating from legacy systems or launching new digital products, our Azure-certified engineers deliver cloud-native architectures that meet the highest standards of performance, security, and compliance. [Let’s Talk](#contact-form) ![azure logo white on transparent background](https://dedicatted.com/wp-content/uploads/2026/01/azure_logo_large.png) ### microsoft azure partner Dedicatted empowers companies across healthcare, fintech, and SaaS to modernize applications, platforms, and DevOps pipelines on Microsoft Azure – enabling secure, scalable, and cost-effective cloud solutions. As a trusted Azure partner, we deliver full-lifecycle support – from strategy and migration to Generative AI use cases and 24 × 7 operations. Our certified team ensures your environments meet strict compliance standards like HIPAA and SOC 2, while leveraging Azure-native services for AI, automation, and hybrid cloud. Whether you’re deploying LLMs or re-architecting legacy systems, we help you unlock the full value of Azure – on time, on budget, and without friction. ![white Microsoft Azure logo on gradient background transitioning from purple to teal with a dark blue center glow](https://dedicatted.com/wp-content/uploads/2026/01/img-3-1024x557.webp) [Discuss your project with Dedicatted technical experts ](#contact-form) ## Strategic Azure Building Blocks ![white shield icon with circular eye-like design on purple circular background representing security or monitoring service](https://dedicatted.com/wp-content/uploads/2025/04/1-1.svg) ### AZURE ARC FOR HYBRID & MULTI-CLOUD Unify on-premises, multi-cloud, and edge environments with Azure Arc. Gain centralized visibility, governance, and compliance across any infrastructure. ![white layered cloud icon on purple circular background representing cloud computing or cloud services](https://dedicatted.com/wp-content/uploads/2025/04/2-1.svg) ### MICROSOFT DEFENDER FOR CLOUD A comprehensive cloud-native security solution that strengthens posture, detects threats, and protects workloads across Azure, AWS, and GCP. ![white stacked database icon on purple circular background representing general data storage or database service](https://dedicatted.com/wp-content/uploads/2025/04/3-1.svg) ### AZURE OPENAI + COGNITIVE SERVICES Harness Generative AI and pre-trained language models through Azure OpenAI. Build intelligent apps with vision, speech, and language APIs – all secured within your cloud boundary. ## Azure Expertise by Focus Area - Azure ML Solutions - Azure Infrastructure - Migrate to Azure ### Azure ML Solutions ##### Unleash the power of AI with Azure Machine Learning and OpenAI services. From predictive analytics to large language model (LLM) integration, we help you build, deploy, and scale intelligent applications tailored to your industry. ![blue cylindrical database icon with white curly braces representing Azure Cosmos DB](https://dedicatted.com/wp-content/uploads/2025/04/icon-8.webp) ##### Azure OpenAI Service ![blue cloud icon with white magnifying glass representing Azure Cognitive Search](https://dedicatted.com/wp-content/uploads/2025/04/icon-9.webp) ##### Azure Machine Learning ![blue cylindrical database icon with white SQL text representing Azure SQL Database](https://dedicatted.com/wp-content/uploads/2025/04/icon-10.webp) ##### Cognitive Services (Vision, Speech, Language) ### Azure Infrastructure ##### Build and scale cloud-native applications on Microsoft Azure's global infrastructure. Leverage fully managed services like Azure SQL Database, Azure Search, and Azure Cosmos DB (DocumentDB) to accelerate deployment, enhance resilience, and optimize performance at the edge and in the cloud. ![blue and white compass icon on blue circular background representing Azure Data Explorer](https://dedicatted.com/wp-content/uploads/2025/04/icon-11.webp) ##### Azure SQL Database ![white rocket icon on blue circular background representing Azure App Service](https://dedicatted.com/wp-content/uploads/2025/04/icon-12.webp) ##### Azure Search ![two overlapping white squares icon on blue circular background representing Azure Managed Applications](https://dedicatted.com/wp-content/uploads/2025/04/icon-13.webp) ##### Azure Cosmos DB ### Migrate to Azure ##### Accelerate your cloud journey with secure and efficient migration strategies. Whether you're rehosting, replatforming, or refactoring, our Azure experts guide you from on-premise systems to fully optimized Azure environments – minimizing risk and downtime. ![white warehouse icon with stacked boxes next to SQL database cylinder on blue circular background representing SQL data warehouse](https://dedicatted.com/wp-content/uploads/2025/04/icon-14.webp) ##### Azure Migrate & Modernize ![abstract geometric blue logo representing Azure Machine Learning](https://dedicatted.com/wp-content/uploads/2025/04/icon-16.webp) ##### Windows Server & SQL Server Migration ![blue gear icon with three aqua wave lines inside representing Azure Stream Analytics](https://dedicatted.com/wp-content/uploads/2025/04/icon-17.webp) ##### App & Database Refactoring ## Horizontal Solutions ### Data & Analytics [](https://dedicatted.com/services/data)### Data & Analytics Leverage data as a strategic asset to drive smarter decisions, accelerate growth, and gain a 360° view of customer behavior. Leading enterprises use data for demand forecasting, price optimization, personalized marketing, and financial planning – but success requires a strong data-driven culture. With Microsoft Azure’s analytics and AI solutions, organizations gain real-time visibility into key metrics and KPIs. These tools enhance operational intelligence, streamline decision-making, and enable predictive insights through AI/ML – revealing hidden patterns, anticipating maintenance needs, and responding faster to market shifts. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) ### Internet of Things [](https://dedicatted.com/services/iot)### Internet of Things (IoT) Microsoft Azure IoT Hub leads with advanced device management, real-time data ingestion, and AI-driven automation for edge-to-cloud transformation. As a trusted Azure partner, Dedicatted designs and modernizes distributed IoT systems. We support seamless device connectivity, edge management with Azure IoT Edge, and scalable data pipelines. With Microsoft’s capabilities and our engineering expertise, you gain secure, enterprise-ready IoT solutions built for agility, interoperability, and future-proof scale. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) ### AI/ML [](https://dedicatted.com/services/artificial-intelligence)### Artificial Intelligence & Machine Learning (AI/ML) Microsoft Azure AI enables businesses to build intelligent solutions that analyze images, interpret speech, and deliver predictive insights – all with enterprise-grade scalability and security. Azure Machine Learning streamlines ML workflows, enabling rapid model development, deployment, and monitoring. With support for PyTorch, TensorFlow, and Kubernetes, Azure accelerates AI adoption across industries. At Dedicatted, we harness Azure’s AI infrastructure to deliver production-ready ML solutions – from smart automation to real-time analytics – with speed and scale. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) ### DevOps [](https://dedicatted.com/what-we-do/devops/devops-as-a-service)### DevOps Microsoft Azure DevOps streamlines development, automates delivery pipelines, and provides full control over infrastructure and release cycles – while reducing costs. At Dedicatted, DevOps is in our DNA. We combine software engineering with Azure’s CI/CD tools to accelerate deployments, improve collaboration, and boost delivery speed. From GitOps and infrastructure as code to monitoring and rollbacks, we help teams move faster – and safer. By aligning Azure’s DevOps tools with our enterprise expertise, you gain scalable, cost-efficient operations that let your team focus on innovation. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) ### Managed Services [](https://dedicatted.com/what-we-do/devops/devops-as-a-service)### Managed Services Downtime costs money and damages trust. That’s why our Azure-certified engineers provide 24/7 support to keep your critical systems running smoothly. Dedicatted delivers end-to-end managed services – from monitoring and incident response to patching and cost optimization – so you don’t need multiple vendors. Backed by Microsoft’s scalable infrastructure and our expert service managers, your Azure environment stays secure, efficient, and always available. With proactive support and continuous optimization, we ensure your cloud operations are resilient and built for growth. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) ### Application Modernization ### Application Modernization Modernizing applications and platforms is key to maximizing ROI, improving agility, and unlocking future innovation – yet many enterprises delay due to perceived complexity. Microsoft’s Azure Migration Program accelerates this process with strategic guidance and cost-saving tools, including license portability for Windows Server and SQL Server – reducing cloud costs by up to 85%. At Dedicatted, we combine decades of engineering expertise with Azure’s modernization tools to help you refactor, rehost, or replatform applications efficiently – reducing risk and complexity while preparing your business for what’s next. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) ### Security & Compliance ### Security & Compliance Microsoft brings decades of experience running the world’s most critical systems – offering unmatched security and compliance across cloud and hybrid environments. At Dedicatted, we embed security at every stage of solution development. From risk assessments to cloud infrastructure hardening, we ensure your systems are resilient, compliant, and audit-ready. By combining our hands-on expertise with Microsoft’s security ecosystem, you get enterprise-grade protection, threat intelligence, and compliance aligned to standards like HIPAA, ISO 27001, and SOC 2. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) [](https://dedicatted.com/services/data)### Data & Analytics Leverage data as a strategic asset to drive smarter decisions, accelerate growth, and gain a 360° view of customer behavior. Leading enterprises use data for demand forecasting, price optimization, personalized marketing, and financial planning – but success requires a strong data-driven culture. With Microsoft Azure’s analytics and AI solutions, organizations gain real-time visibility into key metrics and KPIs. These tools enhance operational intelligence, streamline decision-making, and enable predictive insights through AI/ML – revealing hidden patterns, anticipating maintenance needs, and responding faster to market shifts. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) [](https://dedicatted.com/services/iot)### Internet of Things (IoT) Microsoft Azure IoT Hub leads with advanced device management, real-time data ingestion, and AI-driven automation for edge-to-cloud transformation. As a trusted Azure partner, Dedicatted designs and modernizes distributed IoT systems. We support seamless device connectivity, edge management with Azure IoT Edge, and scalable data pipelines. With Microsoft’s capabilities and our engineering expertise, you gain secure, enterprise-ready IoT solutions built for agility, interoperability, and future-proof scale. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) [](https://dedicatted.com/services/artificial-intelligence)### Artificial Intelligence & Machine Learning (AI/ML) Microsoft Azure AI enables businesses to build intelligent solutions that analyze images, interpret speech, and deliver predictive insights – all with enterprise-grade scalability and security. Azure Machine Learning streamlines ML workflows, enabling rapid model development, deployment, and monitoring. With support for PyTorch, TensorFlow, and Kubernetes, Azure accelerates AI adoption across industries. At Dedicatted, we harness Azure’s AI infrastructure to deliver production-ready ML solutions – from smart automation to real-time analytics – with speed and scale. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) [](https://dedicatted.com/what-we-do/devops/devops-as-a-service)### DevOps Microsoft Azure DevOps streamlines development, automates delivery pipelines, and provides full control over infrastructure and release cycles – while reducing costs. At Dedicatted, DevOps is in our DNA. We combine software engineering with Azure’s CI/CD tools to accelerate deployments, improve collaboration, and boost delivery speed. From GitOps and infrastructure as code to monitoring and rollbacks, we help teams move faster – and safer. By aligning Azure’s DevOps tools with our enterprise expertise, you gain scalable, cost-efficient operations that let your team focus on innovation. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) [](https://dedicatted.com/what-we-do/devops/devops-as-a-service)### Managed Services Downtime costs money and damages trust. That’s why our Azure-certified engineers provide 24/7 support to keep your critical systems running smoothly. Dedicatted delivers end-to-end managed services – from monitoring and incident response to patching and cost optimization – so you don’t need multiple vendors. Backed by Microsoft’s scalable infrastructure and our expert service managers, your Azure environment stays secure, efficient, and always available. With proactive support and continuous optimization, we ensure your cloud operations are resilient and built for growth. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) ### Application Modernization Modernizing applications and platforms is key to maximizing ROI, improving agility, and unlocking future innovation – yet many enterprises delay due to perceived complexity. Microsoft’s Azure Migration Program accelerates this process with strategic guidance and cost-saving tools, including license portability for Windows Server and SQL Server – reducing cloud costs by up to 85%. At Dedicatted, we combine decades of engineering expertise with Azure’s modernization tools to help you refactor, rehost, or replatform applications efficiently – reducing risk and complexity while preparing your business for what’s next. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) ### Security & Compliance Microsoft brings decades of experience running the world’s most critical systems – offering unmatched security and compliance across cloud and hybrid environments. At Dedicatted, we embed security at every stage of solution development. From risk assessments to cloud infrastructure hardening, we ensure your systems are resilient, compliant, and audit-ready. By combining our hands-on expertise with Microsoft’s security ecosystem, you get enterprise-grade protection, threat intelligence, and compliance aligned to standards like HIPAA, ISO 27001, and SOC 2. ![white stylized triangular Microsoft Azure logo on gradient background transitioning from purple to teal](https://dedicatted.com/wp-content/uploads/2025/04/rectangle_1311-3.webp) ## Industry Expertise ![White factory icon on dark circular background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-3.svg) ### Financial Services To meet evolving industry demands, financial services organizations on Microsoft Azure unlock secure, AI-driven insights through Azure OpenAI, Synapse Analytics, and the Microsoft Cloud for Financial Services. Dedicatted helps banks, asset managers, and insurers modernize legacy systems while meeting compliance, resilience, and audit requirements. [Read More](https://dedicatted.com/services/generative-ai/financial-services) ![A white shield with a cross in the center, on a transparent background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-2.svg) ### Retail Retailers on Microsoft Cloud for Retail unify customer, inventory, and operational data through Dynamics 365, Microsoft Fabric, and Azure OpenAI. Dedicatted builds cloud-native solutions that personalize experiences, optimize supply chains, and accelerate omnichannel growth. [Read More](https://dedicatted.com/services/generative-ai/retail) ![A white circular icon on a transparent background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-1-1.svg) ### Energy, Oil & Gas Leading energy companies are becoming more agile, resilient, and efficient by leveraging Microsoft Azure cloud technologies. Dedicatted partners with energy and utilities organizations to deliver tailored cloud strategies, streamline operations, and deploy cutting-edge Azure integrations that drive innovation and business growth. ![A white shopping cart icon on a transparent background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-4.svg) ### Healthcare Healthcare and life sciences organizations are transforming patient care, research, and operations with Microsoft Azure cloud solutions. Dedicatted helps providers design secure, HIPAA-compliant, and scalable systems aligned with organizational goals, patient needs, and the latest technology capabilities. [Read More](https://dedicatted.com/services/generative-ai/healthcare) ![A white circle on a transparent background.](https://dedicatted.com/wp-content/uploads/2025/04/icon-5.svg) ### Manufacturing & Industrial Manufacturers are optimizing operations, reinventing supply chains, and accelerating digital transformation with Microsoft Azure. Dedicatted leverages expertise in digital twins, AI, robotics, and cloud automation to deliver smart, scalable solutions for complex manufacturing and industrial challenges. [Read More](https://dedicatted.com/services/generative-ai/manufacturing) [## Insights ](https://dedicatted.com/insights) [Blog ### Inside a WAF Challenge Rule: How We Stop DDoS Attacks Without AWS Shield Alone Financial Services Healthcare Retail Cloud Operations Managed Services Security Compliance ![Hooded figure typing at a laptop in a blue-toned data center, screen displaying 'DDoS ATTACK'.](https://dedicatted.com/wp-content/uploads/2026/09/Ddos.png) A real DDoS attack, from first alert to full resolution in under 5 minutes. See how Dedicatted's incident response actually works, step by step. ](https://dedicatted.com/insights/inside-a-waf-challenge-rule-how-we-stop-ddos-attacks-without-aws-shield-alone) [Case study ### Agentic Voice Automation for the Clinic Revenue Cycle at Spike Technologies Healthcare Agentic AI Cloud Security Generative AI Managed Services ![Three glowing translucent spheres of networked nodes linked by threads of light on a dark teal ground](https://dedicatted.com/wp-content/uploads/2026/08/agentic-hero-1024x683.jpg) About project The Challenge The expensive part of the work is a conversation. Scripted IVR navigation and portal scraping retrieve a fraction of eligibility answers and break whenever a payer changes a page. The costly work is the call with a payer representative about one patient’s authorization: IVR navigation, hold time, a conversation that follows \[…\] ](https://dedicatted.com/insights/agentic-voice-automation-for-the-clinic-revenue-cycle) [Case study ### OMO Systems: Sub-300ms Device Control for Smart Buildings on AWS IoT Real Estate Cloud and Infrastructure Security Cloud Modernization DevOps Consulting IoT Platform Managed Services ![An IoT device module with a wireless chip on a workbench](https://dedicatted.com/wp-content/uploads/2026/09/iot-hero-esp32-board-1024x682.jpg) About project Challenges Two seconds means very different things depending on which device is at the other end. On a thermostat it is an annoyance a user forgives. On a lock at a door it is a guest in a corridor pressing the button again because nothing happened. On a valve under a detected leak \[…\] ](https://dedicatted.com/insights/smart-building-aws-iot) [Blog ### The 2026 Enterprise Data Readiness Benchmark: Your Foundation for Successful AI Insurance Retail Cloud Data Migration & Modernization Data Architecture Generative AI ![Abstract digital data corridor with blue light trails and glowing particles suggesting a high-tech network edge-to-edge.](https://dedicatted.com/wp-content/uploads/2026/08/Designing-Platform-for-Instant-Market-Penetration-1.png) Only 7% of enterprises have the data foundation AI needs to scale. See what separates them: real case studies, a maturity framework, next steps ](https://dedicatted.com/insights/the-2026-enterprise-data-readiness-benchmark-your-foundation-for-successful-ai) ## Managed services for Azure Using Microsoft Azure to manage important day to day tasks can be very useful for your engineers, but can also take away valuable time that would be better spent working on new products and thinking of new ideas. That’s where Azure cloud managed services come in handy. By letting someone else handle things like patching, scaling, and monitoring, your team will have more time to create cool stuff, while at the same time keeping your system secure, compliant, and making sure costs are kept as low as possible. If your business uses Microsoft Azure DevOps pipelines a lot, we make sure they always run like clockwork so you can focus on developing new, innovative products. At its core, managed services for Azure is a simple subscription that gets you access to everything Azure has to offer. You get to maintain control, while experienced specialists make sure everything is working around the clock. Do you need something more specific? You can always get a targeted Azure managed service that will let you focus on a single workload or region. In either case, you’ll get the same reliable support that all the best managed services Azure have to offer. Dedicatted is an Azure managed service provider that uses automation and human skills. We add your environment to telemetry pipelines and implement security measures before any issues occur. For companies that want to increase redundancy and coverage, we can also help by collaborating with a variety of verified Azure managed service providers to extend skills whenever they’re needed. How fast things get done is also really important. Our Azure DevOps services combines CI/CD, infrastructure as code, and automated testing with the systems that you’re already using. If you require personalized guidance, then our Azure DevOps consulting will provide you with a complete breakdown of the current pipelines and help you greatly reduce lead times. By connecting release planning to ongoing operations, we create a really helpful situation: each deployment sends data back to the platform, which makes future releases safer and faster. It’s a perfect example of doing managed services in Azure the right way. With Dedicatted, you’re in charge while we handle all the hard work. Our experts can help you keep costs down, make sure rules are followed, and speed things up, without needing to hire an army of cloud operators. Show more ## Get started with a Microsoft cloud consultant Outline your Microsoft and Azure challenge, from migration to licensing and architecture. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Services:** Cloud Data Migration & Modernization, Cloud Migration, Managed Services --- ### [AWS Reseller - Cloud Benefits at No Extra Cost | Dedicatted](https://dedicatted.com/our-partners/aws/aws-reseller) **Published:** July 30, 2026 **Author:** Dedicatted **Content:** # AWS Reseller – Cloud Benefits at No Extra Cost Buy AWS through Dedicatted, an AWS Premier Tier Partner, and get a 5% discount on your bill, a dedicated cloud advisor, and free cost optimization – the same AWS you run today, billed through a partner, for less. [Discover your discount](#calculator) ## What is an AWS reseller? An AWS reseller is an AWS partner that buys AWS at partner rates and passes the savings on to you. You keep the same AWS accounts and services – only your billing moves under the partner. Dedicatted adds a dedicated advisor, free Well-Architected and cost-optimization reviews, and cost guidance on top – so you lower your cloud bill and get expert help without hiring a cloud team. - ✓5% discount on your AWS bill from day one - ✓A dedicated AWS-certified cloud advisor - ✓Free Well-Architected & cost reviews - ✓One consolidated monthly invoice with NET 30 payment terms - ✓Zero-downtime billing migration ## Why choose Dedicatted as your AWS reseller - ### Instant 5% savings A 5% discount on your AWS bill from day one, with no change to how you use AWS. - ### A dedicated cloud advisor A named AWS-certified engineer, plus free Well-Architected and cost reviews. - ### Zero-downtime onboarding We move your billing to a partner account, no downtime or service changes. ## See how much you could save Drag to your monthly AWS spend to estimate your savings as a Dedicatted AWS customer Your monthly AWS spend $8,000/mo $3,000 $100,000+ Estimated annual savings — That's per month — [ Get a tailored quote ](#contact-form) Estimate only – final pricing depends on your architecture and usage. ## Join in a few simple steps ### Get in touch Tell us about your AWS setup and goals through the contact form ### Complete the reseller form A short form so we can set up partner billing ### Accept the AWS Org invite You stay in full control of your accounts ### Onboarding & handover We switch on your 5% discount, monitoring, and your advisor [Get in touch](#contact-form) ## Want your AWS fully managed? Reselling lowers your bill – but if you’d rather not run AWS at all, we’ve got you. Dedicatted’s managed service covers monitoring, security, cost optimization, and SLA-backed support end to end, handled by certified engineers, so your team can focus on building. [Explore managed services](https://msp.dedicatted.com/managed-services)[Get in touch](#contact-form) [## AWS cost-saving insights ](https://dedicatted.com/insights) [Blog ### Cloud Optimization Strategy: How to Take Control of Your Cloud Spend Financial Services Healthcare High Tech Software Retail Cloud Modernization DevOps Consulting Managed Services ![Futuristic cloud computing illustration shows glowing cloud icon. Rests on circuit board with digital data points, implying global network connection data storage. Modern tech design.](https://dedicatted.com/wp-content/uploads/2025/10/Designing-Platform-for-Instant-Market-Penetration-2025-11-14T123410.776.png) Take control of cloud spend with smart optimization strategies. Learn to cut costs, eliminate waste, and boost performance across multi-cloud environments. ](https://dedicatted.com/insights/cloud-optimization-strategy-how-to-take-control-of-your-cloud-spend) [Blog ### ARM vs x86: How AWS Graviton Can Save up to 40% on Cloud Costs Financial Services Healthcare High Tech Software Manufacturing Cloud Data Migration & Modernization Cloud Migration Cloud Modernization Cloud Security ![Close-up view of a computer processor with visible pins placed on a circuit board, highlighted in cool blue lighting](https://dedicatted.com/wp-content/uploads/2025/12/Designing-Platform-for-Instant-Market-Penetration-2025-12-05T164251.927.png) Discover how AWS Graviton4 compares to Intel Xeon and AMD EPYC in real benchmarks. See cost savings, and guidance on choosing the right RDS instance in 2025. ](https://dedicatted.com/insights/arm-vs-x86-how-aws-graviton-can-save-up-to-40-on-cloud-costs) [Blog ### How to Manage AWS SageMaker and Bedrock Costs: A FinOps Playbook for AI Teams Financial Services Healthcare Insurance Cloud Modernization ![Team of professionals collaborating around a table, one person showing charts on a tablet on the table.](https://dedicatted.com/wp-content/uploads/2026/06/foinop.png) Tokens, GPUs, Spot instances, chargeback models – the complete AWS FinOps guide for AI/ML workloads in financial services, insurance, and SaaS. ](https://dedicatted.com/insights/how-to-manage-aws-sagemaker-and-bedrock-costs-a-finops-playbook-for-ai-teams) [Case study ### Smarter Logistics for Travel and Hospitality: Kwiksave AWS Cloud Success Automotive Cloud Modernization Generative AI Security Compliance ![heavy industrial truck semi trailer flatbed platform transport wrecked salvage lorry chassis and cabin machine on common highway road at bright day sky. Scrap collision vehicles disposal](https://dedicatted.com/wp-content/uploads/2025/10/Designing-Platform-for-Instant-Market-Penetration-85.png) Discover how Dedicatted helped Kwiksave streamline logistics, automate workflows, and enhance operational efficiency with AI using AWS Travel and Hospitality solutions. ](https://dedicatted.com/insights/smarter-logistics-for-travel-and-hospitality-kwiksave-aws-cloud-success) [Case study ### Optimized Data Architecture: Transforming Data Costs into Growth High Tech Software Cloud and Infrastructure Security Cloud Modernization Data Architecture Managed Services ![Business presentation in a modern office, man presenting analytics dashboard to a group seated at a boardroom table.](https://dedicatted.com/wp-content/uploads/2025/03/Tarci-Cover-1024x537.webp) Dedicatted cut storage costs by 80% with optimized data architecture. See how we helped a global tech firm scale smarter with automation and cloud efficiency. ](https://dedicatted.com/insights/optimized-data-architecture-transforming-data-costs-into-growth) ## Frequently asked questions ### What is AWS reselling? Buying AWS through a partner (Dedicatted) that gives you partner pricing plus consulting and support – the same AWS, at a lower cost, with more help. ### Does it cost extra? No. You pay the same AWS pricing minus your 5% discount, and the consulting and support come included. ### How do I get the 5% discount? During onboarding we set up your AWS billing through Dedicatted, using the option that best fits your setup. From then on, the 5% discount is applied automatically to your monthly bill – with no change to how you use AWS. ### Will Dedicatted have access to my AWS account and services? It depends on the onboarding option you choose – we agree the exact setup with you up front. In every case you keep ownership of your data and day-to-day control of your workloads, while we handle billing and any support you ask us for. ### How does billing work? You get a single monthly invoice from Dedicatted at partner rates, with the same detailed AWS usage breakdown you have today. ### When will I be invoiced? Monthly, in arrears, after AWS closes the billing period. You get one consolidated invoice with NET 30 payment terms, so you pay on invoice rather than an up-front card charge. ### Can you migrate my existing account or Organization? Yes. We migrate your existing AWS accounts into the setup that fits you best, with no downtime and no changes to how your services run. ### Do I keep my AWS console and services? Completely. Nothing about how you build or operate on AWS changes – only who bills you. ### Is there a contract or lock-in? No long-term lock-in. You can move billing back to AWS at any time. ### Who is it for? Startups, scaleups, and enterprises on AWS that want a lower bill and expert support – without growing an in-house cloud team. ## Find out what you would save on AWS Send us your current AWS spend and we will come back within one business day with what moving your account to Dedicatted would change – at no extra cost to you. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Privacy Policy](https://dedicatted.com/privacy-policy) **Published:** January 8, 2025 **Author:** Dedicatted **Content:** # Privacy Notice Our privacy practices are designed to provide a high level of protection for your personal data ## Privacy Policy Effective date: 2023-01-31 Updated on: 2025-07-08 This Privacy Policy explains the policies of Dedicatted on the collection and use of the information we collect when you access dedicatted.com (the “Service”). This Privacy Policy describes your privacy rights and how you are protected under privacy laws. By using our Service, you are consenting to the collection and use of your information in accordance with this Privacy Policy. Please do not access or use our Service if you do not consent to the collection and use of your information as outlined in this Privacy Policy. This Privacy Policy has been created with the help of [CookieScript Privacy Policy Generator](https://cookie-script.com/privacy-policy-generator). Dedicatted is authorized to modify this Privacy Policy at any time. This may occur without prior notice. Dedicatted will post the revised Privacy Policy on the dedicatted.com website ### Collection and Use of Your Personal Information #### Information We Collect When using our Service, you will be prompted to provide us with personal information used to contact or identify you. dedicatted.com collects the following information: - Usage Data - Name - Email Usage Data includes the following: - Internet Protocol (IP) address of computers accessing the site - Web page requests - Referring web pages - Browser used to access site - Time and date of access #### How We Collect Information dedicatted.com collects and receives information from you in the following manner: - When you fill a registration form or otherwise submit your personal information. Your information will be stored for up to 365 days after it is no longer required to provide you the services. Your information may be retained for longer periods for reporting or record- keeping in accordance with applicable laws. Information which does not identify you personally may be stored indefinitely. #### How We Use Your Information dedicatted.com may use your information for the following purposes: - **Providing and maintaining our Service,** as well as monitoring the usage of our Service. - **For other purposes.** Dedicatted will use your information for data analysis to identify usage trends or determine the effective of our marketing campaigns when reasonable. We will use your information to evaluate and improve our Service, products, services, and marketing efforts. - **To contact you.** Dedicatted will contact you by email, phone, SMS, or another form of electronic communication related to the functions, products, services, or security updates when necessary or reasonable. - **Marketing and promotional initiatives.** Dedicatted will use non-specific information gathered from you in order to improve our marketing efforts. #### How We Share Your Information Dedicatted will share your information, when applicable, in the following situations: - **With your consent.** Dedicatted will share your information for any purpose with your explicit consent. #### Third-party Sharing Your information may be disclosed for additional reasons, including: - Complying with applicable laws, regulations, or court orders. - Responding to claims that your use of our Service violates third-party rights. - Enforcing agreements you make with us, including this Privacy Policy. #### Cookies Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser. - **Strictly necessary cookies.** Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies. - **Performance cookies.** Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor. - **Functionality cookies.** Functionality cookies are used to remember visitor information on the website, eg. language, timezone, enhanced content. You can change your consent to cookie usage below. #### Security Your information’s security is important to us. dedicatted.com utilizes a range of security measures to prevent the misuse, loss, or alteration of the information you have given us. However, because we cannot guarantee the security of the information you provide us, you must access our service at your own risk. Dedicatted is not responsible for the performance of websites operated by third parties or your interactions with them. When you leave this website, we recommend you review the privacy practices of other websites you interact with and determine the adequacy of those practices. #### Contact Us For any questions, please contact us through the following methods: Name: Dedicatted Address: 435 Wellington St W, M5V 0V4, Toronto, Canada Email: contact@dedicatted.com Website: dedicatted.com For any queries or concerns you have related to the processing of your information, you may contact us at: legal@dedicatted.com. --- ### [Contact](https://dedicatted.com/contact) **Published:** February 1, 2025 **Author:** Dedicatted **Content:** # Let’s discuss your project? [ +1 437 427-1824 ]() [ contact@dedicatted.com ]() Please leave this field empty. 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[AI Supply Chain Management and Its Top Applications in the Era of Industry 4.0](https://dedicatted.com/insights/ai-supply-chain-management-and-its-top-applications-in-the-era-of-industry-4-0) - [AI-Powered Fraud Detection for Canadian Banks: A Complete Guide](https://dedicatted.com/insights/ai-powered-fraud-detection-for-canadian-banks-a-complete-guide) - [Amazon Bedrock Best Practices for Building and Scaling Generative AI](https://dedicatted.com/insights/amazon-bedrock-best-practices-for-building-and-scaling-generative-ai) - [Amazon takes on Microsoft and Google in the workplace with new ‘Quick Suite’ business AI platform](https://dedicatted.com/insights/amazon-takes-on-microsoft-and-google-in-the-workplace-with-new-quick-suite-business-ai-platform) - [ARM vs x86: How AWS Graviton Can Save up to 40% on Cloud Costs](https://dedicatted.com/insights/arm-vs-x86-how-aws-graviton-can-save-up-to-40-on-cloud-costs) - [Automated Budget Management for AWS: Introducing Budget Controls for Resource Governance](https://dedicatted.com/insights/automated-budget-management-for-aws-introducing-budget-controls-for-resource-governance) - [AWS Activate for startups: a bonus program to save you up to 100K](https://dedicatted.com/insights/aws-activate-for-startups-a-bonus-program-to-save-you-up-to-100k) - [AWS Proof of Concept: how to try new cloud services for free](https://dedicatted.com/insights/aws-proof-of-concept-how-to-try-new-cloud-services-for-free) - [AWS Well-Architected Framework and Review: best practices to boost your business in the cloud](https://dedicatted.com/insights/aws-well-architected-framework-and-review-best-practices-to-boost-your-business-in-the-cloud) - [CI/CD in 2026: The Pipeline Patterns That Actually Scale](https://dedicatted.com/insights/ci-cd-in-2026-the-pipeline-patterns-that-actually-scale) - [Cloud Optimization Strategy: How to Take Control of Your Cloud Spend](https://dedicatted.com/insights/cloud-optimization-strategy-how-to-take-control-of-your-cloud-spend) - [Cloud-Powered AI Consulting: Dedicatted`s Expert Assessment of Market Predictions](https://dedicatted.com/insights/cloud-powered-ai-consulting-dedicatteds-expert-assessment-of-market-predictions) - [Data Architecture: Key components, tools, frameworks, and strategies](https://dedicatted.com/insights/data-architecture-key-components-tools-frameworks-and-strategies) - [Data Infrastructure 101: Building for Scalability and Security](https://dedicatted.com/insights/data-infrastructure-101-building-for-scalability-and-security) - [Data Lake Migration to the Cloud: What Our Experts at Dedicatted Are Telling Clients Right Now](https://dedicatted.com/insights/data-lake-migration-to-the-cloud-what-our-experts-at-dedicatted-are-telling-clients-right-now) - [Data lake vs data warehouse: Which one to choose for your business?](https://dedicatted.com/insights/data-lake-vs-data-warehouse-which-one-to-choose-for-your-business) - [Dedicatted Achieves Canadian AWS Premier Partner Status: What it Means](https://dedicatted.com/insights/dedicatted-achieves-canadian-aws-premier-partner-status-what-it-means) - [Dedicatted Achieves the AWS Agentic AI Specialization](https://dedicatted.com/insights/dedicatted-achieves-the-aws-agentic-ai-specialization) - [Dedicatted became AWS Delivery Partner in 7 categories to Support Cloud and IoT Solutions](https://dedicatted.com/insights/dedicatted-became-aws-delivery-partner-in-7-categories-to-support-cloud-and-iot-solutions) - [Dedicatted Signs Strategic Collaboration Agreement with AWS to help organizations take Generative AI from Pilot to Production](https://dedicatted.com/insights/dedicatted-signs-strategic-collaboration-agreement-with-aws-to-help-organizations-take-generative-ai-from-pilot-to-production) - [Exploring the Top 10 AWS Cloud Migration Tools and Services in 2023](https://dedicatted.com/insights/exploring-the-top-10-aws-cloud-migration-tools-and-services-in-2023) - [Generative AI for the Rest of Us: A Practical Guide for Companies Without a Data Science Team](https://dedicatted.com/insights/generative-ai-for-the-rest-of-us-a-practical-guide-for-companies-without-a-data-science-team) - [How can DevOps help ML train models cheaper](https://dedicatted.com/insights/how-can-devops-help-ml-to-train-models-cheaper) - [How do you build a data warehouse in 2026: Ultimate Guide by Dedicatted’ Experts](https://dedicatted.com/insights/how-do-you-build-a-data-warehouse-in-2026-ultimate-guide-by-dedicatted-experts) - [How does Agentic AI differ from Generative AI?](https://dedicatted.com/insights/genai-vs-agentic-ai-vs-ai-agents) - [How enterprise automation works: Detailed roadmap for your business](https://dedicatted.com/insights/how-enterprise-automation-works-detailed-roadmap-for-your-business) - [How to build your FinOps strategy for 2026](https://dedicatted.com/insights/how-to-build-your-finops-strategy-for-2026) - [How to Get the Most of Amazon Q for Business? We Did Our Research (PDF Inside)](https://dedicatted.com/insights/how-to-get-the-most-of-amazon-q-for-business-we-did-our-research-pdf-inside) - [How to Manage AWS SageMaker and Bedrock Costs: A FinOps Playbook for AI Teams](https://dedicatted.com/insights/how-to-manage-aws-sagemaker-and-bedrock-costs-a-finops-playbook-for-ai-teams) - [Inside a WAF Challenge Rule: How We Stop DDoS Attacks Without AWS Shield Alone](https://dedicatted.com/insights/inside-a-waf-challenge-rule-how-we-stop-ddos-attacks-without-aws-shield-alone) - [Intelligence that acts: Agentic AI for businesses of tomorrow.](https://dedicatted.com/insights/intelligence-that-acts-agentic-ai-for-businesses-of-tomorrow) - [LLMOps for Enterprises: What It Is and How to Scale GenAI in Production](https://dedicatted.com/insights/llmops-for-enterprises-what-it-is-and-how-to-scale-genai-in-production) - [Managed services vs professional services: Differences and applications](https://dedicatted.com/insights/managed-services-vs-professional-services-differences-and-applications) - [Managing cloud data migration: Tools, Tips, and Expert Help](https://dedicatted.com/insights/managing-cloud-data-migration-tools-tips-and-expert-help) - [No More Black Boxes: Making AI Agents Understandable, Auditable, and Accountable](https://dedicatted.com/insights/no-more-black-boxes-making-ai-agents-understandable-auditable-and-accountable) - [Pharma & Life Sciences: R&D Acceleration with Cloud & AI](https://dedicatted.com/insights/pharma-life-sciences-rd-acceleration-with-cloud-ai) - [RAG Pipelines Explained: Real-Time AI Responses for Enterprises](https://dedicatted.com/insights/rag-pipelines-explained-real-time-ai-responses-for-enterprises) - [Road map of DevOps Transformation](https://dedicatted.com/insights/road-map-of-devops-transformation) - [Roadmap for your business: Accelerate AI innovation with AWS infrastructure](https://dedicatted.com/insights/roadmap-for-your-business-accelerate-ai-innovation-with-aws-infrastructure) - [Scaling e-com for Prime Day: Headless Architecture on AWS](https://dedicatted.com/insights/scaling-e-com-for-prime-day-headless-architecture-on-aws) - [Summary of all the biggest updates from AWS’ big tech show re:Invent 2025](https://dedicatted.com/insights/summary-of-all-the-biggest-updates-from-aws-big-tech-show-reinvent-2025) - [The 2026 Enterprise Data Readiness Benchmark: Your Foundation for Successful AI](https://dedicatted.com/insights/the-2026-enterprise-data-readiness-benchmark-your-foundation-for-successful-ai) - [The agent that can’t act alone: Agentic AI inside a regulated Financial Institution](https://dedicatted.com/insights/the-agent-that-cant-act-alone-agentic-ai-inside-a-regulated-financial-institution) - [The Importance of Disaster Recovery in Cloud Migration](https://dedicatted.com/insights/the-importance-of-disaster-recovery-in-cloud-migration) - [The Intelligent Edge: Where IoT meets Machine Learning](https://dedicatted.com/insights/the-intelligent-edge-where-iot-meets-machine-learning) - [The privacy dilemma in AI: Identifying risks and building trust](https://dedicatted.com/insights/the-privacy-dilemma-in-ai-identifying-risks-and-building-trust) - [Top 5 DevOps Pitfalls – And How Dedicatted Expert Consulting Helps You Avoid Them](https://dedicatted.com/insights/top-5-devops-pitfalls-and-how-dedicatted-expert-consulting-helps-you-avoid-them) - [Top 6 Solutions for Kubernetes Intrusion Protection with IDS and IPS](https://dedicatted.com/insights/top-6-solutions-for-kubernetes-intrusion-protection-with-ids-and-ips) - [Ultimate Guide to AWS vs GCP vs Azure: What Works Best for your business](https://dedicatted.com/insights/ultimate-guide-to-aws-vs-gcp-vs-azure-what-works-best-for-your-business) - [Unlocking the Power of Cloud Migration: Why enterprises are adopting it?](https://dedicatted.com/insights/unlocking-the-power-of-cloud-migration-why-enterprises-are-embracing-the-future) - [Webinar | Practical AI Solutions Every Business Leader Needs to Know](https://dedicatted.com/insights/webinar-practical-ai-solutions-every-business-leader-needs-to-know) - [Why security compliance is important](https://dedicatted.com/insights/why-security-compliance-is-important) - [Winning the Shopper’s Mind: How AI Personalization Drives Loyalty](https://dedicatted.com/insights/winning-the-shoppers-mind-how-ai-personalization-drives-loyalty) - [Your Product Can Explain Itself: An AI Product Expert Inside Any Software You Build](https://dedicatted.com/insights/uniguide-ai-product-expert-inside-any-software) ### Case study - [Advancing Infrastructure Stability and Operational Capabilities with Support Service](https://dedicatted.com/insights/advancing-infrastructure-stability-and-operational-capabilities-with-support-service) - [Agentic Voice Automation for the Clinic Revenue Cycle at Spike Technologies](https://dedicatted.com/insights/agentic-voice-automation-for-the-clinic-revenue-cycle) - [AI That Understands You: Taw9eel’s Bilingual Shopping Assistant Delivers Results](https://dedicatted.com/insights/ai-that-understands-you-taw9eels-bilingual-shopping-assistant-delivers-results) - [Automating remittance workflows and reducing costs with GenAI and AWS](https://dedicatted.com/insights/automating-remittance-workflows-and-reducing-costs-with-genai-and-aws) - [Building a Compliance-Ready Cloud: Healthcare platform AWS to Azure Migration](https://dedicatted.com/insights/building-a-compliance-ready-cloud-aws-to-azure-migration) - [Building a Compliance-Ready Cloud: Zivian Health AWS to Azure Migration](https://dedicatted.com/insights/building-a-compliance-ready-cloud-zivian-health-aws-to-azure-migration) - [Building Scalable Data Architecture for IoT with AFT-Managed Landing Zones](https://dedicatted.com/insights/building-scalable-data-architecture-for-iot-with-aft-managed-landing-zones) - [Custom Computer Vision on AWS: From 5 to 0.73-Second Recognition](https://dedicatted.com/insights/custom-computer-vision-aws-case-study) - [Developer Platform That Unblocks Delivery: Self-Service, Dynamic Environments, and an Embedded AI Assistant](https://dedicatted.com/insights/developer-platform-that-unblocks-delivery-self-service-dynamic-environments-and-an-embedded-ai-assistant) - [Enabling Security Compliance and DevOps Agility in a Regulated Healthcare Environment](https://dedicatted.com/insights/enabling-security-compliance-and-devops-agility-in-a-regulated-healthcare-environment) - [From Disaster Recovery to Production: VVN Group’s Zero-Downtime SAP Migration to AWS](https://dedicatted.com/insights/vvn-group-zero-downtime-sap-migration-aws) - [From Manual to Automated: AI-Powered Workflow Revolution with Amazon Q](https://dedicatted.com/insights/from-manual-to-automated-ai-powered-workflow-revolution-with-amazon-q-2) - [From Zero to Developer-Ready Platform in Two Weeks](https://dedicatted.com/insights/from-zero-to-developer-ready-platform-in-two-weeks) - [GenAI-Powered Claims Processing](https://dedicatted.com/insights/genai-powered-claims-processing) - [GenAI-Powered Video Intelligence Platform for a Global Television Network](https://dedicatted.com/insights/genai-powered-video-intelligence-platform-for-a-global-television-network) - [HealthTech in Action: OkDoc App Enhances Patient Experience and Efficiency](https://dedicatted.com/insights/healthtech-in-action-okdoc-app-enhances-patient-experience-and-efficiency) - [How an Aviation Tech Company Cut POC Time from a Month to Days](https://dedicatted.com/insights/how-an-aviation-tech-company-cut-poc-time-from-a-month-to-days) - [Intelligent AI Assistant for Centralized Corporate Knowledge with Amazon Q](https://dedicatted.com/insights/intelligent-ai-assistant-for-centralized-corporate-knowledge-with-amazon-q) - [Keyless Guest Entry and AI Concierge for Short-Term Rentals](https://dedicatted.com/insights/smart-access-control-for-multifamily-and-short-term-rentals) - [Launching a Flexible, SEO-Optimized Website for Adomik’s Marketing Teams](https://dedicatted.com/insights/launching-a-flexible-seo-optimized-website-for-adomiks-marketing-teams) - [Legacy Data Platform Modernization for a European Pet Insurance Provider](https://dedicatted.com/insights/legacy-data-platform-modernization-for-a-european-pet-insurance-provider) - [Migration of Canadian retail operations platform from IBM to cloud architecture on AWS](https://dedicatted.com/insights/migration-of-canadian-retail-operations-platform-from-ibm-to-cloud-architecture-on-aws) - [OMO Systems: Sub-300ms Device Control for Smart Buildings on AWS IoT](https://dedicatted.com/insights/smart-building-aws-iot) - [Optimized Data Architecture: Transforming Data Costs into Growth](https://dedicatted.com/insights/optimized-data-architecture-transforming-data-costs-into-growth) - [Platform & Data Migration with Security Enhancement](https://dedicatted.com/insights/platform-data-migration-with-security-enhancement) - [Production-Grade GenAI Lead Enrichment on AWS for KwikSave Logistics Operations](https://dedicatted.com/insights/production-grade-genai-lead-enrichment-on-aws-for-kwiksave-logistics-operations) - [Retail Data Modernization: Elevating In‑Store & Digital Operations with AWS](https://dedicatted.com/insights/retail-data-modernization-unlocking-agility-through-scalable-cloud-architecture) - [Scaling Platform Stability and Operational Maturity with Managed Support Services](https://dedicatted.com/insights/scaling-platform-stability-and-operational-maturity-with-managed-support-services) - [Self-Service Clinical Analytics on a FHIR Data Store for a Cardiology Network](https://dedicatted.com/insights/self-service-clinical-analytics-on-a-fhir-data-store-for-a-cardiology-network) - [Smart Environmental Reporting with GenAI](https://dedicatted.com/insights/smart-environmental-reporting-with-genai) - [Smarter Logistics for Travel and Hospitality: Kwiksave AWS Cloud Success](https://dedicatted.com/insights/smarter-logistics-for-travel-and-hospitality-kwiksave-aws-cloud-success) - [Smarter Science: How Dedicatted Brought Agentic AI to Chemical Reporting at Cassen Laboratories](https://dedicatted.com/insights/smarter-science-how-dedicatted-brought-agentic-ai-to-chemical-reporting-at-cassen-laboratories) - [Strengthening Platform Reliability and Operational Excellence through Managed Support Services](https://dedicatted.com/insights/strengthening-platform-reliability-and-operational-excellence-through-managed-support-services) - [Strengthening Platform Reliability and Service Continuity Through Managed Support Services](https://dedicatted.com/insights/strengthening-platform-reliability-and-service-continuity-through-managed-support-services) - [Transforming Platform Reliability and Achieving SOC 2 Compliance for a High-Volume Link Management Service](https://dedicatted.com/insights/transforming-platform-reliability-and-achieving-soc-2-compliance-for-a-high-volume-link-management-service) - [What It Takes to Make a SaaS Platform Compliant and Secure](https://dedicatted.com/insights/what-it-takes-to-make-a-saas-platform-compliant-and-secure) - [When the Product Is Powerful and Nobody Can Use It: Embedding an AI Product Expert](https://dedicatted.com/insights/ai-product-expert-onboarding-complex-platform) ### Whitepaper - [AI Multi-Agent Systems for Data Modeling: Workflows, Metrics, Impact](https://dedicatted.com/insights/ai-multi-agent-systems-for-data-modeling-workflows-metrics-impact) - [AI, Cloud, and the Future of Finance: The Engineering Behind Cloud-Native Financial Systems](https://dedicatted.com/insights/ai-cloud-and-the-future-of-finance-the-engineering-behind-cloud-native-financial-systems) - [Amazon QuickSight BI Review (2025): Pricing, Features, Performance](https://dedicatted.com/insights/amazon-quicksight-bi-review-2025-pricing-features-performance) - [Empowering Customer-First Retailers with the Genius of GenAI](https://dedicatted.com/insights/empowering-customer-first-retailers-with-the-genius-of-genai) - [GenAI for retail: The secret weapon of customer-first brands](https://dedicatted.com/insights/genai-for-retail-the-secret-weapon-of-customer-first-brands) - [How Dedicatted Brought Agentic AI to Chemical Reporting at Cassen Laboratories](https://dedicatted.com/insights/how-dedicatted-brought-agentic-ai-to-chemical-reporting-at-cassen-laboratories) - [How to Get the Most of Amazon Q for Business?](https://dedicatted.com/insights/how-to-get-the-most-of-amazon-q-for-business) - [MAP to the Cloud: Navigating Migration on AWS](https://dedicatted.com/insights/map-to-the-cloud-navigating-migration-on-aws) - [Powering Modern Finance Inside a Cloud-Native Success Story](https://dedicatted.com/insights/powering-modern-finance-inside-a-cloud-native-success-story) - [RLHF for RAG Chatbots: a Guide for Advanced Machine Learning](https://dedicatted.com/insights/rlhf-for-rag-chatbots-a-guide-for-advanced-machine-learning) - [Smarter Shopping Assistants: Retrieval-Augmented AI and AR for Retail](https://dedicatted.com/insights/smarter-shopping-assistants-retrieval-augmented-ai-and-ar-for-retail) - [The State of AI: How Far GenAI Has Come in 2026?](https://dedicatted.com/insights/the-state-of-ai-how-far-genai-has-gone-in-2026) - [Transform Your Infrastructure with Dedicatted’s AWS Accelerator](https://dedicatted.com/insights/transform-your-infrastructure-with-dedicatteds-aws-accelerator) - [Why Financial Institutions need DevOps now: Insights & FAQ](https://dedicatted.com/insights/why-financial-institutions-need-devops-now-insights-faq) --- ### [Our Partners](https://dedicatted.com/our-partners) **Published:** April 28, 2025 **Author:** Dedicatted --- ### [Industries](https://dedicatted.com/industries) **Published:** June 12, 2025 **Author:** Dedicatted --- ### [Cloud Services](https://dedicatted.com/what-we-do/cloud-services) **Published:** July 15, 2025 **Author:** Dedicatted --- ### [Key Technologies](https://dedicatted.com/solutions/key-technologies) **Published:** July 14, 2025 **Author:** Dedicatted --- ### [Solutions](https://dedicatted.com/solutions) **Published:** July 14, 2025 **Author:** Dedicatted --- ### [Consulting](https://dedicatted.com/what-we-do/consulting) **Published:** July 14, 2025 **Author:** Dedicatted --- ### [DevOps](https://dedicatted.com/what-we-do/devops) **Published:** March 11, 2025 **Author:** Dedicatted --- ## Services ### [Internet of Things](https://dedicatted.com/services/iot) **Published:** April 30, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. Internet of Things IoT services · AWS IoT, edge to cloud # Internet of Things Services Device to cloud to decision, with the platform proven before a fleet is connected. Dedicatted connects devices, applications and data into one platform on AWS: secure device connectivity, edge-to-cloud data flow, a governed telemetry data lake, and the analytics and alerts your operations run on. Simulators and load tests are generated before a fleet is connected, and engineers own the control loops end to end. [Talk to an IoT architect](#contact-form) [See the service lines](#services) ![An IoT device module with a wireless chip on a workbench](https://dedicatted.com/wp-content/uploads/2026/09/iot-hero-esp32-board-1024x682.jpg) AWS IoT Delivery Partner Delivery Partner in three AWS IoT categories. - AWS IoT Delivery Partner Delivery Partner in three AWS IoT categories, alongside the Premier Tier - From $5,000 Discovery scoped and priced before any build; three packages up to a full foundation - 3 months To a fully operational AWS data lake for IoT telemetry in a recent delivery - SOC 2 + ISO 27001 Device identity, encryption and access under audited practices ## What are IoT services? - ### What are IoT services? IoT services design, build and run the platforms that connect devices, applications and data: device provisioning and connectivity, edge-to-cloud pipelines, telemetry storage and processing, and the analytics, alerts and control loops on top. - ### What do IoT services include? They include discovery of devices and protocols, a proof of concept with your own devices, device management at scale with the edge included, telemetry collection with hot, warm and cold paths, a data lake for IoT telemetry, and dashboards, alerts and remote control. - ### What does an IoT services company do? An IoT services company turns a fleet of devices into a system a business can run: it connects and secures the devices, moves their data to the cloud reliably, stores and processes it, and delivers the analytics and actions the operation needs. ## Our core IoT services - ### IoT Platforms Build the platform your devices talk to on AWS IoT: provisioning and management at scale, edge included, in three fixed-scope packages from discovery to a production foundation. AWS IoT Core*·*Device management*·*Greengrass at the edge*·*Packages from $5,000 [See the IoT platform packages→](https://dedicatted.com/services/iot/platforms) - ### Device Connectivity & Edge Connect controllers, sensors and gateways over MQTT and industrial protocols, with device identity, encryption and over-the-air updates handled from the first device. MQTT*·*Device identity and certificates*·*OTA updates*·*Edge processing - ### Telemetry Data Lake & Analytics Collect telemetry with hot, warm and cold paths, store it in a governed data lake and put dashboards, alerts and machine learning on top of it. Kinesis and Timestream*·*S3 data lake*·*Dashboards and alerts*·*Predictive maintenance - ### Remote Monitoring & Control Monitor sites, fleets and assets in real time and act on them remotely, with sub-second control loops the engineers own end to end and simulators built before the fleet is connected. Fleet and asset tracking*·*Remote control*·*Digital twins and simulators*·*Load testing Need something not listed? [Ask an IoT architect](#contact-form). One business day to a first read. ## Our offering Three fixed-scope packages. Each one de-risks the next, and you can stop after any of them. - ### Discovery Assess, scope and price the build Starting from $5,000 Your devices, protocols and requirements assessed; the proof of concept scoped. - ✓ Assessment of requirements and existing IoT assets - ✓ Protocol and interface compatibility of your devices - ✓ Scope and design for the proof of concept [Get the offer](#contact-form) - ### Proof of Concept Your devices on the platform Starting from $15,000 Two to four device types connected, with telemetry stored and analytics shown. - ✓ Typical devices, controllers and sensors connected - ✓ Telemetry stored and processed within the PoC scope - ✓ Integration, visualization and analytics demonstrated [Get the offer](#contact-form) - Most popular### IoT Foundation Production platform, handed over Starting from $40,000 A cloud-native IoT platform fitted to your stack, edge included. - ✓ Cloud-native IoT solution fitted to your stack - ✓ Device provisioning and management at scale, edge included - ✓ Telemetry collection with hot, warm and cold paths [Get the offer](#contact-form) ## How are IoT services delivered? - ### How do companies use IoT services? Companies use them for predictive maintenance, smart manufacturing, fleet and asset tracking, remote monitoring and control, connected products, and analytics on the telemetry their equipment already produces. - ### What is the IoT services process? The process runs in three packages: discovery that assesses devices, protocols and requirements and prices the build; a proof of concept with two to four device types connected end to end; then the IoT foundation, the production platform built and handed over. - ### How long do IoT projects take? Discovery is scoped and priced first. A proof of concept follows in weeks, and a recent foundation reached a fully operational AWS data lake for IoT telemetry in three months. ## How an engagement runs Engineers use AI at every stage. A person owns every control loop that ships. 1. 1 Discover### Devices, protocols and requirements - ✓ Existing assets and security requirements assessed - ✓ Protocol and interface compatibility checked - ✓ The proof of concept scoped and priced 2. 2 Prove### Your devices on the platform - ✓ Two to four device types connected end to end - ✓ Telemetry stored and processed - ✓ Analytics and visualization demonstrated 3. 3 Build### The production foundation - ✓ Provisioning and management at scale, edge included - ✓ Hot, warm and cold telemetry paths - ✓ Simulators and load tests before the fleet connects 4. 4 Operate### Run and extended - ✓ Dashboards, alerts and remote control in use - ✓ The data lake open to analytics and AI - ✓ Managed operations on published tiers [Tell us about your devices](#contact-form) ## What are the benefits of IoT services? - ### Why use IoT services? They put a production-grade platform behind your devices without building an IoT team: connectivity, security, data flow and analytics designed by engineers who have shipped them on AWS, in fixed-scope packages you can stop after. - ### What are the key benefits of IoT services? Equipment failures predicted before they happen, assets tracked in real time, remote control of sites and devices, telemetry available to analytics and AI, and device identity and encryption handled under audited practices. - ### How do IoT services improve business performance? By turning telemetry into decisions: less downtime, fewer site visits, better utilization of assets, and new connected-product revenue, measured against a baseline taken before the platform goes live. ## The stack behind the practice Connectivity Telemetry Analytics Delivery - ![aws iot core](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws-iot-core.svg) AWS IoT Core - ![iot greengrass](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/iot-greengrass.svg) IoT Greengrass - ![mqtt](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mqtt.svg) MQTT - ![iot sitewise](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/iot-sitewise.svg) IoT SiteWise - ![amazon kinesis](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-kinesis.svg) Amazon Kinesis - ![timestream](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/timestream.svg) Timestream - ![amazon s3](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-s3.svg) Amazon S3 - ![lambda](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/lambda.svg) Lambda - ![iot analytics](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/iot-analytics.svg) IoT Analytics - ![quicksight](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/quicksight.svg) QuickSight - ![grafana](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/grafana.svg) Grafana - ![sagemaker](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sagemaker.svg) SageMaker - ![terraform](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/terraform.svg) Terraform - ![docker](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/docker.svg) Docker - ![gitlab](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/gitlab.svg) GitLab - ![datadog](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/datadog.svg) Datadog ## Featured technology partners ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only GenAI & MSP Partner in Canada As the only AWS GenAI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) - ![Vanta](https://dedicatted.com/wp-content/uploads/2025/01/vanta.webp) - ![Datadog](https://dedicatted.com/wp-content/uploads/2025/01/datadog.webp) Bring us one device type and one question you want the data to answer. An IoT architect replies within one business day with relevant experience and a first read. [ Book a free meeting ↗ ](#contact-form) [## Insights ](https://dedicatted.com/insights) [Blog ### Inside a WAF Challenge Rule: How We Stop DDoS Attacks Without AWS Shield Alone Financial Services Healthcare Retail Cloud Operations Managed Services Security Compliance ![Hooded figure typing at a laptop in a blue-toned data center, screen displaying 'DDoS ATTACK'.](https://dedicatted.com/wp-content/uploads/2026/09/Ddos.png) A real DDoS attack, from first alert to full resolution in under 5 minutes. See how Dedicatted's incident response actually works, step by step. ](https://dedicatted.com/insights/inside-a-waf-challenge-rule-how-we-stop-ddos-attacks-without-aws-shield-alone) [Case study ### Agentic Voice Automation for the Clinic Revenue Cycle at Spike Technologies Healthcare Agentic AI Cloud Security Generative AI Managed Services ![Three glowing translucent spheres of networked nodes linked by threads of light on a dark teal ground](https://dedicatted.com/wp-content/uploads/2026/08/agentic-hero-1024x683.jpg) About project The Challenge The expensive part of the work is a conversation. Scripted IVR navigation and portal scraping retrieve a fraction of eligibility answers and break whenever a payer changes a page. The costly work is the call with a payer representative about one patient’s authorization: IVR navigation, hold time, a conversation that follows \[…\] ](https://dedicatted.com/insights/agentic-voice-automation-for-the-clinic-revenue-cycle) [Case study ### Self-Service Clinical Analytics on a FHIR Data Store for a Cardiology Network Healthcare Cloud Security Data Architecture Generative AI Security Compliance ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) About project The Challenge Three copies of the record, and none of them queryable. The clinical data existed in three disconnected forms: inside each device vendor’s proprietary analysis software, as PDF reports sent to referring physicians, and as files accumulating in Amazon S3 with no data model and no query layer over them. Clinic records \[…\] ](https://dedicatted.com/insights/self-service-clinical-analytics-on-a-fhir-data-store-for-a-cardiology-network) [Case study ### OMO Systems: Sub-300ms Device Control for Smart Buildings on AWS IoT Real Estate Cloud and Infrastructure Security Cloud Modernization DevOps Consulting IoT Platform Managed Services ![An IoT device module with a wireless chip on a workbench](https://dedicatted.com/wp-content/uploads/2026/09/iot-hero-esp32-board-1024x682.jpg) About project Challenges Two seconds means very different things depending on which device is at the other end. On a thermostat it is an annoyance a user forgives. On a lock at a door it is a guest in a corridor pressing the button again because nothing happened. On a valve under a detected leak \[…\] ](https://dedicatted.com/insights/smart-building-aws-iot) ## FAQ ### Which devices and protocols do you support? Controllers, sensors and gateways over MQTT and the industrial protocols they speak, connected through AWS IoT Core with device identity and certificates from the first device. Discovery checks compatibility before anything is built. ### Can we start without buying new hardware? Yes. Discovery assesses the devices you already have, and the proof of concept connects two to four of their types. Simulators stand in for devices that are not on the bench yet. ### What do the packages cost? Discovery is $5,000, the proof of concept $15,000 and the IoT foundation $40,000, each fixed scope, and you can stop after any of them. ### Where does the data live? In your AWS account: telemetry collected with hot, warm and cold paths into a governed data lake that your analytics and AI teams can read. ### How do you secure the fleet? Device identity and certificates, encryption in transit and at rest, and least-privilege access, under the same SOC 2 and ISO 27001 practices we hold ourselves to. ### Do you run the platform afterwards? Yes, on the same managed operations tiers as the rest of the platform work, or we hand it over to your team with the code and the runbooks. ### How do you use AI in delivery? Our engineers use AI at every stage: generating simulators and load tests, drafting pipelines and dashboards, documenting the platform. Every result is verified by an engineer before it ships. Question not answered? [Ask an IoT architect](#contact-form). Same-day reply on feasibility questions. ## What are IoT services? IoT services help businesses connect devices, applications and data into one platform: device connectivity and management at the edge, edge-to-cloud pipelines, telemetry data lakes and the real-time analytics, alerts and control loops on top. Dedicatted delivers [IoT platforms](https://dedicatted.com/services/iot/platforms) on AWS IoT as an AWS IoT Delivery Partner, in fixed-scope packages from discovery to a production foundation. ## IoT platforms, edge to cloud, on AWS Dedicatted helps manufacturers, fleet operators and product companies predict equipment failures, track assets, monitor and control sites remotely, and turn telemetry into decisions. Our engineers combine AWS IoT Core and Greengrass, secure device connectivity, data engineering and analytics to deliver IoT platforms that are proven with simulators and load tests before a fleet is connected, and priced in fixed-scope packages from $5,000. **Our IoT services include:** - IoT platform design and build on AWS IoT - device connectivity, identity, edge processing and over-the-air updates - telemetry pipelines and IoT data lakes - real-time dashboards, alerts and remote control - predictive maintenance and fleet and asset tracking - discovery, proof of concept and IoT foundation packages Show more ## Get started with an IoT architect Tell us what your devices measure and what you need to know from them. Our team responds within one business day with relevant experience and a first read. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Data](https://dedicatted.com/services/data) **Published:** April 6, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. Data & Analytics Data & Analytics services · AWS, Azure, Google Cloud # Data & Analytics Services The foundation designed once, every pipeline tested before it runs on your data. Dedicatted designs, builds and migrates the data platforms enterprises run on: architecture, lakehouse platforms on Databricks and the AWS data stack, cloud data migration and the governance that comes with them, delivered by engineers who work with AI and prove every pipeline against a baseline before cutover. [Talk to a data architect](#contact-form) [See the service lines](#services) ![Data center aisle with tall server racks on both sides and a monitor on a mobile cart in the center.](https://dedicatted.com/wp-content/uploads/2026/08/app-mod-cloud-native-data-center-racks-1024x683.jpg) Lakehouse-first method Object storage with Redshift, Snowflake or Databricks on top. - 2-day snapshot Your current-state architecture visualized, with next steps, in two days - From $10,000 Fixed-scope migration assessment; the price is set before any migration work - AWS Premier Tier Partnered with AWS, Microsoft Azure, Google Cloud and Databricks - SOC 2 + ISO 27001 HIPAA, PCI and GDPR zones designed into the architecture, not bolted on ## What are data and analytics services? - ### What are data and analytics services? Data and analytics services design, build and run the platforms that collect, store, govern and analyze a company’s data: data architecture, warehouses and lakehouses, pipelines, migration to the cloud, and the reporting and analytics on top. - ### What do data and analytics services include? They include current-state assessment, target architecture, lakehouse and warehouse build on Databricks, Redshift or Snowflake, streaming and CDC pipelines, catalog and lineage, governance and compliance zones, and cloud data migration in fixed-scope phases. - ### What does a data and analytics company do? A data and analytics company turns scattered systems into one governed platform: it profiles the estate, designs the target model, builds and tests the pipelines against a baseline, migrates the data and trains the team to run it. ## Our core data and analytics services - ### Data Architecture Design the foundation once: how data is collected, stored, organized, integrated and accessed across the organization, with the target model set by engineers and a roadmap your team keeps. Current-state snapshot*·*Target architecture*·*C1 and C2 diagrams*·*Roadmap [Design your data foundation→](https://dedicatted.com/services/data/architecture) - ### Lakehouse & Data Platforms Build the platform on object storage with Redshift, Snowflake or Databricks on top, with streaming and CDC pipelines, a catalog with lineage, and infrastructure as code from day one. Databricks*·*Redshift and Snowflake*·*Streaming and CDC*·*Catalog and lineage*·*DataOps - ### Cloud Data Migration Move warehouses, pipelines, reports and databases to AWS, Azure or Google Cloud in fixed-scope phases, proved on a proof of concept first and reconciled before anything is switched off. Assessment from $10,000*·*Parallel runs*·*Reconciliation reports*·*Cutover and training [Plan your data migration→](https://dedicatted.com/services/data/cloud-data-migration) - ### Governance, Security & Compliance Design HIPAA, PCI and GDPR zones into the architecture, put access control and lineage in place, and keep the platform audit-ready under the same practices we hold ourselves to. Compliance zones*·*Access control*·*Lineage*·*Audit readiness Need something not listed? [Ask a data architect](#contact-form). One business day to a first read. ## Our accelerators Three ways to start. Each has a fixed scope and ends in something your team keeps. - ### Architecture snapshot 2 days · fixed scope Your current-state architecture visualized, with next steps, in two days. - ✓ Day 1: problem framing and event storming - ✓ Day 2: C1 view and integration points - ✓ A starting point for the plan [ Get the snapshot → ](#contact-form) - ### Modernization jump start 4 weeks · fixed scope Discovery, diagrams, target-state options and a draft roadmap. - ✓ Weeks 1 to 2: discovery and diagrams - ✓ Weeks 3 to 4: target state and roadmap - ✓ A visual and strategic view, ready for action [ Book the jump start → ](#contact-form) - ### Data migration assessment From $10,000 · fixed scope Every object and pipeline inventoried, the migration scoped and priced in phases. - ✓ Inventory of objects and pipelines - ✓ Phases with parallel runs planned - ✓ A price before any work begins [ See the migration page → ](https://dedicatted.com/services/data/cloud-data-migration) ## How are data and analytics services delivered? - ### How do companies use data and analytics services? Companies use them to consolidate reporting on one platform, to move warehouses and pipelines to the cloud, to build the governed foundation AI needs, and to bring lineage and access control to data that regulators ask about. - ### What is the data and analytics process? The process runs snapshot, design, build, migrate: the current state visualized in two days, a target architecture and roadmap in four weeks, pipelines built and tested against a baseline, then a phased migration with parallel runs and reconciliation before cutover. - ### How long do data projects take? The architecture snapshot takes two days and the modernization jump start four weeks. Migration phases are scoped after the assessment and typically run in months, with each phase priced before it starts. ## How an engagement runs Engineers use AI at every stage. A person owns every pipeline that ships. 1. 1 Snapshot### The current state, visualized - ✓ Problem framing and event storming - ✓ C1 view of systems and integration points - ✓ The starting point for the plan 2. 2 Design### The target model set by engineers - ✓ Bounded contexts and target-state options - ✓ Compliance zones designed in - ✓ A roadmap with phases and prices 3. 3 Build### Pipelines proved before they run on your data - ✓ Lakehouse or warehouse built as code - ✓ Every pipeline tested against a baseline - ✓ Catalog, lineage and access control in place 4. 4 Migrate and run### Cutover with parallel runs - ✓ Row counts, checksums and reconciliation reports - ✓ Cutover only when the numbers match - ✓ Your team trained to run the platform [Tell us about your data estate](#contact-form) ## What are the benefits of data and analytics services? - ### Why use data and analytics services? They put a governed, cloud-native data platform in place without a year of internal build: the architecture set by engineers who have done it, and every pipeline proven against a baseline before it runs on your data. - ### What are the key benefits of data and analytics services? One source of truth for reporting, faster and cheaper analytics on cloud storage, compliance zones designed in, lineage that answers auditors, and a foundation ready for machine learning. - ### How do data and analytics services improve business performance? By shortening the path from a question to a trusted answer, retiring the systems that slowed reporting, and giving AI and analytics teams data they can rely on. ## The stack behind the practice Platforms Pipelines Storage and formats Governance and BI - ![databricks](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/databricks.svg) Databricks - ![amazon redshift](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-redshift.svg) Amazon Redshift - ![snowflake](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/snowflake.svg) Snowflake - ![bigquery](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/bigquery.svg) BigQuery - ![kafka](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kafka.svg) Kafka - ![debezium](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/debezium.svg) Debezium - ![airflow](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/airflow.svg) Airflow - ![dbt](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/dbt.svg) dbt - ![amazon s3](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-s3.svg) Amazon S3 - ![apache iceberg](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/apache-iceberg.svg) Apache Iceberg - ![spark](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/spark.svg) Spark - ![trino](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/trino.svg) Trino - ![unity catalog](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/unity-catalog.svg) Unity Catalog - ![datahub](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/datahub.svg) DataHub - ![great expectations](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/great-expectations.svg) Great Expectations - ![quicksight](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/quicksight.svg) QuickSight ## Featured technology partners ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only GenAI & MSP Partner in Canada As the only AWS GenAI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) - ![Vanta](https://dedicatted.com/wp-content/uploads/2025/01/vanta.webp) - ![Datadog](https://dedicatted.com/wp-content/uploads/2025/01/datadog.webp) Show us the systems your reports come from. We will map them in two days. A data architect replies within one business day with relevant experience and a first read. [ Book a free meeting ↗ ](#contact-form) [## Insights ](https://dedicatted.com/insights) [Blog ### Inside a WAF Challenge Rule: How We Stop DDoS Attacks Without AWS Shield Alone Financial Services Healthcare Retail Cloud Operations Managed Services Security Compliance ![Hooded figure typing at a laptop in a blue-toned data center, screen displaying 'DDoS ATTACK'.](https://dedicatted.com/wp-content/uploads/2026/09/Ddos.png) A real DDoS attack, from first alert to full resolution in under 5 minutes. See how Dedicatted's incident response actually works, step by step. ](https://dedicatted.com/insights/inside-a-waf-challenge-rule-how-we-stop-ddos-attacks-without-aws-shield-alone) [Case study ### Agentic Voice Automation for the Clinic Revenue Cycle at Spike Technologies Healthcare Agentic AI Cloud Security Generative AI Managed Services ![Three glowing translucent spheres of networked nodes linked by threads of light on a dark teal ground](https://dedicatted.com/wp-content/uploads/2026/08/agentic-hero-1024x683.jpg) About project The Challenge The expensive part of the work is a conversation. Scripted IVR navigation and portal scraping retrieve a fraction of eligibility answers and break whenever a payer changes a page. The costly work is the call with a payer representative about one patient’s authorization: IVR navigation, hold time, a conversation that follows \[…\] ](https://dedicatted.com/insights/agentic-voice-automation-for-the-clinic-revenue-cycle) [Case study ### Self-Service Clinical Analytics on a FHIR Data Store for a Cardiology Network Healthcare Cloud Security Data Architecture Generative AI Security Compliance ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) About project The Challenge Three copies of the record, and none of them queryable. The clinical data existed in three disconnected forms: inside each device vendor’s proprietary analysis software, as PDF reports sent to referring physicians, and as files accumulating in Amazon S3 with no data model and no query layer over them. Clinic records \[…\] ](https://dedicatted.com/insights/self-service-clinical-analytics-on-a-fhir-data-store-for-a-cardiology-network) [Case study ### OMO Systems: Sub-300ms Device Control for Smart Buildings on AWS IoT Real Estate Cloud and Infrastructure Security Cloud Modernization DevOps Consulting IoT Platform Managed Services ![An IoT device module with a wireless chip on a workbench](https://dedicatted.com/wp-content/uploads/2026/09/iot-hero-esp32-board-1024x682.jpg) About project Challenges Two seconds means very different things depending on which device is at the other end. On a thermostat it is an annoyance a user forgives. On a lock at a door it is a guest in a corridor pressing the button again because nothing happened. On a valve under a detected leak \[…\] ](https://dedicatted.com/insights/smart-building-aws-iot) ## FAQ ### Which platforms do you build on? Object storage on AWS, Azure or Google Cloud with Redshift, Snowflake or Databricks on top, chosen for your workloads rather than a default. We are partnered with AWS and Databricks. ### Do we have to migrate everything at once? No. Migration runs in fixed-scope phases, each proved on a proof of concept and priced before it starts, with parallel runs until the numbers reconcile. ### How do you keep the data accurate through a migration? Row counts, checksums and reconciliation reports at every phase, and cutover only when they match. Pipelines are tested against a baseline before they run on your data. ### Who owns the platform afterwards? You do. Infrastructure, pipelines and models are delivered as code into your repositories and accounts, and your team is trained to run them. ### Can you handle regulated data? Yes. HIPAA, PCI and GDPR zones are designed into the architecture, access follows your policies, and our own delivery is audited under SOC 2 and ISO 27001. ### Where do we start if we are not ready for a migration? With the two-day architecture snapshot. It maps the systems your reports come from and gives you a starting point for a plan, with no migration commitment. ### How do you use AI in delivery? Our engineers use AI at every stage: profiling an estate, drafting pipelines, tests and documentation. Every result is verified by an engineer against a baseline before it ships. Question not answered? [Ask a data architect](#contact-form). Same-day reply on feasibility questions. ## What are data and analytics services? Data and analytics services help businesses design, build and migrate the platforms their data runs on: [data architecture](https://dedicatted.com/services/data/architecture), scalable lakehouse and warehouse platforms on Databricks, Amazon Redshift and Snowflake, streaming pipelines, data governance and [cloud data migration](https://dedicatted.com/services/data/cloud-data-migration) in fixed-scope phases. Dedicatted delivers data and analytics services on AWS, Microsoft Azure and Google Cloud for enterprise clients, with the architecture set by engineers and every pipeline tested against a baseline. ## Data architecture, cloud data migration and analytics Dedicatted helps organizations consolidate reporting on one governed, scalable data platform, move warehouses, pipelines and databases to the cloud, and build the secure data foundation that analytics and AI depend on. Our data engineers and architects combine data architecture, lakehouse engineering, streaming and CDC pipelines, catalog and lineage, governance for HIPAA, PCI and GDPR, and cloud data migration with parallel runs and reconciliation before cutover. **Our data and analytics services include:** - data architecture and a two-day current-state snapshot - lakehouse and data platform build on Databricks, Redshift, Snowflake and BigQuery - streaming, CDC and data pipeline engineering - data catalog, lineage and governance - cloud data migration and modernization in fixed-scope, priced phases - analytics and BI on AWS, Azure and Google Cloud Show more ## Get started with a data architect Tell us which systems your reports come from and what slows them down. Our team responds within one business day with relevant experience and a first read. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Cloud & DevOps Services](https://dedicatted.com/services/cloud-devops) **Published:** June 27, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. Cloud & DevOps Services Cloud & DevOps services · AWS, Azure, Google Cloud # Cloud & DevOps Services Launch faster. Scale smarter. Operate with confidence. Dedicatted builds and runs the infrastructure behind software companies and enterprise platforms: landing zones, CI/CD, Kubernetes and cost control on AWS, Azure and Google Cloud, written as code by engineers who work with AI and reviewed before it ships. Managed operations on published monthly tiers. [Talk to a DevOps consultant](#contact-form) [See the service lines](#services) ![Two coworkers sit at a desk, reviewing code on a computer monitor in a modern office setting.](https://dedicatted.com/wp-content/uploads/2026/08/devops-platform-engineering-pairing-1024x683.jpg) AWS Premier Tier + MSP Premier Tier Services Partner and the only GenAI and MSP partner in Canada. - AWS Premier Tier Partnered with AWS, Microsoft Azure and Google Cloud - Published tiers Managed operations priced on the page, with an estimator to size yours - SOC 2 + ISO 27001 Audited delivery practices, held and renewed for multiple consecutive years - 24/7 operations Monitoring, incident management and release support under SLA ## What are cloud and DevOps services? - ### What are cloud and DevOps services? Cloud and DevOps services design, build and run the infrastructure and delivery pipelines behind software: cloud environments, CI/CD, infrastructure as code, Kubernetes, observability and cost control, on AWS, Azure and Google Cloud. - ### What do cloud and DevOps services include? They include cloud architecture and landing zones, migration, CI/CD pipeline design, infrastructure as code, container platforms, monitoring and incident response, FinOps, and managed operations under an SLA. - ### What does a cloud and DevOps company do? A cloud and DevOps company takes over the platform work so product teams can ship: it builds the environments and pipelines, automates releases and rollbacks, keeps systems observable and secure, and runs them day to day. ## Our core cloud and DevOps services - ### DevOps as a Service Run your platform on managed operations for published monthly tiers: monitoring, incident response and release support, on AWS, Azure, Google Cloud or on-premises. 24/7 monitoring*·*Incident response*·*Release support*·*Published tiers [See the managed service tiers→](https://dedicatted.com/what-we-do/devops/devops-as-a-service) - ### Cloud Migration Move applications, databases and workloads from on-premise or another cloud to AWS, Azure or Google Cloud, with a landing zone, a cutover plan and minimal downtime. Landing zones*·*Cutover planning*·*Rehost to refactor*·*Minimal downtime [Plan your migration→](https://dedicatted.com/services/cloud-devops/cloud-migration) - ### DevOps Consulting Put CI/CD, infrastructure as code, monitoring and security in place with certified engineers, so releases ship faster without breaking what works. CI/CD pipelines*·*Infrastructure as code*·*GitOps*·*Security gates [Ship faster with DevOps consulting→](https://dedicatted.com/what-we-do/consulting/devops-consulting) - ### Kubernetes Consulting Design, harden and run your clusters, with the pipelines that deploy to them and the observability that watches them, on cloud or on-prem. EKS and certified distributions*·*Helm and GitOps*·*High availability*·*Managed clusters [See Kubernetes consulting→](https://dedicatted.com/solutions/key-technologies/kubernetes) - ### Cloud Optimization Take control of cloud spend without giving up speed: waste found, automation put in, and every dollar tied to what the business uses. FinOps*·*Rightsizing*·*Autoscaling*·*Budget enforcement [Cut your cloud bill→](https://dedicatted.com/what-we-do/cloud-services/cloud-optimization) - ### Observability & Monitoring See your whole estate through logs, metrics and traces, from Datadog to Grafana, with SLO dashboards your team reads and faster incident response. Datadog*·*Grafana*·*SLO reporting*·*Faster MTTR Need something not listed? [Ask a DevOps consultant](#contact-form). One business day to a first read. ## Where engagements start Three ways to start. Each has a fixed scope and ends in something your team keeps. - ### Infrastructure assessment Fixed scope · priced in the first call Cloud, IaC, CI/CD and security reviewed, with a costed plan of what to fix first. - ✓ Architecture and cloud-readiness review - ✓ IaC, CI/CD and security audit - ✓ Prioritized, costed recommendations [ Book an assessment call → ](#contact-form) - ### Pipeline and platform build Priced after the assessment CI/CD, GitOps and infrastructure as code, delivered into your repositories. - ✓ CI/CD pipelines for your stack - ✓ Terraform, CDK and GitOps - ✓ Automated testing, rollback and recovery [ Book a pipeline call → ](#contact-form) - ### Managed operations Monthly tiers · published prices Your platform run to an SLA you can read before you sign. - ✓ Three priced packages - ✓ Estimator to size yours - ✓ AWS, Azure, Google Cloud or on-premises [ See the tiers → ](https://dedicatted.com/what-we-do/devops/devops-as-a-service) ## How are cloud and DevOps services delivered? - ### How do companies use cloud and DevOps services? Companies use them to move to the cloud, to ship releases through automated pipelines, to run Kubernetes without an in-house platform team, and to bring cloud spend under control. - ### What is the cloud and DevOps process? The process runs explore, implement, operate, optimize: objectives and current pipelines reviewed, the roadmap turned into code with security gates built in, systems monitored under SLA, then spend and performance tuned continuously. - ### How long do cloud and DevOps projects take? An infrastructure assessment is scoped in the first call and runs in weeks. Migrations and pipeline builds depend on the estate and are scoped after the assessment; managed operations start on a monthly tier. ## How an engagement runs Engineers use AI at every stage. A person owns every change that ships. 1. 1 Explore### Objectives, risks and a roadmap - ✓ Surface key drivers, risks and KPIs - ✓ Review current pipelines, tools and environments - ✓ Set priorities and an actionable delivery timeline 2. 2 Implement### The roadmap turned into code - ✓ CI/CD pipelines with built-in security gates - ✓ Infrastructure codified with Terraform, CDK and GitOps - ✓ Workloads containerized and deployed to Kubernetes or the cloud 3. 3 Operate### Run under SLA - ✓ 24/7 monitoring, alerting and SLO tracking - ✓ Incident management and release support - ✓ Automated patching and friction-free upgrades 4. 4 Optimize### Cost and performance tuned - ✓ Continuous FinOps optimization - ✓ Rightsizing and autoscaling - ✓ Performance tuning against the SLOs [Tell us your stack](#contact-form) ## What are the benefits of cloud and DevOps services? - ### Why use cloud and DevOps services? They give a product team a platform team without hiring one: environments, pipelines and operations run by certified engineers, with the security and cost discipline built in from the first commit. - ### What are the key benefits of cloud and DevOps services? Faster and safer releases, higher availability, reproducible environments, audit-ready change history, lower cloud bills, and incidents handled around the clock. - ### How do cloud and DevOps services improve business performance? By shortening the path from code to production, removing manual release work, keeping systems observable and resilient, and tying cloud spend to what the business actually uses. ## The stack behind the practice Cloud platforms Infrastructure as code Containers and delivery Observability - ![aws](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws.svg) AWS - ![microsoft azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/microsoft-azure.svg) Microsoft Azure - ![google cloud](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/google-cloud.svg) Google Cloud - ![vmware](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/vmware.svg) VMware - ![terraform](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/terraform.svg) Terraform - ![ansible](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ansible.svg) Ansible - ![helm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/helm.svg) Helm - ![vault](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/vault.svg) Vault - ![kubernetes](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kubernetes.svg) Kubernetes - ![docker](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/docker.svg) Docker - ![gitlab](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/gitlab.svg) GitLab - ![github actions](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/github-actions.svg) GitHub Actions - ![datadog](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/datadog.svg) Datadog - ![grafana](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/grafana.svg) Grafana - ![prometheus](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/prometheus.svg) Prometheus - ![pagerduty](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/pagerduty.svg) PagerDuty ## Featured technology partners ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only GenAI & MSP Partner in Canada As the only AWS GenAI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) - ![Vanta](https://dedicatted.com/wp-content/uploads/2025/01/vanta.webp) - ![Datadog](https://dedicatted.com/wp-content/uploads/2025/01/datadog.webp) Tell us where your pipeline or your cloud bill is holding you back. A DevOps consultant replies within one business day with relevant experience and a first read. [ Book a free meeting ↗ ](#contact-form) [## Insights ](https://dedicatted.com/insights) [Blog ### Inside a WAF Challenge Rule: How We Stop DDoS Attacks Without AWS Shield Alone Financial Services Healthcare Retail Cloud Operations Managed Services Security Compliance ![Hooded figure typing at a laptop in a blue-toned data center, screen displaying 'DDoS ATTACK'.](https://dedicatted.com/wp-content/uploads/2026/09/Ddos.png) A real DDoS attack, from first alert to full resolution in under 5 minutes. See how Dedicatted's incident response actually works, step by step. ](https://dedicatted.com/insights/inside-a-waf-challenge-rule-how-we-stop-ddos-attacks-without-aws-shield-alone) [Case study ### Agentic Voice Automation for the Clinic Revenue Cycle at Spike Technologies Healthcare Agentic AI Cloud Security Generative AI Managed Services ![Three glowing translucent spheres of networked nodes linked by threads of light on a dark teal ground](https://dedicatted.com/wp-content/uploads/2026/08/agentic-hero-1024x683.jpg) About project The Challenge The expensive part of the work is a conversation. Scripted IVR navigation and portal scraping retrieve a fraction of eligibility answers and break whenever a payer changes a page. The costly work is the call with a payer representative about one patient’s authorization: IVR navigation, hold time, a conversation that follows \[…\] ](https://dedicatted.com/insights/agentic-voice-automation-for-the-clinic-revenue-cycle) [Case study ### Self-Service Clinical Analytics on a FHIR Data Store for a Cardiology Network Healthcare Cloud Security Data Architecture Generative AI Security Compliance ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) About project The Challenge Three copies of the record, and none of them queryable. The clinical data existed in three disconnected forms: inside each device vendor’s proprietary analysis software, as PDF reports sent to referring physicians, and as files accumulating in Amazon S3 with no data model and no query layer over them. Clinic records \[…\] ](https://dedicatted.com/insights/self-service-clinical-analytics-on-a-fhir-data-store-for-a-cardiology-network) [Case study ### OMO Systems: Sub-300ms Device Control for Smart Buildings on AWS IoT Real Estate Cloud and Infrastructure Security Cloud Modernization DevOps Consulting IoT Platform Managed Services ![An IoT device module with a wireless chip on a workbench](https://dedicatted.com/wp-content/uploads/2026/09/iot-hero-esp32-board-1024x682.jpg) About project Challenges Two seconds means very different things depending on which device is at the other end. On a thermostat it is an annoyance a user forgives. On a lock at a door it is a guest in a corridor pressing the button again because nothing happened. On a valve under a detected leak \[…\] ](https://dedicatted.com/insights/smart-building-aws-iot) ## FAQ ### Which clouds do you work on? AWS first, where we hold the Premier Tier and MSP designations, plus Microsoft Azure and Google Cloud. Private cloud and on-premises estates are run under the same managed service. ### Do you take over an existing platform? Yes. Most managed engagements start with a platform someone else built. The assessment maps it, the first month stabilizes it, and changes are made through code your team reviews. ### What does managed operations cost? The monthly tiers are published on the DevOps as a Service page, with an estimator to size yours. Project work is scoped and priced after the assessment. ### Do we keep ownership of the infrastructure code? Yes. Terraform, pipelines and runbooks live in your repositories and accounts from the first commit; nothing is held in ours. ### How do you handle security and compliance? Security gates run in the pipelines, infrastructure follows CIS and AWS Well-Architected controls, and our own delivery is audited under SOC 2 and ISO 27001. The security practice joins when a framework is in scope. ### Can we start with one piece, such as CI/CD or monitoring? Yes. Pipelines, observability, cost optimization or a single migration are each scoped on their own; the rest joins under the same team when the work calls for it. ### How do you use AI in delivery? Our engineers use AI at every stage: reading an estate, drafting infrastructure code, runbooks and tests. Every change is reviewed by an engineer against your standards before it ships. Question not answered? [Ask a DevOps consultant](#contact-form). Same-day reply on feasibility questions. ## What are cloud and DevOps services? Cloud and DevOps services help businesses build, automate and run cloud infrastructure and software delivery: cloud architecture and [cloud migration](https://dedicatted.com/services/cloud-devops/cloud-migration), [DevOps consulting](https://dedicatted.com/what-we-do/consulting/devops-consulting) with CI/CD and infrastructure as code, Kubernetes, security, observability, cloud cost optimization and [managed DevOps services](https://dedicatted.com/what-we-do/devops/devops-as-a-service) under SLA. Dedicatted delivers cloud and DevOps services on AWS, Microsoft Azure and Google Cloud as an AWS Premier Tier Services Partner and the only GenAI and MSP partner in Canada. ## Cloud and DevOps consulting and managed services Dedicatted helps software companies and enterprises migrate to the cloud, automate software delivery, run Kubernetes platforms and control cloud spend. Our certified cloud engineers combine cloud infrastructure design, DevOps automation, CI/CD pipelines, infrastructure as code, site reliability engineering and FinOps, and run the result as managed DevOps operations on published monthly tiers. **Our cloud and DevOps services include:** - cloud architecture, landing zones and cloud migration to AWS, Azure and Google Cloud - DevOps consulting: CI/CD automation, GitOps and infrastructure as code with Terraform and AWS CDK - Kubernetes consulting and managed clusters - observability, monitoring and incident response - cloud security automation and compliance - cloud cost optimization and FinOps - DevOps as a Service: managed operations under SLA Show more ## Get started with a cloud and DevOps consultant Outline where your infrastructure or delivery pipeline is holding you back. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [AI Services](https://dedicatted.com/services/artificial-intelligence) **Published:** April 28, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. AI Services AI services · generative, agentic, computer vision, ML # Artificial Intelligence & Machine Learning Services Production AI on your own data, with an engineer accountable for every model that ships. Dedicatted designs, builds and runs AI systems for enterprises: generative and agentic applications, computer vision and predictive models, on AWS and in your own accounts. Every model ships with its evaluation set written first and passes the same review and sign-off as any other change. [Talk to an AI consultant](#contact-form) [See the service lines](#services) ![Macro photograph of glowing fibre-optic strands](https://dedicatted.com/wp-content/uploads/2026/08/genai-fibre-optic-hero-1024x768.jpg) AWS GenAI + Agentic AI The only GenAI and MSP partner in Canada, with the Agentic AI specialization. - AWS GenAI + Agentic Both AWS competencies, held by the practice that delivers - 20+ enterprise clients AI in production across healthcare, logistics, retail and finance - SOC 2 + ISO 27001 Your data and models handled under audited practices - 1 business day A first read on your problem from an AI engineer ## What are AI services? - ### What are AI services? AI services cover the design, development and operation of artificial intelligence systems: generative AI applications, AI agents, computer vision and machine learning models, built on a company’s own data and run in production. - ### What do AI services include? They include use-case discovery, data readiness, model selection and fine-tuning, retrieval and agent architectures, evaluation, deployment on cloud infrastructure, and monitoring and retraining once live. - ### What does an AI services company do? An AI services company turns a business problem into a working AI system: it finds where AI pays off, builds and evaluates the model or agent, integrates it with existing systems and keeps it accurate and secure in production. ## Our core AI services - ### Generative AI Move from a first proof of concept to custom LLMs you own, on Amazon Bedrock or OpenAI, with your data and IP under your control. RAG*·*Bedrock and OpenAI integration*·*LLM fine-tuning*·*Domain chatbots*·*Content generation [Build with generative AI→](https://dedicatted.com/services/generative-ai) - ### Agentic AI Put multi-agent systems to work across a whole workflow: planning, reasoning and acting under guardrails for security and compliance. Multi-agent orchestration*·*Workflow automation*·*Document processing*·*AI copilots [Put AI agents to work→](https://dedicatted.com/services/artificial-intelligence/agentic-ai) - ### Computer Vision Turn cameras into instruments: quality inspection, object detection, document processing and video analytics, built on your footage and run in your cloud or on the edge. Quality inspection*·*Object detection*·*Document processing*·*Video analytics*·*Edge deployment [See computer vision services→](https://dedicatted.com/services/artificial-intelligence/computer-vision) - ### ML & Predictive Analytics Forecast demand, failures and churn from your own data, with the MLOps platform that deploys, monitors and retrains the models. Demand forecasting*·*Predictive maintenance*·*Anomaly detection*·*Customer churn*·*MLOps Need something not listed? [Ask an AI consultant](#contact-form). One business day to a first read. ## Where engagements start Three ways to start. Each has a fixed scope and ends in something your team keeps. - ### AI readiness assessment 4 weeks · AWS-funded Your AI use cases ranked, your data checked, a roadmap to production. - ✓ Opportunity map - ✓ Data readiness report - ✓ Roadmap and first build scoped [ See the assessment → ](https://dedicatted.com/services/ai-readiness-assessment) - ### Generative AI pilot Scoped in the first call A working assistant on your documents, in your AWS account, measured first. - ✓ Evaluation set before the model - ✓ Runs in your account - ✓ A path to production [ Book a pilot call → ](#contact-form) - ### Agentic workflow One process · scoped in the first call An agent for one high-volume process, with guardrails and human sign-off. - ✓ Process mapped and instrumented - ✓ Guardrails and approvals built in - ✓ Hours saved, reported [ Book a workflow call → ](#contact-form) ## How are AI services delivered? - ### How do companies use AI services? Companies use AI services to automate document-heavy processes, add assistants and agents to their products and operations, inspect and detect with computer vision, and forecast demand, churn and failures from their own data. - ### What is the AI services process? The process runs assess, pilot, production, operate: opportunities ranked and data checked, a pilot with its evaluation set written first, deployment in the client’s cloud account under review and sign-off, then monitoring and retraining. - ### How long do AI projects take? The assessment takes four weeks. A pilot is scoped in the first call against the process you bring, in weeks rather than months; production rollout depends on integrations and data and is scoped after the pilot. ## How an engagement runs Engineers use AI at every stage. A person owns every result that ships. 1. 1 Assess### Opportunities and data - ✓ Use cases ranked by value and effort - ✓ Data and integration readiness checked - ✓ The first build scoped and priced 2. 2 Pilot### Evaluation set first, then the model - ✓ Success criteria and test set written up front - ✓ Model, retrieval or agent built against them - ✓ Results reported against the numbers 3. 3 Production### Shipped under your controls - ✓ Review and sign-off like any other change - ✓ Deployed in your cloud account - ✓ Security and compliance built in 4. 4 Operate### Kept accurate and affordable - ✓ Monitoring for drift and cost - ✓ Retraining and prompt updates on a schedule - ✓ Managed operations on published tiers [Tell us your process](#contact-form) ## What are the benefits of AI services? - ### Why use AI services? AI services put working models into production faster than an in-house team can hire for, with the evaluation, security and operations discipline that keeps them accurate and compliant. - ### What are the key benefits of AI services? Hours returned from manual work, faster decisions from your own data, new product capabilities such as assistants and agents, and a lower cost per task where AI replaces repeated human effort. - ### How do AI services improve business performance? By automating high-volume processes, surfacing forecasts and anomalies early, and giving staff and customers assistants grounded in company knowledge, measured against a baseline taken before the work starts. ## The stack behind the practice Models and platforms Data and retrieval Computer vision MLOps and delivery - ![Bedrock](https://dedicatted.com/wp-content/uploads/2026/05/Bedrock-300x106.png) Amazon Bedrock - ![sagemaker](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sagemaker.svg) SageMaker - ![hugging face](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/hugging-face.svg) Hugging Face - ![pytorch](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/pytorch.svg) PyTorch - ![databricks](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/databricks.svg) Databricks - ![amazon s3](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-s3.svg) Amazon S3 - ![postgresql](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/postgresql.svg) PostgreSQL - ![aws glue](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws-glue.svg) AWS Glue - ![opencv](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/opencv.svg) OpenCV - ![roboflow](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/roboflow.svg) Roboflow - ![detectron2](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/detectron2.svg) Detectron2 - ![onnx runtime](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/onnx-runtime.svg) ONNX Runtime - ![mlflow](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mlflow.svg) MLflow - ![kubernetes](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kubernetes.svg) Kubernetes - ![gitlab](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/gitlab.svg) GitLab - ![datadog](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/datadog.svg) Datadog ## Featured technology partners ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only GenAI & MSP Partner in Canada As the only AWS GenAI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) - ![Vanta](https://dedicatted.com/wp-content/uploads/2025/01/vanta.webp) - ![Datadog](https://dedicatted.com/wp-content/uploads/2025/01/datadog.webp) Bring us one process. We will tell you in a day whether AI pays for itself there. Tell us the process or the data. An AI consultant replies within one business day with relevant experience and a first read. [ Book a free meeting ↗ ](#contact-form) [## Insights ](https://dedicatted.com/insights) [Case study ### Agentic Voice Automation for the Clinic Revenue Cycle at Spike Technologies Healthcare Agentic AI Cloud Security Generative AI Managed Services ![Three glowing translucent spheres of networked nodes linked by threads of light on a dark teal ground](https://dedicatted.com/wp-content/uploads/2026/08/agentic-hero-1024x683.jpg) About project The Challenge The expensive part of the work is a conversation. Scripted IVR navigation and portal scraping retrieve a fraction of eligibility answers and break whenever a payer changes a page. The costly work is the call with a payer representative about one patient’s authorization: IVR navigation, hold time, a conversation that follows \[…\] ](https://dedicatted.com/insights/agentic-voice-automation-for-the-clinic-revenue-cycle) [Case study ### Self-Service Clinical Analytics on a FHIR Data Store for a Cardiology Network Healthcare Cloud Security Data Architecture Generative AI Security Compliance ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) About project The Challenge Three copies of the record, and none of them queryable. The clinical data existed in three disconnected forms: inside each device vendor’s proprietary analysis software, as PDF reports sent to referring physicians, and as files accumulating in Amazon S3 with no data model and no query layer over them. Clinic records \[…\] ](https://dedicatted.com/insights/self-service-clinical-analytics-on-a-fhir-data-store-for-a-cardiology-network) [Blog ### The 2026 Enterprise Data Readiness Benchmark: Your Foundation for Successful AI Insurance Retail Cloud Data Migration & Modernization Data Architecture Generative AI ![Abstract digital data corridor with blue light trails and glowing particles suggesting a high-tech network edge-to-edge.](https://dedicatted.com/wp-content/uploads/2026/08/Designing-Platform-for-Instant-Market-Penetration-1.png) Only 7% of enterprises have the data foundation AI needs to scale. See what separates them: real case studies, a maturity framework, next steps ](https://dedicatted.com/insights/the-2026-enterprise-data-readiness-benchmark-your-foundation-for-successful-ai) [Blog ### Pharma & Life Sciences: R&D Acceleration with Cloud & AI Healthcare Agentic AI Cloud Modernization Data Architecture Generative AI ![Close-up of glass pipettes transferring liquid in a dimly lit science lab.](https://dedicatted.com/wp-content/uploads/2026/08/Designing-Platform-for-Instant-Market-Penetration.png) This playbook breaks down the reference architecture, use cases by R&D stage, and implementation roadmap for cloud and AI acceleration in pharma ](https://dedicatted.com/insights/pharma-life-sciences-rd-acceleration-with-cloud-ai) ## FAQ ### How do you keep our data out of public models? Models run in your AWS account or ours under contract; nothing you share is used to train a public model. Retrieval systems read your documents at query time rather than baking them into weights, and access follows your existing permissions. ### Do you build on AWS only? AWS is our primary platform and where our competencies are held. We also deliver on Azure and Google Cloud, and integrate OpenAI models where they fit. ### Who owns the model and the code? You do. Code, prompts, evaluation sets, fine-tuned weights and the infrastructure definitions are delivered into your repositories and accounts. ### How do you measure whether it worked? Every engagement starts with a baseline and an evaluation set written before the model. Results are reported against them, and the same numbers are monitored once the system is live. ### What does a pilot cost and how long does it take? The assessment is fixed-scope over four weeks and AWS-funded. Pilots are scoped in the first call against the process you bring; you get a written scope and price before any work starts. ### Can we start with one process? Yes. Most engagements start with one high-volume process or one assistant, measured end to end. The next use case joins under the same team once the first is in production. ### How do you use AI in delivery? Our engineers use AI at every stage: reading data and systems, drafting code, tests and documentation. Every result is verified by an engineer against your standards before it ships. Question not answered? [Ask an AI consultant](#contact-form). Same-day reply on feasibility questions. ## What are AI services? AI services help enterprises put artificial intelligence into production: [generative AI](https://dedicatted.com/services/generative-ai) solutions built on your own data, [agentic AI](https://dedicatted.com/services/artificial-intelligence/agentic-ai) that automates workflows and decision making, computer vision, and machine learning models with the MLOps platform to run them. Dedicatted delivers AI services and consulting on AWS for enterprise clients in healthcare, logistics, retail, manufacturing and finance, from use case to production. ## Enterprise AI services and consulting Dedicatted is an AI services company and AWS partner with the Generative AI and Agentic AI competencies. Our AI engineers design, build and operate generative AI applications, multi-agent systems, computer vision solutions and predictive models that improve operational efficiency, customer experience and real-time decision making. Every model ships with its evaluation set written first, runs in your AWS account under your security controls, and is monitored and retrained once live. **Our AI services include:** - generative AI consulting and development on Amazon Bedrock and OpenAI - agentic AI and workflow automation, including intelligent document processing - computer vision for quality inspection, detection and video analytics - machine learning, predictive analytics and MLOps platforms - AI opportunity and readiness assessments - AI-augmented software development Show more ## Get started with an AI consultant Tell us the process or the data. Our team responds within one business day with relevant experience and a proposed first step. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Services:** Generative AI --- ### [Engineering](https://dedicatted.com/services/engineering) **Published:** April 29, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. Engineering Engineering services · software, QA, architecture, operations, product # Engineering Services Six disciplines, one accountable team, from idea to end of life. Engineering services help businesses design, develop, test, modernize and scale software applications, cloud infrastructure and digital platforms. Dedicatted covers software engineering, quality assurance, solution architecture, business analysis, operations support and product management, with engineers using AI at every stage. [Talk to an engineering consultant](#contact-form) [See the service lines](#services) ![Two developers reviewing code on a monitor](https://dedicatted.com/wp-content/uploads/2026/09/eng-hero-code-review-1024x682.jpg) AI-augmented delivery Our engineers, working alongside AI at every stage. - 6 disciplines Software, testing, architecture, analysis, operations and product, one team - AWS Premier Tier Partnered with AWS, Microsoft Azure, Google Cloud and Oracle - SOC 2 + ISO 27001 Audited delivery practices, held and renewed for multiple consecutive years - 24/7 operations Monitoring, incident management and release support under SLA ## What are engineering services? - ### What are engineering services? Engineering services involve designing, developing, and maintaining software systems, infrastructure, and digital solutions to support business operations and innovation. - ### What do engineering services include? Engineering services include software development, system architecture design, cloud engineering, DevOps implementation, testing, and ongoing support. - ### What does an engineering services company do? An engineering services company builds and optimizes digital products, develops scalable systems, and helps businesses deliver reliable and high-performing software solutions. ## Our core engineering services - ### Software Development Ship reliable, scalable software across the full cycle, from concept and design to build, testing and support, with engineers writing alongside AI and owning every line. Full-cycle delivery*·*AI-augmented development*·*Iterative releases*·*Review and sign-off [See AI-augmented development→](https://dedicatted.com/services/ai-augmented-development) - ### Application Modernization Modernize legacy applications and mainframe estates with AI doing the reading and the drafting, and engineers proving parity on production data before cutover. COBOL, PL/I and RPG*·*Blu Age*·*CAST Imaging*·*Refactor and re-platform [Plan your modernization→](https://dedicatted.com/services/engineering/mainframe-modernization) - ### Software Testing Find and prevent the issues behind delays and rework with testing strategies built on proven frameworks and automation, so your software works as expected. Test strategy*·*Automation frameworks*·*Regression and performance*·*Quality risk prevention - ### Solution Architecture Balance technical requirements, business goals and performance in one plan, from concept through implementation, with systems that scale and stay easy to maintain. Architecture review*·*Technical debt*·*Modernization pathways*·*Architectural governance - ### Business Analysis & Product Management Capture business context, user needs and value early, then run the product lifecycle from prototype to post-launch improvement with agile delivery matched to business goals. Discovery and requirements*·*Roadmaps and business cases*·*Prototyping*·*Agile delivery - ### Operations Support Keep your platform secure, stable and performing with managed operations: 24/7 monitoring, incident response and release support in three priced packages. 24/7 monitoring*·*Incident management*·*Release support*·*SLA-backed [See the managed service tiers→](https://dedicatted.com/what-we-do/devops/devops-as-a-service) Need something not listed? [Ask an engineering consultant](#contact-form). One business day to a first read. ## How are engineering services delivered? - ### How do companies use engineering services? Companies use engineering services to design and develop applications, modernize systems, implement cloud solutions, and improve overall software delivery processes. - ### What is the engineering services process? The process includes requirements analysis, architecture design, development, testing, deployment, and continuous improvement. - ### How long do engineering projects take? Project timelines vary from a few weeks to several months depending on project scope, complexity, and business requirements. ## Tailored for your lifecycle stage Organizations have different needs at each stage of a bespoke application, digital product or platform. Tell us where yours is. 1. 1 Concept### Ideation - ✓ Leverage deep technical and domain expertise - ✓ Data quality and availability - ✓ Technology trade-offs and architecture 2. 2 Build### Develop and launch - ✓ Deliver stable, predictable product launches - ✓ Share delivery ownership - ✓ Increase cost-effectiveness 3. 3 Scale### Grow and mature - ✓ Streamline team roles and increase efficiency - ✓ Transfer application delivery ownership - ✓ Enable efficient transformation 4. 4 Retire### End of life - ✓ Optimize resource allocation and operational efficiency - ✓ Enable transfer of portfolio delivery ownership - ✓ Greatly increase cost-effectiveness [Tell us your stage](#contact-form) ## What are the benefits of engineering services? - ### Why use engineering services? Engineering services help businesses accelerate development, improve system performance, and deliver scalable and reliable digital solutions. - ### What are the key benefits of engineering services? Key benefits include faster time-to-market, improved product quality, enhanced scalability, reduced operational risks, and efficient use of resources. - ### How do engineering services improve business performance? Engineering services improve performance by enabling innovation, optimizing development workflows, and ensuring stable and efficient systems. ## Featured technology partners ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only GenAI & MSP Partner in Canada As the only AWS GenAI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) - ![Vanta](https://dedicatted.com/wp-content/uploads/2025/01/vanta.webp) - ![Datadog](https://dedicatted.com/wp-content/uploads/2025/01/datadog.webp) Skip past trial and error when adopting tech Tell us what you are building and the standard it has to meet. An engineering consultant replies within one business day with relevant experience and a first read. [ Book a free meeting ↗ ](#contact-form) [## Insights ](https://dedicatted.com/insights) [Case study ### OMO Systems: Sub-300ms Device Control for Smart Buildings on AWS IoT Real Estate Cloud and Infrastructure Security Cloud Modernization DevOps Consulting IoT Platform Managed Services ![An IoT device module with a wireless chip on a workbench](https://dedicatted.com/wp-content/uploads/2026/09/iot-hero-esp32-board-1024x682.jpg) About project Challenges Two seconds means very different things depending on which device is at the other end. On a thermostat it is an annoyance a user forgives. On a lock at a door it is a guest in a corridor pressing the button again because nothing happened. On a valve under a detected leak \[…\] ](https://dedicatted.com/insights/smart-building-aws-iot) [Case study ### How an Aviation Tech Company Cut POC Time from a Month to Days High Tech Software Manufacturing DevOps Consulting Generative AI Managed Services ![Two engineers review data on a tablet inside a large aircraft hangar with a plane in the background.](https://dedicatted.com/wp-content/uploads/2026/08/airline-2.png) An aviation software company cut idea validation from month to days with an internal POC factory: two-click environments and AI-DLC builder on Amazon Bedrock. ](https://dedicatted.com/insights/how-an-aviation-tech-company-cut-poc-time-from-a-month-to-days) [Blog ### CI/CD in 2026: The Pipeline Patterns That Actually Scale Financial Services Healthcare High Tech Software Cloud Modernization DevOps Consulting Managed Services Security Compliance ![CI/CD in 2026: The Pipeline Patterns That Actually Scale](https://dedicatted.com/wp-content/uploads/2026/07/cd.png) Discover 6 CI/CD patterns that actually scale in 2026. Stop adding tools. Learn how to fix fragmented pipelines with architecture, not tooling. ](https://dedicatted.com/insights/ci-cd-in-2026-the-pipeline-patterns-that-actually-scale) [Case study ### Developer Platform That Unblocks Delivery: Self-Service, Dynamic Environments, and an Embedded AI Assistant Retail DevOps Consulting Generative AI Managed Services Security Compliance ![Woman interacting with a digital touchscreen displaying clothing items in a smart retail environment.](https://dedicatted.com/wp-content/uploads/2026/01/Designing-Platform-for-Instant-Market-Penetration-2026-01-19T163109.872.png) Designing an Internal Developer Platform with Backstage and AI to enable self-service, faster onboarding, and reduced DevOps dependency in retail ](https://dedicatted.com/insights/developer-platform-that-unblocks-delivery-self-service-dynamic-environments-and-an-embedded-ai-assistant) ## FAQ ### What technologies are used in engineering services? Technologies include programming languages, cloud platforms, containerization tools, DevOps pipelines, and modern frameworks for building scalable applications. ### Are engineering services suitable for startups? Yes, engineering services are suitable for startups and enterprises, helping teams build and scale products efficiently. ### Can engineering services support cloud development? Yes, engineering services support cloud-native development, including application design, deployment, and optimization in cloud environments. ### How do engineering services ensure quality? Quality is ensured through testing, code reviews, automation, monitoring, and adherence to best development practices. ### Can engineering services integrate with existing systems? Yes, engineering services can integrate new solutions with existing systems using APIs, microservices, and modern integration approaches. ### Can we start with one discipline? Yes. Most engagements start with one: a testing team, an architecture review, operations support for a platform you already run. The other disciplines join when the work calls for them, under the same account team. ### How do you use AI in delivery? Our engineers use AI at every stage: reading an estate, drafting code and tests, documenting systems. Every result is verified by an engineer against your standards before it ships. The AI-augmented development page describes the lifecycle in detail. Question not answered? [Ask an engineering consultant](#contact-form). Same-day reply on feasibility questions. ## What are Engineering Services? Engineering services help businesses design, develop, modernize, and scale software applications, cloud infrastructure, and enterprise platforms through software engineering, [DevOps](https://dedicatted.com/what-we-do/devops/devops-as-a-service), quality assurance, cloud architecture, and digital transformation strategies. Dedicatted delivers engineering services for enterprise clients across cloud-native, data, AI, and infrastructure environments. ## Enterprise Engineering Services Dedicatted provides engineering services that help organizations accelerate product development, modernize legacy systems, optimize cloud operations, and build scalable digital platforms. Our engineering teams combine expertise in software development, DevOps, cloud infrastructure, quality assurance, and solution architecture to deliver reliable and high-performing enterprise systems. **We support businesses with:** - custom software development - enterprise application modernization - cloud-native engineering - DevOps and CI/CD automation - infrastructure optimization - QA automation and testing - platform engineering and scalability Our approach focuses on building secure, scalable, and maintainable systems aligned with business goals and long-term operational efficiency. ## Software Development and Product Engineering Software engineering services help businesses create modern digital products and enterprise applications optimized for performance, scalability, and user experience. Dedicatted develops cloud-native applications, APIs, backend systems, and distributed platforms using modern engineering practices and agile delivery models. **Our software engineering expertise includes:** - full-stack application development - backend and API engineering - microservices architecture - cloud-native application development - enterprise platform engineering - SaaS product development - performance optimization and scalability We help organizations accelerate development cycles while maintaining software quality, operational reliability, and infrastructure efficiency. ## DevOps and Cloud Engineering Modern engineering requires scalable infrastructure, deployment automation, and operational visibility. Dedicatted provides DevOps and cloud engineering services that improve software delivery, automate infrastructure management, and optimize cloud operations across [AWS](https://dedicatted.com/insights/migration-of-canadian-retail-operations-platform-from-ibm-to-cloud-architecture-on-aws), Azure, and hybrid environments. **Our DevOps engineering capabilities include:** - CI/CD pipeline implementation - infrastructure as code - Kubernetes and container orchestration - cloud infrastructure automation - observability and monitoring - cloud cost optimization - deployment reliability and scalability As an [AWS Premier Tier Partner](https://dedicatted.com/our-partners/aws), Dedicatted designs cloud and infrastructure solutions aligned with enterprise scalability, security, and operational best practices. ## QA Automation and Engineering Quality Quality assurance is critical for stable and reliable software delivery. Dedicatted integrates QA automation and testing into engineering workflows to reduce deployment risks, improve release quality, and support continuous delivery practices. This approach helps businesses improve software reliability, reduce production issues, and accelerate release cycles. ### Application Modernization and Infrastructure Optimization Legacy systems often limit scalability, operational agility, and cloud adoption. [Dedicatted helps organizations](https://dedicatted.com/insights) modernize applications, optimize infrastructure, and migrate workloads to cloud-native environments using modern engineering and DevOps practices. We help businesses reduce technical debt, improve operational performance, and accelerate digital transformation initiatives. ### Engineering Expertise for Enterprise Growth Dedicatted delivers engineering services for enterprise organizations operating in cloud, [AI](https://dedicatted.com/services/generative-ai), data, IoT, DevOps, and modern infrastructure environments. Our teams work closely with stakeholders, architects, and engineering leaders to align technical implementation with business strategy and operational goals. Whether you are building new digital products, modernizing enterprise systems, scaling cloud infrastructure, or improving DevOps operations, Dedicatted provides engineering expertise designed for scalability, reliability, security, and long-term business growth. Show more ## Get started with an engineering consultant Outline what you need built and the standard it has to meet. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Services:** Cloud and Infrastructure Security, DevOps Consulting, IoT Platform --- ### [Security](https://dedicatted.com/services/security) **Published:** April 23, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. Security Cloud security, compliance & DevSecOps # Cybersecurity & Compliance Services Compliance that runs as code, not as a yearly fire drill. Cybersecurity and compliance services protect cloud infrastructure, applications and enterprise data from threats while keeping SOC 2, ISO 27001, GDPR and other standards met, through continuous monitoring, DevSecOps and cloud security automation. Dedicatted embeds the controls in your pipelines, not bolted on after. [Talk to a security engineer](#contact-form) [See the assessments](#assessments) ![An engineer working at two monitors of code in a dim room](https://dedicatted.com/wp-content/uploads/2026/09/sec-hero-dual-screens-1024x682.jpg) We hold what we sell SOC 2 Type 2 and ISO 27001, audited independently. - ![soc2 badge](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/svg/soc2-badge.svg) SOC 2 Type 2 Independently audited for data security and privacy - ![iso27001 badge](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/svg/iso27001-badge.svg) ISO 27001 Certified for information security management - ![Vanta partner](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/badges/vanta-white.png) Vanta partner Continuous evidence collection, so the audit becomes a report - ![AWS Premier Tier Services Partner](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/badges/aws-premier-tier-white.png) AWS Premier Tier Cloud security on AWS, Microsoft Azure and Google Cloud ## What are security services? - ### What are security services? Security services include a range of solutions designed to protect applications, infrastructure, and data from cyber threats, vulnerabilities, and unauthorized access. - ### What do security services include? Security services include threat detection, vulnerability assessment, penetration testing, cloud security, compliance management, and continuous monitoring. - ### What does a security services company do? A security services company assesses risks, implements security controls, monitors systems, and ensures compliance with industry standards to protect business operations. ## Our core security services - ### Application Security Add DevSecOps checks to every stage of the secure SDLC and run them inside your GitHub or GitLab pipelines, so vulnerabilities are found early and releases stay compliant with SOC 2, ISO 27001 and OWASP. Secure SDLC*·*Threat modeling*·*SAST / DAST*·*Container security*·*Penetration testing - ### Security Compliance Turn SOC 2, ISO 27001, HIPAA, PCI DSS and GDPR requirements into policy as code with continuous evidence collection through Vanta, so the audit becomes a report instead of a project. SOC 2*·*ISO 27001*·*HIPAA*·*PCI DSS*·*GDPR*·*Policy as code - ### Cloud & Infrastructure Security Build CIS and AWS Well-Architected controls into your Terraform pipelines, harden Kubernetes and enforce least-privilege IAM, with continuous drift scanning. AWS Well-Architected*·*IaC guardrails*·*Zero trust*·*Kubernetes hardening*·*IAM and SSO - ### Managed Detection & Response Get around-the-clock monitoring of cloud and on-prem workloads with investigation and containment included: a security operations capability tuned to your environment, without building the team. 24/7 monitoring*·*Threat detection*·*Incident containment*·*Post-incident reports ## Where engagements start Three entry points, each scoped in the first call and each ending in an artefact your team keeps. - ### Security readiness assessment Where most engagements start Automated scanning plus expert review of your cloud accounts, pipelines and access patterns. - ✓ Prioritized findings with real exploitability context - ✓ Critical misconfigurations, exposed surfaces and IAM sprawl, ranked - ✓ A remediation plan as Terraform changes your team reviews [ Book a readiness call → ](#contact-form) - ### Compliance readiness with Vanta SOC 2, ISO 27001, HIPAA, PCI DSS, GDPR Framework requirements translated into monitored controls with continuous evidence collection. - ✓ Gap analysis against the framework you are pursuing - ✓ Policy as code enforced in your pipelines - ✓ Evidence collected automatically all year, audit-ready [ Book a readiness call → ](#contact-form) - ### DevSecOps pipeline hardening Security that ships with the code Scanning and secure templates wired into CI/CD, so every commit is checked without slowing delivery. - ✓ SCA, SAST and secrets scanning in every build - ✓ Secure CI/CD templates teams actually adopt - ✓ Gates that block dangerous changes, not releases [ Book a readiness call → ](#contact-form) ## Regulations never sleep, and neither should your controls Pick the framework; the machinery underneath is the same: requirements become policy as code, and evidence collects itself year-round with Vanta. 1. SOC 2Monitored SaaS and services selling to enterprise 2. ISO 27001Monitored The international information security baseline 3. HIPAAMonitored Healthcare data in the US 4. PCI DSSMonitored Anyone touching card payments 5. GDPRMonitored Personal data of EU residents 1. Requirement What the framework demands 2. Policy as code Enforced in your pipelines 3. Evidence, 24/7 Collected continuously via Vanta 4. Audit = a report Not a months-long project [Which framework applies to you? Ask us →](#contact-form) ## How are security services implemented? - ### How do companies implement security services? Companies implement security services by identifying risks, deploying security tools, integrating protection into infrastructure, and establishing monitoring and response processes. - ### What is the security implementation process? The process includes risk assessment, security strategy development, tool selection, implementation, testing, and continuous monitoring and improvement. - ### How long does it take to implement security services? Implementation timelines vary from a few weeks to several months depending on system complexity, regulatory requirements, and security maturity. ## How security lands in your estate Four steps, each ending in an artefact your team keeps. Scope and order are set in a free consultation first. 1. 1 Prioritized findings### Assess the posture - ✓ Automated scanning of cloud accounts, pipelines and access - ✓ Expert review with real exploitability context - ✓ A ranked findings list, not a 400-page PDF 2. 2 Terraform changes### Fix what matters first - ✓ Critical misconfigurations and exposed surfaces closed first - ✓ IAM sprawl reduced to least privilege - ✓ Changes as code your team reviews, no console edits that drift back 3. 3 Evidence in Vanta### Automate the controls - ✓ CIS and Well-Architected controls in the pipelines - ✓ Compliance evidence wired into Vanta - ✓ Security scanning added to CI 4. 4 Monthly posture report### Watch it continuously - ✓ Drift detection and vulnerability scanning - ✓ Optional 24/7 detection and response - ✓ A posture report your leadership can read [Start with a security readiness assessment](#assessments) ## What are the benefits of security services? - ### Why use security services? Security services help protect sensitive data, reduce cyber risks, ensure compliance, and maintain business continuity. - ### What are the key benefits of security services? Key benefits include improved threat protection, reduced vulnerabilities, enhanced compliance, better visibility into systems, and faster incident response. - ### How do security services improve business performance? Security services improve performance by minimizing downtime, protecting reputation, ensuring system reliability, and enabling safe digital operations. ## The stack behind the practice Posture and compliance Pipeline security Secrets and identity Detection and response - ![Vanta logo](https://dedicatted.com/wp-content/uploads/2025/01/vanta.webp) Vanta - ![aws security hub](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws-security-hub.svg) AWS Security Hub - ![guardduty](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/guardduty.svg) GuardDuty - ![terraform](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/terraform.svg) Terraform - ![snyk](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/snyk.svg) Snyk - ![trivy](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/trivy.svg) Trivy - ![gitlab](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/gitlab.svg) GitLab - ![github actions](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/github-actions.svg) GitHub Actions - ![vault](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/vault.svg) Vault - ![aws iam](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws-iam.svg) AWS IAM - ![aws kms](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws-kms.svg) AWS KMS - ![okta](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/okta.svg) Okta - ![datadog](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/datadog.svg) Datadog - ![falco](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/falco.svg) Falco - ![cloudtrail](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/cloudtrail.svg) CloudTrail - ![pagerduty](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/pagerduty.svg) PagerDuty ## Featured technology partners ![Vanta](https://dedicatted.com/wp-content/uploads/2025/01/vanta.webp) Vanta In collaboration with Vanta, we strengthen cloud security, implement compliance frameworks, and protect critical data. [ Read more ](#) - ![Vanta](https://dedicatted.com/wp-content/uploads/2025/01/vanta.webp) - ![The Only GenAI & MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Datadog](https://dedicatted.com/wp-content/uploads/2025/01/datadog.webp) Shield your business from risk exposure with our architects Tell us what keeps you up: an audit deadline, a posture worry, an incident. A security engineer replies within one business day with relevant experience and a first read. [ Book a security readiness call ↗ ](#contact-form) [## Insights ](https://dedicatted.com/insights) [Blog ### Inside a WAF Challenge Rule: How We Stop DDoS Attacks Without AWS Shield Alone Financial Services Healthcare Retail Cloud Operations Managed Services Security Compliance ![Hooded figure typing at a laptop in a blue-toned data center, screen displaying 'DDoS ATTACK'.](https://dedicatted.com/wp-content/uploads/2026/09/Ddos.png) A real DDoS attack, from first alert to full resolution in under 5 minutes. See how Dedicatted's incident response actually works, step by step. ](https://dedicatted.com/insights/inside-a-waf-challenge-rule-how-we-stop-ddos-attacks-without-aws-shield-alone) [Case study ### Self-Service Clinical Analytics on a FHIR Data Store for a Cardiology Network Healthcare Cloud Security Data Architecture Generative AI Security Compliance ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) About project The Challenge Three copies of the record, and none of them queryable. The clinical data existed in three disconnected forms: inside each device vendor’s proprietary analysis software, as PDF reports sent to referring physicians, and as files accumulating in Amazon S3 with no data model and no query layer over them. Clinic records \[…\] ](https://dedicatted.com/insights/self-service-clinical-analytics-on-a-fhir-data-store-for-a-cardiology-network) [Case study ### OMO Systems: Sub-300ms Device Control for Smart Buildings on AWS IoT Real Estate Cloud and Infrastructure Security Cloud Modernization DevOps Consulting IoT Platform Managed Services ![An IoT device module with a wireless chip on a workbench](https://dedicatted.com/wp-content/uploads/2026/09/iot-hero-esp32-board-1024x682.jpg) About project Challenges Two seconds means very different things depending on which device is at the other end. On a thermostat it is an annoyance a user forgives. On a lock at a door it is a guest in a corridor pressing the button again because nothing happened. On a valve under a detected leak \[…\] ](https://dedicatted.com/insights/smart-building-aws-iot) [Blog ### CI/CD in 2026: The Pipeline Patterns That Actually Scale Financial Services Healthcare High Tech Software Cloud Modernization DevOps Consulting Managed Services Security Compliance ![CI/CD in 2026: The Pipeline Patterns That Actually Scale](https://dedicatted.com/wp-content/uploads/2026/07/cd.png) Discover 6 CI/CD patterns that actually scale in 2026. Stop adding tools. Learn how to fix fragmented pipelines with architecture, not tooling. ](https://dedicatted.com/insights/ci-cd-in-2026-the-pipeline-patterns-that-actually-scale) ## FAQ ### What types of threats do security services protect against? Security services protect against malware, ransomware, phishing attacks, data breaches, and unauthorized access. ### What tools are used in security services? Common tools include firewalls, intrusion detection systems, SIEM platforms, endpoint protection, and cloud security tools. ### Are security services necessary for small businesses? Yes, security services are important for businesses of all sizes to protect data and prevent cyber threats. ### What is cloud security? Cloud security involves protecting cloud-based systems, applications, and data through access controls, encryption, and monitoring. ### How fast can we get SOC 2 ready? Readiness is measured in weeks once evidence collection is automated: the controls are mapped, the gaps closed and the evidence accumulates in Vanta instead of in screenshots. A Type 2 report then needs the observation window your auditor sets, typically three to twelve months, during which the automation does the work. ### Do you replace our security team or work with it? Either. Teams with security staff use us for the engineering-heavy work: infrastructure hardening, pipeline integration, posture automation. Teams without one get the full practice, including managed detection and response. ### Will security gates slow down our developers? Done right, the opposite: secure templates and automated checks remove the back-and-forth with security reviews. Gates block genuinely dangerous changes; everything else ships with findings attached, not held hostage. ### Can you work in regulated environments? Yes. Healthcare (HIPAA), payments (PCI DSS) and EU personal data (GDPR) engagements are routine, and we hold SOC 2 Type 2 and ISO 27001 ourselves, so our own delivery practices pass your vendor review. Question not answered? [Ask a security engineer](#contact-form). Same-day reply on scoping questions. ## What are Security Services? Security services help businesses protect cloud infrastructure, applications, networks, and enterprise data from cyber threats, unauthorized access, vulnerabilities, and operational risks through continuous monitoring, threat detection, compliance management, and security automation. Dedicatted provides enterprise security services for cloud, DevOps, and modern infrastructure environments. ## Enterprise Security Services Dedicatted delivers security services that help organizations secure cloud-native environments, improve infrastructure resilience, and maintain compliance across enterprise systems. Our security experts work with businesses to identify vulnerabilities, reduce operational risk, and implement scalable security frameworks aligned with modern cloud and DevOps operations. **We help organizations protect:** - cloud infrastructure - enterprise applications - Kubernetes environments - CI/CD pipelines - APIs and microservices - databases and sensitive data - multi-cloud and hybrid systems Our approach combines proactive monitoring, automation, governance, and cloud security best practices to improve overall security posture and operational reliability. ## Cloud Security Services Cloud security services help businesses secure workloads, applications, and infrastructure across AWS, Azure, and [Google Cloud](https://dedicatted.com/our-partners/google-cloud) environments. Dedicatted designs and implements scalable cloud security architectures focused on visibility, compliance, access management, and threat prevention. **Our cloud security capabilities include:** - cloud infrastructure security - IAM and access management - network security and segmentation - workload protection - cloud compliance monitoring - security automation and remediation - backup and disaster recovery security We help organizations reduce cloud security risks while maintaining performance, scalability, and operational flexibility. ## DevSecOps and Infrastructure Security DevSecOps integrates security directly into software delivery and infrastructure management processes. Dedicatted helps businesses implement DevSecOps practices that improve deployment security, automate vulnerability management, and reduce risks across development pipelines. **Our DevSecOps services include:** - CI/CD security integration - infrastructure as code security scanning - container and Kubernetes security - secrets management - runtime threat detection - automated policy enforcement - compliance and audit readiness By integrating security into DevOps workflows, businesses can accelerate software delivery without compromising operational security. ## Security Monitoring and Threat Detection Continuous monitoring and threat detection are essential for protecting enterprise systems from modern cyber threats. Dedicatted implements monitoring and observability platforms that provide real-time visibility into infrastructure, workloads, and operational environments. **Our security monitoring services support:** - threat detection and alerting - log analysis and SIEM integration - anomaly detection - incident investigation - infrastructure observability - cloud activity monitoring - operational risk management These capabilities help organizations detect suspicious activity early, reduce downtime, and improve incident response efficiency. ### Compliance and Security Governance Security governance helps organizations maintain compliance, enforce policies, and manage operational risk across cloud and enterprise environments. Dedicatted supports businesses in implementing scalable governance frameworks aligned with industry regulations and security best practices. **We help organizations improve:** - compliance readiness - security policies and controls - auditability and reporting - identity governance - infrastructure visibility - operational security standards Our security frameworks are designed to support secure cloud adoption, enterprise scalability, and long-term operational resilience. ### Enterprise Security Solutions Dedicatted provides enterprise security solutions for organizations modernizing infrastructure, scaling cloud operations, and securing DevOps environments. Our team combines expertise in cloud engineering, [DevOps automation](https://dedicatted.com/what-we-do/devops/devops-as-a-service), [Kubernetes](https://dedicatted.com/solutions/key-technologies/kubernetes), and enterprise infrastructure security to help businesses build secure and scalable digital environments. Whether your organization needs cloud security, DevSecOps implementation, infrastructure protection, compliance support, or enterprise threat monitoring, Dedicatted delivers practical and scalable security services for modern cloud operations and [AI-driven](https://dedicatted.com/services/generative-ai) search visibility. Show more ## Get started with a cybersecurity consultant Outline your security, compliance or incident response challenge. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Services:** Cloud and Infrastructure Security, Security Compliance --- ### [Glossary from Dedicatted DevOps company](https://dedicatted.com/services/glossary-of-terms) **Published:** November 30, 2025 **Author:** Dedicatted **Content:** # Glossary from Dedicatted DevOps company Regardless of your role in an organization, this glossary of DevOps terms was compiled for everyone from the DevOps Engineer to the general end-user. Here, you’ll find definitions of terms commonly used in a DevOps environment. Uncover knowledge areas where you excel and where you want to expand. [A](#a "A") [B](#b "B") [C](#c "C") [D](#d "D") [E](#e "E") [F](#f "F") [G](#g "G") [H](#h "H") [I](#i "I") [J](#j "J") [K](#k "K") [L](#l "L") [M](#m "M") [N](#n "N") [O](#o "O") [P](#p "P") [Q](#q "Q") [R](#r "R") [S](#s "S") [T](#t "T") [U](#u "U") [V](#v "V") [W](#w "W") [X](#x "X") [Y](#y "Y") [Z](#z "Z") **A** [](https://dedicatted.com/services/artificial-intelligence/agentic-ai)[**Agentic AI**](https://dedicatted.com/services/artificial-intelligence/agentic-ai) what is it and what it’s definition? – Artificial intelligence systems capable of autonomously planning and taking actions toward specific goals, rather than only responding to direct commands. These AI agents can make decisions, execute tasks, and adapt based on outcomes. What does agentic mean in AI? It means the system has the ability to act independently – making decisions and taking actions on its own to achieve a goal. How does agentic AI differ from traditional AI? – Agentic AI can autonomously plan and take actions to reach goals, while traditional AI mainly reacts to input and follows predefined instructions without independent decision-making. **Agile** is a software development methodology. This methodology delivers iteratively. It remains flexible. It collaborates closely among teams. [**App Modernization**](https://dedicatted.com/what-we-do/cloud-services/app-modernisation) – the process of updating legacy applications with modern technologies, architectures, and practices to improve performance, security, scalability, and user experience. **API Application Programming Interface** is rules for communication and data exchange between software components applications or systems. **API Gateway** – An application that manages and controls incoming traffic from users for backend services, commonly used among microservices. **Automation** – The use of tools and scripts to remove manual steps in development, testing, deployment, and infrastructure management. **Autoscaling** – Automatically adjusting computing resources based on demand to improve performance and optimize cost. **Artifact** – built output exists from a software project. Examples are binaries, containers, or packages for deployment. These are stored and consumed as needed. **Artifact Repository** – A storage system for versioning and managing build artifacts (e.g., JFrog Artifactory, Nexus). **Audit Logging** – Tracking and recording activity within systems to ensure security, compliance, and troubleshooting capabilities. **Ansible** – An open-source automation tool used for configuration management, app deployment, and orchestration. [**AWS**](https://dedicatted.com/our-partners/aws) **(Amazon Web Services)** what is? – The world’s most widely adopted enterprise cloud platform, with DevOps-supporting infrastructure and services. **ALM (Application Lifecycle Management)** – Refers to a collection of processes and tools that manage applications from planning and development through retirement. [**Azure managed service**](https://dedicatted.com/our-partners/microsoft) what is it? – A fully managed cloud solution provided and maintained by Microsoft, where Azure handles infrastructure, updates, security, and scaling so users can focus on running their applications instead of managing the underlying systems. **B** **Backup** – A copy of data stored separately to ensure recovery in case of loss or failure. **Bare-Metal Server** – A physical server dedicated to a single tenant without a virtualization layer. **Blue-Green Deployment** – A release strategy using two identical environments to switch traffic with zero downtime. **Bottleneck (Lean)** – A stage in the workflow where limited capacity slows down overall delivery and reduces efficiency. **Branching Strategy** – Rules for managing code versions and collaboration (e.g., GitFlow, trunk-based development). **Build Automation** – Tools and processes that compile code, run tests, and package artifacts without manual actions. **Build Pipeline** – a sequence of automated stages that convert code commits into deployable builds. **Business Continuity** – readiness to maintain operations during outages, disasters, system failures, or other disruptions. **Bug Tracking** – system to submit, prioritize, and address software bugs and issues. **Baseline Configuration** – a configuration that is designated and approved as the basis for further activities. **C** **CI/CD (Continuous Integration / Continuous Delivery/Deployment)** – Practices that automate code integration, testing, and release to speed up delivery. **Cloud Computing** – Delivery of computing resources (servers, storage, databases, etc.) over the internet on demand. **Cloud Migration** – Moving systems, data, and workloads from on-premises environments to the cloud. **Cloud-Native** – Applications designed to run in the cloud using microservices, containers, and automation. **Configuration Management** – Controlling and maintaining system settings to ensure consistency across environments. **Container** – A lightweight environment that packages code and dependencies to run reliably across systems. **Container Orchestration** – Automated management of containerized applications at scale (e.g., Kubernetes). **Canary Deployment** – A gradual rollout of changes to a subset of users to reduce risk. **Compliance** – Meeting regulatory and industry standards for security, privacy, and operations. **Chaos Engineering** – Intentional failure injection to test system resilience and reliability. **Change Management** – Processes ensuring controlled, documented changes to infrastructure or software. **Cluster** – A group of linked servers or nodes working together as a single system. **Cost Optimization** – Techniques to reduce cloud spending while maintaining performance. **Continuous Monitoring** – Real-time tracking of system health, performance, and security. **Command-Line Interface (CLI)** – A text-based tool for interacting with systems, typically used for automation and operations. **D** [**Data Architecture**](https://dedicatted.com/services/data/architecture) **–** The structured design and organization of data, systems, and processes to ensure efficient data storage, management, integration, and accessibility across an organization. [**Data Migration& Modernization**](https://dedicatted.com/services/data/cloud-data-migration) **–** transferring data from outdated systems to modern platforms while improving its structure, quality, and performance to support current and future business needs. [**Devops as a service**](https://dedicatted.com/what-we-do/devops/devops-as-a-service) – what is it? (DaaS) a cloud solution where a provider automates and manages the software delivery lifecycle, helping companies release updates faster and with fewer manual efforts. [**Devops consulting**](https://dedicatted.com/what-we-do/consulting/devops-consulting) – what is? – Guidance and support to help companies adopt DevOps practices, automate delivery processes, and improve software performance and reliability. **Data Center** – Physical facility hosting servers and network infrastructure. **Deployment** – Releasing new or updated software into an environment. **Disaster Recovery (DR)** – Processes to restore systems after failures or outages. **Docker** – A platform for building and running containerized applications. **Downtime** – Period when a service is unavailable. **E** **Environment** – A specific environment which the app runs on (dev, test, staging, prod). **Elasticity** – Ability to automatically scale resources up or down as needed. **Encryption** – The process of encoding a message into an unreadable format. [**Engineering Services**](https://dedicatted.com/services/engineering) – specialized technical and consulting services that support the full lifecycle of systems, software, and infrastructure – including research, design, development, integration, deployment, optimization, and ongoing maintenance. These services help organizations build reliable, scalable, and innovative solutions that meet business, operational, and regulatory requirements. **Endpoint** – A network-accessible resource such as an API URL or device. **F** **Failover** – Automatic switching to a backup system upon failure of the primary. **Feature Flag** – A switch to enable or disable a feature, without redeploying. **Feedback loop** – Continual user feedback or results monitoring improves delivery. **Firewall** – Security system controlling allowed network traffic. **G** [**Generative AI**](https://dedicatted.com/services/generative-ai) what is? – It is a type of artificial intelligence (AI) that can create content such as text, images, code, or audio content based on learned patterns in data rather than being specifically programmed to perform a task. Generative AI can create new content from prompts. DevOps with generative AI can be a powerful tool for services and consulting. **Generative AI in Azure Machine Learning** – A suite of services and tools that enable developers to build, deploy and manage applications powered by generative AI (large-language models, image models, etc.) using Azure. **Git** – A version control system for tracking code changes. **GitOps** – Managing infrastructure and deployments declaratively with Git. **Golden Image** – Pre-configured template for creating identical environments. [**Google cloud managed services**](https://dedicatted.com/our-partners/google-cloud) what is it? – Cloud solutions where Google handles the infrastructure, maintenance, security, and scaling, allowing organizations to run applications without managing the underlying systems. **H** **High Availability (HA)** – A system designed to minimize downtime. **Hybrid Cloud** – A combination of public and private cloud. **Helm** – Package manager for Kubernetes applications. **I** **IaC (Infrastructure as Code)** – Provisioning infrastructure via code instead of manually configuring beyond it. [**IoT Platforms**](https://dedicatted.com/services/iot/platforms) – software systems that connect, manage, and analyze data from Internet-of-Things devices to enable monitoring, automation, and integration with other applications. **Immutable Infrastructure** – Resources should not be modified, only replaced in order to ensure consistency. **J** **JSON** – A lightweight data interchange format commonly used by APIs. **Jenkins** – A popular automation server for CI/CD. **K** [**Kubernetes**](https://dedicatted.com/solutions/key-technologies/kubernetes) – (K8s) acts as a container orchestration platform for automating deploying and scaling. **KPI (Key Performance Indicator)** – Metrics used to measure operational performance. **L** **Latency** – Request and response time. **Load Balancer** – Tool that distributes traffic across multiple servers. **Log Management** – Collecting and analyzing logs for troubleshooting and security. **Lean** – A methodology focused on eliminating waste in workflows. **M** **Microservices** – Architect applications as services that are small and independent. **Monitoring** – Tracking system health and performance in real time. **Multi-Cloud** – Using multiple cloud providers for flexibility and redundancy. **N** **Namespace** – Logical grouping of resources, often used in Kubernetes. **Network Policy** – Rules governing connectivity and security between workloads. **Node** – A server (physical or virtual) running workloads in a cluster. **O** **Observability** – Understanding the internal state of a system using logs, metrics and traces. **Orchestration** – Automatic coordination of complex deployments and scaling. **On-Premises** – Infrastructure in a company’s physical premises. **P** **Pipeline** – Automated series of steps for building, testing, and deploying code. **Provisioning** – Allocating and configuring cloud resources or servers. **Platform Engineering** – Building self-service platforms to improve developer productivity. **Q** **Quality Assurance (QA)** – Ensuring software quality through testing practices. **Query** – Request for information from a database or monitoring system. **Quarantine Environment** – Isolated system for testing risky changes. **R** **Rollback** – Reverting to a previously deployed stable version following issues. **Runtime** – The environment surrounding an application when it is run. **Release Management** – Coordinating and controlling software releases. **S** [**Security Compliance**](https://dedicatted.com/services/security) – The practice of ensuring that an organization follows required laws, regulations, standards, and internal policies to protect data, systems, and operations from security risks. **Software as a service (SaaS)** – Software delivered over the internet. **SRE (Site Reliability Engineering)** – Engineering service reliability through automation. **Scalability** – Ability to grow system capacity efficiently. **Serverless** – Running code without managing servers directly. **Security Hardening** – Enhancing system security by reducing vulnerabilities. **T** **Terraform** – IaC tool for provisioning cloud infrastructure. **Telemetry** – Collection of performance and operational data for monitoring. **Testing Automation** – Running tests automatically during the development cycle. **U** **Uptime** – The percentage of time a system remains online and functional. **Unified Monitoring** – View logs, metrics, and traces across systems in one place. **Upgrade** – Updating software or systems with improved versions. **V** **Version Control** – For tracking and managing changes to code and configuration. **Virtual Machine (VM) –** An emulated computing environment running on a physical server. **Vulnerability Scan** – Automated detection of security weaknesses. **W** **Workflow Automation** – Using automation to accomplish repeatable tasks. **Webhooks** – Event-based triggers for external service notifications. **White-Box Monitoring** – Monitoring based on internal system data. **X** **XML** – Markup language for structured data exchange. **XaaS (Anything as a Service)** – General term for cloud services. **Y** **YAML** – Human-readable configuration format used in CI/CD and Kubernetes. **Yottabyte** – A unit of data equal to 10²⁴ bytes. **Z** **Zero-Downtime Deployment** – deploying new code without service interruption. **Zero Trust** – security model requiring users to authenticate for every action. ## Talk to a Dedicatted engineer Tell us what you are trying to solve. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Agentic AI Solutions](https://dedicatted.com/services/artificial-intelligence/agentic-ai) **Published:** June 19, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. Agentic AI Solutions Agentic AI Solutions & Consulting # Agentic AI Autonomy you can hold accountable Agentic AI consulting is a service that helps businesses implement autonomous AI agents that automate workflows, make decisions, and execute complex tasks using real-time data and large language models, delivered by Dedicatted, an AWS Premier Tier Partner specializing in enterprise AI, cloud, and DevOps solutions. [Talk to an AI consultant](#contact-form) ![Three glowing translucent spheres of networked nodes linked by threads of light on a dark teal ground](https://dedicatted.com/wp-content/uploads/2026/08/agentic-hero-1024x683.jpg) AWS Agentic AI Specialization Selected Canadian partner · bounded autonomy · you own the code ## Understanding agentic AI - ### What is agentic AI? Agentic AI refers to intelligent systems that can autonomously make decisions, take actions, and complete tasks based on goals, context, and real-time data. - ### What are agentic AI services? Agentic AI services include solution design, AI agent development, workflow automation, system integration, and deployment of autonomous AI systems for business use cases. - ### What does an agentic AI company do? An agentic AI company builds and deploys autonomous AI agents that can perform tasks such as data analysis, customer interaction, process automation, and decision support. ## Why agentic AI programmes stall Gartner expects **over 40% of agentic AI projects to be cancelled by the end of 2027**, and names three causes. The rest of this page answers them. 1. Escalating cost Agents that run unbounded burn tokens, compute, and engineering time with no unit economics anyone signed off on. 2. Unclear business value The use case was never scored against a number the business wanted to move, so nobody can say whether it worked. 3. Inadequate risk controls No one can answer what the agent may do alone, who is accountable when it acts wrong, and how its actions are audited. Source: Gartner press release, June 2025, agentic AI project cancellation forecast. ## our process Dedicatted provides Agentic AI consulting services that help businesses build autonomous AI agents for workflow automation and decision-making using large language models, real-time data, and enterprise AI infrastructure. ![white icon of the lightning bulb on the purple circle padding](https://dedicatted.com/wp-content/uploads/2025/06/icon-2.svg) ### 1. Explore #### Identify high-impact agentic AI use cases aligned with your business goals, data readiness, and existing systems. - Identify high-impact, feasible use cases aligned with business priorities - Assess data availability, workflows, and system readiness for AI integration - Define agent roles, integration points, and compliance or security requirements [Get our offer](#contact-form) ![white icon of the turning gear on the purple circle padding](https://dedicatted.com/wp-content/uploads/2025/06/icon-5.svg) ### 2. Implement #### Build, integrate, and deploy autonomous AI agents into your cloud and DevOps stack with full observability. - Build and fine-tune agents using domain-specific data and feedback loops - Establish observability, perform rigorous testing, and validate with stakeholders - Seamlessly deploy and integrate within your existing technology stack [Contact us](#contact-form) ![white icon of the chip with AI word written in the middle on the purple circle padding](https://dedicatted.com/wp-content/uploads/2025/06/icon-1.svg) ### 3. Maintain #### Monitor, optimize, and scale your AI agents with continuous governance, compliance, and performance tuning. - Track agent behavior, refine logic, and update training data - Optimize systems for cost-efficiency, speed, and output quality - Maintain governance, security, and compliance across all AI operations [Get offer](#contact-form) ## Our agentic AI solution ![Infographic showing an AI-driven document automation workflow. On the left, documents are ingested. AI parses the documents for signals, then plans the optimal flow, and converts data into decisions. The system presents and delivers the results to the destination. At the top, AI tunes itself over time by learning from data, updating policies, and adjusting logic, creating a continuous improvement cycle.](https://dedicatted.com/wp-content/uploads/2025/06/image-338-scaled.png)One continuous loop: every decision the agent delivers feeds back into how it parses and plans the next one. See it in action ## Bounded autonomy and accountability Autonomous does not mean unaccountable. Every agent we ship has three answers written down before it touches production. ### What the agent does alone - A distinct digital identity per agent — scoped credentials, not shared service accounts - Narrow, modular design — one job per agent, composed rather than monolithic - Authority capped — anything above a pre-set limit routes to a human ### What a person signs off - A named owner, validator, and steward for every agent in production - Explicit kill switches — any agent can be stopped instantly, by role, not by ticket - Escalation paths agreed before launch, not invented during an incident ### How it stays auditable - Every action logged with the reasoning behind it — replayable end to end - Integration with your existing model-risk review, not a parallel process - Runs in your perimeter — your data stays in your account, you own the code - 50+ AI use cases shipped to production - Month → days proof-of-concept time on an aviation platform, once agents took over provisioning and validation - 100% of the intellectual property assigned to the client ## Benefits for your business ![Rocket icon in gradient purple-teal circle representing launch, startup, or acceleration](https://dedicatted.com/wp-content/uploads/2025/03/ai-offering-card.svg) ### Increased Efficiency #### Agentic AI systems can manage hundreds of user interactions simultaneously with minimal human intervention ![Data-driven decisions icon](https://dedicatted.com/wp-content/uploads/2026/05/data-driven-icon-v2.svg) ### Data-driven decisions #### AI agents uncover actionable insights from interactions, driving smarter, faster, and fully compliant decisions. ![Overlapping squares icon in gradient purple-teal circle representing integration or connectivity](https://dedicatted.com/wp-content/uploads/2025/03/ai-offering-card-2.svg) ### Scalability #### AI agents can adjust their processing capacity to match demand, tackle complex tasks, and automate workflows ![star icon favorite rating bookmark gradient purple teal](https://dedicatted.com/wp-content/uploads/2025/04/icon-7.svg) ### Enhanced customer satisfaction #### By analyzing data from various touchpoints and past interactions, AI-powered agents can predict customer needs and offer proactive solutions. ![compass icon navigation direction guidance gradient purple teal](https://dedicatted.com/wp-content/uploads/2025/04/icon-6.svg) ### Around-the-clock service availability #### This enables businesses to eliminate wait times and service gaps often occurring during off-peak hours or high-demand periods. ![IoT lightbulb icon for smart device connectivity solutions](https://dedicatted.com/wp-content/uploads/2025/04/icon-8.svg) ### Ready to See Agentic AI in Action? #### Experience how Agentic AI made by Dedicatted can think, plan, and act across complex tasks – without constant prompting. [Explore our Demo](https://dedicatted.com/contact) ## How is agentic AI implemented? - ### How do companies implement agentic AI? Companies implement agentic AI by identifying use cases, preparing data, selecting models, designing agent workflows, and integrating AI agents into existing systems. - ### What is the agentic AI implementation process? The process includes discovery, use case definition, data preparation, model selection, agent development, system integration, deployment, and continuous optimization. - ### How long does it take to implement agentic AI? Implementation typically takes 4–12 weeks depending on system complexity, data availability, and integration requirements. ## What are the benefits of agentic AI? - ### Why use agentic AI services? Agentic AI services help automate complex workflows, reduce manual effort, improve decision-making speed, and enable scalable autonomous operations. - ### What are the key benefits of agentic AI? Key benefits include autonomous task execution, real-time decision-making, improved operational efficiency, scalability, and enhanced user experiences. - ### How does agentic AI improve business performance? Agentic AI improves performance by streamlining processes, reducing errors, enabling continuous operations, and accelerating time-to-value. ## Industry Expertise ### Healthcare [](https://dedicatted.com/services/generative-ai/healthcare)### Healthcare Clinical and administrative work still moves at the speed of a person reading a document. Agentic AI monitors device and record data for anomalies, drafts prior authorisations and discharge summaries, and routes anything clinical to a named human before it counts — so the agent does the reading and your team does the deciding. [ Read more→ ](https://dedicatted.com/services/generative-ai/healthcare) ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) ### Manufacturing [](https://dedicatted.com/services/generative-ai/manufacturing)### Manufacturing Agents watch equipment telemetry continuously, predict maintenance before a line stops, and adjust operating parameters inside limits you set. Anything outside those limits routes to a human rather than executing — which is what makes autonomy on a production line something a plant manager will sign off. [ Read more→ ](https://dedicatted.com/services/generative-ai/manufacturing) ![Blue industrial robot arm on a modern factory production line](https://dedicatted.com/wp-content/uploads/2026/08/manufacturing-819x1024.jpg) ### Financial [](https://dedicatted.com/services/generative-ai/financial-services)### Financial Multi-agent workflows review alerts, analyse transaction patterns and draft findings, with authority capped so an agent recommends and executes within a pre-set limit and everything above it goes to a person. Every action is logged with the reasoning behind it, and the review plugs into your existing model-risk process. [ Read more→ ](https://dedicatted.com/services/generative-ai/financial-services) ![Worms-eye view looking up at modern glass office towers](https://dedicatted.com/wp-content/uploads/2026/08/financial-819x1024.jpg) ### Retail [](https://dedicatted.com/services/generative-ai/retail)### Retail Agents monitor stock in real time, read demand from sales patterns and adjust orders without waiting for a weekly review. In the supply chain they track shipments, predict delays and reschedule around them — continuously, rather than when someone happens to look. [ Read more→ ](https://dedicatted.com/services/generative-ai/retail) ![Bright modern retail store interior with product displays](https://dedicatted.com/wp-content/uploads/2026/08/retail-819x1024.jpg) ### Insurance [](https://dedicatted.com/services/generative-ai/insurance)### Insurance Agents process claims, detect fraud through real-time pattern analysis and personalise policy recommendations, while continuously monitoring risk indicators. Underwriting decisions above a threshold route to an underwriter, and every one carries the reasoning that produced it. [ Read more→ ](https://dedicatted.com/services/generative-ai/insurance) ![Abstract glowing blue data wave pattern on a dark background](https://dedicatted.com/wp-content/uploads/2026/08/insurance-819x1024.jpg) ### Supply Chain [](https://dedicatted.com/services/generative-ai/supply-chain)### Supply Chain Agents coordinate across suppliers, carriers and warehouses at once: tracking shipments, predicting disruption and re-planning routes as conditions change. The work that used to wait for a planner to notice now happens continuously, inside the constraints you define. [ Read more→ ](https://dedicatted.com/services/generative-ai/supply-chain) ![Automated warehouse robot carrying a storage rack](https://dedicatted.com/wp-content/uploads/2026/08/supply-chain-819x1024.jpg) [](https://dedicatted.com/services/generative-ai/healthcare)### Healthcare Clinical and administrative work still moves at the speed of a person reading a document. Agentic AI monitors device and record data for anomalies, drafts prior authorisations and discharge summaries, and routes anything clinical to a named human before it counts — so the agent does the reading and your team does the deciding. [ Read more→ ](https://dedicatted.com/services/generative-ai/healthcare) ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) [](https://dedicatted.com/services/generative-ai/manufacturing)### Manufacturing Agents watch equipment telemetry continuously, predict maintenance before a line stops, and adjust operating parameters inside limits you set. Anything outside those limits routes to a human rather than executing — which is what makes autonomy on a production line something a plant manager will sign off. [ Read more→ ](https://dedicatted.com/services/generative-ai/manufacturing) ![Blue industrial robot arm on a modern factory production line](https://dedicatted.com/wp-content/uploads/2026/08/manufacturing-819x1024.jpg) [](https://dedicatted.com/services/generative-ai/financial-services)### Financial Multi-agent workflows review alerts, analyse transaction patterns and draft findings, with authority capped so an agent recommends and executes within a pre-set limit and everything above it goes to a person. Every action is logged with the reasoning behind it, and the review plugs into your existing model-risk process. [ Read more→ ](https://dedicatted.com/services/generative-ai/financial-services) ![Worms-eye view looking up at modern glass office towers](https://dedicatted.com/wp-content/uploads/2026/08/financial-819x1024.jpg) [](https://dedicatted.com/services/generative-ai/retail)### Retail Agents monitor stock in real time, read demand from sales patterns and adjust orders without waiting for a weekly review. In the supply chain they track shipments, predict delays and reschedule around them — continuously, rather than when someone happens to look. [ Read more→ ](https://dedicatted.com/services/generative-ai/retail) ![Bright modern retail store interior with product displays](https://dedicatted.com/wp-content/uploads/2026/08/retail-819x1024.jpg) [](https://dedicatted.com/services/generative-ai/insurance)### Insurance Agents process claims, detect fraud through real-time pattern analysis and personalise policy recommendations, while continuously monitoring risk indicators. Underwriting decisions above a threshold route to an underwriter, and every one carries the reasoning that produced it. [ Read more→ ](https://dedicatted.com/services/generative-ai/insurance) ![Abstract glowing blue data wave pattern on a dark background](https://dedicatted.com/wp-content/uploads/2026/08/insurance-819x1024.jpg) [](https://dedicatted.com/services/generative-ai/supply-chain)### Supply Chain Agents coordinate across suppliers, carriers and warehouses at once: tracking shipments, predicting disruption and re-planning routes as conditions change. The work that used to wait for a planner to notice now happens continuously, inside the constraints you define. [ Read more→ ](https://dedicatted.com/services/generative-ai/supply-chain) ![Automated warehouse robot carrying a storage rack](https://dedicatted.com/wp-content/uploads/2026/08/supply-chain-819x1024.jpg) ## Insights [Case study ### Agentic Voice Automation for the Clinic Revenue Cycle at Spike Technologies Healthcare Agentic AI Cloud Security Generative AI Managed Services ![Three glowing translucent spheres of networked nodes linked by threads of light on a dark teal ground](https://dedicatted.com/wp-content/uploads/2026/08/agentic-hero-1024x683.jpg) About project The Challenge The expensive part of the work is a conversation. Scripted IVR navigation and portal scraping retrieve a fraction of eligibility answers and break whenever a payer changes a page. The costly work is the call with a payer representative about one patient’s authorization: IVR navigation, hold time, a conversation that follows \[…\] ](https://dedicatted.com/insights/agentic-voice-automation-for-the-clinic-revenue-cycle) [Case study ### Self-Service Clinical Analytics on a FHIR Data Store for a Cardiology Network Healthcare Cloud Security Data Architecture Generative AI Security Compliance ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) About project The Challenge Three copies of the record, and none of them queryable. The clinical data existed in three disconnected forms: inside each device vendor’s proprietary analysis software, as PDF reports sent to referring physicians, and as files accumulating in Amazon S3 with no data model and no query layer over them. Clinic records \[…\] ](https://dedicatted.com/insights/self-service-clinical-analytics-on-a-fhir-data-store-for-a-cardiology-network) [Blog ### The 2026 Enterprise Data Readiness Benchmark: Your Foundation for Successful AI Insurance Retail Cloud Data Migration & Modernization Data Architecture Generative AI ![Abstract digital data corridor with blue light trails and glowing particles suggesting a high-tech network edge-to-edge.](https://dedicatted.com/wp-content/uploads/2026/08/Designing-Platform-for-Instant-Market-Penetration-1.png) Only 7% of enterprises have the data foundation AI needs to scale. See what separates them: real case studies, a maturity framework, next steps ](https://dedicatted.com/insights/the-2026-enterprise-data-readiness-benchmark-your-foundation-for-successful-ai) [Blog ### Pharma & Life Sciences: R&D Acceleration with Cloud & AI Healthcare Agentic AI Cloud Modernization Data Architecture Generative AI ![Close-up of glass pipettes transferring liquid in a dimly lit science lab.](https://dedicatted.com/wp-content/uploads/2026/08/Designing-Platform-for-Instant-Market-Penetration.png) This playbook breaks down the reference architecture, use cases by R&D stage, and implementation roadmap for cloud and AI acceleration in pharma ](https://dedicatted.com/insights/pharma-life-sciences-rd-acceleration-with-cloud-ai) Our expertise yields meaningful results for clients Free consultation · Response within one business day [ Get started now ↗ ](#contact-form) ## FAQ ### What is an AI agent? An AI agent is a system that perceives its environment, processes information, and takes actions to achieve specific goals without constant human input. ### What technologies are used in agentic AI? Agentic AI uses technologies such as large language models, machine learning, APIs, vector databases, and orchestration frameworks for autonomous workflows. ### Is agentic AI suitable for all businesses? Agentic AI can be applied across industries, but its effectiveness depends on clearly defined processes and availability of structured or semi-structured data. ### What is the difference between agentic AI and traditional AI? Traditional AI focuses on predictions or outputs, while agentic AI systems can take actions, make decisions, and execute multi-step workflows autonomously. ### How is agentic AI integrated into existing systems? Agentic AI is integrated through APIs, cloud platforms, and workflow orchestration tools that connect AI agents with business systems and data sources. ### Who owns the code and the data? It runs inside your own perimeter, so your data stays in your account and you own the resulting code. Intellectual property from the engagement is assigned to you. ### What happens when an agent gets something wrong? Every agent in production has a named owner, validator and steward, an explicit kill switch that any of them can pull by role rather than by ticket, and an authority cap — anything above a pre-set limit routes to a human before it executes. ### How do you keep agent activity auditable? Each agent has its own digital identity rather than a shared service account, and every action is logged with the reasoning behind it so a decision can be replayed end to end. The review plugs into your existing model-risk process instead of running alongside it. ## Agentic AI Solutions and Consulting Services Agentic AI is an AI architecture that enables autonomous agents to plan, reason, make decisions, and execute multi-step tasks using large language models, tools, APIs, and real-time data. Dedicatted provides enterprise Agentic AI solutions for enterprise automation, Agentic AI integration services, DevOps operations, and intelligent workflow orchestration. Dedicatted delivers agentic ai solutions that help businesses automate operations, optimize workflows, and improve decision-making using autonomous AI agents integrated with cloud and DevOps environments. Our consulting services focus on practical enterprise use cases, secure deployments, and scalable AI operations rather than experimental prototypes. We design and implement agentic ai platforms that integrate with CI/CD pipelines, observability systems, cloud infrastructure, ticketing platforms, and internal enterprise tools. These systems help organizations reduce manual operations, improve deployment reliability, and accelerate operational workflows. ### Agentic AI Platforms Choosing the right agentic ai platforms is critical for secure and scalable AI adoption. As an Agentic AI development company, Dedicatted evaluates platforms based on integration capabilities, security controls, scalability, observability, and compatibility with enterprise DevOps environments. ### Our team helps businesses: - evaluate agentic ai platforms - design AI deployment strategies - implement governance and security controls - integrate AI agents with enterprise systems - optimize infrastructure for AI operations - create rollout and migration plans We focus on building enterprise-ready AI environments that balance operational efficiency, security, scalability, and measurable business value. ## What are Agentic AI Tools? Agentic ai tools are AI-powered systems that can plan actions, use external tools, process data, and execute workflows autonomously. Dedicatted implements agentic ai tools that integrate with cloud platforms, observability stacks, deployment pipelines, and enterprise applications. ### Dedicatted implements agentic ai tools for: - infrastructure automation - incident response workflows - deployment validation - cloud cost optimization - observability and monitoring analysis - engineering productivity automation - compliance-friendly documentation ## Agentic AI Architecture A scalable agentic ai architecture combines large language models, planning logic, memory layers, tool integrations, and operational safeguards. Through our Agentic AI implementation services, Dedicatted designs agentic ai architecture patterns that fit secure enterprise environments and modern cloud operations. ### Our architectures include: - large language models and reasoning engines - memory and context management - CI/CD and cloud integrations - policy enforcement and approval workflows - monitoring and auditability - secure enterprise API integrations Agentic AI works by combining reasoning, planning, and tool use to complete tasks autonomously. Agents perceive context, decide next actions, call tools or APIs, evaluate outcomes, and iterate until goals are achieved. ### Common agentic ai use cases include: - automated incident triage - infrastructure drift correction - deployment validation - DevOps workflow automation - cloud operations management - internal knowledge retrieval - compliance and documentation workflows - healthcare and financial process automation ## AWS Agentic AI Cloud Services AWS agentic AI cloud services provide the foundation for secure, scalable, and production-grade agentic AI deployments. Dedicatted designs AWS-native agentic AI architectures that integrate with existing cloud environments and DevOps pipelines. ### We deliver: - AI infrastructure deployment - AI agent orchestration - cloud-native AI integrations - DevOps automation for AI operations - observability and monitoring platforms - multi-agent coordination systems - enterprise workflow integrations Show more ## Get started with an Agentic AI consultant Briefly outline your challenge — our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Services:** Agentic AI, Generative AI --- ### [IoT Platforms](https://dedicatted.com/services/iot/platforms) **Published:** April 29, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. IoT Platforms IoT solutions · AWS IoT, edge to cloud # IoT Platforms A thousand devices talking, and a platform that listens. IoT platforms connect devices, applications and data into one ecosystem that improves operational efficiency, automation and real-time decisions. Dedicatted builds them end to end on AWS: secure device connectivity, edge-to-cloud data flow, a governed data lake, and the analytics and alerts your operations run on. [Talk to an IoT architect](#contact-form) [See the packages](#packages) ![An IoT device module with a wireless chip on a workbench](https://dedicatted.com/wp-content/uploads/2026/09/iot-hero-esp32-board-1024x682.jpg) AWS IoT Delivery Partner Delivery Partner in three AWS IoT categories. - AWS IoT Delivery Partner Delivery Partner in three AWS IoT categories, alongside the Premier Tier - From $5,000 Discovery scoped and priced before any build; three packages up to a full foundation - 3 months To a fully operational AWS data lake for IoT telemetry in a recent delivery - SOC 2 + ISO 27001 Device identity, encryption and access under audited practices ## What are IoT platforms? - ### What is an IoT platform? An IoT platform is a software solution that connects, manages, and processes data from IoT devices, enabling communication, automation, and real-time analytics. - ### What are IoT platform services? IoT platform services include platform design, device integration, data processing, cloud connectivity, analytics setup, and ongoing platform management. - ### What does an IoT platform company do? An IoT platform company develops and manages IoT ecosystems, connects devices to cloud infrastructure, and enables data-driven automation and insights. ## Our offering Three fixed-scope packages. Each one de-risks the next, and you can stop after any of them. - ### Discovery Assess, scope and price the build Starting from $5,000 - ✓ Assessment of business and security requirements and existing IoT assets - ✓ Communication protocol and interface compatibility of your devices - ✓ Scope and design for the proof of concept: timeline, cost, success criteria [Get offer](#contact-form) - ### Proof of Concept Your devices on the platform, end to end Starting from $15,000 - ✓ Typical devices, controllers and sensors connected (2 to 4 device types) - ✓ Telemetry stored and processed within the PoC scope - ✓ Telemetry integration, visualization and analytics demonstrated [Get offer](#contact-form) - Most popular### IoT Foundation The production platform, built and handed over Starting from $40,000 - ✓ A cloud-native IoT solution, low-code or fully customized PaaS, fitted to your stack - ✓ Device provisioning and management at scale, edge included - ✓ Telemetry collection with hot, warm and cold paths for processing and transformation - ✓ Optional: a prototype of an intelligent edge solution - ✓ Optional: simulators of your devices, created or adopted [Get offer](#contact-form) ## IoT solutions we deliver 01 Predictive maintenance Sensor telemetry (vibration, temperature, current draw) streams into models that flag equipment wear before it becomes… 02 Smart manufacturing Every machine, line and shift connected: real-time OEE, quality control on live data and closed-loop optimization… 03 Fleet and asset tracking Location, condition and custody of every asset in real time, from vehicles and cargo to equipment across sites. 04 Remote monitoring and control Distributed installations (pumps, meters, chargers, towers) report status continuously and accept commands remotely. 05 Smart products and connected devices A hardware product becomes a connected service: provisioning at manufacturing scale, secure over-the-air updates, usage… 06 IoT data lake and analytics Device telemetry lands in a governed cloud data lake, catalogued, secured and queryable alongside your business data. ### Predictive maintenance ### Predictive maintenance Sensor telemetry (vibration, temperature, current draw) streams into models that flag equipment wear before it becomes downtime. Maintenance happens on the schedule you set, not on the machine's. #### What it does for you - ✓ Failures predicted ahead, not discovered - ✓ Maintenance windows planned around production - ✓ Spare-parts inventory driven by actual wear - ✓ Downtime cost visible per asset ### Smart manufacturing ### Smart manufacturing Every machine, line and shift connected: real-time OEE, quality control on live data and closed-loop optimization, turning plant-floor data into decisions across discrete and process industries. #### What it does for you - ✓ Shift-by-shift visibility into utilization, throughput and yield - ✓ Anomaly detection on every line - ✓ Less energy and material waste through closed-loop optimization - ✓ Plant-floor data joined with business context ### Fleet and asset tracking ### Fleet and asset tracking Location, condition and custody of every asset in real time, from vehicles and cargo to equipment across sites. Geofencing and condition alerts turn losses into notifications. #### What it does for you - ✓ Live location for every tagged asset - ✓ Temperature and shock condition monitoring - ✓ Geofence alerts on unexpected movement - ✓ Utilization data to right-size the fleet ### Remote monitoring and control ### Remote monitoring and control Distributed installations (pumps, meters, chargers, towers) report status continuously and accept commands remotely. Site visits become the exception, not the routine. #### What it does for you - ✓ 24/7 visibility across every remote site - ✓ Remote configuration and firmware updates - ✓ Alarm-driven dispatch instead of routine rounds - ✓ Compliance logging built into operations ### Smart products and connected devices ### Smart products and connected devices A hardware product becomes a connected service: provisioning at manufacturing scale, secure over-the-air updates, usage analytics, and the cloud APIs your app teams build on. #### What it does for you - ✓ Provisioning pipeline from factory to field - ✓ Secure over-the-air update infrastructure - ✓ Usage analytics feeding product decisions - ✓ APIs your mobile and web teams consume ### IoT data lake and analytics ### IoT data lake and analytics Device telemetry lands in a governed cloud data lake, catalogued, secured and queryable alongside your business data. The foundation for every dashboard, model and report downstream. #### What it does for you - ✓ All telemetry queryable in one governed lake - ✓ Real-time and historical analysis on one platform - ✓ Device data joined with business context - ✓ Ready for ML and AI workloads from day one ## From sensor to decision in five stages Every platform we build follows the same edge-to-cloud spine, hardened at each stage, so scale never means fragility. 1. Devices and edge FreeRTOS · Greengrass · Azure IoT Edge 2. 3. Connectivity MQTT · LoRaWAN · IoT Core / IoT Hub 4. 5. Streaming Kafka · Flink · Kinesis / Event Hubs 6. 7. Data lake Iceberg · S3 / ADLS · Athena / Synapse 8. 9. Insight and action Grafana · Power BI · SageMaker / Vertex AI What happens here Every device gets a unique certificate identity at provisioning, and latency-critical logic runs locally, so alarms and shutoffs still work with the network down and data is buffered through the gaps. The failure it prevents A fleet you cannot trust or update: spoofed devices, bricked firmware rollouts, and safety logic that dies with the connection. What happens here Devices authenticate with mutual TLS and publish over MQTT, and the broker absorbs millions of concurrent connections while applying per-device permissions. The failure it prevents The classic IoT breach: a flat network where any device, or anyone impersonating one, sees the whole fleet. What happens here Telemetry is routed, filtered and enriched in motion: threshold alerts fire in seconds, malformed readings are quarantined, and each consumer gets only the stream it needs. The failure it prevents Hearing about the failure at end of shift, because the data sat in a batch queue while the machine burned. What happens here Telemetry lands catalogued, partitioned and governed, queryable with SQL alongside your business data, with retention and access rules enforced automatically. The failure it prevents A swamp of raw JSON nobody can query: years of sensor history that costs money to store and returns nothing. What happens here Dashboards for the floor, alerts for on-call, models that predict failures before thresholds trip, and automated responses closing the loop back to the devices. The failure it prevents A platform that only reports: people staring at dashboards while decisions that could be automatic wait for a meeting. ## How are IoT platforms implemented? - ### How do companies implement IoT platforms? Companies implement IoT platforms by connecting devices, setting up communication protocols, integrating cloud services, and building data processing and analytics pipelines. - ### What is the IoT platform implementation process? The process includes requirements analysis, platform architecture design, device integration, data pipeline setup, testing, deployment, and continuous monitoring. - ### How long does it take to implement an IoT platform? Implementation timelines range from a few weeks to several months depending on device complexity, scale, and integration requirements. ## How an engagement runs Discovery, implementation, evolution: each step maps to a package, so you know the scope and the price before it starts. 1. 1 From $5,000### Discovery - ✓ Learn and investigate: requirements, security, existing assets - ✓ Evaluate and define: protocols and device compatibility - ✓ Design and plan: PoC scope, timeline, cost, success criteria 2. 2 From $15,000### Proof of concept - ✓ 2 to 4 device types connected to the platform - ✓ Telemetry stored and processed within the PoC scope - ✓ Integration, visualization and analytics demonstrated 3. 3 From $40,000### Foundation - ✓ MVP built and tested on your cloud account - ✓ Provisioning and management at scale, edge included - ✓ Launch and scale: hot, warm and cold data paths 4. 4 Managed Services### Operate and optimize - ✓ 24/7 monitoring and incident response - ✓ Fleet, firmware and cost management - ✓ New device types onboarded without a project [Start with Discovery](#packages) ## What are the benefits of IoT platforms? - ### Why use IoT platform services? IoT platform services help businesses collect and analyze real-time data, automate operations, improve efficiency, and enable smart decision-making. - ### What are the key benefits of IoT platforms? Key benefits include real-time monitoring, scalability, improved operational efficiency, predictive maintenance, and better data visibility. - ### How do IoT platforms improve business performance? IoT platforms improve performance by enabling data-driven insights, reducing downtime, optimizing processes, and supporting automation at scale. [ Featured case study ↗ ### Building scalable data architecture for IoT devices A fully operational AWS data lake within three months for a global healthcare analytics company: device telemetry streaming into governed analytics on AFT-managed landing zones. IoT Data architecture AWS ![Close-up of a person adjusting a smartwatch, representing wearable IoT devices used in clinical data collection.](https://dedicatted.com/wp-content/uploads/2025/03/VivoSense-Cover.webp) ](https://dedicatted.com/insights/building-scalable-data-architecture-for-iot-with-aft-managed-landing-zones)## The stack behind the platform Device and edge Streaming Storage and analytics ML and operations - ![aws iot core](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws-iot-core.svg) AWS IoT Core - ![iot greengrass](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/iot-greengrass.svg) IoT Greengrass - ![mqtt](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mqtt.svg) MQTT - ![docker](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/docker.svg) Docker - ![amazon kinesis](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-kinesis.svg) Amazon Kinesis - ![kafka](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kafka.svg) Kafka - ![apache flink](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/apache-flink.svg) Apache Flink - ![lambda](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/lambda.svg) Lambda - ![amazon s3](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-s3.svg) Amazon S3 - ![aws glue](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws-glue.svg) AWS Glue - ![athena](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/athena.svg) Athena - ![timestream](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/timestream.svg) Timestream - ![quicksight](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/quicksight.svg) QuickSight - ![sagemaker](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sagemaker.svg) SageMaker - ![grafana](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/grafana.svg) Grafana - ![terraform](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/terraform.svg) Terraform - ![kubernetes](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kubernetes.svg) Kubernetes Want to know how we would solve your challenge? Tell us what your devices should report and what decisions the data should drive. An IoT architect replies within one business day with relevant experience and a first read. [ Book a free meeting ↗ ](#contact-form) [## Insights ](https://dedicatted.com/insights) [Case study ### OMO Systems: Sub-300ms Device Control for Smart Buildings on AWS IoT Real Estate Cloud and Infrastructure Security Cloud Modernization DevOps Consulting IoT Platform Managed Services ![An IoT device module with a wireless chip on a workbench](https://dedicatted.com/wp-content/uploads/2026/09/iot-hero-esp32-board-1024x682.jpg) About project Challenges Two seconds means very different things depending on which device is at the other end. On a thermostat it is an annoyance a user forgives. On a lock at a door it is a guest in a corridor pressing the button again because nothing happened. On a valve under a detected leak \[…\] ](https://dedicatted.com/insights/smart-building-aws-iot) [Case study ### Keyless Guest Entry and AI Concierge for Short-Term Rentals High Tech Software Cloud Modernization IoT Platform Managed Services ![SmartphoneKey’s App-Free Operation lets you access your property simply and securely, without the need to download or manage a mobile app. Just use your SmartphoneKey key card or your smartphone’s digital wallet, and you’re in—no apps, no updates, no fuss.](https://dedicatted.com/wp-content/uploads/2025/10/Designing-Platform-for-Instant-Market-Penetration-86.png) How Dedicatted built SmartphoneKey's keyless entry and Amazon Bedrock AI concierge for short-term rentals: 45% fewer service visits, 50% lower deployment cost, ROI in 18 months. ](https://dedicatted.com/insights/smart-access-control-for-multifamily-and-short-term-rentals) [Blog ### Data lake vs data warehouse: Which one to choose for your business? Financial Services High Tech Software Insurance Retail Data Architecture IoT Platform ![Integration of databases for system backup](https://dedicatted.com/wp-content/uploads/2025/06/Data-Lake-vs-Dat-Warehouse.-Cover-1-e1751554039626-1024x613.jpg) Data lake vs data warehouse: comparison raw vs structured storage, analytics, cost, use cases to choose the optimal data strategy for your business. ](https://dedicatted.com/insights/data-lake-vs-data-warehouse-which-one-to-choose-for-your-business) [Blog ### The Intelligent Edge: Where IoT meets Machine Learning Automotive Financial Services Healthcare Manufacturing Generative AI IoT Platform ![Industrial engineer using tablet to manage IoT devices in a smart factory with robotics and automation – Dedicatted intelligent edge services.](https://dedicatted.com/wp-content/uploads/2025/03/IoT-Cover-2-1024x537.webp) Discover how IoT devices and machine learning create business opportunities across manufacturing, urban planning, and healthcare sectors. ](https://dedicatted.com/insights/the-intelligent-edge-where-iot-meets-machine-learning) ## FAQ ### How many devices can the platform handle? AWS IoT Core scales to millions of concurrent connections. The architecture we build is the same for 100 devices and 100,000; only the streaming and storage tiers are resized. A pilot starts small without re-platforming later. ### What about devices already deployed in the field? Existing fleets connect through protocol bridges or gateway devices, no forklift replacement. Where a device cannot hold a certificate, a gateway handles identity and security on its behalf. ### How is device data secured? Every device gets a unique certificate identity; data is encrypted in transit and at rest; access follows least-privilege policies. Our practices are SOC 2 Type 2 and ISO 27001 audited, which matters when telemetry touches regulated environments. ### Can the platform work with intermittent connectivity? Yes. Edge components buffer data locally and sync when the connection returns, and critical logic such as alarms and shutoffs runs at the edge, so it works fully offline. ### How long does a first deployment take? A recent engagement delivered a fully operational AWS data lake for IoT telemetry within three months. The Proof of Concept package connects 2 to 4 device types and demonstrates the pipeline before any scale-out; Discovery fixes its scope and price first. ### What technologies are used in IoT platforms? Sensors and gateways at the edge, MQTT and cellular or LoRaWAN connectivity, AWS IoT Core and Greengrass, streaming with Kinesis or Kafka, a data lake on S3 with Glue and Athena, and dashboards and models on QuickSight, Grafana and SageMaker. The set is chosen to fit your devices, not a fixed toolkit. ### Are IoT platforms scalable? Yes. The platform is designed for large fleets and high data volumes from the start: managed ingestion, streaming that scales with load, and storage tiers that grow with usage instead of hardware purchases. ### How secure are IoT platforms? Encryption, certificate-based authentication and access control protect data and devices from unauthorized access, with audit logging built into operations. Security is designed into each stage of the platform, not added after. Question not answered? [Ask an IoT architect](#contact-form). Same-day reply on feasibility questions. ## IoT Platforms for Smarter Business Operations Modern businesses rely on connected devices, real-time analytics, and automated infrastructure to improve operational efficiency and accelerate digital transformation. However, fragmented IoT ecosystems, outdated infrastructure, and disconnected data streams often create security risks, operational delays, and scalability challenges. At [Dedicatted](https://dedicatted.com/insights?category=case-study), we build scalable IoT platforms that connect devices, applications, and cloud infrastructure into secure, high-performance ecosystems designed for modern business operations. Our IoT engineering and managed cloud services help organizations improve visibility, automate workflows, and gain real-time insights from connected environments. ## End-to-End IoT Platform Services We deliver cloud-native IoT solutions for industries including manufacturing, logistics, healthcare, retail, and smart infrastructure. **Our IoT platform services include:** - IoT architecture design - Device integration and management - Real-time data processing - Cloud-native IoT infrastructure - IoT security and compliance - Edge computing implementation - Monitoring and observability - Infrastructure automation Using scalable cloud environments powered by Amazon Web Services, [Microsoft Azure](https://dedicatted.com/our-partners/microsoft), and [Google Cloud Platform](https://dedicatted.com/our-partners/google-cloud), we help businesses manage large-scale connected ecosystems securely and efficiently. ## Real-Time Insights & Operational Efficiency Modern IoT platforms allow businesses to monitor operations continuously and respond faster to changing conditions. **Our IoT solutions help organizations:** - Reduce operational downtime - Improve asset tracking and monitoring - Automate business workflows - Optimize infrastructure performance - Improve predictive maintenance - Strengthen operational visibility - Lower infrastructure and maintenance costs Real-time analytics and cloud-native scalability help businesses make faster, data-driven decisions across connected environments. ## Secure & Scalable IoT Infrastructure IoT environments require strong security, high availability, and scalable architecture. **Dedicatted implements:** - Secure device communication - Cloud infrastructure automation - Kubernetes and containerization - Continuous monitoring - Disaster recovery planning - Compliance-ready infrastructure Our engineers design IoT ecosystems capable of supporting millions of connected events while maintaining performance, reliability, and data security. ### Why Businesses Choose Dedicatted [Dedicatted](https://dedicatted.com) combines cloud engineering, DevOps expertise, and IoT platform development to help organizations build future-ready connected systems. We help businesses modernize operations with scalable IoT infrastructure, cloud-native architecture, and continuous optimization designed for long-term digital growth. Show more ## Get started with an IoT consultant Outline your device, connectivity or platform challenge. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Services:** IoT Platform --- ### [Cloud Data Migration & Modernization](https://dedicatted.com/services/data/cloud-data-migration) **Published:** April 3, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. Cloud Data Migration & Modernization Data & Analytics Services · AWS, Azure, Google Cloud # Cloud Data Migration & Modernization Warehouses, pipelines, reports and databases moved to the cloud in fixed-scope phases, priced before you commit. Cloud Data Migration & Modernization helps businesses move outdated data systems to scalable, cloud-native environments designed for modern operations, analytics, and long-term growth. Legacy databases and fragmented on-prem infrastructure slow innovation, raise maintenance costs and limit access to critical insights. [See the offers](#managed-service-packages) [Check your stack](#capabilities) ![Blade servers in a data center rack](https://dedicatted.com/wp-content/uploads/2025/04/cdm-hero-blade-servers-1024x682.jpg) Assessment from $10,000 Scope and price fixed before any migration work begins. - From $10,000 Fixed-scope assessment; the price is set before any migration work - 3 clouds, 4 layers AWS, Azure and Google Cloud; warehouse, pipelines, BI and databases - SOC 2 + ISO 27001 AWS Premier Tier, held and renewed for multiple consecutive years - 1 business day Response with relevant migration experience included ## What is cloud data migration? - ### What is cloud data migration? Cloud data migration is the process of transferring data from on-premises systems, legacy databases, or other environments to cloud platforms such as AWS, Azure, or Google Cloud. - ### What are cloud data migration services? Cloud data migration services include data assessment, migration planning, data transfer, validation, database migration, and post-migration optimization in cloud environments. - ### What does a cloud data migration company do? A cloud data migration company provides consulting, develops migration strategies, ensures secure data transfer, and optimizes data storage and performance in the cloud. ## Our offering Three fixed-scope entry points. Each one de-risks the next, and you can stop after any of them. Start small, prove it, scale The assessment feeds the design; the proof of concept proves the design; the pilot migrates a real workload. No big-bang commitment. - ![SOC 2 Type 2](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/svg/soc2-badge.svg) - ![ISO 27001](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/svg/iso27001-badge.svg) SOC 2 Type 2 and ISO 27001, held and renewed for multiple consecutive years. ### Smart Assessment From $10,000 Infrastructure and data audit: - ✓Automated inventory of warehouse, ETL and BI objects - ✓Dependency analysis across reports, pipelines and schemas - ✓Migration feasibility, risk and cost evaluation - ✓Compliance and security check [Get offer](#contact-form) Most popular### Migration Design & PoC From $25,000 Detailed migration roadmap: - ✓Target architecture on AWS, Azure or Google Cloud - ✓Proof of concept on selected workloads - ✓Performance and compatibility testing - ✓Security and access planning [Get offer](#contact-form) ### Migration Pilot From $80,000 Workload migration in phases: - ✓Pilot migration of a production workload - ✓Pipelines converted to cloud-native processing - ✓Cost and performance optimization - ✓Post-migration monitoring and support [Get offer](#contact-form) Smart Assessment From $10,000 Migration Design & PoC From $25,000 Migration Pilot From $80,000 Automated discovery of your data estate ✓ ✓ ✓ Dependency mapping across reports, ETL pipelines and warehouse objects ✓ ✓ ✓ Target architecture and migration roadmap ✖ ✓ ✓ Proof of concept on selected workloads ✖ ✓ ✖ Pilot migration of a production workload ✖ ✖ ✓ Team 3 engineers 4 engineers 8 engineers Starting points; the exact scope and price are fixed in the assessment before any migration work begins. ## Our approach Your vision, our framework: each phase pairs what you want with how we deliver it, and what you walk away with. - 01### Assessment & planning Learn · Plan We inventory every object, dependency and pipeline in the data platform with assessment tooling, then scope the migration and modernization with Dedicatted's framework: what moves as is, what is rebuilt, what retires. - ✓ Strategy - ✓ Processes - 02### Migration execution Validate · Execute The scope is proved on a proof of concept first, then executed in phases with parallel runs: row counts, checksums and reconciliation reports before anything is decommissioned. - ✓ Handbooks - ✓ Delivery teams - 03### Cutover & support Support · Improve Cost optimization is designed in from the first phase, not added after. After cutover your team is trained to run the platform, with monitoring and support through the first months. - ✓ Best practices - ✓ Training programs ## Everything that moves, moves together A data platform is more than a database. We migrate and modernize all five layers in step, so nothing breaks the day after cutover. 1. Data warehouse Schemas, history and workloads move to a cloud warehouse 2. Applications Connections repointed and tested against the new platform 3. Databases Licensed and open-source engines re-platformed or upgraded 4. Reports and BI Dashboards rebuilt or repointed, no analytics blackout 5. Data pipelines ETL jobs converted to cloud-native processing ## How is cloud data migration implemented? - ### How do companies migrate data to the cloud? Companies migrate data by analyzing existing data sources, selecting appropriate migration tools, preparing data for transfer, and executing migration with minimal downtime. - ### What is the cloud data migration process? The process includes data discovery, assessment, migration strategy selection, data cleansing, transfer, validation, and continuous monitoring. - ### How long does cloud data migration take? Cloud data migration timelines vary from a few days to several weeks depending on data volume, complexity, and system integration requirements. ## What are the benefits of cloud data migration? - ### Why use cloud data migration services? Cloud data migration services help improve data accessibility, enhance scalability, reduce infrastructure costs, and support advanced analytics and cloud-native applications. - ### What are the key benefits of cloud data migration? Key benefits include improved data availability, better performance, enhanced security, cost efficiency, and easier integration with modern cloud services. - ### How does cloud data migration improve business performance? Cloud data migration improves performance by enabling real-time data access, supporting data-driven decision-making, and increasing operational efficiency. ## Migration technology capabilities Pick your target cloud. Every on-prem layer on the left has a proven landing zone on the right. AWS Azure Google Cloud Layer On-prem source ![aws](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws.svg) AWS target Data warehouse Warehouse and lakehouse ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) SQL Server ![oracle](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/oracle.svg) Oracle ![teradata](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/teradata.svg) Teradata ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) Netezza ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) DB2 ![hive](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/hive.svg) Hadoop / Hive ![sap](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sap.svg) SAP BW V Vertica → ![snowflake](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/snowflake.svg) Snowflake ![databricks](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/databricks.svg) Databricks ![amazon redshift](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-redshift.svg) Amazon Redshift ETL / pipelines Batch and ELT I Informatica ![talend](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/talend.svg) Talend ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) SSIS ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) IBM DataStage P Pentaho A Ab Initio ![alteryx](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/alteryx.svg) Alteryx ![sas](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sas.svg) SAS → ![aws glue](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws-glue.svg) AWS Glue ![databricks](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/databricks.svg) Databricks ![dbt](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/dbt.svg) dbt ![airflow](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/airflow.svg) Airflow Reporting & BI Reports and dashboards ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) SSRS / SSAS ![microstrategy](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/microstrategy.svg) MicroStrategy ![qlik](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/qlik.svg) Qlik ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) Cognos ![sap](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sap.svg) SAP BusinessObjects ![sisense](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sisense.svg) Sisense → ![quicksight](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/quicksight.svg) QuickSight ![tableau](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/tableau.svg) Tableau ![power bi](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/power-bi.svg) Power BI Databases Licensed and open-source ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) SQL Server ![oracle](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/oracle.svg) Oracle ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) DB2 ![sybase](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sybase.svg) Sybase ![mysql](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mysql.svg) MySQL ![postgresql](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/postgresql.svg) PostgreSQL ![mongodb](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mongodb.svg) MongoDB ![cassandra](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/cassandra.svg) Cassandra ![hbase](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/hbase.svg) HBase → ![amazon aurora](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-aurora.svg) Amazon Aurora ![amazon rds](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-rds.svg) Amazon RDS ![dynamodb](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/dynamodb.svg) DynamoDB Streaming Events and messaging ![kafka](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kafka.svg) Kafka ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) IBM MQ ![tibco](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/tibco.svg) TIBCO ![rabbitmq](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/rabbitmq.svg) RabbitMQ → ![amazon msk](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-msk.svg) Amazon MSK ![amazon kinesis](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-kinesis.svg) Kinesis ![spark](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/spark.svg) Spark Tooling on AWSAWS DMS and SCT for databases, AWS Glue and dbt for pipelines, SnowConvert AI for Snowflake targets Layer On-prem source ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) Azure target Data warehouse Warehouse and lakehouse ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) SQL Server ![oracle](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/oracle.svg) Oracle ![teradata](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/teradata.svg) Teradata ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) Netezza ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) DB2 ![hive](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/hive.svg) Hadoop / Hive ![sap](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sap.svg) SAP BW V Vertica → ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) Microsoft Fabric ![snowflake](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/snowflake.svg) Snowflake ![databricks](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/databricks.svg) Databricks ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) Azure Synapse ETL / pipelines Batch and ELT I Informatica ![talend](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/talend.svg) Talend ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) SSIS ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) IBM DataStage P Pentaho A Ab Initio ![alteryx](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/alteryx.svg) Alteryx ![sas](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sas.svg) SAS → ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) Azure Data Factory ![databricks](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/databricks.svg) Databricks ![dbt](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/dbt.svg) dbt ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) Fabric Data Factory Reporting & BI Reports and dashboards ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) SSRS / SSAS ![microstrategy](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/microstrategy.svg) MicroStrategy ![qlik](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/qlik.svg) Qlik ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) Cognos ![sap](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sap.svg) SAP BusinessObjects ![sisense](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sisense.svg) Sisense → ![power bi](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/power-bi.svg) Power BI ![tableau](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/tableau.svg) Tableau Databases Licensed and open-source ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) SQL Server ![oracle](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/oracle.svg) Oracle ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) DB2 ![sybase](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sybase.svg) Sybase ![mysql](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mysql.svg) MySQL ![postgresql](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/postgresql.svg) PostgreSQL ![mongodb](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mongodb.svg) MongoDB ![cassandra](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/cassandra.svg) Cassandra ![hbase](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/hbase.svg) HBase → ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) Azure SQL Managed Instance ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) Azure Database for PostgreSQL ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) Cosmos DB Streaming Events and messaging ![kafka](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kafka.svg) Kafka ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) IBM MQ ![tibco](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/tibco.svg) TIBCO ![rabbitmq](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/rabbitmq.svg) RabbitMQ → ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) Event Hubs ![ms azure](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-azure.svg) Fabric Real-Time ![spark](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/spark.svg) Spark Tooling on AzureAzure Database Migration Service, Data Factory and dbt; SnowConvert AI for Snowflake targets Layer On-prem source ![gcp](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/gcp.svg) Google Cloud target Data warehouse Warehouse and lakehouse ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) SQL Server ![oracle](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/oracle.svg) Oracle ![teradata](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/teradata.svg) Teradata ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) Netezza ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) DB2 ![hive](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/hive.svg) Hadoop / Hive ![sap](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sap.svg) SAP BW V Vertica → ![bigquery](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/bigquery.svg) BigQuery ![snowflake](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/snowflake.svg) Snowflake ![databricks](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/databricks.svg) Databricks ETL / pipelines Batch and ELT I Informatica ![talend](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/talend.svg) Talend ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) SSIS ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) IBM DataStage P Pentaho A Ab Initio ![alteryx](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/alteryx.svg) Alteryx ![sas](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sas.svg) SAS → ![gcp](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/gcp.svg) Dataflow ![airflow](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/airflow.svg) Cloud Composer ![dbt](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/dbt.svg) dbt ![databricks](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/databricks.svg) Databricks Reporting & BI Reports and dashboards ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) SSRS / SSAS ![microstrategy](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/microstrategy.svg) MicroStrategy ![qlik](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/qlik.svg) Qlik ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) Cognos ![sap](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sap.svg) SAP BusinessObjects ![sisense](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sisense.svg) Sisense → ![looker](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/looker.svg) Looker ![tableau](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/tableau.svg) Tableau ![power bi](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/power-bi.svg) Power BI Databases Licensed and open-source ![ms sql server](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ms-sql-server.svg) SQL Server ![oracle](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/oracle.svg) Oracle ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) DB2 ![sybase](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/sybase.svg) Sybase ![mysql](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mysql.svg) MySQL ![postgresql](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/postgresql.svg) PostgreSQL ![mongodb](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mongodb.svg) MongoDB ![cassandra](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/cassandra.svg) Cassandra ![hbase](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/hbase.svg) HBase → ![gcp](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/gcp.svg) Cloud SQL ![gcp](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/gcp.svg) AlloyDB ![spanner](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/spanner.svg) Cloud Spanner Streaming Events and messaging ![kafka](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kafka.svg) Kafka ![ibm](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ibm.svg) IBM MQ ![tibco](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/tibco.svg) TIBCO ![rabbitmq](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/rabbitmq.svg) RabbitMQ → ![gcp](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/gcp.svg) Pub/Sub ![gcp](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/gcp.svg) Dataflow ![spark](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/spark.svg) Spark Tooling on Google CloudGoogle Database Migration Service, Dataflow and dbt; BigQuery Migration Service for warehouses Source stacks shown are the most common. If yours is not listed it is almost certainly still migratable. [Ask us](#contact-form). Your stack is not on the list? Send the source and the target you have in mind. A data migration consultant replies within one business day with the landing zone and the first risks. [ Ask a consultant ↗ ](#contact-form) [## Insights ](https://dedicatted.com/insights) [Blog ### The 2026 Enterprise Data Readiness Benchmark: Your Foundation for Successful AI Insurance Retail Cloud Data Migration & Modernization Data Architecture Generative AI ![Abstract digital data corridor with blue light trails and glowing particles suggesting a high-tech network edge-to-edge.](https://dedicatted.com/wp-content/uploads/2026/08/Designing-Platform-for-Instant-Market-Penetration-1.png) Only 7% of enterprises have the data foundation AI needs to scale. See what separates them: real case studies, a maturity framework, next steps ](https://dedicatted.com/insights/the-2026-enterprise-data-readiness-benchmark-your-foundation-for-successful-ai) [Blog ### Dedicatted Signs Strategic Collaboration Agreement with AWS to help organizations take Generative AI from Pilot to Production Cloud Data Migration & Modernization Cloud Modernization ![Two people shaking hands in a blurred business setting, signaling a deal or partnership.](https://dedicatted.com/wp-content/uploads/2026/08/agree-2.png) Dedicatted and AWS sign a strategic collaboration agreement to help organizations move generative AI from pilot to production with AWS technology and expertise ](https://dedicatted.com/insights/dedicatted-signs-strategic-collaboration-agreement-with-aws-to-help-organizations-take-generative-ai-from-pilot-to-production) [Blog ### Data Lake Migration to the Cloud: What Our Experts at Dedicatted Are Telling Clients Right Now Automotive Healthcare Retail Cloud Data Migration & Modernization Cloud Modernization Data Architecture ![Rows of server racks in a data center with purple-lit screens displaying code, extending into the distance.](https://dedicatted.com/wp-content/uploads/2026/07/Lake-Migration.png) Data lake migration to the cloud, explained by Dedicatted's experts: real cost savings, risk management, and a proven 5-phase process. ](https://dedicatted.com/insights/data-lake-migration-to-the-cloud-what-our-experts-at-dedicatted-are-telling-clients-right-now) [Blog ### Dedicatted Achieves Canadian AWS Premier Partner Status: What it Means Cloud Data Migration & Modernization Cloud Migration Cloud Modernization ![AWS Partner Premier Tier Services badge centered on a dark blue background.](https://dedicatted.com/wp-content/uploads/2026/07/look.png) Dedicatted is now AWS Premier Tier certified. See what that actually requires, how it differs from Advanced tier, and how to evaluate any partner's badge. ](https://dedicatted.com/insights/dedicatted-achieves-canadian-aws-premier-partner-status-what-it-means) ## FAQ ### What tools are used for cloud data migration? Cloud-native services (AWS DMS and SCT, Azure Data Factory and Database Migration Service, Google Database Migration Service) with automated assessment tooling for dependency analysis, plus Databricks, Snowflake and dbt for modernization. The set is chosen to fit your source stack, not a fixed toolkit. ### What types of data can be migrated to the cloud? Structured databases, data warehouses, data lakes, semi-structured and unstructured files, BI models and the ETL pipelines that connect them, including licensed engines such as Oracle and SQL Server and open-source stores such as PostgreSQL and MongoDB. ### Is cloud data migration secure? Data is encrypted in transit and at rest, access is role-based, and migrations run inside your cloud account under your controls. Our practices are SOC 2 Type 2 and ISO 27001 audited. ### What challenges are associated with cloud data migration? Undocumented dependencies, schema drift, downtime windows and cost surprises. The assessment exists to surface these before commitment, which is why we never start with a big-bang migration. ### How is data integrity ensured during migration? Row counts, checksums and reconciliation reports validate every dataset; parallel-run periods compare old and new outputs on live workloads before anything is decommissioned. ### Can large-scale data be migrated without downtime? In most cases yes: change-data-capture replication keeps source and target in sync while workloads move in phases, and the final cutover takes minutes rather than days. Where a window is unavoidable, it is scheduled and rehearsed. ### What does a cloud data migration cost? The assessment is fixed at $10,000, the design and proof of concept from $25,000, the pilot from $80,000. Each phase fixes the scope and price of the next, so there is no open-ended estimate. [See the offers](#managed-service-packages). Question not answered? [Ask a data migration consultant](#contact-form). Same-day reply on feasibility questions. ## Cloud Data Migration & Modernization Services Legacy data systems slow business growth, increase infrastructure costs, and limit access to critical insights. Many organizations still rely on outdated databases, fragmented storage environments, and on-prem infrastructure that cannot support modern analytics, AI workloads, or scalable cloud applications. At [Dedicatted](https://dedicatted.com), we help businesses modernize legacy data infrastructure and migrate workloads to secure, scalable cloud-native platforms. Our cloud data migration services improve performance, reduce operational complexity, and prepare organizations for long-term digital transformation. Modern cloud data migration is not just about transferring information from one environment to another. It is about building flexible, high-performance systems that support faster decision-making, automation, and continuous innovation. ## Cloud Data Migration Services Our engineers help organizations migrate, optimize, and modernize data environments across leading cloud platforms, including: - Amazon Web Services - Microsoft Azure - Google Cloud Platform We provide: - Legacy database migration - Cloud-native data architecture - Data warehouse modernization - Real-time data pipeline implementation - Hybrid and multi-cloud migration - Cloud storage optimization - Data security and compliance - Infrastructure automation Cloud migration frameworks help organizations reduce downtime, improve scalability, and modernize business-critical systems securely. ## Modernize Legacy Data Infrastructure Outdated infrastructure often creates operational bottlenecks and technical debt. Our cloud-native modernization services help businesses: - Eliminate data silos - Improve system performance - Increase infrastructure scalability - Reduce maintenance costs - Strengthen disaster recovery - Enable advanced analytics and AI initiatives We modernize monolithic systems using cloud-native architectures, Kubernetes, Infrastructure as Code (IaC), and [automated DevOps workflows](https://dedicatted.com/what-we-do/devops/devops-as-a-service) to improve long-term operational efficiency. Modern cloud-native environments also improve resilience, observability, and deployment flexibility for enterprise applications. ## Secure & Scalable Cloud Migration Data security and operational continuity are critical during [cloud migration](https://dedicatted.com/services/cloud-devops/cloud-migration) projects. Our migration process includes: 1. Infrastructure and dependency assessment 2. Migration strategy and architecture planning 3. Automated migration and validation 4. Performance optimization and monitoring 5. Continuous cloud support and cost optimization We use automation, CI/CD practices, and cloud observability tools to minimize operational disruption and improve migration reliability. Modern cloud migration platforms increasingly rely on automation and continuous monitoring to reduce risk and improve scalability. ### Why Businesses Choose Dedicatted Organizations choose Dedicatted for cloud data migration and modernization because we combine cloud engineering expertise, DevOps automation, and enterprise infrastructure modernization in one service model. Our team delivers: - Cloud-native architecture expertise - Enterprise migration strategy - DevOps and Kubernetes implementation - Multi-cloud optimization - Infrastructure automation - Continuous cloud support We help businesses transform outdated data systems into scalable cloud platforms built for analytics, [AI](https://dedicatted.com/services/generative-ai), automation, and future growth. Show more ## Get started with a data migration consultant Tell us what data you are moving and where it is stuck. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Services:** Cloud Data Migration & Modernization, Cloud Migration --- ### [Data Architecture](https://dedicatted.com/services/data/architecture) **Published:** April 1, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. Data Architecture Data architecture & engineering services · AWS, Azure, Google Cloud # Data Architecture Design the foundation once, stop paying for rework. Data architecture is the foundation that determines how data is collected, stored, organized, integrated and accessed across an organization. Scattered systems and poorly structured databases slow reporting and make decisions harder. A strong architecture keeps data accurate, accessible, secure and scalable across every platform and workflow. [Talk to a data architect](#contact-form) [See the accelerators](#accelerators) ![A tower server in a blue-lit server room](https://dedicatted.com/wp-content/uploads/2026/09/da-hero-tower-server-1024x682.jpg) Lakehouse-first method Object storage with Redshift, Snowflake or Databricks on top. - 2-day snapshot Your current-state architecture visualized, with next steps, in two days - AWS Premier Tier Partnered with AWS, Microsoft Azure and Google Cloud - SOC 2 + ISO 27001 HIPAA, PCI and GDPR zones designed into the architecture, not bolted on - 1 business day Reply with relevant experience and a first read on your problem ## What is data architecture? - ### What is data architecture? Data architecture is the design and organization of data systems, including how data is collected, stored, processed, and accessed across an organization. - ### What are data architecture services? Data architecture services include data modeling, data platform design, data integration, storage solutions, governance frameworks, and cloud data architecture planning. - ### What does a data architecture company do? A data architecture company designs scalable data systems, defines data strategies, ensures data quality, and enables efficient data flow across business applications. ## What we design and build 01 Data platform architecture The blueprint for your entire data estate: where data lives, how it moves and which teams access what, designed against… 02 Lakehouse design and build Our lakehouse-first method combines affordable object storage with high-performance query engines, Redshift, Snowflake… 03 Streaming and CDC pipelines Change-data-capture pipelines built with Debezium and Kafka stream every insert, update and delete from operational… 04 Data catalog and lineage Every table catalogued in Glue or Hive Metastore, every transformation traceable. 05 Governance, security and compliance Access patterns, encryption, retention and audit trails designed into the architecture, so HIPAA, PCI and GDPR reviews… 06 Infrastructure as code and DataOps The whole platform, storage, compute, pipelines and permissions, lives in version-controlled code with automated tests… ### Data platform architecture ### Data platform architecture The blueprint for your entire data estate: where data lives, how it moves and which teams access what, designed against your workloads, compliance needs and budget rather than a vendor's reference diagram. #### What you get - ✓ Target architecture with lineage diagrams - ✓ Security zones mapped to HIPAA, PCI and GDPR - ✓ Cost model per workload before you build - ✓ A phased build roadmap your team can execute ### Lakehouse design and build ### Lakehouse design and build Our lakehouse-first method combines affordable object storage with high-performance query engines, Redshift, Snowflake or Databricks, so one platform serves analysts, data scientists and applications alike. #### What you get - ✓ Object storage layer with open table formats - ✓ Query engine sized and tuned to your workloads - ✓ Batch and real-time on the same platform - ✓ No more warehouse-versus-lake duplicate stacks ### Streaming and CDC pipelines ### Streaming and CDC pipelines Change-data-capture pipelines built with Debezium and Kafka stream every insert, update and delete from operational databases into the platform, so analytics reflects the business as of now, not as of last night. #### What you get - ✓ CDC from operational databases without load spikes - ✓ Sub-minute data freshness for dashboards - ✓ Replayable streams for recovery and backfill - ✓ Schema-evolution tooling that survives changes ### Data catalog and lineage ### Data catalog and lineage Every table catalogued in Glue or Hive Metastore, every transformation traceable. When a number looks wrong on a dashboard, you trace it to the source in minutes, not in a week of chat archaeology. #### What you get - ✓ Central catalog with ownership and definitions - ✓ Column-level lineage across pipelines - ✓ Impact analysis before schema changes - ✓ Self-service discovery for analysts ### Governance, security and compliance ### Governance, security and compliance Access patterns, encryption, retention and audit trails designed into the architecture, so HIPAA, PCI and GDPR reviews find a system built for them, not around them. #### What you get - ✓ Role- and attribute-based access controls - ✓ Security zones separating regulated data - ✓ Audit-ready logging and retention policies - ✓ Anonymization and masking where required ### Infrastructure as code and DataOps ### Infrastructure as code and DataOps The whole platform, storage, compute, pipelines and permissions, lives in version-controlled code with automated tests and blue-green deployments, so changes ship safely and environments never drift. #### What you get - ✓ Terraform-managed platform, reviewable in Git - ✓ CI/CD for pipelines with automated testing - ✓ Blue-green cutovers with automated rollback - ✓ Identical dev, staging and production ## Three ways to modernize, one assessment decides Every workload gets one of three treatments. The assessment picks which, not a default habit. 1. Lift and shift Fastest. Data and applications move as they are, no changes: the quickest path to the cloud and often the most expensive to run long term. Right for deadline-driven data centre exits and low-change legacy systems. 2. Re-platform Balanced. Targeted changes before the move, managed-service swaps and compatibility fixes: slower than a lift, cheaper every month after. Right for databases and ETL moving to managed cloud equivalents. 3. Re-architect Biggest payoff. A full redesign for cloud-native performance and scale, the option where the lakehouse pays for itself. Right for analytics platforms, AI workloads and systems at their limits. ## How is data architecture implemented? - ### How do companies implement data architecture? Companies implement data architecture by analyzing business requirements, designing data models, selecting technologies, and building scalable data pipelines and storage systems. - ### What is the data architecture process? The process includes data assessment, architecture design, data modeling, technology selection, implementation, and ongoing optimization and governance. - ### How long does it take to implement data architecture? Implementation timelines vary from a few weeks to several months depending on data complexity, system scale, and integration needs. ## How an engagement runs The first two steps are the accelerators below; the rest is scoped from the roadmap they produce. 1. 1 2 days### Visualize the current state - ✓ Problem framing and event storming - ✓ C1 view of the systems and integration points - ✓ Clear next steps for the modernization plan 2. 2 4 weeks### Modernization roadmap - ✓ User journeys and C1 / C2 diagrams - ✓ Bounded contexts and target-state options - ✓ A draft roadmap ready for action 3. 3 Scoped from the roadmap### Design and build as code - ✓ Target architecture in Terraform, reviewable in Git - ✓ CDC pipelines where downtime is not an option - ✓ Lift, re-platform or re-architect per workload 4. 4 Per workload### Validate and cut over - ✓ Row counts, checksums and parallel runs - ✓ Blue-green cutover with a rehearsed rollback - ✓ Your team trained to run the platform [Start with the two-day snapshot](#accelerators) ## Our accelerators Two fixed-scope entry points. Each ends with a document your team can act on. - ### Visualize your current architecture state 2 days - ✓ Day 1: problem framing and event storming - ✓ Day 2: C1 view of your systems and integration points - ✓ Outcome: a starting point for the modernization plan [ Get offer → ](#contact-form) - ### Architecture modernization jump start 4 weeks - ✓ Weeks 1-2: discovery, user journeys, C1 / C2 diagrams - ✓ Weeks 3-4: bounded contexts, target-state options, draft roadmap - ✓ Outcome: a visual and strategic view, ready for action [ Get offer → ](#contact-form) ## What are the benefits of data architecture? - ### Why use data architecture services? Data architecture services help organizations manage data efficiently, improve data accessibility, and support analytics and decision-making. - ### What are the key benefits of data architecture? Key benefits include better data organization, improved data quality, enhanced scalability, stronger governance, and support for advanced analytics. - ### How does data architecture improve business performance? Data architecture improves performance by enabling faster data access, supporting real-time insights, and improving operational efficiency. ## The stack behind the architecture Lakehouse and warehouse Query engines Streaming and CDC Transform and orchestration Catalog and data quality Infrastructure and ops - ![amazon s3](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-s3.svg) Amazon S3 - ![apache iceberg mark](https://dedicatted.com/wp-content/uploads/2026/09/apache-iceberg-mark-300x300.png) Apache Iceberg - ![snowflake](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/snowflake.svg) Snowflake - ![databricks](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/databricks.svg) Databricks - ![bigquery](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/bigquery.svg) BigQuery - ![microsoft fabric](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/microsoft-fabric.svg) Microsoft Fabric - ![amazon redshift](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-redshift.svg) Amazon Redshift - ![trino](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/trino.svg) Trino - ![clickhouse](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/clickhouse.svg) ClickHouse - ![duckdb](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/duckdb.svg) DuckDB - ![spark](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/spark.svg) Spark - ![kafka](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kafka.svg) Kafka - ![debezium](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/debezium.svg) Debezium - ![apache flink](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/apache-flink.svg) Apache Flink - ![amazon msk](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-msk.svg) Amazon MSK - ![amazon kinesis](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/amazon-kinesis.svg) Amazon Kinesis - ![dbt](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/dbt.svg) dbt - ![airflow](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/airflow.svg) Airflow - ![airbyte](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/airbyte.svg) Airbyte - ![aws glue](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws-glue.svg) AWS Glue - ![unity catalog](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/unity-catalog.svg) Unity Catalog - ![datahub](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/datahub.svg) DataHub - ![openmetadata mark](https://dedicatted.com/wp-content/uploads/2026/09/openmetadata-mark-300x300.png) OpenMetadata - ![great expectations](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/great-expectations.svg) Great Expectations - ![terraform](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/terraform.svg) Terraform - ![opentofu](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/opentofu.svg) OpenTofu - ![kubernetes](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kubernetes.svg) Kubernetes - ![github actions](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/github-actions.svg) GitHub Actions - ![grafana](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/grafana.svg) Grafana - ![docker](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/docker.svg) Docker Not sure which of the three ways fits your estate? Send a one-paragraph description of your data platform and where it hurts. A data architect replies within one business day with a first read and the right starting point. [ Ask a data architect ↗ ](#contact-form) [## Insights ](https://dedicatted.com/insights) [Case study ### Self-Service Clinical Analytics on a FHIR Data Store for a Cardiology Network Healthcare Cloud Security Data Architecture Generative AI Security Compliance ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) About project The Challenge Three copies of the record, and none of them queryable. The clinical data existed in three disconnected forms: inside each device vendor’s proprietary analysis software, as PDF reports sent to referring physicians, and as files accumulating in Amazon S3 with no data model and no query layer over them. Clinic records \[…\] ](https://dedicatted.com/insights/self-service-clinical-analytics-on-a-fhir-data-store-for-a-cardiology-network) [Blog ### The 2026 Enterprise Data Readiness Benchmark: Your Foundation for Successful AI Insurance Retail Cloud Data Migration & Modernization Data Architecture Generative AI ![Abstract digital data corridor with blue light trails and glowing particles suggesting a high-tech network edge-to-edge.](https://dedicatted.com/wp-content/uploads/2026/08/Designing-Platform-for-Instant-Market-Penetration-1.png) Only 7% of enterprises have the data foundation AI needs to scale. See what separates them: real case studies, a maturity framework, next steps ](https://dedicatted.com/insights/the-2026-enterprise-data-readiness-benchmark-your-foundation-for-successful-ai) [Blog ### Pharma & Life Sciences: R&D Acceleration with Cloud & AI Healthcare Agentic AI Cloud Modernization Data Architecture Generative AI ![Close-up of glass pipettes transferring liquid in a dimly lit science lab.](https://dedicatted.com/wp-content/uploads/2026/08/Designing-Platform-for-Instant-Market-Penetration.png) This playbook breaks down the reference architecture, use cases by R&D stage, and implementation roadmap for cloud and AI acceleration in pharma ](https://dedicatted.com/insights/pharma-life-sciences-rd-acceleration-with-cloud-ai) [Blog ### Data Lake Migration to the Cloud: What Our Experts at Dedicatted Are Telling Clients Right Now Automotive Healthcare Retail Cloud Data Migration & Modernization Cloud Modernization Data Architecture ![Rows of server racks in a data center with purple-lit screens displaying code, extending into the distance.](https://dedicatted.com/wp-content/uploads/2026/07/Lake-Migration.png) Data lake migration to the cloud, explained by Dedicatted's experts: real cost savings, risk management, and a proven 5-phase process. ](https://dedicatted.com/insights/data-lake-migration-to-the-cloud-what-our-experts-at-dedicatted-are-telling-clients-right-now) ## FAQ ### Do we need a full re-architecture, or can we modernize incrementally? Almost always incrementally. The assessment classifies each workload as lift and shift, re-platform or re-architect. Most estates end up a mix, with the full redesign reserved for the systems where it pays for itself. ### How do you keep mission-critical databases running during migration? Change-data-capture replication keeps source and target in sync while workloads move. Blue-green deployment patterns and a rehearsed rollback make the final cutover a minutes-long switch with a known way back. ### Which cloud should we choose? It depends on your workloads, compliance profile and existing skills. We work across AWS, Azure and Google Cloud, with the deepest bench on AWS as a Premier Tier partner. The recommendation comes out of the assessment, with reasoning you can challenge. ### How does this support AI and ML workloads? A lakehouse gives models governed, catalogued, fresh data, which is the single biggest predictor of an AI project reaching production. The same platform serves BI dashboards and feature pipelines, so there is no separate ML data stack to maintain. ### What does an engagement cost? The two-day snapshot and the four-week jump start are fixed-scope offers, quoted in the first call. Build phases are estimated from the roadmap, so you see the number per phase before committing to any of them. [See the accelerators](#accelerators). ### What tools are used in data architecture? Object storage and a query engine (Amazon S3 with Redshift, Snowflake, Databricks or BigQuery), Kafka and Debezium for streaming and CDC, dbt and Airflow for transformation and orchestration, a catalog such as AWS Glue or DataHub, and Terraform for the platform itself. The set is chosen to fit your estate, not a fixed toolkit. ### What is the difference between data architecture and data engineering? Architecture decides the structure: where data lives, how it moves, who can reach it and which controls apply. Engineering builds and runs that structure: the pipelines, the platform code, the tests. We do both, and the architecture is written down before the first pipeline is built. ### What is data governance in data architecture? The policies and controls that keep data accurate, secure and compliant: ownership and definitions in the catalog, role- and attribute-based access, retention and audit trails. Designed into the architecture, governance becomes a document review at audit time rather than a retrofit. Question not answered? [Ask a data architect](#contact-form). Same-day reply on feasibility questions. ## Cloud Architecture & Migration Services Modern businesses need scalable, secure, and flexible infrastructure to support digital transformation, real-time analytics, and cloud-native applications. Legacy on-premise systems often create performance bottlenecks, increase maintenance costs, and limit scalability. At Dedicatted, we provide cloud architecture and migration services that help organizations modernize infrastructure, optimize workloads, and migrate applications and databases to secure cloud environments. Our engineers design future-ready cloud ecosystems built for performance, resilience, and long-term scalability. ## Cloud Architecture for Modern Applications Cloud architecture defines how applications, data platforms, and infrastructure operate across cloud environments. Strong architecture improves operational efficiency, accelerates software delivery, and supports business growth. Our cloud architecture services include: - Cloud-native infrastructure design - Application modernization - Data architecture engineering - Multi-cloud and hybrid cloud solutions - Kubernetes and container orchestration - Infrastructure as Code (IaC) - Security and compliance optimization We help businesses create scalable cloud platforms that support [AI](https://dedicatted.com/services/generative-ai) workloads, analytics, [DevOps automation](https://dedicatted.com/what-we-do/devops/devops-as-a-service), and real-time applications. ## Cloud Migration Services [Cloud migration](https://dedicatted.com/services/cloud-devops/cloud-migration) involves moving applications, workloads, and databases from on-premise infrastructure to modern cloud platforms such as: - Amazon Web Services - Microsoft Azure - Google Cloud Platform Our migration services include: - Application migration - Database migration - Infrastructure modernization - AWS cloud migration - Cloud cost optimization - Disaster recovery planning - Migration automation Successful cloud migration improves scalability, reduces infrastructure costs, and increases operational flexibility. ## Cloud Migration Strategies Every migration project requires the right modernization strategy. ### Lift and Shift Move applications to the cloud with minimal modifications for faster deployment. ### Re-Platforming Optimize existing applications before migration to improve cloud compatibility and efficiency. ### Re-Architecting Redesign applications using microservices, [Kubernetes](https://dedicatted.com/solutions/key-technologies/kubernetes), and cloud-native technologies for maximum scalability and performance. We help businesses choose the most effective migration strategy based on infrastructure complexity, compliance requirements, and long-term business goals. ## Modern Data Architecture & Engineering Modern cloud infrastructure requires scalable data platforms capable of supporting analytics, AI, and real-time processing. Our engineers build cloud-native data ecosystems using technologies such as Redshift, Snowflake, Databricks, Kafka, and Debezium. We implement secure data pipelines, scalable storage architectures, and governance frameworks optimized for compliance standards including HIPAA, PCI, and GDPR. ### Why Choose Dedicatted [Dedicatted](https://dedicatted.com) combines cloud engineering expertise, DevOps automation, and enterprise migration experience to help businesses modernize infrastructure with minimal disruption. Our team delivers: - Secure cloud migration - Scalable cloud architecture - Kubernetes & DevOps expertise - Data platform modernization - Infrastructure automation - Continuous cloud optimization We help organizations build reliable, cloud-native environments designed for performance, scalability, and future digital growth. Show more ## Get started with a data architecture consultant Outline your data platform, warehouse or governance challenge. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Services:** Data Architecture --- ### [Generative AI for Supply Chain](https://dedicatted.com/services/generative-ai/supply-chain) **Published:** February 12, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Generative AI](https://dedicatted.com/services/generative-ai) 3. Generative AI for Supply Chain # Transform Supply Chains With Generative AI Generative AI for supply chain management helps businesses optimize planning, sourcing, manufacturing, and logistics using AI-powered automation, predictive analytics, and real-time operational data to improve efficiency, resilience, sustainability, and cost control. [Book a free meeting](#contact-form) [See our offerings](#offerings) ![Automated storage and retrieval robot moving stock totes on warehouse racking](https://dedicatted.com/wp-content/uploads/2026/08/robotic-solutions-automated-warehouse-1024x683.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner ## What is Generative AI in Supply Chain Management? - ### What is generative AI in supply chain management? Generative AI in supply chain management uses advanced AI models to automate operations, optimize logistics, improve forecasting, and support data-driven decision-making across the supply chain. - ### What are generative AI supply chain services? Generative AI supply chain services include demand forecasting, inventory optimization, logistics automation, supplier management, predictive analytics, and AI-powered reporting solutions. - ### What does a supply chain AI development company do? A supply chain AI company develops and deploys AI-powered solutions that help businesses improve operational efficiency, reduce costs, and build more resilient supply chains. ## Industry Challenges ### Stock Optimization ### Stock Optimization Gen AI helps you achieve optimal inventory levels, meeting customer demand while minimizing holding costs and avoiding stockouts. It drives your supply chain toward accurate demand forecasting, effective communication, and direct coordination between suppliers, manufacturers, and retailers. ![Stocked pallet racking in a distribution warehouse](https://dedicatted.com/wp-content/uploads/2026/08/stock-optimization-warehouse-racking-1024x683.jpg) ### Supplier Reliability ### Supplier Reliability Generative AI helps your supply chain business run smoothly by ensuring timely delivery of high-quality materials and goods. It also makes it much easier to build robust partnerships, keep an eye on supplier performance, and better choose your vendors. ![Stacks of blue shipping pallets outside a distribution warehouse](https://dedicatted.com/wp-content/uploads/2026/08/supplier-reliability-pallet-pool-warehouse-1024x683.jpg) ### Tech Adoption ### Tech Adoption Gen AI helps you achieve extraordinary efficiency and resilience by using the cloud, IoT, and other modern technologies, along with digital twins and control towers. This makes it much easier to manage the supply chain challenges present in today’s dynamic business environment. ![Warehouse worker checking stock with a handheld barcode scanner and a tablet](https://dedicatted.com/wp-content/uploads/2026/08/tech-adoption-warehouse-barcode-scanning-1024x683.jpg) ### Robotic Solutions ### Robotic Solutions Generative AI boosts workforce productivity and addresses labor shortages by expanding the use of supply chain robotics and low-code solutions. ![Automated storage and retrieval robot moving stock totes on warehouse racking](https://dedicatted.com/wp-content/uploads/2026/08/robotic-solutions-automated-warehouse-1024x683.jpg) ### Sustainable Sourcing ### Sustainable Sourcing Gen AI can effectively source materials while minimizing environmental impact and addressing social and labor concerns. Building sustainable, ethical supply chains with AI helps maximize both resource management and minimize harm to the environment. ![Aerial view of solar panels covering the roofs of a distribution facility](https://dedicatted.com/wp-content/uploads/2026/08/sustainable-sourcing-solar-roof-1024x683.jpg) ### Stock Optimization Gen AI helps you achieve optimal inventory levels, meeting customer demand while minimizing holding costs and avoiding stockouts. It drives your supply chain toward accurate demand forecasting, effective communication, and direct coordination between suppliers, manufacturers, and retailers. ![Stocked pallet racking in a distribution warehouse](https://dedicatted.com/wp-content/uploads/2026/08/stock-optimization-warehouse-racking-1024x683.jpg) ### Supplier Reliability Generative AI helps your supply chain business run smoothly by ensuring timely delivery of high-quality materials and goods. It also makes it much easier to build robust partnerships, keep an eye on supplier performance, and better choose your vendors. ![Stacks of blue shipping pallets outside a distribution warehouse](https://dedicatted.com/wp-content/uploads/2026/08/supplier-reliability-pallet-pool-warehouse-1024x683.jpg) ### Tech Adoption Gen AI helps you achieve extraordinary efficiency and resilience by using the cloud, IoT, and other modern technologies, along with digital twins and control towers. This makes it much easier to manage the supply chain challenges present in today’s dynamic business environment. ![Warehouse worker checking stock with a handheld barcode scanner and a tablet](https://dedicatted.com/wp-content/uploads/2026/08/tech-adoption-warehouse-barcode-scanning-1024x683.jpg) ### Robotic Solutions Generative AI boosts workforce productivity and addresses labor shortages by expanding the use of supply chain robotics and low-code solutions. ![Automated storage and retrieval robot moving stock totes on warehouse racking](https://dedicatted.com/wp-content/uploads/2026/08/robotic-solutions-automated-warehouse-1024x683.jpg) ### Sustainable Sourcing Gen AI can effectively source materials while minimizing environmental impact and addressing social and labor concerns. Building sustainable, ethical supply chains with AI helps maximize both resource management and minimize harm to the environment. ![Aerial view of solar panels covering the roofs of a distribution facility](https://dedicatted.com/wp-content/uploads/2026/08/sustainable-sourcing-solar-roof-1024x683.jpg) ## Business Opportunities Five places generative AI pays off in the supply chain, ranked by business value and how easy each is to ship. #### Business Opportunities Use Cases Business Value Case Simplicity 1 Disruption analysis Strategic bet Business Value: 86 Case Simplicity: 36 Gen AI analyzes global data to pinpoint natural disasters, political instability, and economic shifts that could disrupt your supply chain. [Discuss this use case](#contact-form) 2 Demand Forecasting Business Value: 70 Case Simplicity: 50 Gen AI identifies upcoming product shortages and overstock, giving your supply chain planners a prioritized list of potential availability problems. [Discuss this use case](#contact-form) 3 Waste Reduction Quick win Business Value: 82 Case Simplicity: 62 Gen AI minimizes product loss by proactively identifying and resolving imminent expiration risks within your supply chain. [Discuss this use case](#contact-form) 4 Order Tracking Business Value: 58 Case Simplicity: 78 Gen AI intelligently tracks order statuses, delivery dates, transportation schedules, and payments, eliminating the need for daily data extraction from disparate systems. [Discuss this use case](#contact-form) 5 Automated Procurement Business Value: 64 Case Simplicity: 56 Gen AI automates vendor management and selection, cutting the time needed for supplier identification from weeks to days. [Discuss this use case](#contact-form) Business Value Case Simplicity Where each use case lands Strategic bets Quick wins Easy extras 1 Disruption analysis 2 Demand Forecasting 3 Waste Reduction 4 Order Tracking 5 Automated Procurement Business Value ↑ ← Harder to ship Case Simplicity Easier → ## How is Generative AI implemented in supply chain operations? - ### How do companies implement generative AI in supply chain management? Companies implement generative AI by identifying operational challenges, integrating supply chain data, selecting AI models, and deploying intelligent automation workflows. - ### What is the process of generative AI adoption in supply chain operations? The process includes discovery, infrastructure assessment, data preparation, model selection, system integration, testing, deployment, and continuous optimization. - ### How long does it take to implement generative AI in supply chain management? Implementation timelines typically range from several weeks to a few months depending on infrastructure complexity, integrations, and customization requirements. ## What technologies power supply chain AI solutions? - ### What is RAG in supply chain AI? RAG (Retrieval-Augmented Generation) enables AI systems to retrieve and use logistics data, supplier documentation, operational records, and external market information to generate more accurate insights. - ### What is LLMOps in supply chain management? LLMOps refers to the deployment, monitoring, governance, and optimization of large language models used in supply chain environments. - ### What AI models are used in supply chain solutions? Supply chain AI solutions commonly use models such as GPT, Claude, LLaMA, and other foundation models depending on operational and infrastructure requirements. ## Our Generative AI Offerings - ### GenAI Discovery Idea To Use Case: analysis and validation - ✓ Use cases and business priorities evaluation - ✓ Data quality and availability assessment - ✓ Architecture and integration design - ✓ Fast Proof of Concept validation [ Book a discovery call → ](#contact-form) - ### GenAI Development Validation to Production: accelerate time to market - ✓ MVP development - ✓ Solution engineering and deployment - ✓ Scaling and Integration - ✓ Training and adoption support [ Discuss a build → ](#contact-form) - ### GenAI Augmentation Expertise to Execution: consulting and project support - ✓ Augmenting engineering expertise - ✓ Augmenting engineering capacity - ✓ Consulting on all stages of development - ✓ Solution audit and optimization [ Discuss augmentation → ](#contact-form) ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) ## What are Generative AI use cases in supply chain management? - ### What are the main use cases of generative AI in supply chain operations? Common use cases include demand forecasting, inventory management, logistics optimization, procurement automation, supplier communication, and operational reporting. - ### How is generative AI used in logistics and supply chain operations? Businesses use generative AI to automate workflows, improve forecasting accuracy, optimize transportation routes, and streamline supply chain decision-making. - ### Can generative AI automate supply chain workflows? Yes, generative AI can automate tasks such as shipment tracking, inventory reporting, supplier analysis, procurement documentation, and operational planning. ## AI Optimization and Supply Chain Resilience - ### How does generative AI improve supply chain efficiency? Generative AI helps organizations optimize inventory levels, reduce delays, improve forecasting accuracy, and streamline logistics operations. - ### Can generative AI improve supply chain resilience? Yes, generative AI can identify risks, predict disruptions, support contingency planning, and help businesses respond faster to supply chain changes. - ### How does generative AI support logistics optimization? Generative AI can analyze transportation data, optimize delivery routes, reduce operational bottlenecks, and improve resource allocation. Not sure which use case to start with? Tell us where generative AI should help across planning, sourcing and logistics. An engineer replies within one business day with what we would build first and what it takes. [ Book a free meeting ↗ ](#contact-form) ## Insights [Blog ### Webinar | Practical AI Solutions Every Business Leader Needs to Know Automotive Financial Services Healthcare Manufacturing Retail Agentic AI Generative AI Managed Services ![Close-up on AI Solution, which determines anomalies and failures during production at the earliest stage](https://dedicatted.com/wp-content/uploads/2025/08/Designing-Platform-for-Instant-Market-Penetration-48.png) Practical AI solutions for businesses: boost efficiency, reduce costs, and drive growth with expert insights and real cases. ](https://dedicatted.com/insights/webinar-practical-ai-solutions-every-business-leader-needs-to-know) [Whitepaper ### GenAI for retail: The secret weapon of customer-first brands Retail Generative AI ![A beautiful client using the digital interface of augmented reality in a modern shopping center.](https://dedicatted.com/wp-content/uploads/2025/06/Designing-Platform-for-Instant-Market-Penetration-94.png) See how GenAI on AWS helps customer-first retailers boost margins, enable conversational search, AI stylists, and smarter personalization – unlocking up to $390 billion in value. ](https://dedicatted.com/insights/genai-for-retail-the-secret-weapon-of-customer-first-brands) [Case study ### Strengthening Platform Reliability and Operational Excellence through Managed Support Services High Tech Software Cloud and Infrastructure Security Cloud Operations Managed Services Security Compliance ![Adomik case study cover featuring a presenter discussing performance metrics with an engaged audience in a conference room.](https://dedicatted.com/wp-content/uploads/2025/03/Adomik.-Cover-2-1024x537.webp) See how Adomik improved platform stability, security, and compliance with 24/7 Managed Support Services, ensuring seamless operations and growth. ](https://dedicatted.com/insights/strengthening-platform-reliability-and-operational-excellence-through-managed-support-services) [Whitepaper ### Empowering Customer-First Retailers with the Genius of GenAI Retail Generative AI ![Young woman standing in front of holographic product displays in a futuristic in-store environment, symbolizing AI-powered retail and immersive shopping experiences](https://dedicatted.com/wp-content/uploads/2025/05/Designing-Platform-for-Instant-Market-Penetration-34-1024x537.png) Discover how AWS GenAI empowers customer-first retailers with 24/7 conversational support, personalized offers, speech-driven experiences, and scalable service – without ballooning costs. ](https://dedicatted.com/insights/empowering-customer-first-retailers-with-the-genius-of-genai) ## FAQ ### What are the benefits of generative AI for supply chain companies? Benefits include improved operational efficiency, reduced costs, better forecasting, faster decision-making, and increased supply chain resilience. ### What ROI can generative AI deliver in supply chain management? Generative AI can reduce manual workloads, improve logistics efficiency, minimize disruptions, and optimize inventory management. ### Is generative AI suitable for small and mid-sized supply chain businesses? Yes, SMBs can use generative AI to automate operations, improve planning, and optimize logistics without large infrastructure investments. ### How much does generative AI development for supply chain management cost? Costs vary depending on project scope, infrastructure complexity, integrations, and customization requirements. ### What factors affect the cost of supply chain AI solutions? Pricing depends on data complexity, infrastructure scale, AI model selection, integration requirements, customization level, and operational needs. ## What is Generative AI for Supply Chain? Generative AI for supply chain is the use of AI models and automation systems to optimize planning, sourcing, manufacturing, logistics, and operational workflows using real-time data, predictive analytics, and intelligent automation. Dedicatted provides enterprise generative ai for supply chain solutions for logistics, manufacturing, retail, and distribution environments. ## Generative AI in Supply Chain Operations Generative AI in supply chain operations enables organizations to automate workflows, optimize logistics, and accelerate decision-making using large language models, predictive analytics, and AI-powered automation. Dedicatted develops scalable generative AI solutions tailored to the operational complexity and data requirements of modern supply chain environments. Dedicatted develops scalable AI solutions for supply chain organizations of all sizes, including manufacturers, distributors, logistics providers, and global retail operations. Our supply chain AI services support: - Demand forecasting and predictive planning - Inventory optimization and replenishment - Logistics and transportation route optimization - Supplier management and procurement automation - Operational reporting and AI-powered analytics - Supply chain risk monitoring and contingency planning - Shipment tracking and exception handling With our generative AI expertise, supply chain organizations can modernize legacy workflows, reduce manual effort, and deliver faster, more accurate operational decisions while maintaining resilience across planning, sourcing, manufacturing, and distribution. ## Generative AI for Logistics and Manufacturing Generative AI streamlines logistics and manufacturing by automating repetitive tasks, accelerating planning cycles, and supporting decision-making across operations, procurement, and distribution teams. Dedicatted helps organizations integrate AI systems with: - Enterprise resource planning (ERP) platforms - Warehouse management systems (WMS) - Transportation management systems (TMS) - Manufacturing execution systems (MES) - Supplier relationship and procurement platforms - Supply chain analytics and reporting tools By embedding generative AI directly into operational workflows, supply chain leaders can shorten planning cycles, reduce processing costs, and free up specialists to focus on higher-value initiatives such as resilience, sustainability, and continuous improvement. ## Cloud Infrastructure for Supply Chain AI Secure, scalable cloud infrastructure is the foundation of every successful supply chain AI program. Dedicatted helps businesses implement: - Secure cloud architectures aligned with supply chain operational standards - Scalable AI workload orchestration and MLOps pipelines - Data lakes and governed data platforms for AI training - Encryption, access controls, and audit logging - Disaster recovery and high-availability AI deployments - FinOps and cost optimization for supply chain AI infrastructure As an [AWS Premier Tier Partner](https://dedicatted.com/our-partners/aws), Dedicatted designs and operates enterprise-grade AI infrastructure that meets the performance, scalability, and reliability expectations of modern supply chain operations. ### Generative AI for Supply Chain Analytics Generative AI transforms supply chain analytics by turning raw operational, logistics, and supplier data into actionable insights, summaries, and recommendations. Our supply chain analytics capabilities include: - Demand forecasting and predictive trend analysis - Inventory and replenishment analytics - Logistics performance and route optimization analytics - Supplier performance and procurement analytics - Operational risk and disruption analytics - Executive dashboards and natural-language reporting With generative AI built directly into analytics workflows, supply chain leaders gain faster access to the insights they need to make better, data-driven decisions across planning, sourcing, manufacturing, and distribution. Dedicatted partners with supply chain organizations end-to-end, from discovery and operational assessment through deployment, optimization, and ongoing support, to deliver measurable business value from generative AI investments. Show more ## Get started with a generative AI consultant Outline your planning, logistics or supplier use case. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Generative AI and Insurance](https://dedicatted.com/services/generative-ai/insurance) **Published:** February 12, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Generative AI](https://dedicatted.com/services/generative-ai) 3. Generative AI and Insurance # Insurance Innovation with Generative AI Generative AI for insurance helps insurers automate claims processing, risk analysis, fraud detection, underwriting, and customer support using AI-powered workflows and real-time data to improve operational efficiency, compliance, and customer experience. [Book a free meeting](#contact-form) [See our offerings](#offerings) ![Two professionals signing documents across a desk](https://dedicatted.com/wp-content/uploads/2026/08/finserv-regulatory-documents-signing-1024x683.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner ## What is Generative AI in Insurance? - ### What is generative AI in insurance? Generative AI in insurance uses advanced AI models to automate workflows, improve customer interactions, process claims faster, and support data-driven decision-making. - ### What are generative AI insurance services? Generative AI insurance services include claims automation, AI assistant development, policy document processing, fraud detection support, customer service automation, and analytics solutions. - ### What does an insurance AI development company do? An insurance AI company develops and deploys AI-powered solutions that help insurers improve operational efficiency, customer experience, and risk management. ## Industry Challenges ### Customer Experience ### Customer Experience Customer experience in insurance presents a major opportunity for companies. Efficient interactions, proactive support, and personalized solutions tailored to each client’s specific needs help build strong relationships and enhance customer loyalty, ultimately driving business growth. ![Close-up of a monochrome banner with bold text reading “WE HEAR YOU.” printed on it.](https://dedicatted.com/wp-content/uploads/2025/02/jon-tyson-vVSleEYPSGY-unsplash-1024x684.jpg) ### Simplified Onboarding ### Simplified Onboarding Simplifying due diligence and onboarding is a growing trend in insurance. Insurers are utilizing technology and data analytics to accelerate verification processes, minimize paperwork, and shorten response times. ![Eyeglasses resting on an open lined notebook beside a pen and a laptop on a white desk, suggesting a workspace or study setting.](https://dedicatted.com/wp-content/uploads/2025/02/trent-erwin-UgA3Xvi3SkA-unsplash-1024x684.jpg) ### Global Compliance Management ### Global Compliance Management With evolving regulatory requirements across borders, insurers are leveraging AI-driven solutions to simplify compliance management. Automation enhances efficiency, accuracy, and risk mitigation, allowing insurers to navigate multinational operations seamlessly while delivering reliable service to global clients. ![Stamped legal document in German—signifies official approval and validation. Ideal visual for topics like compliance, auditing, or legal workflows.](https://dedicatted.com/wp-content/uploads/2025/02/markus-spiske-7PMGUqYQpYc-unsplash-1024x683.jpg) ### Claim Process Optimization ### Claim Process Optimization Optimizing underwriting and claims processes is a key trend in B2B insurance. Insurers are leveraging AI and big data analytics to accelerate risk assessment, improve accuracy, and deliver tailored coverage solutions. ![Person holding a credit card and typing on a laptop, representing online shopping or digital payment.](https://dedicatted.com/wp-content/uploads/2025/02/rupixen-Q59HmzK38eQ-unsplash-1024x683.jpg) ### Customer Experience Customer experience in insurance presents a major opportunity for companies. Efficient interactions, proactive support, and personalized solutions tailored to each client’s specific needs help build strong relationships and enhance customer loyalty, ultimately driving business growth. ![Close-up of a monochrome banner with bold text reading “WE HEAR YOU.” printed on it.](https://dedicatted.com/wp-content/uploads/2025/02/jon-tyson-vVSleEYPSGY-unsplash-1024x684.jpg) ### Simplified Onboarding Simplifying due diligence and onboarding is a growing trend in insurance. Insurers are utilizing technology and data analytics to accelerate verification processes, minimize paperwork, and shorten response times. ![Eyeglasses resting on an open lined notebook beside a pen and a laptop on a white desk, suggesting a workspace or study setting.](https://dedicatted.com/wp-content/uploads/2025/02/trent-erwin-UgA3Xvi3SkA-unsplash-1024x684.jpg) ### Global Compliance Management With evolving regulatory requirements across borders, insurers are leveraging AI-driven solutions to simplify compliance management. Automation enhances efficiency, accuracy, and risk mitigation, allowing insurers to navigate multinational operations seamlessly while delivering reliable service to global clients. ![Stamped legal document in German—signifies official approval and validation. Ideal visual for topics like compliance, auditing, or legal workflows.](https://dedicatted.com/wp-content/uploads/2025/02/markus-spiske-7PMGUqYQpYc-unsplash-1024x683.jpg) ### Claim Process Optimization Optimizing underwriting and claims processes is a key trend in B2B insurance. Insurers are leveraging AI and big data analytics to accelerate risk assessment, improve accuracy, and deliver tailored coverage solutions. ![Person holding a credit card and typing on a laptop, representing online shopping or digital payment.](https://dedicatted.com/wp-content/uploads/2025/02/rupixen-Q59HmzK38eQ-unsplash-1024x683.jpg) ## Business Opportunities Five places generative AI pays off in insurance, ranked by business value and how easy each is to ship. #### Business Opportunities Use cases Business value Case simplicity 1 Automated Claims Documentation Quick win Business value: 90 Case simplicity: 80 Generative AI streamlines insurance claim documentation by analyzing policy details and claim specifics. It automatically generates accurate forms, loss summaries, and support documents – reducing manual effort, minimizing errors, and improving processing efficiency. [Discuss this use case](#contact-form) 2 Personalized Lead Engagement Business value: 74 Case simplicity: 64 Generative AI customizes communication for leads by considering factors like industry, company size, and insurance needs. It generates tailored emails and messages, improving engagement and boosting conversion rates. [Discuss this use case](#contact-form) 3 Tailored B2B Solutions Business value: 68 Case simplicity: 52 By leveraging B2B client data, Generative AI designs customized insurance solutions. It analyzes industry risks, contracts, and regulations to recommend optimal coverage, terms, and endorsements – enhancing satisfaction and effectively managing unique risk exposures. [Discuss this use case](#contact-form) 4 Multinational Policy Localization Business value: 56 Case simplicity: 44 Generative AI helps global insurers adapt policies to different languages and regulatory environments. Using NLP and machine translation, it generates policy versions that align with diverse legal and compliance requirements. [Discuss this use case](#contact-form) 5 Multinational Compliance Management Strategic bet Business value: 82 Case simplicity: 34 Generative AI supports multinational insurers in maintaining compliance with varying risk management regulations, ensuring adherence to industry standards across different jurisdictions. [Discuss this use case](#contact-form) Business value Case simplicity Where each use case lands Strategic bets Quick wins Easy extras 1 Automated Claims Documentation 2 Personalized Lead Engagement 3 Tailored B2B Solutions 4 Multinational Policy Localization 5 Multinational Compliance Management Business value ↑ ← Harder to ship Case simplicity Easier → ## How is Generative AI implemented in insurance? - ### How do insurance companies implement generative AI? Insurance companies implement generative AI by identifying business use cases, preparing secure datasets, selecting AI models, integrating systems, and deploying compliant AI workflows. - ### What is the process of generative AI adoption in insurance? The process includes discovery, compliance assessment, data preparation, AI model selection, development, integration, testing, deployment, and ongoing optimization. - ### How long does it take to implement generative AI in insurance? Implementation timelines typically range from several weeks to a few months depending on project complexity, compliance requirements, and integrations. ## What technologies power insurance AI solutions? - ### What is RAG in insurance AI? RAG (Retrieval-Augmented Generation) enables AI systems to retrieve and use policy documents, claims data, and insurance knowledge bases to generate more accurate responses. - ### What is LLMOps in insurance? LLMOps refers to the deployment, monitoring, governance, and optimization of large language models in secure insurance environments. - ### What AI models are used in insurance solutions? Insurance AI solutions commonly use models such as GPT, Claude, LLaMA, and industry-specific AI models depending on business and compliance requirements. ## Our Generative AI Offerings - ### GenAI Discovery Idea To Use Case: analysis and validation - ✓ Use cases and business priorities evaluation - ✓ Data quality and availability assessment - ✓ Architecture and integration design - ✓ Fast Proof of Concept validation [ Book a discovery call → ](#contact-form) - ### GenAI Development Validation to Production: accelerate time to market - ✓ MVP development - ✓ Solution engineering and deployment - ✓ Scaling and Integration - ✓ Training and adoption support [ Discuss a build → ](#contact-form) - ### GenAI Augmentation Expertise to Execution: consulting and project support - ✓ Augmenting engineering expertise - ✓ Augmenting engineering capacity - ✓ Consulting on all stages of development - ✓ Solution audit and optimization [ Discuss augmentation → ](#contact-form) ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) ## What are Generative AI use cases in insurance? - ### What are the main use cases of generative AI in insurance? Common use cases include claims processing, customer support automation, underwriting assistance, fraud detection, policy analysis, and document generation. - ### How is generative AI used in insurance companies? Insurance providers use generative AI to automate repetitive workflows, improve customer service, accelerate claims handling, and optimize operations. - ### Can generative AI automate insurance workflows? Yes, generative AI can automate tasks such as claims summaries, policy document generation, customer inquiries, reporting, and compliance workflows. Not sure which use case to start with? Tell us which underwriting, claims or servicing workflow you want generative AI to carry. An engineer replies within one business day with what we would build first and what it takes. [ Book a free meeting ↗ ](#contact-form) ## Insights [Case study ### GenAI-Powered Claims Processing Healthcare Insurance Cloud Modernization Cloud Security Generative AI Security Compliance ![Case study: GenAI-Powered Claims Processing | Dedicatted. Hands holding pen reviewing document paperwork for claims processing, with blue overlay filter](https://dedicatted.com/wp-content/uploads/2025/03/Xodus.-Cover-8-1024x537.webp) How Dedicatted built a GenAI claims adjudication platform for Xodus Travel Services on Amazon Bedrock, cutting manual effort by 70% and lifting adjudicator throughput 85%. ](https://dedicatted.com/insights/genai-powered-claims-processing) [Whitepaper ### Powering Modern Finance Inside a Cloud-Native Success Story Financial Services Cloud and Infrastructure Security DevOps Consulting Security Compliance ![Close up hand of businesswoman using digital pencil hologram for drawing business forex chart interface on dark blue background Digital transformation and trading concept](https://dedicatted.com/wp-content/uploads/2025/06/Designing-Platform-for-Instant-Market-Penetration-2025-11-14T123815.170.png) Explore how a cloud-native overhaul delivered field-level encryption, sub-second trade repository, and $2M AWS support – driving secure, ultra-fast finance modernization. ](https://dedicatted.com/insights/powering-modern-finance-inside-a-cloud-native-success-story) [Whitepaper ### Empowering Customer-First Retailers with the Genius of GenAI Retail Generative AI ![Young woman standing in front of holographic product displays in a futuristic in-store environment, symbolizing AI-powered retail and immersive shopping experiences](https://dedicatted.com/wp-content/uploads/2025/05/Designing-Platform-for-Instant-Market-Penetration-34-1024x537.png) Discover how AWS GenAI empowers customer-first retailers with 24/7 conversational support, personalized offers, speech-driven experiences, and scalable service – without ballooning costs. ](https://dedicatted.com/insights/empowering-customer-first-retailers-with-the-genius-of-genai) [Blog ### LLMOps for Enterprises: What It Is and How to Scale GenAI in Production Financial Services High Tech Software Insurance Manufacturing Generative AI Managed Services ![Code running on computer screens and tablets in an empty data center](https://dedicatted.com/wp-content/uploads/2025/07/Designing-Platform-for-Instant-Market-Penetration-100.png) Unlock scalable GenAI success with enterprise-grade LLMOps – streamline deployment, ensure monitoring, maintain agility, and govern models effectively. ](https://dedicatted.com/insights/llmops-for-enterprises-what-it-is-and-how-to-scale-genai-in-production) ## FAQ ### Is generative AI secure for insurance companies? Yes, insurance AI solutions can be developed with enterprise-grade security, encryption, governance controls, and compliance-focused infrastructure. ### Can generative AI support compliance in insurance? Yes, generative AI can help insurers automate compliance monitoring, documentation workflows, reporting, and policy management processes. ### How is sensitive insurance data protected in AI systems? Sensitive data is protected through encryption, secure cloud infrastructure, access controls, monitoring systems, and governance policies. ### What are the benefits of generative AI for insurance companies? Benefits include faster claims processing, reduced operational costs, improved customer experiences, better risk assessment, and increased efficiency. ### What ROI can generative AI deliver in insurance? Generative AI can improve productivity, reduce manual workloads, accelerate workflows, and optimize customer support operations. ### Is generative AI suitable for small and mid-sized insurance providers? Yes, SMB insurers can use generative AI to automate operations, improve customer interactions, and streamline internal workflows. ### How much does generative AI development for insurance cost? Costs vary depending on project complexity, compliance requirements, infrastructure, integrations, and customization needs. ### What factors affect the cost of insurance AI solutions? Pricing depends on data security requirements, AI model selection, infrastructure complexity, integration scope, compliance standards, and customization level. ## What is Generative AI for Insurance? Generative AI for insurance is the use of AI models and automation systems to improve claims processing, underwriting, customer support, risk analysis, and operational workflows across insurance organizations. Dedicatted provides enterprise generative ai insurance solutions for insurers, brokers, and insurance technology platforms. ## Generative AI in Insurance Generative AI in insurance enables organizations to automate core workflows, accelerate claims processing, and improve customer support using large language models, retrieval-augmented generation, and AI-powered automation. Dedicatted develops scalable generative AI solutions tailored to the operational, regulatory, and compliance requirements of modern insurance providers. Dedicatted develops scalable AI solutions for insurance organizations of all sizes, including national insurers, brokers, and digital-first insurance technology platforms. Our insurance AI services support: - Claims processing automation - AI-powered customer support and virtual assistants - Underwriting assistance and risk analysis - Policy document processing and generation - Fraud detection and anomaly identification - Compliance monitoring and reporting - Insurance analytics and decision support With our generative AI expertise, insurance organizations can modernize legacy processes, reduce manual effort, and deliver faster, more accurate customer experiences while maintaining strict compliance and governance controls. ## Generative AI for Insurance Operations Generative AI streamlines insurance operations by automating repetitive tasks, accelerating claims handling, and supporting decision-making across underwriting, customer service, and compliance teams. Dedicatted helps insurance companies integrate AI systems with: - Policy administration platforms - Claims management systems - Customer relationship management (CRM) tools - Document management and content systems - Risk analytics and reporting platforms - Compliance and governance frameworks By embedding generative AI directly into operational workflows, insurers can shorten cycle times, reduce processing costs, and free up specialists to focus on higher-value cases and strategic initiatives. ### Cloud Infrastructure for Insurance AI Secure, compliant cloud infrastructure is the foundation of every successful insurance AI program. Dedicatted helps insurance organizations implement: - Secure cloud architectures aligned with insurance compliance standards - Scalable AI workload orchestration and MLOps pipelines - Data lakes and governed data platforms for AI training - Encryption, access controls, and audit logging - Disaster recovery and high-availability AI deployments - FinOps and cost optimization for AI infrastructure As an [AWS Premier Tier Partner](https://dedicatted.com/our-partners/aws), Dedicatted designs and operates insurance-grade AI infrastructure that meets enterprise security, performance, and regulatory expectations. ### Generative AI for Insurance Analytics Generative AI transforms insurance analytics by turning raw policy, claims, and customer data into actionable insights, summaries, and recommendations. Our insurance analytics capabilities include: - Claims trend analysis and predictive insights - Customer behavior and segmentation analytics - Risk scoring and underwriting analytics - Fraud pattern detection and anomaly analysis - Compliance reporting and regulatory analytics - Executive dashboards and natural-language reporting With generative AI built directly into analytics workflows, insurance leaders gain faster access to the insights they need to make better, data-driven decisions across underwriting, claims, customer experience, and compliance. Dedicatted partners with insurance organizations end-to-end, from discovery and compliance assessment through deployment, optimization, and ongoing support, to deliver measurable business value from generative AI investments. Show more ## Get started with a generative AI consultant Outline your claims, underwriting or policy servicing use case. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Generative AI for E-commerce](https://dedicatted.com/services/generative-ai/retail) **Published:** February 12, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Generative AI](https://dedicatted.com/services/generative-ai) 3. Generative AI for E-commerce # Generative AI for Retail Generative AI for retail helps businesses automate marketing, ecommerce operations, customer service, supply chain management, and retail analytics using AI-powered workflows, intelligent automation, and real-time data processing. [Book a free meeting](#contact-form) [See our offerings](#offerings) ![Grocery store aisle with a wooden central display and shelves stocked with items on both sides under bright lights.](https://dedicatted.com/wp-content/uploads/2026/08/retail-1-1024x768.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner ## What is Generative AI in Retail? - ### What is generative AI in retail? Generative AI in retail uses advanced AI models to automate operations, personalize customer experiences, optimize inventory management, and improve business decision-making. - ### What are generative AI retail services? Generative AI retail services include AI chatbot development, product recommendation systems, customer support automation, inventory optimization, and retail analytics solutions. - ### What does a retail AI development company do? A retail AI company develops and deploys AI-powered solutions that help retailers improve customer engagement, streamline operations, and increase sales performance. ## Industry Challenges ### Decision Fatigue ### Decision Fatigue Gen AI helps your retail business keep up with evolving consumer buying habits and personalize the shopping experience. By guiding customers with smart choices, you can reduce checkout times and lower bounce rates. ![Laptop showing an online store surrounded by shopping bags](https://dedicatted.com/wp-content/uploads/2026/08/ecommerce-decision-fatigue-online-browsing-1024x683.jpg) ### Supply Chain Efficiency ### Supply Chain Efficiency Gen AI boosts your retail business’s efficiency and responsiveness by optimizing supply chains, improving inventory management, and streamlining logistics. ![Small business workspace with packed box, shipping supplies, and laptops—highlighting e-commerce or startup operations.](https://dedicatted.com/wp-content/uploads/2025/02/bench-accounting-MGaFENpDCsw-unsplash-1024x684.jpg) ### Actionable Analytics ### Actionable Analytics Gen AI helps your retail business overcome the challenge of gathering and using reliable data from many sources. This allows you to gain valuable insights to improve your business. ![Analytics dashboard close-up showing key performance metrics: CTR, quality score, conversions—ideal for data-driven marketing or business intelligence.](https://dedicatted.com/wp-content/uploads/2025/02/stephen-dawson-qwtCeJ5cLYs-unsplash-1024x737.jpg) ### Data Security ### Data Security Gen AI helps retail businesses securely manage large and diverse datasets, respecting customer privacy and complying with data protection regulations. ![Blue-lit server blades in a secure data centre rack](https://dedicatted.com/wp-content/uploads/2026/08/ecommerce-data-security-server-1024x683.jpg) ### Workflow Automation ### Workflow Automation Gen AI improves back-office operations for retail businesses – from marketing and sales to financials and reporting. This frees up resources for creating excellent customer experiences and pursuing strategic growth. ![Ecommerce marketing specialist mapping cart abandonment recovery workflow strategy with email sequence and timing triggers on whiteboard.](https://dedicatted.com/wp-content/uploads/2025/02/campaign-creators-kQ4tBklJI-unsplash-1024x682.jpg) ### Decision Fatigue Gen AI helps your retail business keep up with evolving consumer buying habits and personalize the shopping experience. By guiding customers with smart choices, you can reduce checkout times and lower bounce rates. ![Laptop showing an online store surrounded by shopping bags](https://dedicatted.com/wp-content/uploads/2026/08/ecommerce-decision-fatigue-online-browsing-1024x683.jpg) ### Supply Chain Efficiency Gen AI boosts your retail business’s efficiency and responsiveness by optimizing supply chains, improving inventory management, and streamlining logistics. ![Small business workspace with packed box, shipping supplies, and laptops—highlighting e-commerce or startup operations.](https://dedicatted.com/wp-content/uploads/2025/02/bench-accounting-MGaFENpDCsw-unsplash-1024x684.jpg) ### Actionable Analytics Gen AI helps your retail business overcome the challenge of gathering and using reliable data from many sources. This allows you to gain valuable insights to improve your business. ![Analytics dashboard close-up showing key performance metrics: CTR, quality score, conversions—ideal for data-driven marketing or business intelligence.](https://dedicatted.com/wp-content/uploads/2025/02/stephen-dawson-qwtCeJ5cLYs-unsplash-1024x737.jpg) ### Data Security Gen AI helps retail businesses securely manage large and diverse datasets, respecting customer privacy and complying with data protection regulations. ![Blue-lit server blades in a secure data centre rack](https://dedicatted.com/wp-content/uploads/2026/08/ecommerce-data-security-server-1024x683.jpg) ### Workflow Automation Gen AI improves back-office operations for retail businesses – from marketing and sales to financials and reporting. This frees up resources for creating excellent customer experiences and pursuing strategic growth. ![Ecommerce marketing specialist mapping cart abandonment recovery workflow strategy with email sequence and timing triggers on whiteboard.](https://dedicatted.com/wp-content/uploads/2025/02/campaign-creators-kQ4tBklJI-unsplash-1024x682.jpg) ## Business Opportunities Five places generative AI pays off in e-commerce, ranked by business value and how easy each is to ship. #### Business Opportunities Use Cases Business Value Case Simplicity 1 Virtual Shopping Concierge Quick win Business Value: 88 Case Simplicity: 74 Gen AI helps you create personalized interactions with customers. By recognizing their buying habits and preferences, you can offer custom product recommendations. [Discuss this use case](#contact-form) 2 Customer support Business Value: 76 Case Simplicity: 88 Gen AI can instantly assist with consumer inquiries and complaints, while also providing information about store locations, inventory, and promotions. [Discuss this use case](#contact-form) 3 Innovation in Products and Services Business Value: 82 Case Simplicity: 50 Gen AI streamlines research and design, helping your retail business bring new products and services to market more quickly. [Discuss this use case](#contact-form) 4 AI-Driven Content Generation Business Value: 60 Case Simplicity: 80 With Gen AI, creating marketing and promotional materials tailored to specific locations and customer segments, including translation and localization, becomes much simpler. [Discuss this use case](#contact-form) 5 Predictive Market Trends Strategic bet Business Value: 80 Case Simplicity: 32 Gen AI analyzes consumer, product, and market data to forecast retail trends, giving you actionable insights to develop new products and adjust assortments for a positive business impact. [Discuss this use case](#contact-form) Business Value Case Simplicity Where each use case lands Strategic bets Quick wins Easy extras 1 Virtual Shopping Concierge 2 Customer support 3 Innovation in Products and Services 4 AI-Driven Content Generation 5 Predictive Market Trends Business Value ↑ ← Harder to ship Case Simplicity Easier → ## How is Generative AI implemented in retail? - ### How do retailers implement generative AI? Retailers implement generative AI by identifying business use cases, integrating customer and operational data, selecting AI models, and deploying intelligent retail workflows. - ### What is the process of generative AI adoption in retail? The process includes discovery, data preparation, AI model selection, system integration, testing, deployment, and continuous optimization. - ### How long does it take to implement generative AI in retail? Implementation timelines typically range from a few weeks to several months depending on project complexity, integrations, and customization needs. ## What technologies power retail AI solutions? - ### What is RAG in retail AI? RAG (Retrieval-Augmented Generation) allows AI systems to retrieve and use product catalogs, customer data, and retail knowledge bases to generate more accurate responses and recommendations. - ### What is LLMOps in retail? LLMOps is the practice of deploying, monitoring, maintaining, and optimizing large language models in retail environments. - ### What AI models are used in retail solutions? Retail AI solutions commonly use models such as GPT, Claude, LLaMA, and other foundation models depending on the business requirements and infrastructure. ## Our Generative AI Offerings - ### GenAI Discovery Idea To Use Case: analysis and validation - ✓ Use cases and business priorities evaluation - ✓ Data quality and availability assessment - ✓ Architecture and integration design - ✓ Fast Proof of Concept validation [ Book a discovery call → ](#contact-form) - ### GenAI Development Validation to Production: accelerate time to market - ✓ MVP development - ✓ Solution engineering and deployment - ✓ Scaling and Integration - ✓ Training and adoption support [ Discuss a build → ](#contact-form) - ### GenAI Augmentation Expertise to Execution: consulting and project support - ✓ Augmenting engineering expertise - ✓ Augmenting engineering capacity - ✓ Consulting on all stages of development - ✓ Solution audit and optimization [ Discuss augmentation → ](#contact-form) ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) ## What are Generative AI use cases in retail? - ### What are the main use cases of generative AI in retail? Key use cases include personalized shopping experiences, customer support automation, product recommendations, inventory management, marketing content generation, and demand forecasting. - ### How is generative AI used in ecommerce and retail stores? Retailers use generative AI to automate customer interactions, optimize supply chains, personalize marketing campaigns, and improve operational efficiency. - ### Can generative AI automate retail workflows? Yes, generative AI can automate tasks such as customer support, product descriptions, inventory reporting, order management, and marketing content creation. Not sure which use case to start with? Tell us which part of the shopping or fulfilment journey you want generative AI to carry. An engineer replies within one business day with what we would build first and what it takes. [ Book a free meeting ↗ ](#contact-form) ## Insights [Whitepaper ### GenAI for retail: The secret weapon of customer-first brands Retail Generative AI ![A beautiful client using the digital interface of augmented reality in a modern shopping center.](https://dedicatted.com/wp-content/uploads/2025/06/Designing-Platform-for-Instant-Market-Penetration-94.png) See how GenAI on AWS helps customer-first retailers boost margins, enable conversational search, AI stylists, and smarter personalization – unlocking up to $390 billion in value. ](https://dedicatted.com/insights/genai-for-retail-the-secret-weapon-of-customer-first-brands) [Whitepaper ### Empowering Customer-First Retailers with the Genius of GenAI Retail Generative AI ![Young woman standing in front of holographic product displays in a futuristic in-store environment, symbolizing AI-powered retail and immersive shopping experiences](https://dedicatted.com/wp-content/uploads/2025/05/Designing-Platform-for-Instant-Market-Penetration-34-1024x537.png) Discover how AWS GenAI empowers customer-first retailers with 24/7 conversational support, personalized offers, speech-driven experiences, and scalable service – without ballooning costs. ](https://dedicatted.com/insights/empowering-customer-first-retailers-with-the-genius-of-genai) [Blog ### LLMOps for Enterprises: What It Is and How to Scale GenAI in Production Financial Services High Tech Software Insurance Manufacturing Generative AI Managed Services ![Code running on computer screens and tablets in an empty data center](https://dedicatted.com/wp-content/uploads/2025/07/Designing-Platform-for-Instant-Market-Penetration-100.png) Unlock scalable GenAI success with enterprise-grade LLMOps – streamline deployment, ensure monitoring, maintain agility, and govern models effectively. ](https://dedicatted.com/insights/llmops-for-enterprises-what-it-is-and-how-to-scale-genai-in-production) [Blog ### Winning the Shopper’s Mind: How AI Personalization Drives Loyalty Retail Generative AI Managed Services ![The future of shopping: how AI personalization is changing retail](https://dedicatted.com/wp-content/uploads/2025/08/Designing-Platform-for-Instant-Market-Penetration-44.png) Discover how AI personalization helps retailers win loyalty – by delivering seamless, hyper-personal experiences across channels and operations. ](https://dedicatted.com/insights/winning-the-shoppers-mind-how-ai-personalization-drives-loyalty) ## FAQ ### How does generative AI improve customer experience in retail? Generative AI helps retailers deliver personalized recommendations, faster support, customized promotions, and more engaging shopping experiences. ### Can generative AI improve ecommerce conversions? Yes, AI-powered personalization and automated customer engagement can increase conversion rates and improve customer retention. ### How does generative AI help with retail marketing? Generative AI can create personalized campaigns, product descriptions, email content, and promotional materials at scale. ### What are the benefits of generative AI for retailers? Benefits include improved customer engagement, reduced operational costs, better inventory management, increased efficiency, and higher sales performance. ### What ROI can generative AI deliver in retail? Generative AI can improve productivity, increase conversion rates, automate repetitive tasks, and optimize retail operations. ### Is generative AI suitable for small and mid-sized retail businesses? Yes, SMB retailers can use generative AI to automate workflows, personalize customer experiences, and compete more effectively without large infrastructure investments. ### How much does generative AI development for retail cost? Costs vary depending on project scope, integrations, infrastructure, and customization requirements. ### What factors affect the cost of retail AI solutions? Pricing depends on data complexity, AI model selection, ecommerce integrations, infrastructure, scalability requirements, and customization level. ## What is Generative AI for Retail? Generative AI for retail is the use of AI models and automation systems to improve customer experiences, optimize ecommerce operations, automate workflows, and enhance decision-making across retail businesses. Dedicatted provides enterprise generative ai retail solutions for ecommerce platforms, retail operations, and customer engagement systems. ## Generative AI in Retail Generative AI in retail enables businesses to automate operations, personalize customer experiences, optimize ecommerce performance, and improve decision-making using large language models, retrieval-augmented generation, and AI-powered automation. Dedicatted develops scalable generative AI solutions tailored to the operational, marketing, and customer-engagement needs of modern retail and ecommerce businesses. Dedicatted develops scalable AI solutions for retail businesses of all sizes, including ecommerce platforms, omnichannel retailers, and digital-first retail brands. Our retail AI services support: - AI-powered product recommendations and personalization - Customer support automation and intelligent chatbots - Marketing content generation and campaign automation - Inventory management and demand forecasting - Pricing optimization and promotional analytics - Order management and ecommerce workflow automation - Retail analytics and decision support With our generative AI expertise, retail organizations can modernize legacy processes, automate customer engagement, and deliver more relevant, personalized shopping experiences across every touchpoint. ## Generative AI Ecommerce Solutions Generative AI transforms ecommerce by automating customer interactions, personalizing the shopping journey, and improving the efficiency of marketing, sales, and operational workflows. Dedicatted helps ecommerce and retail businesses implement AI systems integrated with: - Ecommerce platforms and storefronts - Product information management (PIM) systems - Customer relationship management (CRM) tools - Order management and fulfillment platforms - Marketing automation and email systems - Retail analytics and business intelligence platforms By embedding generative AI directly into ecommerce workflows, retailers can increase conversion rates, accelerate customer support, and unlock new revenue opportunities through personalization at scale. ## Gen AI in Retail Operations Generative AI streamlines retail operations by automating repetitive tasks, accelerating workflows, and supporting smarter decision-making across marketing, merchandising, and customer service teams. Common retail generative ai use cases include: - Automated product description and content generation - Personalized shopping assistants and AI chatbots - Dynamic pricing and promotional content automation - Demand forecasting and inventory optimization - Customer feedback analysis and sentiment summaries - Marketing campaign generation and A/B testing - Loyalty program insights and personalization By scaling generative AI across operational workflows, retailers reduce manual effort, improve customer satisfaction, and free up teams to focus on strategic, revenue-driving initiatives. ### Cloud Infrastructure for Retail AI Modern retail AI relies on scalable, secure cloud infrastructure capable of handling high-volume customer data, real-time analytics, and continuous AI model operations. Dedicatted helps retail businesses implement: - Secure cloud architectures aligned with retail compliance and PCI standards - Scalable AI workload orchestration and MLOps pipelines - Data lakes and governed data platforms for retail AI training - Encryption, access controls, and audit logging for customer data - High-availability AI deployments for ecommerce and storefront workloads - FinOps and cost optimization for retail AI infrastructure As an [AWS Premier Tier Partner](https://dedicatted.com/our-partners/aws), Dedicatted designs and operates retail-grade AI infrastructure that meets enterprise security, performance, and scalability expectations. ### AI Solutions for Modern Retail and Ecommerce Dedicatted partners with retail and ecommerce organizations end-to-end, from discovery and use-case prioritization through model selection, deployment, optimization, and ongoing support. Our team combines deep generative AI expertise with retail and ecommerce experience to deliver measurable business value across marketing, sales, customer engagement, and operations. With generative AI built directly into the retail technology stack, brands gain a powerful platform for growth — driving conversions, deepening customer loyalty, and accelerating innovation across every channel. Show more ## Get started with a generative AI consultant Outline your commerce, merchandising or customer service use case. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Generative AI for transformation in Manufacturing](https://dedicatted.com/services/generative-ai/manufacturing) **Published:** February 11, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Generative AI](https://dedicatted.com/services/generative-ai) 3. Generative AI for transformation in Manufacturing # Manufacturing Industry Innovation With Generative AI Generative AI in manufacturing helps businesses optimize production, supply chains, workforce operations, and industrial processes by combining AI models with technologies such as industrial IoT, digital twins, and real-time operational data. [Book a free meeting](#contact-form) [See our offerings](#offerings) ![Yellow industrial robot arm on a factory production line](https://dedicatted.com/wp-content/uploads/2026/08/manufacturing-industrial-robot-arm-1024x683.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner ## What is Generative AI in Manufacturing? - ### What is generative AI in manufacturing? Generative AI in manufacturing uses advanced AI models to automate processes, optimize operations, improve production efficiency, and support data-driven decision-making. - ### What are generative AI manufacturing services? Generative AI manufacturing services include predictive maintenance solutions, AI-powered automation, supply chain optimization, digital twin integration, and intelligent analytics. - ### What does a manufacturing AI development company do? A manufacturing AI company designs and deploys AI-powered systems that help manufacturers improve productivity, reduce downtime, and optimize operational workflows. ## Industry Challenges ### Financial Resilience ### Financial Resilience Gen AI helps you modify pricing, production costs, and wages to stay competitive. It also improves decision-making when rising interest rates threaten growth, and global trade disputes and political instability create issues. ![Hand using digital stylus on tablet displaying data analytics dashboard with colorful pie charts, bar graphs and KPI metrics for business performance tracking.](https://dedicatted.com/wp-content/uploads/2025/02/jakub-zerdzicki-ykgLX_CwtDw-unsplash-1024x683.jpg) ### Sustainable Operations ### Sustainable Operations Gen AI simplifies reducing your organization’s environmental impact, including carbon emissions across your value chain. As ESG reporting requirements approach, AI helps you make smart investments in options and processes. And with customer behaviors changing, it supports lowering the environmental impact of your products. ![Aerial view of a parking lot filled with semi-trailer trucks parked in diagonal rows, adjacent to a road with cars and a sidewalk.](https://dedicatted.com/wp-content/uploads/2025/02/marcin-jozwiak-kGoPcmpPT7c-unsplash-1024x682.jpg) ### Material Shortages ### Material Shortages Generative AI helps build resilience in your supply chain against material shortages and rising prices, while also offering information into its carbon impact for ESG compliance. It also helps you identify key areas, select improved suppliers, and create valuable local partnerships. ![Stack of assorted wooden boards and plywood sheets stored vertically on a shelf in a workshop or lumber area.](https://dedicatted.com/wp-content/uploads/2025/02/elena-rouame-GXi2HxZ35NQ-unsplash-1024x683.jpg) ### Talent Retention ### Talent Retention As an aging workforce leads to expertise loss, Gen AI can handle repetitive processes, expand workflows, and improve internal operations. This helps companies manage the effects of fewer skilled workers and changing wage and workplace expectations. ![The image shows a group of people gathered around a wooden table in a well-lit room, likely during a meeting or collaborative work session. Some are writing on notepads, suggesting brainstorming or note-taking activity. The casual attire and natural light give the setting a relaxed but focused atmosphere.](https://dedicatted.com/wp-content/uploads/2025/02/dylan-gillis-KdeqA3aTnBY-unsplash-1024x683.jpg) ### Tech Advancements ### Tech Advancements Generative AI helps manufacturers meet rising Industry 4.0 demands, increasing productivity and enhancing processes. By aligning Gen AI investments with existing digital projects like IoT, digital twins, and the industrial metaverse, manufacturers can unlock bigger gains. ![Yellow industrial robot arm on a factory production line](https://dedicatted.com/wp-content/uploads/2026/08/manufacturing-industrial-robot-arm-1024x683.jpg) ### Financial Resilience Gen AI helps you modify pricing, production costs, and wages to stay competitive. It also improves decision-making when rising interest rates threaten growth, and global trade disputes and political instability create issues. ![Hand using digital stylus on tablet displaying data analytics dashboard with colorful pie charts, bar graphs and KPI metrics for business performance tracking.](https://dedicatted.com/wp-content/uploads/2025/02/jakub-zerdzicki-ykgLX_CwtDw-unsplash-1024x683.jpg) ### Sustainable Operations Gen AI simplifies reducing your organization’s environmental impact, including carbon emissions across your value chain. As ESG reporting requirements approach, AI helps you make smart investments in options and processes. And with customer behaviors changing, it supports lowering the environmental impact of your products. ![Aerial view of a parking lot filled with semi-trailer trucks parked in diagonal rows, adjacent to a road with cars and a sidewalk.](https://dedicatted.com/wp-content/uploads/2025/02/marcin-jozwiak-kGoPcmpPT7c-unsplash-1024x682.jpg) ### Material Shortages Generative AI helps build resilience in your supply chain against material shortages and rising prices, while also offering information into its carbon impact for ESG compliance. It also helps you identify key areas, select improved suppliers, and create valuable local partnerships. ![Stack of assorted wooden boards and plywood sheets stored vertically on a shelf in a workshop or lumber area.](https://dedicatted.com/wp-content/uploads/2025/02/elena-rouame-GXi2HxZ35NQ-unsplash-1024x683.jpg) ### Talent Retention As an aging workforce leads to expertise loss, Gen AI can handle repetitive processes, expand workflows, and improve internal operations. This helps companies manage the effects of fewer skilled workers and changing wage and workplace expectations. ![The image shows a group of people gathered around a wooden table in a well-lit room, likely during a meeting or collaborative work session. Some are writing on notepads, suggesting brainstorming or note-taking activity. The casual attire and natural light give the setting a relaxed but focused atmosphere.](https://dedicatted.com/wp-content/uploads/2025/02/dylan-gillis-KdeqA3aTnBY-unsplash-1024x683.jpg) ### Tech Advancements Generative AI helps manufacturers meet rising Industry 4.0 demands, increasing productivity and enhancing processes. By aligning Gen AI investments with existing digital projects like IoT, digital twins, and the industrial metaverse, manufacturers can unlock bigger gains. ![Yellow industrial robot arm on a factory production line](https://dedicatted.com/wp-content/uploads/2026/08/manufacturing-industrial-robot-arm-1024x683.jpg) ## Business Opportunities Five places generative AI pays off in manufacturing, ranked by business value and how easy each is to ship. #### Business Opportunities Use Cases Business Value Case Simplicity 1 Insight Generation Quick win Business Value: 86 Case Simplicity: 78 Gen AI uses structured and unstructured data to improve your organization’s information systems. By translating this data, it helps employees work more efficiently. [Discuss this use case](#contact-form) 2 Process Efficiency Business Value: 80 Case Simplicity: 60 Generative AI simplifies operations by examining production data, enhancing workflows, and streamlining approvals. Ongoing monitoring enables flexible adjustments, reducing resource use. [Discuss this use case](#contact-form) 3 Optimizing Supply Chain Business Value: 74 Case Simplicity: 46 Gen AI evaluates data, forecasts, and projections to suggest supply chain improvements. Inventory and schedules then adjust to meet demand and address external issues. [Discuss this use case](#contact-form) 4 Assisted Equipment Maintenance Business Value: 62 Case Simplicity: 70 Predictive analytics in Gen AI uses data to detect irregularities and trends that signal possible equipment failures or maintenance requirements. This lets your team make sure maintenance happens on time. [Discuss this use case](#contact-form) 5 Rapid Prototyping Business Value: 54 Case Simplicity: 56 Generative AI accelerates product design, enhances engineering with goal-focused components, and shortens development and testing phases for faster results. [Discuss this use case](#contact-form) Business Value Case Simplicity Where each use case lands Strategic bets Quick wins Easy extras 1 Insight Generation 2 Process Efficiency 3 Optimizing Supply Chain 4 Assisted Equipment Maintenance 5 Rapid Prototyping Business Value ↑ ← Harder to ship Case Simplicity Easier → ## How is Generative AI implemented in manufacturing? - ### How do manufacturers implement generative AI? Manufacturers implement generative AI by identifying operational challenges, integrating production data, selecting AI models, and deploying intelligent automation solutions. - ### What is the process of generative AI adoption in manufacturing? The process includes discovery, infrastructure assessment, data preparation, model selection, system integration, testing, deployment, and continuous optimization. - ### How long does it take to implement generative AI in manufacturing? Implementation timelines typically range from several weeks to a few months depending on system complexity, factory infrastructure, and integration requirements. ## What technologies power manufacturing AI solutions? - ### What is RAG in manufacturing AI? RAG (Retrieval-Augmented Generation) enables AI systems to retrieve and use production data, operational manuals, and technical documentation to generate more accurate insights. - ### What is LLMOps in manufacturing? LLMOps refers to the deployment, monitoring, maintenance, and optimization of large language models used in manufacturing environments. - ### What AI models are used in manufacturing solutions? Manufacturing AI solutions commonly use models such as GPT, Claude, LLaMA, and industry-specific AI systems depending on operational and infrastructure needs. ## Our Generative AI Offerings - ### GenAI Discovery Idea To Use Case: analysis and validation - ✓ Use cases and business priorities evaluation - ✓ Data quality and availability assessment - ✓ Architecture and integration design - ✓ Fast Proof of Concept validation [ Book a discovery call → ](#contact-form) - ### GenAI Development Validation to Production: accelerate time to market - ✓ MVP development - ✓ Solution engineering and deployment - ✓ Scaling and Integration - ✓ Training and adoption support [ Discuss a build → ](#contact-form) - ### GenAI Augmentation Expertise to Execution: consulting and project support - ✓ Augmenting engineering expertise - ✓ Augmenting engineering capacity - ✓ Consulting on all stages of development - ✓ Solution audit and optimization [ Discuss augmentation → ](#contact-form) ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) ## What are Generative AI use cases in manufacturing? - ### What are the main use cases of generative AI in manufacturing? Key use cases include predictive maintenance, production planning, quality control, supply chain optimization, process automation, and industrial reporting. - ### How is generative AI used in factories and industrial operations? Factories use generative AI to automate repetitive workflows, improve equipment monitoring, optimize resource allocation, and support operational decision-making. - ### Can generative AI automate manufacturing processes? Yes, generative AI can automate tasks such as production scheduling, maintenance reporting, inventory analysis, and operational documentation. Not sure which use case to start with? Tell us where generative AI should help on the shop floor or in your supply base. An engineer replies within one business day with what we would build first and what it takes. [ Book a free meeting ↗ ](#contact-form) ## Insights [Case study ### Automating remittance workflows and reducing costs with GenAI and AWS Manufacturing Cloud Modernization Cloud Security Generative AI ![Case study: Hercules Crane & Lifting Supplies - Automating remittance workflows and reducing costs with GenAI and AWS. | Dedicatted](https://dedicatted.com/wp-content/uploads/2025/03/Hercules.-Cover-1024x537.webp) How Dedicatted streamlined financial operations with AWS and GenAI, cutting costs by 40% and improving efficiency through automated document processing. ](https://dedicatted.com/insights/automating-remittance-workflows-and-reducing-costs-with-genai-and-aws) [Blog ### How to Get the Most of Amazon Q for Business? We Did Our Research (PDF Inside) Automotive Financial Services Healthcare High Tech Software Retail Agentic AI Cloud and Infrastructure Security Generative AI Managed Services Security Compliance ![‘Amazon Q’ and the new era of AI for business: A picture of AWS VP Matt Wood](https://dedicatted.com/wp-content/uploads/2025/09/Designing-Platform-for-Instant-Market-Penetration-70.png) Learn how to maximize Amazon Q for Business. Our research shows 300% ROI with real use cases, pilot steps, and a free executive guide (PDF). ](https://dedicatted.com/insights/how-to-get-the-most-of-amazon-q-for-business-we-did-our-research-pdf-inside) [Blog ### Webinar | Practical AI Solutions Every Business Leader Needs to Know Automotive Financial Services Healthcare Manufacturing Retail Agentic AI Generative AI Managed Services ![Close-up on AI Solution, which determines anomalies and failures during production at the earliest stage](https://dedicatted.com/wp-content/uploads/2025/08/Designing-Platform-for-Instant-Market-Penetration-48.png) Practical AI solutions for businesses: boost efficiency, reduce costs, and drive growth with expert insights and real cases. ](https://dedicatted.com/insights/webinar-practical-ai-solutions-every-business-leader-needs-to-know) [Case study ### From Manual to Automated: AI-Powered Workflow Revolution with Amazon Q Manufacturing Generative AI ![Engineers and contractors on construction site, following the progess of bridge and road infrastructure development. Two experts on construction platform in reflective wear with hardhats discussing project phases and successful previous developments.](https://dedicatted.com/wp-content/uploads/2025/09/Designing-Platform-for-Instant-Market-Penetration-63.png) Discover how Dedicatted's customer boosted efficiency with Amazon Q. AI-powered knowledge management now streamlines reports, saves time, and ensures security. ](https://dedicatted.com/insights/from-manual-to-automated-ai-powered-workflow-revolution-with-amazon-q-2) ## FAQ ### How does generative AI work with industrial IoT? Generative AI can analyze data from industrial IoT devices to improve predictive maintenance, monitor equipment performance, and optimize manufacturing operations. ### What are digital twins in manufacturing AI? Digital twins are virtual representations of physical systems that help manufacturers simulate operations, analyze performance, and improve decision-making using AI. ### Can generative AI improve supply chain management? Yes, generative AI helps manufacturers forecast demand, identify risks, optimize logistics, and improve supply chain efficiency. ### What are the benefits of generative AI for manufacturers? Benefits include increased operational efficiency, reduced downtime, improved product quality, lower operational costs, and faster decision-making. ### What ROI can generative AI deliver in manufacturing? Generative AI can reduce manual workloads, optimize production processes, minimize equipment failures, and improve overall productivity. ### Is generative AI suitable for small and mid-sized manufacturers? Yes, SMB manufacturers can use generative AI to automate operations, improve efficiency, and gain insights without large infrastructure investments. ### How much does generative AI development for manufacturing cost? Costs vary depending on project complexity, infrastructure, integrations, and customization requirements. ### What factors affect the cost of manufacturing AI solutions? Pricing depends on production scale, data availability, infrastructure complexity, AI model selection, integration needs, and customization level. ## What is Generative AI in Manufacturing? Generative AI in manufacturing is the use of AI models and automation systems to optimize production processes, improve operational efficiency, automate workflows, and support data-driven manufacturing decisions. Dedicatted provides enterprise generative ai manufacturing solutions for industrial operations, smart factories, and manufacturing platforms. ## Generative AI Manufacturing Solutions Our manufacturing AI services support: - Predictive maintenance and equipment monitoring - Production planning and scheduling automation - Quality control and defect detection - Supply chain optimization and logistics - Industrial process automation - Operational reporting and analytics - Digital twin integration and simulation ## Generative AI for Manufacturing Operations Dedicatted helps manufacturing companies integrate AI systems with: - Industrial IoT platforms and sensor networks - ERP, MES, and SCADA manufacturing systems - Production data platforms and historians - CAD, CAM, and digital twin environments - Warehouse management and logistics systems - Workforce management and operational tools ## Cloud and AI Infrastructure for Manufacturing Dedicatted helps manufacturers implement: - Scalable cloud architectures for industrial AI workloads - AI workload orchestration and MLOps pipelines - Manufacturing data lakes and governed data platforms - Encryption, access controls, and operational security - High-availability AI deployments for factory environments - FinOps and cost optimization for manufacturing AI infrastructure As an [AWS Premier Tier Partner](https://dedicatted.com/our-partners/aws), Dedicatted designs and operates manufacturing-grade AI systems that meet enterprise security, performance, and operational expectations. ### Enterprise Generative AI Manufacturing Services Dedicatted partners with manufacturing organizations end-to-end, from discovery and infrastructure assessment through deployment, optimization, and ongoing support, to deliver measurable business value from generative AI investments. Our enterprise generative ai manufacturing services are designed for scale, reliability, and long-term operational impact across smart factories, industrial operations, and manufacturing technology platforms. Show more ## Get started with a generative AI consultant Outline your production, quality or maintenance use case. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Generative AI for Healthcare](https://dedicatted.com/services/generative-ai/healthcare) **Published:** February 11, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Generative AI](https://dedicatted.com/services/generative-ai) 3. Generative AI for Healthcare # The Future of Healthcare is AI Generative AI for healthcare helps providers and payers process and analyze clinical notes, medical images, patient records, and other healthcare data using AI models to improve operational efficiency, decision-making, and healthcare workflows. [Book a free meeting](#contact-form) [See our offerings](#offerings) ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner ## What is Generative AI for Healthcare? - ### What is generative AI in healthcare? Generative AI in healthcare uses advanced AI models to automate clinical workflows, analyze medical data, generate documentation, and improve patient care. - ### What are generative AI healthcare services? Generative AI healthcare services include AI assistant development, medical document automation, clinical data analysis, patient support solutions, and AI system integration. - ### What does a healthcare AI development company do? A healthcare AI company designs and deploys AI-powered solutions that help healthcare providers improve operational efficiency, patient engagement, and decision-making. ## Industry Challenges ### Cost Constraints ### Cost Constraints Staffing shortages, rising costs, and reduced reimbursement rates create financial challenges for health systems, impacting patient experience, health equity, care quality, and availability. Generative AI can help alleviate these pressures. ![U.S. one dollar bill with George Washington's portrait and green seal](https://dedicatted.com/wp-content/uploads/2025/02/kenny-eliason-8fDhgAN5zG0-unsplash-1024x802.jpg) ### Billing Complexities ### Billing Complexities Gen AI can help address challenges in healthcare such as shifting to value-based care, changing reimbursement rates, lack of price transparency, limited patient payment options, and the need to balance quality care with affordability. ![Customer making a contactless payment using Google Pay on a smartphone at a modern point-of-sale terminal.](https://dedicatted.com/wp-content/uploads/2025/02/clay-banks-XvS-uKUoUao-unsplash-1024x683.jpg) ### Customized Treatments ### Customized Treatments Patients now have access to more medical information than ever before, and they want to be involved in their own healthcare decisions. Gen AI makes it easier to collaborate with these informed patients, enabling personalized diagnoses and treatments tailored to their individual needs. ![Close-up of a female doctor in a white coat using a smartphone, with a stethoscope around her neck and red nail polish on her fingers.](https://dedicatted.com/wp-content/uploads/2025/02/national-cancer-institute-cQ8FfVNvbew-unsplash-1024x683.jpg) ### Error-Free Processing ### Error-Free Processing Processing healthcare claims is often a slow, hard, and error-prone process. Gen AI makes it easier to automate this, correctly apply claims processing rules within regulatory guidelines, and execute contracted agreements. ![Person working on a desktop computer displaying code on a large monitor, suggesting software development or programming in progress.](https://dedicatted.com/wp-content/uploads/2025/02/lee-campbell-6njoEbtarec-unsplash-1024x684.jpg) ### Provider Quality ### Provider Quality Gen AI helps you build and maintain comprehensive, high-performing provider networks, giving you better control over costs and ensuring quality of care. It also allows to effectively analyze the provider’s performance, which is extremely important for improving the network and managing contracts. ![Healthcare team joining hands together showing medical collaboration doctors nurses unity and patient care commitment.](https://dedicatted.com/wp-content/uploads/2025/01/image_healthcare-1024x418.webp) ### Cost Constraints Staffing shortages, rising costs, and reduced reimbursement rates create financial challenges for health systems, impacting patient experience, health equity, care quality, and availability. Generative AI can help alleviate these pressures. ![U.S. one dollar bill with George Washington's portrait and green seal](https://dedicatted.com/wp-content/uploads/2025/02/kenny-eliason-8fDhgAN5zG0-unsplash-1024x802.jpg) ### Billing Complexities Gen AI can help address challenges in healthcare such as shifting to value-based care, changing reimbursement rates, lack of price transparency, limited patient payment options, and the need to balance quality care with affordability. ![Customer making a contactless payment using Google Pay on a smartphone at a modern point-of-sale terminal.](https://dedicatted.com/wp-content/uploads/2025/02/clay-banks-XvS-uKUoUao-unsplash-1024x683.jpg) ### Customized Treatments Patients now have access to more medical information than ever before, and they want to be involved in their own healthcare decisions. Gen AI makes it easier to collaborate with these informed patients, enabling personalized diagnoses and treatments tailored to their individual needs. ![Close-up of a female doctor in a white coat using a smartphone, with a stethoscope around her neck and red nail polish on her fingers.](https://dedicatted.com/wp-content/uploads/2025/02/national-cancer-institute-cQ8FfVNvbew-unsplash-1024x683.jpg) ### Error-Free Processing Processing healthcare claims is often a slow, hard, and error-prone process. Gen AI makes it easier to automate this, correctly apply claims processing rules within regulatory guidelines, and execute contracted agreements. ![Person working on a desktop computer displaying code on a large monitor, suggesting software development or programming in progress.](https://dedicatted.com/wp-content/uploads/2025/02/lee-campbell-6njoEbtarec-unsplash-1024x684.jpg) ### Provider Quality Gen AI helps you build and maintain comprehensive, high-performing provider networks, giving you better control over costs and ensuring quality of care. It also allows to effectively analyze the provider’s performance, which is extremely important for improving the network and managing contracts. ![Healthcare team joining hands together showing medical collaboration doctors nurses unity and patient care commitment.](https://dedicatted.com/wp-content/uploads/2025/01/image_healthcare-1024x418.webp) ## Business Opportunities Five places generative AI pays off in healthcare, ranked by business value and how easy each is to ship. #### Business Opportunities Use Cases Business Value Case Simplicity 1 Member Acquisition Quick win Business Value: 88 Case Simplicity: 86 Gen AI helps to grab the attention of new members with customized benefit plans, which will improve member retention, and create great opportunities for intermediaries who are just entering the market. [Discuss this use case](#contact-form) 2 Compliance Assurance Business Value: 84 Case Simplicity: 48 Gen AI analyzes contracts and CMS guidelines to ensure accurate payments and identify opportunities to improve patient care and optimize costs by examining provider practice patterns. [Discuss this use case](#contact-form) 3 Admin Efficiency Business Value: 74 Case Simplicity: 52 Gen AI greatly improves administrative tasks such as scheduling, billing, prior authorization, claims tracking, and payment processing. [Discuss this use case](#contact-form) 4 Rapid Processing Business Value: 66 Case Simplicity: 56 Gen AI streamlines healthcare administration by managing and synthesizing care notes, creating comprehensive member profiles for payment authorization, and generating support materials for payment justification and reporting. This simplifies and speeds up payment processes. [Discuss this use case](#contact-form) 5 Automated Support Business Value: 58 Case Simplicity: 64 AI-powered bots automate time-consuming tasks such as form completion, answering routine questions, streamlining complex processes, and improving the overall experience for healthcare professionals, patients, care teams, and administrative staff. [Discuss this use case](#contact-form) Business Value Case Simplicity Where each use case lands Strategic bets Quick wins Easy extras 1 Member Acquisition 2 Compliance Assurance 3 Admin Efficiency 4 Rapid Processing 5 Automated Support Business Value ↑ ← Harder to ship Case Simplicity Easier → ## How is Generative AI implemented in healthcare? - ### How do healthcare organizations implement generative AI? Healthcare organizations implement generative AI by identifying clinical or operational use cases, preparing compliant datasets, integrating AI models, and deploying secure workflows. - ### What is the process of generative AI adoption in healthcare? The adoption process includes discovery, compliance assessment, data preparation, AI model selection, system integration, testing, deployment, and monitoring. - ### How long does it take to implement generative AI in healthcare? Implementation timelines typically range from several weeks to a few months depending on system complexity, compliance requirements, and integrations. ## What technologies power healthcare AI solutions? - ### What is RAG in healthcare AI? RAG (Retrieval-Augmented Generation) allows AI systems to retrieve and use medical knowledge, clinical guidelines, and healthcare data to generate more accurate responses. - ### What is LLMOps in healthcare? LLMOps is the practice of deploying, monitoring, securing, and optimizing large language models in healthcare environments. - ### What AI models are used in healthcare solutions? Healthcare AI solutions commonly use models such as GPT, Claude, LLaMA, and domain-specific medical AI models depending on compliance and business needs. ## Our Generative AI Offerings - ### GenAI Discovery Idea To Use Case: analysis and validation - ✓ Use cases and business priorities evaluation - ✓ Data quality and availability assessment - ✓ Architecture and integration design - ✓ Fast Proof of Concept validation [ Book a discovery call → ](#contact-form) - ### GenAI Development Validation to Production: accelerate time to market - ✓ MVP development - ✓ Solution engineering and deployment - ✓ Scaling and Integration - ✓ Training and adoption support [ Discuss a build → ](#contact-form) - ### GenAI Augmentation Expertise to Execution: consulting and project support - ✓ Augmenting engineering expertise - ✓ Augmenting engineering capacity - ✓ Consulting on all stages of development - ✓ Solution audit and optimization [ Discuss augmentation → ](#contact-form) ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) ## What are Generative AI use cases in healthcare? - ### What are the main use cases of generative AI in healthcare? Key use cases include medical documentation automation, patient communication, clinical decision support, appointment management, and healthcare data summarization. - ### How is generative AI used in hospitals and clinics? Hospitals and clinics use generative AI to automate administrative workflows, improve patient support, assist medical staff, and optimize operational efficiency. - ### Can generative AI automate healthcare workflows? Yes, generative AI can automate tasks such as clinical note generation, medical record summarization, patient triage, and report creation. Not sure which use case to start with? Tell us which clinical or operational workflow you want generative AI to carry. An engineer replies within one business day with what we would build first and what it takes. [ Book a free meeting ↗ ](#contact-form) ## Insights [Case study ### HealthTech in Action: OkDoc App Enhances Patient Experience and Efficiency Healthcare Managed Services ![Female doctor discussing medical record with patient in clinic exam room, enhanced with the "OkDoc" logo](https://dedicatted.com/wp-content/uploads/2025/09/Designing-Platform-for-Instant-Market-Penetration-62.png) See how Dedicatted developed OkDoc, a HealthTech app that streamlines patient care and integrates with hospital legacy systems. ](https://dedicatted.com/insights/healthtech-in-action-okdoc-app-enhances-patient-experience-and-efficiency) [Case study ### Building a Compliance-Ready Cloud: Healthcare platform AWS to Azure Migration Healthcare Cloud Data Migration & Modernization Cloud Migration Cloud Modernization ![Healthcare professional conducting a virtual patient consultation via laptop with stethoscope and 'Doctor Online' signage – telehealth compliance setup](https://dedicatted.com/wp-content/uploads/2025/09/Designing-Platform-for-Instant-Market-Penetration-98.png) See how Dedicatted modernized Healthcare SaaS platform with AWS to Azure Migration, ensuring compliance, cost savings, and faster delivery. ](https://dedicatted.com/insights/building-a-compliance-ready-cloud-aws-to-azure-migration) [Blog ### LLMOps for Enterprises: What It Is and How to Scale GenAI in Production Financial Services High Tech Software Insurance Manufacturing Generative AI Managed Services ![Code running on computer screens and tablets in an empty data center](https://dedicatted.com/wp-content/uploads/2025/07/Designing-Platform-for-Instant-Market-Penetration-100.png) Unlock scalable GenAI success with enterprise-grade LLMOps – streamline deployment, ensure monitoring, maintain agility, and govern models effectively. ](https://dedicatted.com/insights/llmops-for-enterprises-what-it-is-and-how-to-scale-genai-in-production) [Case study ### Smart Environmental Reporting with GenAI Healthcare Cloud Modernization Cloud Security Generative AI ![Case study: Smarter Environmental Reporting with GenAI, with laboratory microscope and company logo on blue background](https://dedicatted.com/wp-content/uploads/2025/03/Designing-Platform-for-Instant-Market-Penetration-2025-11-14T122225.965.png) See how CASSEN Labs used GenAI to cut lab report time by 80%, triple analysis capacity, and expand to B2C markets in this AI transformation case study. ](https://dedicatted.com/insights/smart-environmental-reporting-with-genai) ## FAQ ### Is generative AI secure for healthcare organizations? Yes, healthcare AI solutions can be built with enterprise-grade security, encryption, and compliance measures to protect sensitive patient data. ### Can generative AI solutions comply with HIPAA requirements? Yes, generative AI systems can be designed to support HIPAA compliance through secure infrastructure, access controls, and data governance practices. ### How is patient data protected in AI healthcare systems? Patient data is protected through encryption, secure cloud environments, role-based access control, and compliance-focused data management policies. ### What are the benefits of generative AI for healthcare providers? Benefits include reduced administrative workload, improved patient experience, faster access to information, and increased operational efficiency. ### What ROI can generative AI deliver in healthcare? Generative AI can significantly reduce manual processes, lower operational costs, and improve staff productivity across healthcare organizations. ### Is generative AI suitable for small healthcare practices? Yes, small and mid-sized healthcare providers can use generative AI to automate repetitive tasks and improve patient communication without large infrastructure investments. ### How much does generative AI development for healthcare cost? Costs depend on project scope, compliance requirements, integrations, and customization needs, ranging from pilot projects to enterprise-scale implementations. ### What factors affect the cost of healthcare AI solutions? Pricing is influenced by data complexity, regulatory requirements, infrastructure, AI model selection, integrations, and security standards. ## What is Generative AI for Healthcare? Generative AI for healthcare is the use of AI models, large language models, and healthcare data systems to automate clinical workflows, improve decision-making, analyze medical data, and optimize healthcare operations. Dedicatted provides enterprise generative ai healthcare solutions for providers, healthcare platforms, medical operations, and healthcare technology companies. ## Generative AI in Healthcare Generative AI in healthcare enables organizations to automate clinical and administrative workflows, analyze medical data, and improve patient experiences using large language models, retrieval-augmented generation, and AI-powered automation. Dedicatted develops scalable generative AI solutions tailored to the operational, regulatory, and compliance requirements of modern healthcare providers and payers. ## Generative AI and Healthcare Operations Generative AI streamlines healthcare operations by automating documentation, supporting clinical staff, and accelerating administrative workflows across providers, payers, and healthcare technology platforms. Our generative ai for healthcare services support: - Medical documentation and clinical note automation - AI-powered patient communication and virtual assistants - Healthcare data summarization and analysis - Clinical decision support and knowledge retrieval - Appointment and workflow management automation - Compliance monitoring and reporting - Healthcare analytics and operational intelligence By embedding generative AI directly into healthcare workflows, organizations can reduce administrative workload, improve patient experience, and free clinicians to focus on care delivery and complex cases. ## Generative AI in Medicine Generative AI in medicine supports clinicians, researchers, and healthcare technology teams by accelerating access to medical knowledge, automating documentation, and enabling more efficient analysis of clinical and operational data. Our healthcare AI expertise includes: - Clinical workflow automation and documentation - Medical knowledge retrieval and clinical decision support - AI-powered patient engagement and triage tools - Healthcare data integration and interoperability - Compliance-focused AI architecture and governance - Custom medical AI model development and fine-tuning - Healthcare AI deployment, monitoring, and optimization As an [AWS Premier Tier Partner](https://dedicatted.com/our-partners/aws), Dedicatted designs and operates healthcare-grade AI systems that meet enterprise security, performance, and regulatory expectations. ## Healthcare AI Infrastructure and Cloud Solutions Secure, compliant cloud infrastructure is the foundation of every successful healthcare AI program. Our engineers help organizations implement: - HIPAA-aligned cloud architectures for healthcare workloads - Scalable AI workload orchestration and MLOps pipelines - Healthcare data lakes and governed data platforms - Encryption, access controls, and audit logging - Disaster recovery and high-availability AI deployments - FinOps and cost optimization for healthcare AI infrastructure Dedicatted delivers healthcare-ready cloud and AI infrastructure that supports compliance, performance, and operational reliability at scale. ### Generative AI for Healthcare Analytics Generative AI transforms healthcare analytics by turning raw clinical, operational, and patient data into actionable insights, summaries, and recommendations. Our healthcare analytics capabilities include: - Clinical trend analysis and predictive insights - Operational performance and utilization analytics - Population health and patient segmentation analytics - Quality, safety, and outcomes reporting - Compliance reporting and regulatory analytics - Executive dashboards and natural-language reporting With generative AI built directly into analytics workflows, healthcare leaders gain faster access to the insights they need to make better, data-driven decisions across clinical operations, patient experience, and compliance. ### Enterprise Generative AI Healthcare Solutions Dedicatted partners with healthcare organizations end-to-end, from discovery and compliance assessment through deployment, optimization, and ongoing support, to deliver measurable business value from generative AI investments. Our enterprise generative ai healthcare solutions are designed for scale, security, and long-term operational impact across providers, payers, and healthcare technology platforms. Show more ## Get started with a generative AI consultant Outline your clinical or administrative use case. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Generative AI for Financial Services](https://dedicatted.com/services/generative-ai/financial-services) **Published:** February 11, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Generative AI](https://dedicatted.com/services/generative-ai) 3. Generative AI for Financial Services # Boosting Financial Services with Generative AI Generative AI helps financial organizations improve customer experience, modernize legacy workflows, automate complex processes, and deliver more responsive and agile financial services. It also enables companies to innovate faster while adapting to changing customer needs. [Book a free meeting](#contact-form) [See our offerings](#offerings) ![Team reviewing work together on a laptop in an office](https://dedicatted.com/wp-content/uploads/2026/08/finserv-team-shared-workflow-1024x683.jpg) AWS Generative AI The only GenAI and MSP partner in Canada AWS Agentic AI Specialization Selected Canadian partner ## What is Generative AI in Financial Services? - ### What is generative AI in financial services? Generative AI in financial services uses advanced AI models to automate workflows, analyze financial data, improve customer experiences, and support faster decision-making. - ### What are generative AI financial services? Generative AI financial services include AI assistant development, fraud detection support, financial document automation, customer service solutions, and intelligent analytics. - ### What does a financial AI development company do? A financial AI company develops and deploys AI-powered solutions that help banks, fintech companies, insurers, and financial institutions improve efficiency and operational performance. ## Industry Challenges ### Regulatory Agility ### Regulatory Agility Failing to comply with regulations often results in hefty fines and other serious consequences for your financial service organization. But with Gen AI, you will be able to create systems that make it easy to adhere to the complicated, ever-changing regulations. ![Two professionals signing documents across a desk](https://dedicatted.com/wp-content/uploads/2026/08/finserv-regulatory-documents-signing-1024x683.jpg) ### Custom Service Delivery ### Custom Service Delivery Generative AI helps financial service providers enhance the customer experience, attracting and retaining clients by simplifying personalized, speedy, and convenient service delivery. ![Adviser talking through paperwork with clients](https://dedicatted.com/wp-content/uploads/2026/08/finserv-advisor-client-consultation-1024x683.jpg) ### Cost-Effective Assistance ### Cost-Effective Assistance Providing excellent customer support can be expensive, but Generative AI can help your financial services organization invest wisely in both skilled representatives and the technology they need to assist your customers effectively. ![Client meeting with an accountant over a laptop](https://dedicatted.com/wp-content/uploads/2026/08/finserv-accountant-client-meeting-1024x683.jpg) ### Unified Workflows ### Unified Workflows Many financial services organizations struggle with internal processes that operate in silos. Gen AI can help reduce these difficulties by streamlining workflows and improving overall efficiency, thus diminishing errors and customer service delays. ![Team reviewing work together on a laptop in an office](https://dedicatted.com/wp-content/uploads/2026/08/finserv-team-shared-workflow-1024x683.jpg) ### Intelligent Data Use ### Intelligent Data Use Financial services organizations collect vast amounts of data but often struggle to manage it effectively. Gen AI makes it easier to use that data to make informed decisions and comply with regulations. ![Candlestick market chart displayed on a laptop screen](https://dedicatted.com/wp-content/uploads/2026/08/finserv-market-data-analysis-1024x683.jpg) ### Regulatory Agility Failing to comply with regulations often results in hefty fines and other serious consequences for your financial service organization. But with Gen AI, you will be able to create systems that make it easy to adhere to the complicated, ever-changing regulations. ![Two professionals signing documents across a desk](https://dedicatted.com/wp-content/uploads/2026/08/finserv-regulatory-documents-signing-1024x683.jpg) ### Custom Service Delivery Generative AI helps financial service providers enhance the customer experience, attracting and retaining clients by simplifying personalized, speedy, and convenient service delivery. ![Adviser talking through paperwork with clients](https://dedicatted.com/wp-content/uploads/2026/08/finserv-advisor-client-consultation-1024x683.jpg) ### Cost-Effective Assistance Providing excellent customer support can be expensive, but Generative AI can help your financial services organization invest wisely in both skilled representatives and the technology they need to assist your customers effectively. ![Client meeting with an accountant over a laptop](https://dedicatted.com/wp-content/uploads/2026/08/finserv-accountant-client-meeting-1024x683.jpg) ### Unified Workflows Many financial services organizations struggle with internal processes that operate in silos. Gen AI can help reduce these difficulties by streamlining workflows and improving overall efficiency, thus diminishing errors and customer service delays. ![Team reviewing work together on a laptop in an office](https://dedicatted.com/wp-content/uploads/2026/08/finserv-team-shared-workflow-1024x683.jpg) ### Intelligent Data Use Financial services organizations collect vast amounts of data but often struggle to manage it effectively. Gen AI makes it easier to use that data to make informed decisions and comply with regulations. ![Candlestick market chart displayed on a laptop screen](https://dedicatted.com/wp-content/uploads/2026/08/finserv-market-data-analysis-1024x683.jpg) ## Business Opportunities Seven places generative AI pays off in financial services, ranked by business value and how easy each is to ship. #### Business Opportunities Use cases Business value Case simplicity 1 Proactive Compliance Business value: 86 Case simplicity: 62 Gen AI helps financial services organizations stay ahead of regulatory changes and automate compliance tasks, reducing the risk of violations and strengthening their overall compliance. Use Personal data? **NO** [Discuss this use case](#contact-form) 2 Dynamic Client Care Business value: 80 Case simplicity: 74 Gen AI makes customer support much easier for your financial services organization by automating tasks, providing real-time assistance, and creating personalized responses. Use Personal data? **YES** [Discuss this use case](#contact-form) 3 Instant Client Setup Business value: 72 Case simplicity: 78 Gen AI makes onboarding easier for financial services organizations by automating paperwork, personalizing welcome materials, and offering immediate support. Use Personal data? **NO** [Discuss this use case](#contact-form) 4 AI Collaboration Tools Business value: 68 Case simplicity: 66 Think of Gen AI as a co-pilot for your financial services organization. It automates tasks, provides valuable insights and recommendations, helps with complex work, and collaborates with your employees. Use Personal data? **IN SOME CASES** [Discuss this use case](#contact-form) 5 Co-pilots Quick win Business value: 90 Case simplicity: 82 DevOps Consulting focuses on identifying bottlenecks, improving workflows, and implementing industry-leading tools. Our team collaborates with your business to ensure smoother operations, improved efficiency, and optimized resource allocation. Whether you’re looking to modernize your infrastructure or integrate new technologies, we provide expert guidance to achieve your goals. Use Personal data? **No** [Discuss this use case](#contact-form) 6 Customized offerings creation Business value: 76 Case simplicity: 54 Generative AI empowers B2B financial services companies to customize their offerings directly at the lead generation stage. By analyzing customer data, it facilitates personalized outreach campaigns and automates lead nurturing processes, making initial client interactions more effective. Use Personal data? **NO** [Discuss this use case](#contact-form) 7 Management reporting Strategic bet Business value: 82 Case simplicity: 36 Gen AI automates data collection, analysis, and report generation, delivering valuable insights and recommendations while enhancing reporting efficiency. It streamlines workflows, accelerates decision-making, and optimizes time and resource usage. Use Personal data? **NO** [Discuss this use case](#contact-form) Business value Case simplicity Where each use case lands Strategic bets Quick wins Easy extras 1 Proactive Compliance 2 Dynamic Client Care 3 Instant Client Setup 4 AI Collaboration Tools 5 Co-pilots 6 Customized offerings creation 7 Management reporting Business value ↑ ← Harder to ship Case simplicity Easier → ## How is Generative AI implemented in financial services? - ### How do financial companies implement generative AI? Financial companies implement generative AI by identifying business use cases, preparing secure datasets, selecting AI models, integrating systems, and deploying compliant AI workflows. - ### What is the process of generative AI adoption in finance? The process includes discovery, compliance assessment, data preparation, AI model selection, development, integration, testing, deployment, and monitoring. - ### How long does it take to implement generative AI in financial services? Implementation timelines usually range from several weeks to a few months depending on system complexity, regulatory requirements, and integrations. ## What technologies power financial AI solutions? - ### What is RAG in financial AI? RAG (Retrieval-Augmented Generation) enables AI systems to retrieve and use financial documents, policies, and market data to generate more accurate responses and insights. - ### What is LLMOps in financial services? LLMOps refers to the deployment, monitoring, governance, and optimization of large language models in secure financial environments. - ### What AI models are used in financial solutions? Financial AI solutions commonly use models such as GPT, Claude, LLaMA, and industry-specific AI models depending on compliance and infrastructure requirements. ## Our Generative AI Offerings - ### GenAI Discovery Idea To Use Case: analysis and validation - ✓ Use cases and business priorities evaluation - ✓ Data quality and availability assessment - ✓ Architecture and integration design - ✓ Fast Proof of Concept validation [ Book a discovery call → ](#contact-form) - ### GenAI Development Validation to Production: accelerate time to market - ✓ MVP development - ✓ Solution engineering and deployment - ✓ Scaling and Integration - ✓ Training and adoption support [ Discuss a build → ](#contact-form) - ### GenAI Augmentation Expertise to Execution: consulting and project support - ✓ Augmenting engineering expertise - ✓ Augmenting engineering capacity - ✓ Consulting on all stages of development - ✓ Solution audit and optimization [ Discuss augmentation → ](#contact-form) ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) ## What are Generative AI use cases in financial services? - ### What are the main use cases of generative AI in finance? Common use cases include customer support automation, fraud detection assistance, financial reporting, risk analysis, document processing, and personalized banking experiences. - ### How is generative AI used in banks and fintech companies? Banks and fintech companies use generative AI to automate workflows, improve customer communication, streamline operations, and enhance decision-making. - ### Can generative AI automate financial processes? Yes, generative AI can automate tasks such as report generation, document summarization, customer inquiries, compliance workflows, and data analysis. Not sure which use case to start with? Tell us where generative AI should help in your financial services operation. An engineer replies within one business day with what we would build first and what it takes. [ Book a free meeting ↗ ](#contact-form) ## Insights [Whitepaper ### Why Financial Institutions need DevOps now: Insights & FAQ Financial Services DevOps Consulting ![Modern finance digital bank online financial institution offering convenient efficient banking services without physical branches revolutionizing access accounts payments financial management](https://dedicatted.com/wp-content/uploads/2025/06/Designing-Platform-for-Instant-Market-Penetration-2025-11-14T124130.169.png) Discover how security-first DevOps helps financial institutions tackle rising compliance costs, streamline legacy migration, boost uptime. Answers to top FAQs inside. ](https://dedicatted.com/insights/why-financial-institutions-need-devops-now-insights-faq) [Blog ### AWS Activate for startups: a bonus program to save you up to 100K Financial Services Healthcare Insurance Retail Cloud and Infrastructure Security Cloud Data Migration & Modernization Cloud Migration Cloud Modernization Managed Services ![Close-up view of a laptop featuring AWS Activate homepage with headline “Grow your startup with AWS” and a video preview image of a businesswoman.](https://dedicatted.com/wp-content/uploads/2024/01/AWS-Activate.-Cover-1024x537.webp) Discover how AWS Activate offers startups up to $100,000 in credits, tools, and support to build and scale their business on AWS cloud services. ](https://dedicatted.com/insights/aws-activate-for-startups-a-bonus-program-to-save-you-up-to-100k) [Whitepaper ### Powering Modern Finance Inside a Cloud-Native Success Story Financial Services Cloud and Infrastructure Security DevOps Consulting Security Compliance ![Close up hand of businesswoman using digital pencil hologram for drawing business forex chart interface on dark blue background Digital transformation and trading concept](https://dedicatted.com/wp-content/uploads/2025/06/Designing-Platform-for-Instant-Market-Penetration-2025-11-14T123815.170.png) Explore how a cloud-native overhaul delivered field-level encryption, sub-second trade repository, and $2M AWS support – driving secure, ultra-fast finance modernization. ](https://dedicatted.com/insights/powering-modern-finance-inside-a-cloud-native-success-story) [Blog ### Automated Budget Management for AWS: Introducing Budget Controls for Resource Governance Automotive Financial Services Manufacturing Retail DevOps Consulting Managed Services ![Amidst a sea of financial documents, a meticulous individual calculates figures, ensuring every detail is accounted for. The calculator, a trusted companion, aids in navigating the complexities of numbers and reports, as the pen hovers, ready to jot down important notes.](https://dedicatted.com/wp-content/uploads/2025/09/Designing-Platform-for-Instant-Market-Penetration-65.png) Discover how automated AWS budget controls, tagging, and compliance policies help cut costs, prevent surprise bills, and keep cloud spend in check. ](https://dedicatted.com/insights/automated-budget-management-for-aws-introducing-budget-controls-for-resource-governance) ## What is Generative AI for Financial Services? Generative AI for financial services is the use of AI models and automation systems to improve banking operations, financial analytics, customer experiences, compliance workflows, and enterprise decision-making. Dedicatted provides enterprise generative ai financial services solutions for banks, fintech platforms, insurers, and financial operations teams. ## Generative AI in Financial Services Our AI services support: - Banking operations and customer engagement - Fintech platforms and digital financial products - Financial analytics and reporting - AI-powered document processing - Compliance and governance workflows - Risk management and fraud detection support - Enterprise financial automation ## Generative AI for Banking Operations Dedicatted helps financial organizations implement AI systems integrated with: - Core banking platforms - Payment processing systems - CRM and customer engagement tools - Compliance and regulatory infrastructure - Financial reporting systems - Internal automation tools ## Cloud Infrastructure for Financial AI Dedicatted helps financial organizations implement: - Secure cloud infrastructure on AWS as an AWS Premier Tier Partner - Scalable AI environments for financial workloads - Data lakes and analytics pipelines for financial data - Encrypted storage and access controls - Monitoring and observability for AI systems - Compliance-ready cloud architectures ### Generative AI for Financial Analytics Our financial analytics capabilities include: - Automated financial reporting - Market data analysis and summarization - Customer behavior insights - Risk scoring and forecasting support - Document and policy analysis - AI-assisted operational intelligence ### Enterprise Generative AI Financial Services Solutions Show more ## FAQ ### Is generative AI secure for financial institutions? Yes, financial AI solutions can be developed with enterprise-grade security, encryption, governance controls, and compliance-focused infrastructure. ### Can generative AI support regulatory compliance in finance? Yes, generative AI can help automate compliance monitoring, reporting workflows, policy analysis, and document management processes. ### How is sensitive financial data protected in AI systems? Sensitive financial data is protected through encryption, access controls, secure cloud environments, monitoring systems, and governance policies. ### What are the benefits of generative AI for financial companies? Benefits include improved operational efficiency, reduced manual workloads, faster customer support, better risk management, and lower operational costs. ### What ROI can generative AI deliver in financial services? Generative AI can improve productivity, reduce operational inefficiencies, accelerate workflows, and optimize customer interactions across financial organizations. ### Is generative AI suitable for small financial businesses and fintech startups? Yes, SMBs and fintech startups can use generative AI to automate operations, improve customer experiences, and scale services efficiently. ### How much does generative AI development for financial services cost? Costs depend on project complexity, compliance requirements, infrastructure, integrations, and customization needs. ### What factors affect the cost of financial AI solutions? Pricing is influenced by data security requirements, infrastructure complexity, AI model selection, integration scope, and regulatory compliance standards. ## Get started with a generative AI consultant Outline your use case and the compliance constraints around it. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Mainframe Modernization with AI](https://dedicatted.com/services/engineering/mainframe-modernization) **Published:** August 27, 2026 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. [Engineering](https://dedicatted.com/services/engineering) 4. Mainframe Modernization Mainframe Modernization with AI # AI-augmented mainframe modernization Our AI-augmented delivery model pairs mainframe engineers with AI at every stage: reading the estate, extracting the business rules and drafting the translation across COBOL, PL/I, RPG, Natural and Assembler. Engineers verify every result against production behaviour before cutover, with scope, price and a week-level timeline published before you sign. [Get the readiness assessment](#contact-form) ![Enterprise mainframe storage array with status lights](https://dedicatted.com/wp-content/uploads/2026/08/mainframe-hero-storage-array-1024x683.jpg) AI-augmented delivery Our mainframe engineers, working alongside AI Parity before cutover Every rule traced to source, every output tested [ 01Your options ](#paths) [ 02The process ](#process) [ 03What's included ](#included) [ 04Our approach ](#verification) [ 05Packages & pricing ](#pricing) [ 06FAQ ](#faq) ## What AI Changes for a Mainframe Programme Our engineers use AI at every stage. Four published results; the assessment produces yours. - 60% less time on assessment and code analysis with CAST Imaging - 100% of standard COBOL, RPG and CL converted automatically with Blu Age - 10x lower run cost after a Blu Age refactor, COBOL to Java in 13 months - 70% of modernization cost removed by 2027 with generative AI, Gartner predicts ## What is AI-powered mainframe modernization? - ### What is AI-powered mainframe modernization? It is the practice of moving mainframe and midrange workloads, written in COBOL, PL/I, RPG, Natural or Assembler with the JCL, CICS, IMS, Db2 and VSAM around them, to a modern platform using AI for the mechanical work: analysing the codebase, extracting business rules with a trace back to the source line, drafting the translation, and generating the tests that prove the new system behaves like the old one. Engineers own the architecture, the data migration and the cutover decision. - ### How is it different from a traditional mainframe migration? A traditional migration spends its first year on discovery: reading undocumented code, reconstructing what each job does, and hand-writing a test suite that never existed. AI compresses that phase and generates the regression tests, which is where most of a mainframe budget historically went. The engineering judgement, what to move, in what order, and what to leave, does not change. - ### Which tools do you use, and why not just one? The transformation engine is chosen per language and per target in the assessment: commercial translators where they are mature for the language, model-assisted refactoring where they are not, and our own analysis and test-generation pipeline across all of them. The choice is written down with the reasoning, so it can be challenged and, if the estate changes, swapped. ## Six Honest Paths for a Mainframe Workload No estate takes one path. The assessment assigns a path per workload, with the reasoning and the cost behind each one written down. - 01### Refactor Translate the code to a modern language, Java or C# depending on your target, with AI-assisted tooling and the behaviour held identical. The default for large, stable batch and transaction estates where the business logic is still correct. - 02### Reimagine Extract the business rules, then rebuild the workload cloud-native from those requirements instead of translating line by line. For systems whose logic is worth keeping but whose structure is not. - 03### Replatform Move the workload to a cloud runtime with minimal change, through an emulation or rehosting layer, when the driver is exiting the hardware and the licence, not improving the code. - 04### Replace Sometimes the honest answer is a product you buy. Payroll, core insurance administration and similar workloads often have a mature SaaS equivalent that beats any rewrite. We will say so. - 05### Retire Estates carry jobs nobody has run in years. Analysis finds the dead code and low-utilization workloads before anyone pays to migrate them. - 06### Retain Some workloads should stay on the mainframe for now, integrated rather than moved. A modernization plan that cannot say this about anything is a sales plan. - Which path fits your workloads? [The assessment answers it →](#contact-form) ## How Mainframe Modernization Works With Us ### Assess Weeks 1 to 3 AI-assisted codebase analysis: dependencies, dead code A path per workload, with the reasoning Wave plan by business priority and risk Fixed-price proposal, yours to keep ### Pilot Weeks 4 to 10 One workload transformed end to end AI-drafted, engineer-reviewed code Parity tested against production data Acceptance criteria published upfront ### Modernize Per wave Incremental delivery, wave by wave A delivery pod per wave Engineer sign-off gate per wave Your ops team trained as each wave lands ### Cutover Per wave Old and new run side by side on real volume Batch windows and response times measured Rollback path kept open throughout Runbooks and handover included ## What's Included 01 Portfolio Assessment The codebase read in full before anyone proposes a plan: what runs, what calls what, what has not executed in years… 02 Business Rule Extraction Decades of undocumented logic turned into readable specifications, each rule traced back to the source file and line it… 03 Code Transformation COBOL, PL/I, RPG or Natural translated to Java or C# with AI-assisted tooling chosen per language, then refactored by… 04 Testing & Parity Proof The part that decides whether a modernization succeeds. 05 Data Migration Db2, VSAM, IMS and Adabas moved to cloud databases with the parity checks applied to data as strictly as to code. 06 Landing Zone & Operations The cloud platform underneath, on AWS, Azure, Google Cloud or private cloud, and the handover that decides whether your… ### Portfolio Assessment ### Portfolio Assessment The codebase read in full before anyone proposes a plan: what runs, what calls what, what has not executed in years, and what each workload would cost to move. #### What's included - ✓ Full inventory: programs, copybooks, JCL, CICS, IMS, Db2, VSAM and IDMS - ✓ Dependency and complexity mapping across the estate - ✓ Dead-code and low-utilization census - ✓ Wave plan and fixed-price proposal you keep either way ### Business Rule Extraction ### Business Rule Extraction Decades of undocumented logic turned into readable specifications, each rule traced back to the source file and line it came from, so an auditor can follow it. #### What's included - ✓ Functional documentation per program - ✓ Business rules traced to exact source lines - ✓ Entry points and functional groups identified - ✓ Specifications your own team can review ### Code Transformation ### Code Transformation COBOL, PL/I, RPG or Natural translated to Java or C# with AI-assisted tooling chosen per language, then refactored by engineers into code your team will still be able to read in five years. #### What's included - ✓ Transformation engine selected per language and target - ✓ Engineer review and refactoring of generated code - ✓ Target architecture designed before translation begins - ✓ Coding standards and structure your team agrees to ### Testing & Parity Proof ### Testing & Parity Proof The part that decides whether a modernization succeeds. Characterization tests capture what the mainframe does today; the new system has to match it, on production data. #### What's included - ✓ Characterization tests generated from current behaviour - ✓ Parity runs on production data, old versus new - ✓ Performance testing: batch windows and response times - ✓ Regression environment maintained through the programme ### Data Migration ### Data Migration Db2, VSAM, IMS and Adabas moved to cloud databases with the parity checks applied to data as strictly as to code. #### What's included - ✓ Db2, VSAM, IMS and Adabas to managed cloud databases - ✓ Schema conversion and data type mapping - ✓ Row-level reconciliation between source and target - ✓ Cutover rehearsal before the real one ### Landing Zone & Operations ### Landing Zone & Operations The cloud platform underneath, on AWS, Azure, Google Cloud or private cloud, and the handover that decides whether your team owns this afterwards or you call us forever. #### What's included - ✓ Landing zone, networking and security baseline - ✓ Infrastructure as Code in Terraform - ✓ Observability, alerting and runbooks - ✓ Ops-team enablement: your mainframe operators trained on the new stack ## Our Approach: AI-Augmented Delivery Dedicatted engineers run AI through every stage of a mainframe programme: understanding the estate, documenting it, translating it, proving parity. The mechanical work gets faster and cheaper; the decisions and the gates stay with engineers. Manual effort With AI assistance Code discovery and analysis Dependency graph, dead code and the business logic hidden across the estate 60% less analysis time What the AI doesCAST Imaging reads the whole estate and builds the dependency graph: call chains, data flows, dead code, the rules buried in batch jobs and stored procedures. What engineers doValidate the graph against runtime and the scheduler, interview the remaining owners, flag what must survive untouched. What you getAn estate map: dependency graph per module, dead-code inventory, and a risk-ranked list of what the modernization must not break. Documentation and business rules Program documentation and rules traced to the source line Weeks to map 12M lines What the AI doesCAST Imaging and a language model turn structure into plain-English documentation and business rules, each traced to file and line. What engineers doReview the rules with your domain owners, mark the ones that must survive, sign the specification. What you getSpecifications your auditors can follow back to the code. Code translation and refactoring COBOL, RPG, PL/I and Natural to Java or C# 100% standard code automated What the AI doesBlu Age converts standard COBOL, RPG, CL, PL/I and Natural to Java automatically, data access and screens included. What engineers doRefactor the generated code to the agreed architecture and coding standards. Nothing merges unreviewed. What you getJava or C# your team can maintain, with the behaviour held identical. Testing and parity proof Characterization tests generated; parity proven on production data Same parity run, by design What the AI doesGenerates characterization tests from current behaviour, so the regression suite exists before anything changes. What engineers doRun parity on production data, measure the batch window and response times, sign the gate. What you getEvidence an auditor can follow: parity runs recorded with inputs, outputs and a named sign-off. [Get your numbers →](#contact-form) ## Where This Fits, and Where It Does Not A shorter list of what we do not take on is worth more than a longer list of what we do. ### A good fit - z/OS estates in COBOL or PL/I, with JCL batch and CICS or IMS transactions - IBM i estates in RPG and CL, and Natural/Adabas on the mainframe - Db2, VSAM, IMS and IDMS data that has to move with the applications - Batch-heavy portfolios where the nightly window is the real constraint - Teams facing a retirement cliff in mainframe skills - Organizations that need the audit trail as much as the migration ### Scoped separately, or not at all - Estates with no test data and no way to generate it: parity cannot be proven against nothing - Programmes that need a single big-bang cutover on a fixed date - Workloads whose regulator has not been consulted about leaving the mainframe - Assembler-heavy estates and Unisys or Fujitsu platforms: scoped case by case after the assessment, never assumed - A rewrite whose only driver is the licence bill, where replatforming would do - Anything where the honest answer is Retain, and the decision has already been made otherwise ## Packages & Pricing Published because a mainframe programme is the last place a buyer should have to guess. The assessment price is fixed and the deliverables are yours whether or not you continue with us. - Most popular### Readiness Assessment Three weeks, fixed scope, yours to keep Fixed $15,000 - ✓ Full inventory and dependency map of the estate - ✓ A path assigned per workload, with the reasoning - ✓ Wave plan sequenced on business priority and risk - ✓ Fixed-price proposal for the pilot and the waves - ✓ Up to roughly 1M lines of code or 10 applications [Book the assessment](#contact-form) - ### Pilot Modernization One workload, proven in parallel From $60,000 - ✓ One business-critical workload transformed end to end - ✓ Characterization tests and parity runs on production data - ✓ Performance tested against your batch window - ✓ Parallel run beside the mainframe - ✓ Acceptance criteria published before we start [Scope a pilot](#contact-form) - ### Modernization Waves Per wave, scoped in the assessment Priced per wave - ✓ A delivery pod per wave: mainframe SME, target-stack engineers, data engineer - ✓ Transformation, data migration and parity testing - ✓ Engineer sign-off gate at the end of each wave - ✓ Your ops team enabled as each wave lands - ✓ Runbooks and handover included [Talk to an engineer](#contact-form) ## The three objections every mainframe team raises - ### Will the new system still finish the nightly batch in time? That is a gate criterion, not a hope. During the parallel run the modernized workload processes the same volume as the mainframe and the batch window is measured. If it does not fit, the wave does not advance; tuning or a different target architecture comes first. - ### What happens to our mainframe team? They are the people who know what the code is supposed to do, and the programme depends on them. Ops-team enablement is a workstream, not a footnote: your operators are trained on the new stack as each wave lands, and the runbooks are written with them rather than handed to them. - ### How do we prove to an auditor that nothing changed? Every extracted business rule carries a trace to the source file and line it came from, every parity run is recorded with its inputs and outputs, and each wave ends with a named engineer sign-off. That evidence trail is a deliverable, not a by-product. ## Mainframe Modernization, Answered ### How much does mainframe modernization cost? The readiness assessment is $15,000 and takes three weeks, and it produces the number for everything after it: effort per workload, the wave sequence, and a fixed-price proposal. A pilot on one business-critical workload starts at $60,000. Waves are priced individually because the honest drivers are estate size, language mix, data complexity, integration count and how much test data exists. Any tooling licence is itemised in the proposal rather than hidden in the day rate. ### How long does mainframe modernization take with AI? Historically, years, and many never finish: only 22% of started mainframe modernizations were called a success in a 2023 survey of 400 executives. AI compresses the two phases that consumed that calendar, discovery and test writing. It does not shorten the parallel run, and should not. Our assessment takes three weeks and a first pilot workload runs weeks four to ten. After that, delivery is wave by wave rather than one date, so modernized workloads reach production within months while the rest of the estate is still being worked. How many waves your estate needs is what the assessment tells you before you commit. ### Which languages and platforms can you modernize? COBOL, PL/I, Natural and Assembler on z/OS, with the JCL, CICS, IMS, Db2, VSAM and IDMS around them, and RPG and CL on IBM i. The transformation engine differs per language, which is why the assessment names the tooling per workload instead of assuming one translator covers the estate. Where a language has no mature translator, the path is usually Reimagine: extract the rules, rebuild from them, and prove parity the same way. ### Can AI translate COBOL to Java automatically? Partly, and the distinction matters. AI tooling produces code that compiles and a first draft of the tests, and it does that in days rather than months. What it cannot do on its own is guarantee that the new program produces the same numbers as the old one on your data. A 2024 ICSE study of 1,700 translated samples found the language models it tested produced correct translations between 2.1% and 47.3% of the time, and Gartner predicts that more than 70% of mainframe exit projects started in 2026 will miss their intended benefits because generative AI capabilities are overestimated. That is why generated code is reviewed by engineers and gated on parity tests before it goes anywhere near production. ### How do you prove the new system behaves identically to the old one? Three ways, in order. Every extracted business rule is traced to the source file and line it came from. Characterization tests capture the current behaviour before anything changes, and the modernized workload has to match it on production data. Then the two run in parallel on real volume, including the nightly batch, until a named engineer signs the wave off. Parity is a gate, not a report. ### Will the modernized system meet our batch windows and response times? Functional parity is not enough on a mainframe, so performance is part of the gate criteria. During the parallel run we measure batch completion against your actual window and transaction response times against your current baseline. If either misses, the wave does not advance until the architecture or the tuning fixes it. ### Where does our source code go, and is it used to train AI models? Analysis and transformation run in a cloud account or environment you control, with an audit trail of what each tool produced and which source lines it came from. Model-training, retention and data-residency terms for every tool in the chain are reviewed with your security team and agreed in writing before any code is uploaded. ### Which cloud do you modernize to? AWS, Azure, Google Cloud or a private cloud. The target is chosen in the assessment on your constraints, not on a partnership: data residency, the platforms your teams already run, the licensing you already hold, and where the data the workload depends on lives. We deliver on all of them, and the landing zone, Infrastructure as Code and observability work is the same discipline whichever you pick. ### Do we have to move everything off the mainframe? No, and a partner who says otherwise is selling. Retain is one of the six paths, and for some workloads it is the right one: integrated with the cloud estate rather than migrated. Retire is another. Most estates carry jobs nobody has run in years, and the analysis finds them before you pay to move them. ### What if we choose another partner after the assessment? Then you take the deliverables with you. The assessment is fixed-price and zero lock-in: the inventory, the dependency map, the path per workload with its reasoning, the wave plan and the priced proposal are yours to use with any partner. The pilot works the same way, with acceptance criteria published before it starts. We would rather win the waves on the pilot’s results than on a contract clause. Question not answered? [Ask a mainframe engineer directly](#contact-form). A senior engineer replies within one business day. ## Mainframe Modernization Services with AI Dedicatted delivers mainframe and legacy modernization services with AI-assisted engineering: COBOL, PL/I, RPG and Natural translated to modern languages, business rule extraction with source-line traceability, Db2, VSAM and IMS data migration, and the parity testing that proves a modernized workload behaves exactly like the system it replaced. We are based in Toronto and deliver to organizations across Canada and the United States, on AWS, Azure, Google Cloud or private cloud. Typical engagements start with a fixed-price readiness assessment covering the full estate: programs, copybooks, JCL, CICS and IMS transactions, Db2, VSAM and IDMS. It produces a wave plan that assigns each workload one of six paths: refactor, reimagine, replatform, replace, retire or retain. Delivery runs wave by wave with parallel running and engineer sign-off at each cutover, never a single big-bang migration. Related services: [AI-driven application modernization](/what-we-do/cloud-services/app-modernisation) for distributed and cloud-hosted legacy systems, and [cloud migration](/services/cloud-devops/cloud-migration) for infrastructure and data platform moves. ## What We Deliver - Mainframe and IBM i portfolio assessment and dependency mapping - COBOL, PL/I, RPG and Natural transformation to Java or C# - Business rule extraction with source-line traceability - Characterization testing and functional parity proof - Performance parity: batch windows and transaction response times - Db2, VSAM, IMS and Adabas migration to managed cloud databases - Landing zone, Infrastructure as Code and observability on AWS, Azure, Google Cloud or private cloud - Parallel running, wave cutover and ops-team enablement Show more ## featured technology partners ![The Only GenAI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only GenAI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only GenAI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) ## Talk to a mainframe engineer Tell us what the estate looks like: languages, rough size, what is forcing the question. A senior engineer, not a sales rep, replies within one business day. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [Generative AI Solutions and Consulting Services](https://dedicatted.com/services/generative-ai) **Published:** February 1, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. Generative AI Solutions and Consulting Services # Generative AI that ships to production Generative AI consulting helps you find where AI pays off, then build it: content generation, workflow automation and better decisions, using LLMs, RAG and foundation models. We take you from use case to production, with governance and reporting in place. [Book a free meeting](#contact-form) [Start with an assessment](https://dedicatted.com/services/ai-readiness-assessment) ![Macro photograph of glowing fibre-optic strands](https://dedicatted.com/wp-content/uploads/2026/08/genai-fibre-optic-hero-1024x768.jpg) AWS Premier Tier one of four clouds we build on Weeks, not quarters Use case to working prototype ## What is generative AI? - ### What is generative AI consulting? Generative AI consulting helps businesses design, implement, and scale AI systems that generate content, automate workflows, and improve decision-making. - ### What are generative AI services? Generative AI services include solution design, model integration, RAG implementation, fine-tuning, and deployment of AI systems for business use cases. - ### What does a generative AI company do? A generative AI company builds and deploys AI-powered solutions such as chatbots, document automation tools, and intelligent assistants using large language models. ## Everyone is adopting generative AI. Far fewer are getting value from it. Adoption is no longer the hard part. The hard part is turning a working prototype into something that runs in production, meets your security and compliance rules, and shows up in the numbers. That is where most programmes stall. We work in that gap: picking the use cases that pay off, building them on the cloud you already run, and putting governance and reporting in place so the result holds up. Our generative AI services help businesses: - ✓ Automate repetitive and time-consuming workflows - ✓ Improve operational efficiency and reduce costs - ✓ Personalise customer engagement with AI - ✓ Speed up software delivery and engineering work - ✓ Improve internal knowledge management and decision support - ✓ Scale AI adoption across your cloud environments 88%of organisations now use AI in at least one business function 39%can point to any measurable EBIT impact from it 1 in 3have scaled AI beyond pilots across the enterprise 5.5%attribute more than 5% of EBIT to their AI use ## Our generative AI services We build generative AI on the cloud you already run — AWS, Azure, Google Cloud or Oracle — and take it all the way through to production. - ### Generative AI consulting and discovery We work out which generative AI use cases are worth building, check whether your data and platform are ready, and prove the shortlist with a fast proof of concept. - ✓ Use cases and business priorities evaluation - ✓ Data quality and availability assessment - ✓ Architecture and integration design - ✓ Fast proof of concept validation [ Read the case study → ](https://dedicatted.com/insights/smart-environmental-reporting-with-genai) - ### Generative AI development We take a validated use case through to production: MVP, solution engineering, deployment on your cloud, and the training your team needs to run it. - ✓ MVP development - ✓ Solution engineering and deployment - ✓ Scaling and integration - ✓ Training and adoption support [ Read the case study → ](https://dedicatted.com/insights/automating-remittance-workflows-and-reducing-costs-with-genai-and-aws) - ### Generative AI team augmentation We add generative AI engineers and architects to your team, or review and optimise a solution you have already built. - ✓ Augmenting engineering expertise - ✓ Augmenting engineering capacity - ✓ Consulting on all stages of development - ✓ Solution audit and optimisation [ Read the case study → ](https://dedicatted.com/insights/intelligent-ai-assistant-for-centralized-corporate-knowledge-with-amazon-q) ## Generative AI: the journey with us Five phases from first workshop to a system your team runs without us. 01 GenAI Discovery Workshop 02 Rapid Prototyping and Validation 03 Solution Design and Prepare to Scale 04 Implementation and Empowerment 05 Ongoing Optimisation ### GenAI Discovery Workshop Goal Find where generative AI brings the most return in your business, and confirm you are ready to build it. Duration 1-6 workshops [ See the four-week assessment → ](https://dedicatted.com/services/ai-readiness-assessment) What we do - ✓ Identify and qualify GenAI use cases - ✓ Define vision, priorities and success criteria - ✓ Assess readiness and data availability - ✓ Bring stakeholders up to speed on GenAI Reference team - GenAI Engineer - Project Manager - Product Manager ### Rapid Prototyping and Validation Goal Test the idea against real users and real data, so each step proves its value before you spend more. Duration 2-8 weeks What we do - ✓ Build a fast proof of concept - ✓ Validate with real users and real data - ✓ Refine the scope based on what comes back Reference team - GenAI Engineer - Project Manager - Software Engineer - UX/UI Designer ### Solution Design and Prepare to Scale Goal Set the architecture, roadmap and governance the solution needs to run in production. Duration 2-4 weeks What we do - ✓ Outline the implementation roadmap - ✓ Define the target architecture - ✓ Plan integrations and governance Reference team - GenAI Engineer - Solution Architect - Business Analyst - Project Manager ### Implementation and Empowerment Goal Build and deploy the solution so it is fully operational and your team can run it from day one. Duration 2-4 months What we do - ✓ Develop and integrate the validated solution - ✓ Deploy into your cloud environment - ✓ Train your team on the new technology Reference team - GenAI Engineer - Solution Architect - Software Engineer - DevOps Engineer - QA Engineer ### Ongoing Optimisation Goal Track how the solution performs in production and adapt it as your data, users and market change. Duration On demand What we do - ✓ Learn from real user feedback - ✓ Fine-tuning and prompt engineering - ✓ Monitoring and observability Reference team - GenAI Engineer - Support Engineer - Project Manager Dedicatted AI assessment ## Is your business ready for generative AI? Most generative AI projects stall on the foundations rather than the model: data that is not ready, a platform that cannot carry the workload, or a use case nobody sharpened. Four questions, written by the engineers who do this work, to show you where you actually stand and what to fix first. - 4 questions - about 2 minutes - no email required Start the assessment → Start over Your dataSelect every statement that is true today Our data sits in one place, or is joined up well enough to query across systems It is clean and consistent enough to ground a model on without heavy clean-up first We know who owns each dataset and there are rules for keeping it accurate Access, retention and compliance are handled automatically rather than by hand Your cloud and platformSelect every statement that is true today We run on a modern cloud with APIs, and can add inference or GPU workloads Our core systems can be integrated with, without rebuilding them first The platform handles today's load and we can see what it is doing How clear is the use case?Pick the one that fits best We are still working out where generative AI could help We have areas in mind, but no specific use case or measure of success We have named use cases, each tied to a business outcome We have a prioritised set, each with an owner and a number we can track Ownership and governancePick the one that fits best Generative AI is new to us and nobody owns it yet There is interest, but no clear owner, skills or process Someone owns it and a few people have hands-on experience We have an owner, skilled people, and rules for how models get approved and monitored ← Back Where we’d start together [](#contact-form) Retake the assessment Rather talk it through? Leave your business email and we will come back to you about what this result means for your situation. Please leave this field empty. ## How is generative AI implemented? - ### How do companies implement generative AI? Companies implement generative AI by identifying use cases, preparing data, selecting models, integrating systems, and deploying AI workflows. - ### What is the process of generative AI adoption? The process includes discovery, data preparation, model selection, development, deployment, and ongoing monitoring. - ### How long does it take to implement generative AI? Implementation typically takes 2–8 weeks depending on complexity, data availability, and integration requirements. ## What technologies power generative AI solutions? - ### What is RAG in generative AI? RAG (Retrieval-Augmented Generation) is a method that combines large language models with external data sources to produce more accurate and up-to-date responses. - ### What is LLMOps? LLMOps is the practice of managing, deploying, monitoring, and optimizing large language models in production environments. - ### What models are used in generative AI solutions? Common models include GPT, Claude, LLaMA, and other foundation models depending on the use case and infrastructure. ## Generative AI for your industry ### Healthcare [](https://dedicatted.com/services/generative-ai/healthcare)### Healthcare Your staff is overloaded with paperwork, between HIPAA rules and endless charts. Give your staff a helping hand by using AI to automate prior authorizations, flag abnormal lab results, and create discharge summaries, which will free up a lot of their time so they can focus on patient care. Need tools specifically designed for your needs? Train models on your EHR data to triage high-risk cases or manage drug inventory. All hosted on HIPAA-ready servers with 24/7 monitoring, because patients are more important than IT problems. [ Discover GenAI for healthcare→ ](https://dedicatted.com/services/generative-ai/healthcare) ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) ### Manufacturing [](https://dedicatted.com/services/generative-ai/manufacturing)### Manufacturing Tired of equipment failures? Generative AI can predict them before they happen. Train AI on your data to automate quality checks, reduce waste, and stay on schedule. We connect AI to your sensors and systems, turning data into actionable insights. Focus on making great products, we’ll handle the tech stuff. No need for a PhD to get started! [ Discover GenAI for manufacturing→ ](https://dedicatted.com/services/generative-ai/manufacturing) ![Blue industrial robot arm on a modern factory production line](https://dedicatted.com/wp-content/uploads/2026/08/manufacturing-819x1024.jpg) ### Financial [](https://dedicatted.com/services/generative-ai/financial-services)### Financial Fraud happens quickly. Your team can’t keep up. Our AI looks at transactions and finds suspicious activity without bothering your team with false positives. Automate your loan approvals, audit trails, and risk reports while following the rules. Use your past transactions to teach our models to predict credit risks or create personalized client portfolios. We’ll keep your data safe on AWS/Azure, so you don’t have to worry. [ Discover GenAI for financial services→ ](https://dedicatted.com/services/generative-ai/financial-services) ![Worms-eye view looking up at modern glass office towers](https://dedicatted.com/wp-content/uploads/2026/08/financial-819x1024.jpg) ### Retail [](https://dedicatted.com/services/generative-ai/retail)### Retail Tired of guessing what your customers want? Let AI do the hard work for you. By analyzing sales trends, weather patterns and what’s going on around the world, AI can predict demand with great accuracy. Automate your restocking process, send personalized promotions to your customers, and let your chatbots handle all the simple questions. Connect AI to your POS and supplier systems to minimize your expenses and keep your shelves full. We’ll also make sure your system doesn’t crash on big shopping days like Black Friday. [ Discover GenAI for retail→ ](https://dedicatted.com/services/generative-ai/retail) ![Bright modern retail store interior with product displays](https://dedicatted.com/wp-content/uploads/2026/08/retail-819x1024.jpg) ### Insurance [](https://dedicatted.com/services/generative-ai/insurance)### Insurance Insurance processes can be slow and complex. AI helps streamline underwriting, claims processing, and customer service by automating repetitive tasks and improving decision-making. Free your team from paperwork so they can focus on serving clients. Train AI on your historical data to assess risks, personalize policies, and optimize operations. All hosted on secure, industry-compliant cloud platforms, so you can focus on protection, not IT management. [ Discover GenAI for insurance→ ](https://dedicatted.com/services/generative-ai/insurance) ![Abstract glowing blue data wave pattern on a dark background](https://dedicatted.com/wp-content/uploads/2026/08/insurance-819x1024.jpg) ### Supply Chain [](https://dedicatted.com/services/generative-ai/supply-chain)### Supply Chain Generative AI is transforming the supply chain industry by enhancing logistics, reducing inefficiencies, and improving demand forecasting. Companies leveraging AI-driven solutions gain a competitive edge by optimizing inventory, reducing costs, and ensuring seamless supply chain operations. [ Discover GenAI for supply chain→ ](https://dedicatted.com/services/generative-ai/supply-chain) ![Automated warehouse robot carrying a storage rack](https://dedicatted.com/wp-content/uploads/2026/08/supply-chain-819x1024.jpg) [](https://dedicatted.com/services/generative-ai/healthcare)### Healthcare Your staff is overloaded with paperwork, between HIPAA rules and endless charts. Give your staff a helping hand by using AI to automate prior authorizations, flag abnormal lab results, and create discharge summaries, which will free up a lot of their time so they can focus on patient care. Need tools specifically designed for your needs? Train models on your EHR data to triage high-risk cases or manage drug inventory. All hosted on HIPAA-ready servers with 24/7 monitoring, because patients are more important than IT problems. [ Discover GenAI for healthcare→ ](https://dedicatted.com/services/generative-ai/healthcare) ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) [](https://dedicatted.com/services/generative-ai/manufacturing)### Manufacturing Tired of equipment failures? Generative AI can predict them before they happen. Train AI on your data to automate quality checks, reduce waste, and stay on schedule. We connect AI to your sensors and systems, turning data into actionable insights. Focus on making great products, we’ll handle the tech stuff. No need for a PhD to get started! [ Discover GenAI for manufacturing→ ](https://dedicatted.com/services/generative-ai/manufacturing) ![Blue industrial robot arm on a modern factory production line](https://dedicatted.com/wp-content/uploads/2026/08/manufacturing-819x1024.jpg) [](https://dedicatted.com/services/generative-ai/financial-services)### Financial Fraud happens quickly. Your team can’t keep up. Our AI looks at transactions and finds suspicious activity without bothering your team with false positives. Automate your loan approvals, audit trails, and risk reports while following the rules. Use your past transactions to teach our models to predict credit risks or create personalized client portfolios. We’ll keep your data safe on AWS/Azure, so you don’t have to worry. [ Discover GenAI for financial services→ ](https://dedicatted.com/services/generative-ai/financial-services) ![Worms-eye view looking up at modern glass office towers](https://dedicatted.com/wp-content/uploads/2026/08/financial-819x1024.jpg) [](https://dedicatted.com/services/generative-ai/retail)### Retail Tired of guessing what your customers want? Let AI do the hard work for you. By analyzing sales trends, weather patterns and what’s going on around the world, AI can predict demand with great accuracy. Automate your restocking process, send personalized promotions to your customers, and let your chatbots handle all the simple questions. Connect AI to your POS and supplier systems to minimize your expenses and keep your shelves full. We’ll also make sure your system doesn’t crash on big shopping days like Black Friday. [ Discover GenAI for retail→ ](https://dedicatted.com/services/generative-ai/retail) ![Bright modern retail store interior with product displays](https://dedicatted.com/wp-content/uploads/2026/08/retail-819x1024.jpg) [](https://dedicatted.com/services/generative-ai/insurance)### Insurance Insurance processes can be slow and complex. AI helps streamline underwriting, claims processing, and customer service by automating repetitive tasks and improving decision-making. Free your team from paperwork so they can focus on serving clients. Train AI on your historical data to assess risks, personalize policies, and optimize operations. All hosted on secure, industry-compliant cloud platforms, so you can focus on protection, not IT management. [ Discover GenAI for insurance→ ](https://dedicatted.com/services/generative-ai/insurance) ![Abstract glowing blue data wave pattern on a dark background](https://dedicatted.com/wp-content/uploads/2026/08/insurance-819x1024.jpg) [](https://dedicatted.com/services/generative-ai/supply-chain)### Supply Chain Generative AI is transforming the supply chain industry by enhancing logistics, reducing inefficiencies, and improving demand forecasting. Companies leveraging AI-driven solutions gain a competitive edge by optimizing inventory, reducing costs, and ensuring seamless supply chain operations. [ Discover GenAI for supply chain→ ](https://dedicatted.com/services/generative-ai/supply-chain) ![Automated warehouse robot carrying a storage rack](https://dedicatted.com/wp-content/uploads/2026/08/supply-chain-819x1024.jpg) ## Insights [Case study ### Agentic Voice Automation for the Clinic Revenue Cycle at Spike Technologies Healthcare Agentic AI Cloud Security Generative AI Managed Services ![Three glowing translucent spheres of networked nodes linked by threads of light on a dark teal ground](https://dedicatted.com/wp-content/uploads/2026/08/agentic-hero-1024x683.jpg) About project The Challenge The expensive part of the work is a conversation. Scripted IVR navigation and portal scraping retrieve a fraction of eligibility answers and break whenever a payer changes a page. The costly work is the call with a payer representative about one patient’s authorization: IVR navigation, hold time, a conversation that follows \[…\] ](https://dedicatted.com/insights/agentic-voice-automation-for-the-clinic-revenue-cycle) [Case study ### Self-Service Clinical Analytics on a FHIR Data Store for a Cardiology Network Healthcare Cloud Security Data Architecture Generative AI Security Compliance ![Clinician reviewing patient information on a tablet](https://dedicatted.com/wp-content/uploads/2026/08/genai-healthcare-clinician-tablet-1024x683.jpg) About project The Challenge Three copies of the record, and none of them queryable. The clinical data existed in three disconnected forms: inside each device vendor’s proprietary analysis software, as PDF reports sent to referring physicians, and as files accumulating in Amazon S3 with no data model and no query layer over them. Clinic records \[…\] ](https://dedicatted.com/insights/self-service-clinical-analytics-on-a-fhir-data-store-for-a-cardiology-network) [Blog ### The 2026 Enterprise Data Readiness Benchmark: Your Foundation for Successful AI Insurance Retail Cloud Data Migration & Modernization Data Architecture Generative AI ![Abstract digital data corridor with blue light trails and glowing particles suggesting a high-tech network edge-to-edge.](https://dedicatted.com/wp-content/uploads/2026/08/Designing-Platform-for-Instant-Market-Penetration-1.png) Only 7% of enterprises have the data foundation AI needs to scale. See what separates them: real case studies, a maturity framework, next steps ](https://dedicatted.com/insights/the-2026-enterprise-data-readiness-benchmark-your-foundation-for-successful-ai) [Blog ### Pharma & Life Sciences: R&D Acceleration with Cloud & AI Healthcare Agentic AI Cloud Modernization Data Architecture Generative AI ![Close-up of glass pipettes transferring liquid in a dimly lit science lab.](https://dedicatted.com/wp-content/uploads/2026/08/Designing-Platform-for-Instant-Market-Penetration.png) This playbook breaks down the reference architecture, use cases by R&D stage, and implementation roadmap for cloud and AI acceleration in pharma ](https://dedicatted.com/insights/pharma-life-sciences-rd-acceleration-with-cloud-ai) Talk to someone who has put generative AI into production Free consultation · Response within one business day [ Book a free meeting ↗ ](#contact-form) ## What are generative AI use cases in business? - ### What are the main use cases of generative AI in business? Key use cases include customer support automation, document processing, code generation, data analysis, and content creation. - ### How is generative AI used in enterprises? Enterprises use generative AI to automate workflows, improve decision-making, enhance customer experience, and reduce operational costs. - ### Can generative AI automate business processes? Yes, generative AI can automate tasks such as answering customer queries, summarizing documents, and generating reports. ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. Top 2% of AWS partners worldwide. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) ## FAQ ### What are the benefits of generative AI for companies? Benefits include increased efficiency, cost reduction, faster decision-making, and improved customer experience. ### What ROI can generative AI deliver? Generative AI can reduce manual workload by 30–60% and significantly improve productivity depending on the use case. ### Is generative AI worth it for small and mid-sized businesses? Yes, generative AI can deliver value to SMBs by automating repetitive tasks and improving operational efficiency. ### How much does generative AI consulting cost? Costs vary depending on scope, but projects typically range from a few thousand to enterprise-level investments. ### What factors affect the cost of generative AI solutions? Costs depend on data complexity, model choice, infrastructure, integration needs, and customization level. ## Generative AI for Business Generative AI consulting from Dedicatted helps organizations in Toronto improve operational efficiency, automate workflows, enhance decision-making, and accelerate innovation through secure and scalable AI adoption. Our mission is to help businesses unlock the full potential of generative ai solutions while ensuring governance, reliability, and long-term business value across cloud and enterprise environments. As an experienced generative ai consultant and technology partner, Dedicatted is a generative AI consulting company that delivers enterprise-ready generative ai consulting services tailored to real business challenges. We help organizations implement practical gen ai solutions that optimize operations, support engineering teams, improve customer experiences, and drive measurable business outcomes. ### Our generative ai services and solutions help businesses: - automate repetitive and time-consuming workflows - improve operational efficiency and reduce costs - enhance customer engagement with AI-powered personalization - accelerate software delivery and engineering productivity - improve internal knowledge management and decision support - enable scalable AI adoption across cloud environments ## Generative AI Models for Business Use Cases Choosing the right AI platform and architecture is critical for successful implementation. Our generative ai consultants assess existing infrastructure, cloud environments, data sources, compliance requirements, and scalability needs to recommend the most effective approach. We design and deploy generative ai solutions that balance performance, security, cost efficiency, and operational value. From AI assistants and automation platforms to enterprise knowledge systems and intelligent analytics workflows, we help businesses implement production-ready AI systems aligned with long-term goals. ## DevOps Generative AI and Cloud Operations Dedicatted specializes in DevOps generative ai implementations that combine AI automation with modern DevOps and cloud engineering practices. By integrating DevOps with generative ai, organizations can streamline CI/CD workflows, automate operational tasks, improve infrastructure visibility, and accelerate incident response. ### Our DevOps generative ai expertise includes: - AI-powered log and monitoring analysis - automated incident investigation and remediation - intelligent infrastructure optimization - engineering productivity automation - AI-assisted documentation generation - cloud operations and deployment optimization These gen ai solutions help businesses reduce manual workload, improve deployment reliability, and increase operational agility across cloud-native environments. ### Generative AI Consulting Services for Enterprise Adoption Many businesses hesitate to move Generative AI into production due to concerns around security, governance, hallucinations, and operational complexity. With extensive experience delivering generative ai consulting projects across regulated industries, cloud environments, and enterprise systems, Dedicatted follows proven implementation frameworks designed for secure and scalable AI adoption. Our AI consulting in Toronto work focuses on bringing secure, reliable, and, most importantly, scalable AI systems that businesses can actually use. ### Our generative ai consulting services address: - data readiness and governance - access control and compliance requirements - AI model evaluation and validation - hallucination mitigation strategies - auditability and observability - secure enterprise integrations By addressing business and technical risks early, we help organizations deploy AI systems confidently and efficiently. ### Generative AI for Business, Analytics, and Data Platforms Dedicatted delivers generative ai for business applications that go beyond generic automation use cases. We implement domain-specific AI systems for DevOps, cloud operations, healthcare, financial services, analytics, and enterprise knowledge management. Our expertise in generative ai for analytics and generative ai for data analytics enables organizations to transform large volumes of operational and business data into actionable insights. We design architectures using: - retrieval-augmented generation (RAG) - vector databases - enterprise search systems - AI-powered analytics workflows - secure internal knowledge retrieval - modern MLOps and orchestration frameworks These generative ai solutions improve relevance, reduce hallucinations, and support reliable AI-driven insights grounded in enterprise data. ## Generative AI for Cloud Solutions and Azure Environments Cloud infrastructure is essential for scalable and secure AI adoption. Dedicatted provides generative ai for cloud solutions across AWS, Azure, and Google Cloud environments, helping businesses deploy enterprise AI systems with flexibility and operational control. Our team also specializes in generative ai azure implementations, enabling organizations to build AI-powered workflows, analytics platforms, and automation systems within Microsoft Azure ecosystems while maintaining scalability, compliance, and cloud security best practices. As an AWS Premier Tier Partner, we design AI architectures aligned with AWS best practices to ensure enterprise-grade scalability, security, and performance. We also extend AI ecosystems with enterprise integrations, observability platforms, deployment pipelines, and collaboration tools that support real-world operational environments. ### Enterprise Generative AI Services and Solutions We leverage a modern ecosystem of large language models, foundation models, cloud-native AI platforms, vector databases, and orchestration frameworks to deliver secure and scalable generative ai services and solutions for enterprise organizations. Whether your business is exploring AI adoption, optimizing cloud operations, modernizing workflows, or building advanced AI-powered platforms, Dedicatted provides practical, governed, and scalable generative ai consulting designed to accelerate innovation and deliver predictable business value. Show more ## Get a generative AI implementation plan Briefly outline your challenge. Our team responds within one business day with relevant experience and a first read on the technical approach. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Services:** Generative AI --- ### [Cloud Migration](https://dedicatted.com/services/cloud-devops/cloud-migration) **Published:** December 5, 2025 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. Cloud Migration Cloud Migration Services # Priced upfront, migrated in phases, measured after Cloud migration is the process of moving applications, data, infrastructure, and enterprise workloads from on-premise systems or legacy environments to cloud platforms such as AWS, Azure, or Google Cloud to improve scalability, performance, cost efficiency, and operational flexibility. [Get in touch with our cloud experts](#contact-form) [Calculate your migration ROI](https://migration.dedicatted.com/?utm_source=dedicatted.com&utm_medium=referral&utm_campaign=cloud-migration-page&utm_content=hero) ![Server racks lining a data center aisle](https://dedicatted.com/wp-content/uploads/2026/08/cloudmig-hero-datacenter-aisle-1-1024x682.jpg) AWS Premier Tier Partner — 50+ AWS projects Minimal downtime Phased cutovers, rollback at every step [ 01Why migrate ](#why) [ 02Benefits ](#benefits) [ 03The 7 R's ](#strategy) [ 04Roadmap ](#roadmap) [ 05Offering & pricing ](#pricing) [ 06FAQ ](#faq) ## Why Do You Need To Adopt The Cloud - 45% average cost savings are achieved by migrating workloads and streamlining operations to the cloud - 1 month the average time it takes for organizations to start seeing productivity gains after cloud adoption - 4x faster — companies release new features or products up to four times faster in the cloud ## What is cloud migration? - ### What is cloud migration? Cloud migration is the process of moving applications, data, and infrastructure from on-premises or legacy systems to cloud platforms such as AWS, Azure, or Google Cloud. - ### What are cloud migration services? Cloud migration services include cloud consulting, infrastructure assessment, data migration, application migration, rehosting, replatforming, and cloud environment optimization. - ### What does a cloud migration company do? A cloud migration company provides cloud migration consulting services, develops migration strategies, executes workload migration, and ensures secure and efficient cloud adoption. ## How Cloud Migration Benefits Your Organization ![Macro purple particles glitter in water running motion blur festive glitter luxury aesthetic flickering lights](https://dedicatted.com/wp-content/uploads/2025/04/image_169.webp) #### Cut operational costs The flexible architecture of cloud enables businesses to effectively manage resources, easily scaling to match need and size. ![Elegant purple blue 3d swirl with curved ends. Bright purple neon light in the dark. Flowing silk membrane. Great as background for cover cards for electronic presentations.](https://dedicatted.com/wp-content/uploads/2025/04/image_169-1.webp) #### Improve data security 84% of enterprises have already adopted a multi-cloud strategy and on-premise infrastructure, for added security. ![Neon navy abstract creative background light design](https://dedicatted.com/wp-content/uploads/2025/04/image_169-4.webp) #### Increase process efficiency The cloud’s flexibility and agility improve and increases an organization’s processes efficiency, lowers costs, and drives innovation for product development. ![Purple and blue abstract background with a whirlwind of smoke.](https://dedicatted.com/wp-content/uploads/2025/04/image_169-5.webp) #### Collaborate more effectively Our cloud migration service helps you maintain a workflow with your colleagues, partners and stakeholders all over the world, in real time. ![Marble texture background Blue white purple and pink pattern](https://dedicatted.com/wp-content/uploads/2025/04/image_169-2.webp) #### Improve infrastructure scalability Expand your computing capacities up or out by scaling resources according to demand to accommodate fluctuating workloads and support dynamic growth. ![Black dark purple blue rose magenta sky](https://dedicatted.com/wp-content/uploads/2025/04/image_169-3.webp) #### Disaster recovery and business continuity Take advantage of inherent disaster recovery mechanisms implemented in cloud platforms to guarantee consistent data backup and recovery in the event of failure. ## The 7 R's of Cloud Migration Strategy No single strategy fits a whole portfolio. The assessment maps every workload to the R with the best cost-risk balance. - R1### Rehost Move existing applications to the cloud as-is — the fastest path with no code changes, ideal when speed and minimal disruption matter most. - R2### Replatform Make targeted optimizations during the move (managed databases, autoscaling) without rearchitecting the application. - R3### Refactor Re-architect applications to fully use cloud-native services for scalability, resilience, and long-term cost efficiency. - R4### Repurchase Replace legacy apps with cloud-native SaaS alternatives — e.g. move CRM to Salesforce, HR to Workday. - R5### Retire Decommission applications no longer providing business value to reduce maintenance overhead and security exposure. - R6### Retain Keep selected applications on-premises temporarily when there is no immediate business case to migrate — re-evaluate later. - R7### Relocate Move VMware workloads to VMware Cloud on AWS at the hypervisor level — no OS, code, or operational changes required. - Which R fits your workloads? [Run the ROI calculator →](https://migration.dedicatted.com/?utm_source=dedicatted.com&utm_medium=referral&utm_campaign=cloud-migration-page&utm_content=seven-rs) ## Our Cloud Migration Roadmap ![Gradient of dynamic purple background llines](https://dedicatted.com/wp-content/uploads/2025/06/image-168-1.jpg) ### Discovery Weeks 1–2Understand what you run today and what the cloud needs to deliver before anything moves. Assess current infrastructure, applications, dependencies and business needs Define cloud transformation goals and key performance metrics Identify potential risks and outline mitigation strategies ![Gradient of dynamic purple background llines](https://dedicatted.com/wp-content/uploads/2025/06/image-168-1.jpg) ### Planning Weeks 3–4Map every workload to a strategy and sequence the move to keep the business running. Map each workload to the right migration strategy (7 R’s) Design the target architecture and landing zone Sequence waves to minimize downtime and risk ![Gradient of dynamic purple background llines](https://dedicatted.com/wp-content/uploads/2025/06/image-168-1.jpg) ### Execution From week 5Migrate in phases with validation at every step — then optimize what landed. Migrate in waves with rollback paths at each cutover Validate performance, security, and cost against the baseline Optimize and hand over with runbooks and training Whitepaper ![Abstract cloud online network data warehouse with AWS Partner Migration Services Competency](https://dedicatted.com/wp-content/uploads/2025/06/Designing-Platform-for-Instant-Market-Penetration-33-1024x537.png) - Whitepaper AWS MAP explained - Free Instant PDF access ## 85% of cloud migrations go over budget or off schedule. This guide to AWS MAP, the Migration Acceleration Program, shows how to migrate with full visibility and zero surprises. Download it and map your success on AWS before you migrate. - ✓ Predict future TCO and calculate ROI - ✓ Optimize licenses, databases, and infrastructure - ✓ Execute low-risk migrations backed by expert guidance Please leave this field empty. Full nameBusiness email \* By submitting my details I accept the [Terms & Conditions](https://dedicatted.com/privacy-policy) and agree to receive relevant news from Dedicatted. I can unsubscribe at any time – see our [Privacy Notice](https://dedicatted.com/privacy-policy). Published by Dedicatted, AWS Premier Tier Services Partner. Instant PDF access. Your download is starting. [Click here if the download hasn't started automatically](https://dedicatted.com/wp-content/uploads/2025/06/MAP-to-the-Cloud.pdf) ## Our Offering Three fixed-scope entry points — you know the price and deliverables before signing anything. - ### Smart Assessment Infrastructure and data audit Starting from $10,000 - ✓ Migration feasibility analysis - ✓ Cloud strategy and planning - ✓ Risk and cost evaluation - ✓ Compliance and security check [Get Offer](#contact-form) - Most popular### Migration Design & PoC Detailed migration roadmap Starting from $25,000 - ✓ Target architecture design - ✓ Proof of concept for selected workloads - ✓ Performance and compatibility testing - ✓ Security and access planning [Partner with us](#contact-form) - ### Migration Pilot Workload migration in phases Starting from $80,000 - ✓ Automation of data pipelines - ✓ Modernization of legacy systems - ✓ Cost and performance optimization - ✓ Post-migration monitoring and support [Get Offer](#contact-form) ## Pricing and Features #### Features #### Smart Assessment $10,000 #### Migration design & PoC $25,000 #### Migration Pilot $80,000 Automated discovery of your estate Dependency mapping across reports, ETL pipelines and warehouse objects Target architecture and migration roadmap Proof of concept on selected workloads Pilot migration of a production workload Team 3 engineers 4 engineers 8 engineers [## Insights ](https://dedicatted.com/insights) [Blog ### Dedicatted Signs Strategic Collaboration Agreement with AWS to help organizations take Generative AI from Pilot to Production Cloud Data Migration & Modernization Cloud Modernization ![Two people shaking hands in a blurred business setting, signaling a deal or partnership.](https://dedicatted.com/wp-content/uploads/2026/08/agree-2.png) Dedicatted and AWS sign a strategic collaboration agreement to help organizations move generative AI from pilot to production with AWS technology and expertise ](https://dedicatted.com/insights/dedicatted-signs-strategic-collaboration-agreement-with-aws-to-help-organizations-take-generative-ai-from-pilot-to-production) [Blog ### Data Lake Migration to the Cloud: What Our Experts at Dedicatted Are Telling Clients Right Now Automotive Healthcare Retail Cloud Data Migration & Modernization Cloud Modernization Data Architecture ![Rows of server racks in a data center with purple-lit screens displaying code, extending into the distance.](https://dedicatted.com/wp-content/uploads/2026/07/Lake-Migration.png) Data lake migration to the cloud, explained by Dedicatted's experts: real cost savings, risk management, and a proven 5-phase process. ](https://dedicatted.com/insights/data-lake-migration-to-the-cloud-what-our-experts-at-dedicatted-are-telling-clients-right-now) [Blog ### Dedicatted Achieves Canadian AWS Premier Partner Status: What it Means Cloud Data Migration & Modernization Cloud Migration Cloud Modernization ![AWS Partner Premier Tier Services badge centered on a dark blue background.](https://dedicatted.com/wp-content/uploads/2026/07/look.png) Dedicatted is now AWS Premier Tier certified. See what that actually requires, how it differs from Advanced tier, and how to evaluate any partner's badge. ](https://dedicatted.com/insights/dedicatted-achieves-canadian-aws-premier-partner-status-what-it-means) [Blog ### Scaling e-com for Prime Day: Headless Architecture on AWS Retail Cloud Data Migration & Modernization Cloud Migration Cloud Modernization ![Amazon Prime delivery driver in a blue uniform holding a Prime box beside a Prime van.](https://dedicatted.com/wp-content/uploads/2026/06/pri.png) Headless commerce on AWS explained: MACH architecture, AWS service stack, migration roadmap, and peak scaling patterns. Complete implementation guide for retail ](https://dedicatted.com/insights/scaling-e-com-for-prime-day-headless-architecture-on-aws) ## FAQ ### What are the main cloud migration strategies? The industry framework is the 7 R’s: rehost (lift-and-shift), replatform, refactor, repurchase, retire, retain, and relocate. Most portfolios use several — the assessment maps each workload to the strategy with the best cost-risk balance. ### What tools are used for cloud migration? Native tooling (AWS Migration Hub, Application Migration Service, Database Migration Service; Azure Migrate; Google Migration Center), plus Terraform for landing zones and CI/CD for repeatable deployments — chosen to fit your stack and target cloud. ### Is cloud migration suitable for small businesses? Yes — smaller estates often migrate faster and see savings sooner. The Smart Assessment scales down: for a small portfolio it typically compresses to days, not weeks, with the same fixed-price clarity. ### What challenges are associated with cloud migration? The common ones: undiscovered dependencies between applications, data gravity, compliance constraints, downtime windows, and skills gaps. Every one of them is addressed in the assessment phase — before migration begins, not during. ### How is data secured during cloud migration? Encryption in transit and at rest, private connectivity (VPN or Direct Connect), least-privilege access controls, and validation checksums on every transfer — aligned with our SOC 2 and ISO 27001 certified practices. ### Can legacy systems be migrated to the cloud? Almost always. Some rehost as-is, some need replatforming, and truly end-of-life systems (aging Unix, out-of-support Windows Server, legacy databases) get a modernization path — the 7 R’s framework decides per system. Question not answered? [Ask a cloud engineer directly](#contact-form) — same-day reply on migration questions. ## Cloud Migration Services Modernize your infrastructure with enterprise-grade cloud migration services in Canada from Dedicatted. We help businesses migrate applications, databases, and workloads to AWS, Azure, and Google Cloud with minimal downtime, maximum security, and long-term scalability. Our cloud engineers specialize in complex infrastructure migration, cloud-native modernization, Kubernetes, DevOps automation, and AI-ready environments designed for performance and growth. ### What We Deliver - Cloud infrastructure migration - Application and database migration - Hybrid and multi-cloud solutions - Kubernetes and container migration - Legacy system modernization - DevOps and CI/CD implementation - Cloud security and compliance - Post-migration optimization and support ## Why Businesses Choose Dedicatted ### Deep Cloud Expertise Dedicatted combines cloud architecture, DevOps, Kubernetes, and software engineering expertise to deliver secure and scalable cloud environments tailored to business goals. ### AI-Ready Infrastructure We build modern cloud infrastructures optimized for AI workloads, analytics, automation, and high-performance applications. ### Secure, Low-Risk Migration Our proven migration process minimizes downtime, protects critical data, and ensures operational continuity throughout every stage of migration. ### Cloud-Native Approach We help businesses move beyond simple “lift-and-shift” migration by modernizing applications with containers, microservices, Infrastructure as Code (IaC), and Kubernetes. ### Cloud Platforms We Support - AWS Cloud Migration - Microsoft Azure Migration - Google Cloud Platform (GCP) Migration Our AWS cloud migration services help businesses move their infrastructure to Amazon Web Services and modernize their workloads along the way ### Our Cloud Migration Process - Infrastructure assessment and migration planning - Cloud architecture design - Secure workload and data migration - Optimization, automation, and monitoring - Ongoing cloud management and support ### Benefits of Cloud Migration - Improved scalability and flexibility - Reduced infrastructure costs - Faster deployment cycles - Enhanced security and compliance - Better disaster recovery and reliability - Stronger foundation for AI and digital transformation At Dedicatted, we help companies across Canada transform legacy infrastructure into secure, scalable, and cloud-native environments that support innovation, automation, and business growth. Show more ## Our cloud migration partners to speed up your move to the cloud ![AWS Premier Tier Partner](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) AWS Premier Tier Partner With almost 100 AWS-certified experts on board, we have delivered 50+ successful AWS projects over the years. [ Explore ](https://dedicatted.com/our-partners/aws) - ![AWS Premier Tier Partner](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Microsoft Solutions Partner](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud Partner](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) ## Get started with a cloud migration consultant Tell us what you are moving and what is blocking it. Our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Services:** Cloud Data Migration & Modernization, Cloud Migration --- ### [AI-Augmented Development Services](https://dedicatted.com/services/ai-augmented-development) **Published:** August 23, 2026 **Author:** Dedicatted **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. AI-Augmented Development Services # From traditional SDLC to AI-first delivery Dedicatted transforms engineering delivery across the entire SDLC by embedding AI into your team's workflows through a staged, metrics-driven approach. As an AWS AI-DLC partner and Ambassador, we implement the AI-driven development lifecycle AWS publishes – helping you automate workflows, reduce costs, and ship faster. [Reach out](#contact-form) [See the approach](#get-the-guide) ![Two engineers review data on a tablet inside a large aircraft hangar with a plane in the background.](https://dedicatted.com/wp-content/uploads/2026/08/airline-2.png) AWS AI-DLC Partner & Ambassador Days, not months Idea to working prototype ## What is AI-augmented development? AI-augmented software development embeds AI tools directly into your delivery lifecycle – from code generation and intelligent completion to automated testing, proactive bug detection, and DevOps automation – inside a structured, measured workflow. ## Your problem vs our solution Adoption ✕Around half of GenAI initiatives never reach production ✓A staged AI-DLC rollout, measured at every phase – not licences and hope Licences ✕30-50% of AI tool seats go unused within months ✓Usage tracked from week one; shelfware surfaced and eliminated Productivity ✕Tools alone deliver single-digit gains ✓Structured enablement on real workflows, on your live codebase ROI ✕Nobody can prove the spend changed anything ✓A baseline agreed up front and measured before and after ## Our AI-augmented consulting services Dedicatted is an AWS Premier Tier Partner specializing in cloud, DevOps and Generative AI for enterprise clients. - ### Rapid maturity assessment We audit your development ecosystem to pinpoint where AI delivers the most immediate value. - ✓ Workflow, tooling and delivery audit - ✓ Baseline metrics agreed up front - ✓ Prioritised adoption roadmap [ Explore the assessment → ](https://dedicatted.com/services/ai-readiness-assessment) - ### AI-native team uplift We evolve your engineering culture from traditional methods to an AI-first way of working. - ✓ Hands-on co-implementation with your teams - ✓ AI champions identified and coached - ✓ Bolt cadence replacing sprint ceremony where it earns its place [ Discuss team uplift → ](#contact-form) - ### Advisory & tool evaluation We monitor the volatile AI landscape on your behalf and vet the tools before you commit. - ✓ Continuous strategic updates - ✓ Technical vetting of GenAI tooling - ✓ Fit judged against your stack, not the hype cycle [ Ask about advisory → ](#contact-form) - ### Agentic AI PoC factory We build and validate autonomous AI agents tailored to your specific operational needs. - ✓ Agent use cases scoped and scored - ✓ Working proofs of concept in weeks - ✓ Human-in-the-loop guardrails by default [ Explore Agentic AI → ](https://dedicatted.com/services/artificial-intelligence/agentic-ai) - ### Training & enablement Your teams master the AI-augmented SDLC by shipping with it, not by sitting through decks. - ✓ Mob elaboration on your live backlog - ✓ Real prototypes shipped during enablement - ✓ Internal leads run the cadence at handover [ Plan enablement → ](#contact-form) - ### AI governance & security Every line of generated code protected by standards that apply from the first commit. - ✓ Guardrails and code provenance - ✓ Security review embedded in the pipeline - ✓ Data boundaries your compliance team can sign [ Talk governance → ](#contact-form) ## Beyond consulting: AI-augmented development services Hands-on delivery, not just advice – AI embedded into testing, pipelines, modernization and the full SDLC. - ### AI-augmented testing & QA Quality that scales with generation speed instead of falling behind it. - ✓ Automated test creation and maintenance - ✓ AI-driven security practices - ✓ Effective human-in-the-loop review workflows [ Discuss QA → ](#contact-form) - ### AI-augmented DevOps & CI/CD AI helping your team establish IaC and automate the pipeline work that eats sprint time. - ✓ Infrastructure as Code generation - ✓ Deployment and backup scripting automated - ✓ Pipeline optimization measured end to end [ Explore Cloud & DevOps → ](https://dedicatted.com/services/cloud-devops) - ### AI-accelerated legacy modernization AI-assisted engineering that speeds up and de-risks modernizing legacy applications – without a rewrite. - ✓ Codebase understanding at machine speed - ✓ Incremental migration paths, validated early - ✓ Modernization grounded in your target architecture [ Discuss modernization → ](#contact-form) - ### AI-first SDLC management End-to-end delivery on a governed platform: planning, AI-powered architecture, development, testing, deployment and maintenance. - ✓ Context layer carrying your standards over MCP - ✓ Production-parity environments on demand - ✓ Builders on Amazon Bedrock, grounded in your rules [ Talk to an architect → ](#contact-form) ## Case studies [ All case studies ↗ ](https://dedicatted.com/insights) [ ![Two engineers review data on a tablet inside a large aircraft hangar with a plane in the background.](https://dedicatted.com/wp-content/uploads/2026/08/airline-2.png) Case study Featured ↗ ### How an Aviation Tech Company Cut POC Time from a Month to Days An AI-DLC platform took idea-to-prototype from roughly a month to days, with production-parity environments two clicks away. - DevOps Consulting - Generative AI - Managed Services ](https://dedicatted.com/insights/how-an-aviation-tech-company-cut-poc-time-from-a-month-to-days) [ ![Two programmers seated at desks coding on multiple monitors in a bright office environment.](https://dedicatted.com/wp-content/uploads/2026/08/app-mod-pipeline-engineers-monitors.jpg) Case study ↗ ### When the Product Is Powerful and Nobody Can Use It: Embedding an AI Product Expert An agentic product expert embedded inside the interface, turning a powerful but hard-to-learn platform into one teams could actually operate. - Agentic AI - Generative AI ](https://dedicatted.com/insights/ai-product-expert-onboarding-complex-platform) [ ![Warehouse loading dock scene showing a pallet jack moving a shrink-wrapped pallet of cardboard boxes into the back of an open delivery truck. The pallet sits on a lift gate, and the boxes feature a logo combining a maple leaf and French flag colors, suggesting shipping or logistics operations.](https://dedicatted.com/wp-content/uploads/2026/02/Designing-Platform-for-Instant-Market-Penetration-2026-02-10T155621.133.png) Case study ↗ ### Production-Grade GenAI Lead Enrichment on AWS for KwikSave Logistics Operations A production GenAI enrichment pipeline on AWS that turned raw logistics leads into qualified, actionable records. - Cloud Modernization - Generative AI ](https://dedicatted.com/insights/production-grade-genai-lead-enrichment-on-aws-for-kwiksave-logistics-operations) ## Addressing critical business challenges 1. 01### Chaotic adoption, invisible ROI Tools bought team by team, nobody measuring. We bring order: oversight, a baseline, and measurement frameworks that show what changed. 2. 02### Escalating competitive pressure Competitors ship faster every quarter. We accelerate your delivery pipeline so you stop playing catch-up. 3. 03### Unpredictable engineering output AI makes some teams faster and some slower. We stabilise delivery by moving beyond basic tool access to a structured lifecycle. 4. 04### Security and governance uncertainty Generated code with unknown provenance. We secure the AI journey so every line produced is protected by your standards. > Buying licences is the easy part. A model does not know your architecture, so what it writes does not survive code review – and the team quietly goes back to writing it by hand. We fix that by giving the model your standards to work from, and by making a prototype and a production service the same build. Then we measure it. If we cannot show the change in numbers, we do not claim it. > > ![Dmytro Petlichenko, COO at Dedicatted](https://dedicatted.com/wp-content/uploads/2026/08/dmytro-petlichenko-150x150.webp) Dmytro Petlichenko COO, Dedicatted – AI-DLC AWS Ambassador ## AI-augmented development at Dedicatted: our integration-focused approach 1. 1 Week 1-2### Inception - ✓ Mob elaboration: AI turns business intent into units of work, the team validates - ✓ Audit how delivery works today; surface the standards living only in people's heads - ✓ Agree the baseline we will measure against 2. 2 Week 3-6### Construction - ✓ Work moves in bolts – hours or days, not sprint-weeks - ✓ Stand up the context layer and the first golden path - ✓ Put two or three real workflows through it on live code 3. 3 Week 4-8### Construction at width - ✓ Extend to more teams and more paths - ✓ Harden governance and cost controls - ✓ Infrastructure ready for organisation-wide use 4. 4 Ongoing### Operations, then handover - ✓ Advanced agentic workflows where they earn their place - ✓ Your platform team takes ownership of templates, context layer and governance - ✓ We leave the critical path [Reach out](#contact-form) Engineering guide ![Cover of the Dedicatted guide: From AI-assisted to AI-driven](https://dedicatted.com/wp-content/uploads/2026/08/ai-dlc-guide-cover-723x1024.webp) - 15 pages AWS AI-DLC framework - Free Instant PDF access ## Your developers got faster. Your delivery numbers did not move. Written for CTOs and engineering leaders who bought the licences, watched developers get faster, and are still waiting for delivery to catch up. What AI-DLC is, what actually changes, what it will not fix, and how to run it on a real workload in 90 days. - ✓ What AI-DLC actually changes in the lifecycle. - ✓ What it will not fix, stated plainly. - ✓ How to run it on a real workload in 90 days. Please leave this field empty. Full nameBusiness email \* By submitting my details I accept the [Terms & Conditions](https://dedicatted.com/privacy-policy) and agree to receive relevant news from Dedicatted. I can unsubscribe at any time – see our [Privacy Notice](https://dedicatted.com/privacy-policy). Written by Dmytro Petlichenko, AI-DLC AWS Ambassador. Instant PDF access. Your download is starting. [Click here if the download hasn't started automatically](https://dedicatted.com/wp-content/uploads/2026/08/Dedicatted-AI-DLC-Guide-1.pdf) ## FAQ ### What is AI-augmented software development? AI-augmented software development embeds AI tools directly into your delivery lifecycle – from code generation and intelligent completion to automated testing, proactive bug detection, and DevOps automation – inside a structured, measured workflow. ### What are the key benefits of using AI in software development? Speed, quality and cost efficiency: AI automates code reviews, generates test cases, flags security vulnerabilities early and reduces rework – provided the lifecycle around it changes too, which is what AI-DLC structures. ### What types of AI tools are commonly used? AI-assisted code generation and completion, automated test creation, AI-driven bug detection, and CI/CD pipeline optimization. The AI-DLC steering rules we implement run on Kiro, Amazon Q, Cursor, Claude Code and Copilot, so you are not locked to one assistant. ### What is AI-DLC? AI-DLC is the AI-driven development lifecycle AWS publishes as an open methodology: three phases – Inception, Construction, Operations – with work moving in short cycles called bolts and AI proposing plans the team validates. Dedicatted implements it as an accredited AWS partner, so what we run is a public standard you can read, not a vendor framework you have to trust. ### How can AI-augmented development give my business a competitive advantage? AI-augmented pipelines let your teams ship faster, catch defects earlier and modernize legacy systems without the cost and risk of a full rewrite – and a measured rollout means you can prove it. ### How can my company get started? The best starting point is a rapid assessment of your current development process to identify where AI delivers the most immediate impact. We run that in the first two weeks and agree the baseline every later claim is measured against. ## What are AI-augmented development services? AI-augmented development services embed AI into the software delivery lifecycle itself: grounding code generation in your architecture, provisioning production-parity environments on demand, and enforcing security standards from the first commit. Dedicatted delivers this as an accredited AWS AI-DLC partner in Canada, implementing the AI-driven development lifecycle that AWS publishes as an open methodology. Show more ### AI-DLC: the AWS methodology for AI-first delivery AI-DLC restructures delivery into three phases – Inception, Construction and Operations – with work moving in short cycles called bolts and AI proposing plans that engineering teams validate. Dedicatted implements AI-DLC on AWS with a governed context layer exposed over MCP, Backstage golden-path environments on EKS, RDS and S3, and builders on Amazon Bedrock grounded in each client’s own standards. The result: prototypes and production services are the same build, and AI-written code carries your architecture, data rules and security posture from its first commit. ## Make what AI writes safe to ship Briefly outline your challenge – our team responds within one business day with relevant experience and initial technical insights. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. --- ### [AI Opportunity & Readiness Assessment](https://dedicatted.com/services/ai-readiness-assessment) **Published:** August 17, 2026 **Author:** Dmytro Petlichenko **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. AI Opportunity & Readiness Assessment AI Opportunity & Readiness Assessment # Find where AI actually pays off in your business In four weeks, we find the AI use cases worth doing in your business, tell you honestly whether your data is ready, and map the path to production. [Book your assessment](#contact-form) The engagement at a glance Duration 4 weeks Your team's time ~6 hours total Deliverables 4 documents, yours to keep Your cost AWS-funded 1. You pay $0 No fee, no procurement cycle 2. AWS funds it Comparable engagements run $40k-$120k 3. You keep everything All four documents, no strings Funded through AWS partner programmes for qualifying businesses — [see the FAQ for how it works](#faq) ## Where AI creates value - ### Knowledge & document search Your team asks in plain language and gets answers pulled straight from your manuals, contracts, SOPs and records. - ### Forecasting & optimization Forecast demand, inventory and asset needs from your own data instead of guesswork. - ### Process & document automation High-volume, repetitive work handled by AI: invoices, orders, claims and back-office tasks. - ### Monitoring & decision support AI flags the exceptions and patterns that matter and puts the full picture in one place, so your team decides faster. ## Why start with an assessment Most AI initiatives don't fail on the model — they fail on foundations nobody checked first. - >80% of AI projects fail — roughly twice the failure rate of non-AI IT projects RAND Corporation, 2024 - 48% of AI projects make it out of prototype and into production Gartner, 2024 - 74% of companies struggle to achieve and scale value from AI investments BCG, 2024 What the other 20% looks like — one of ours, in production [Full case study↗](https://dedicatted.com/insights/custom-computer-vision-aws-case-study) - 61% lower cost per scan than the vendor we replaced - 0.73s average recognition, down from roughly five seconds - 2-3M scans processed per day in production - 100% of the intellectual property assigned to the client Measured in a like-for-like production comparison at live traffic volume. ## Exactly what you walk away with Not a maturity score and a slide about the future of AI. Four documents you can act on, and they are yours to keep whatever you decide to do next. 1. 01 / USE CASES### Prioritised use-case register Three to five candidate use cases, each scored for business value and technical feasibility, including the ones we recommend you do not pursue and why. 8-12 pages PDF + XLSX 2. 02 / READINESS### Readiness scorecard and gap register Your data, platform, security and operating model rated against the AWS Cloud Adoption Framework for AI, with every gap listed by severity and owner. 14-18 pages PDF 3. 03 / ARCHITECTURE### Target architecture and business case A reference architecture on AWS, sized and costed, with a three-year TCO model and the AWS funding programmes you qualify for. 10-14 pages PDF + diagram 4. 04 / ROADMAP### Sequenced roadmap and board readout A twelve-month delivery sequence with dependencies and decision points, plus a one-page executive summary written for a board rather than for engineers. 1 page + deck PDF + PPTX [Book your assessment](#contact-form) ## Four weeks, start to readout About six hours of your team's time in total, concentrated in the first two weeks. Week 1 Discovery and prioritization Week 2 Readiness assessment Week 3 Architecture and ROI Week 4 Roadmap and readout ### Discovery and prioritization Goal Understand your priorities, workflows and data, and agree which candidate use cases are worth scoring. Your time ~3 hours What we do - ✓ Map your priorities, workflows and data - ✓ Shortlist the candidate use cases - ✓ Score them by value and feasibility ### Readiness assessment Goal Establish honestly what your data, platform and operating model can carry today, and what they cannot. Your time ~2 hours What we do - ✓ Assess data, technology and AI readiness - ✓ Flag the gaps and risks honestly - ✓ Cover security and governance where it matters ### Architecture and ROI Goal Design what the first use case actually runs on, and what it costs against what it returns. Your time ~1 hour What we do - ✓ Design the target AWS architecture - ✓ Build the business case and ROI - ✓ Identify applicable AWS funding ### Roadmap and readout Goal Hand over a sequenced twelve-month plan and walk your leadership through it. Your time Readout only What we do - ✓ Sequence the path to production - ✓ Executive readout with recommendations - ✓ Optional 2-4 week feasibility prototype [Book your assessment](#contact-form) ## Whether this is worth your four weeks Roughly six hours of your team's time in total, concentrated in the first two weeks. ### A good fit if - You have executive intent for AI but no agreed plan, and competing internal opinions about where to start. - You are running AI pilots that have not reached production, and it is not clear why. - An AI feature is live but the unit economics or a vendor dependency has become the problem. - You need a costed, defensible case before a board or investment committee will release budget. ### Not a fit if - You already know the use case and want it built. Skip the assessment and talk to us about delivery instead. - You are looking for licence discounts or procurement leverage rather than an engineering opinion. - Nobody can commit around six hours across four weeks. The assessment needs your people in the room. Not sure yet? ## Score your AI readiness in two minutes Before you book four weeks, get a first read on where you stand — the same four dimensions we assess in the full engagement: data, platform, use case, and ownership. - 4 questions - about 2 minutes - see your result instantly Start the quick check → Start over Your dataSelect every statement that is true today Our data sits in one place, or is joined up well enough to query across systems It is clean and consistent enough to ground a model on without heavy clean-up first We know who owns each dataset and there are rules for keeping it accurate Access, retention and compliance are handled automatically rather than by hand Your cloud and platformSelect every statement that is true today We run on a modern cloud with APIs, and can add inference or GPU workloads Our core systems can be integrated with, without rebuilding them first The platform handles today's load and we can see what it is doing How clear is the use case?Pick the one that fits best We are still working out where AI could help We have areas in mind, but no specific use case or measure of success We have named use cases, each tied to a business outcome We have a prioritised set, each with an owner and a number we can track Ownership and governancePick the one that fits best AI is new to us and nobody owns it yet There is interest, but no clear owner, skills or process Someone owns it and a few people have hands-on experience We have an owner, skilled people, and rules for how models get approved and monitored ← Back Where we’d start together [](#contact-form) Retake Rather talk it through? Leave your business email and we will send your result with a short note on what it means for your situation. Please leave this field empty. ## Regulated sectors get the regulated version Same four weeks, same deliverables. The difference is that every gap we find comes mapped to the frameworks your auditors already use, so the readiness work lands straight in your existing compliance process. ### Financial services 4 frameworks - Model governance, explainability and the human-in-the-loop controls a regulator will ask about. - Gap register mapped to OSFI B-13 NIST AI RMF 1.0 ISO/IEC 42001:2024 SOC 2 ### Healthcare 4 frameworks - PHI handling, clinical-safety review and third-party AI risk, with a board-ready risk summary. - Gap register mapped to HIPAA Security Rule HITRUST CSF NIST AI RMF 1.0 ISO/IEC 42001 ### Retail and commerce 4 frameworks - Personalisation, forecasting and automated decisioning where consent and peak-season cost both bite. - Gap register mapped to PCI DSS 4.0 GDPR Art. 22 SOC 2 ISO/IEC 42001:2024 In manufacturing, insurance, energy, or the public sector? The same mapping works for your frameworks — [tell us which ones apply](#contact-form). ## Why Dedicatted ![AWS Premier Tier Services Partner badge](https://dedicatted.com/wp-content/uploads/2026/07/aws-premier-tier-services-partner-badge-300x222.png) ### The only Generative AI and MSP partner in Canada Top 2% of AWS partners worldwide — the team assessing your readiness is the same one that ships AI to production. [Read more](https://dedicatted.com/our-partners/aws) ### 100+ #### AWS certifications across the team ### 50+ #### AI use cases shipped to production ## Proven results [Case study ### Custom Computer Vision on AWS: From 5 to 0.73-Second Recognition High Tech Software Generative AI Managed Services ![Custom Computer Vision on AWS: From 5 to 0.73-Second Recognition](https://dedicatted.com/wp-content/uploads/2026/08/card-3.png) How we replaced a specialist AI vendor with a custom SageMaker recognition pipeline on AWS: 61% lower cost per scan, 0.73s latency, full client ownership. ](https://dedicatted.com/insights/custom-computer-vision-aws-case-study) [Case study ### Production-Grade GenAI Lead Enrichment on AWS for KwikSave Logistics Operations Automotive High Tech Software Cloud Modernization Generative AI ![Warehouse loading dock scene showing a pallet jack moving a shrink-wrapped pallet of cardboard boxes into the back of an open delivery truck. The pallet sits on a lift gate, and the boxes feature a logo combining a maple leaf and French flag colors, suggesting shipping or logistics operations.](https://dedicatted.com/wp-content/uploads/2026/02/Designing-Platform-for-Instant-Market-Penetration-2026-02-10T155621.133.png) AI-powered AWS platform for Kwiksave Logistics turns delivery data into qualified leads, reducing manual work, protecting CRM quality, and enabling growth. ](https://dedicatted.com/insights/production-grade-genai-lead-enrichment-on-aws-for-kwiksave-logistics-operations) [Case study ### GenAI-Powered Video Intelligence Platform for a Global Television Network High Tech Software Data Architecture Generative AI ![GenAI-Powered Video Intelligence Platform for a Global Television Network](https://dedicatted.com/wp-content/uploads/2026/02/Designing-Platform-for-Instant-Market-Penetration-2026-02-11T133414.933.png) GenAI video intelligence for a global TV network: semantic search, faster content discovery, improved archive use, and scalable AI-driven media workflows. ](https://dedicatted.com/insights/genai-powered-video-intelligence-platform-for-a-global-television-network) [Case study ### Migration of Canadian retail operations platform from IBM to cloud architecture on AWS Retail Cloud Data Migration & Modernization Cloud Migration ![Two warehouse workers in blue uniforms load cardboard boxes from a delivery van onto a pallet inside a warehouse.](https://dedicatted.com/wp-content/uploads/2026/04/Designing-Platform-for-Instant-Market-Penetration-2026-04-27T210007.521.png) Discover how a national retailer migrated legacy IBM systems to a modern cloud architecture, achieving 50% faster POS and seamless e-commerce integration. ](https://dedicatted.com/insights/migration-of-canadian-retail-operations-platform-from-ibm-to-cloud-architecture-on-aws) ## Questions, answered ### Do we need to be using AWS already? No. The assessment is about your business and your data. You don’t need to be an AWS customer to take part. ### What is the catch if AWS sponsors it? No catch. AWS supports these assessments so organizations find real, high-value AI opportunities. You work with Dedicatted the whole way through. ### What if our data is not ready? That’s exactly what we check. You get an honest readiness picture and a roadmap that accounts for it, so nothing surprises you later. ### How much of our team time does it take? A few working sessions across the four weeks, with your business and technology people. ### Do we own the deliverables? Yes. The report, roadmap and architecture are yours, and you’re under no obligation to build with us. ### What if we are multi-cloud? That’s fine. The architecture we recommend is AWS-based, but the opportunities and roadmap are yours to run anywhere. ### Can you sign an NDA? Yes, before we look at anything sensitive. ## See where AI can create value in your business Briefly outline your challenge. Our team responds within one business day with relevant experience and a first read on the technical approach. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. Our team replies within one business day, with relevant experience and a first read on your problem. **Services:** Agentic AI, Generative AI --- ### [Computer Vision](https://dedicatted.com/services/artificial-intelligence/computer-vision) **Published:** August 24, 2026 **Author:** Dmytro Petlichenko **Content:** 1. [Home](https://dedicatted.com/) 2. [Services](https://dedicatted.com/services) 3. Computer Vision Development Computer Vision Development Services # Computer Vision Turn cameras into instruments, and footage into decisions Computer vision development services help businesses automate visual tasks — quality inspection, object detection, document processing, and video analytics — using deep learning models built on your data and deployed in your cloud or on the edge. [Talk to a CV expert](#contact-form) ![Silver robotic arm extends its clamp-style gripper toward the camera, blue tag visible near the joints, against a gray background.](https://dedicatted.com/wp-content/uploads/2026/08/hero-1024x684.jpg) Your footage stays yours Built in your cloud by an AWS Premier Tier Partner — SOC 2 and ISO 27001 audited - AI on AWS, Premier Tier The only GenAI and MSP partner in Canada - Your footage, your cloud Trained and deployed in your account — SOC 2 + ISO 27001 - 2-week feasibility check Honest verdict on your footage before you commit budget - Cloud or edge Models run where your cameras are — even offline ## What is computer vision development? - ### What it is Computer vision development is the process of building AI systems that interpret images and video — detecting objects, reading documents, spotting defects, and tracking movement — so machines can act on what they see without human review. - ### What a development partner covers A computer vision development company handles the full lifecycle: data collection and annotation, model training and validation, deployment to cloud or edge devices, and continuous monitoring and retraining in production. ## Our Computer Vision Expertise Seven core vision tasks. Most production systems combine two or three — scoped to the problem, not the buzzword. 01 Object Detection & Recognition We build models that find and identify specific items in images and video — products, parts, vehicles, people — with… 02 Visual Anomaly Detection Models trained on what ‘good’ looks like flag anything that deviates — scratches, dents, missing… 03 Image Classification We automate the categorization of images into predefined classes using deep learning — replacing manual sorting with… 04 Image Segmentation Pixel-level understanding of what is where — so systems can measure, count, and act on the exact shape and extent of… 05 OCR & Document Intelligence We extend detection precision to text-bearing objects — labels, packaging, invoices, forms — for accurate data… 06 Video Analytics Real-time video stream analysis with event detection and pattern recognition — automated alerts and live dashboards… 07 Pose Estimation & Tracking We detect and track human and object keypoints in video, enabling systems that understand movement, posture, and… ![Aerial view of a multi-lane highway with cars and I-80 and I-75 shields on the road; long shadows from structures cross the lanes.](https://dedicatted.com/wp-content/uploads/2026/08/cap-detection.jpg) ### Object Detection & Recognition ### Object Detection & Recognition We build models that find and identify specific items in images and video — products, parts, vehicles, people — with the precision your inventory, quality control, and safety systems depend on. #### Common uses - ✓ Automated inventory and shelf audits - ✓ Assembly verification on production lines - ✓ Vehicle and license plate recognition - ✓ PPE and safety compliance monitoring ![Worker in a glove uses a red handheld welding/grinding tool on a metal rail, sparks flying and a blue arc visible nearby.](https://dedicatted.com/wp-content/uploads/2026/08/cap-anomaly.jpg) ### Visual Anomaly Detection ### Visual Anomaly Detection Models trained on what ‘good’ looks like flag anything that deviates — scratches, dents, missing components, contamination — including defect types too rare to collect examples of. #### Common uses - ✓ Surface defect detection at full line speed - ✓ Predictive maintenance from visual wear - ✓ Contamination checks in food and pharma - ✓ Weld and joint quality verification ![Workers sort red apples on a multi-conveyor packing line in a fruit processing plant, with yellow stairs and blue conveyors nearby.](https://dedicatted.com/wp-content/uploads/2026/08/cap-classification.jpg) ### Image Classification ### Image Classification We automate the categorization of images into predefined classes using deep learning — replacing manual sorting with consistent, auditable decisions at any volume. #### Common uses - ✓ Product catalog auto-tagging - ✓ Medical image triage and routing - ✓ Damage assessment from photos - ✓ Content moderation at scale ![Aerial view of a yellow canola field on the left, a narrow dirt path in the middle, and a green crop field with tracks on the right.](https://dedicatted.com/wp-content/uploads/2026/08/cap-segmentation.jpg) ### Image Segmentation ### Image Segmentation Pixel-level understanding of what is where — so systems can measure, count, and act on the exact shape and extent of objects, not just their presence. #### Common uses - ✓ Precise dimension and area measurement - ✓ Crop and land analysis from aerial imagery - ✓ Tumor and lesion boundary mapping - ✓ Background removal for product imagery ![Desk with a pile of papers and orange receipts held by a gold binder clip, on a white desk with a pen, pencil, sticky notes, and a blue folder nearby](https://dedicatted.com/wp-content/uploads/2026/08/cap-ocr.jpg) ### OCR & Document Intelligence ### OCR & Document Intelligence We extend detection precision to text-bearing objects — labels, packaging, invoices, forms — for accurate data extraction that feeds straight into your systems of record. #### Common uses - ✓ Invoice and form data extraction - ✓ Label, batch code, and serial verification - ✓ ID document processing and validation - ✓ Shipping paperwork automation ![Man stands with hands clasped behind neck, facing a large illuminated screen in a dim control room or office.](https://dedicatted.com/wp-content/uploads/2026/08/cap-video.jpg) ### Video Analytics ### Video Analytics Real-time video stream analysis with event detection and pattern recognition — automated alerts and live dashboards instead of someone watching monitors. #### Common uses - ✓ Real-time safety incident alerts - ✓ Customer flow and queue analytics - ✓ Perimeter and access monitoring - ✓ Process compliance verification ![Three male sprinters start a race from starting blocks on a red track, wearing black and red uniforms, with blocks numbered 4, 3 and 2 nearby.](https://dedicatted.com/wp-content/uploads/2026/08/cap-pose.jpg) ### Pose Estimation & Tracking ### Pose Estimation & Tracking We detect and track human and object keypoints in video, enabling systems that understand movement, posture, and orientation — even in cluttered environments. #### Common uses - ✓ Ergonomics and injury-risk monitoring - ✓ Sports and physiotherapy motion analysis - ✓ Robot guidance and bin picking - ✓ Fall detection in care environments ## What Vision Automation Is Worth Industry benchmarks for computer vision in operations — what mature deployments deliver against manual inspection and monitoring. - Less machine downtime 30–50% Visual monitoring catches wear and anomalies before they stop the line. - Of units inspected 100% Every item at full line speed, same criteria from first unit to last — not a sample. - Higher labor productivity 15–30% Repetitive visual checks automated; people move to work that needs judgment. - From event to alert Seconds Defects, safety incidents, and stockouts surface in real time, not at end-of-shift. Ranges: McKinsey industry benchmarks for AI-driven visual operations [What would these numbers mean on your line? →](#contact-form) ## Our Computer Vision Development Services End to end or any stage alone — from validating the use case to keeping models accurate in production. - 01### CV Strategy & Feasibility We align vision capabilities with your business goals — pinpointing the right use cases, datasets, and models, and validating feasibility before you commit budget. - 02### Data Services Data collection, annotation, augmentation, and synthetic datasets — so your models train on data optimized for precision, not just whatever footage exists. - 03### Custom Model Development We design custom vision systems tailored to your environment and integrate them with your current infrastructure — from prototype to production-grade system. - 04### Model Optimization Hyperparameter tuning, compression, quantization, and ONNX conversion — so models run fast enough and cheap enough for real production workloads. - 05### Deployment & MLOps CI/CD pipelines, human-in-the-loop workflows, and edge deployment to cameras, devices, and industrial sensors — cloud or fully on-premises. - 06### Monitoring & Retraining Drift detection, accuracy monitoring, and scheduled retraining keep models reliable as your products, lighting, and environments change. ## What the Camera Catches Every deployment watches for one costly event. Six real detections our systems make — and the call each one triggers. - ![Manufacturing](https://dedicatted.com/wp-content/uploads/2026/08/cam1-a-1024x683.jpg) CAM 01 · LINE 3 LIVE glass\_crack · 0.96 Manufacturing Flags the defect and holds the batch — before it ships. - ![Retail](https://dedicatted.com/wp-content/uploads/2026/08/retail-1-1024x768.jpg) CAM 02 · AISLE 7 LIVE empty\_facing · 0.91 Retail Spots the gap on the shelf and fires the restock task. - ![Healthcare](https://dedicatted.com/wp-content/uploads/2026/08/cam3.jpg) SCAN 03 · RADIOLOGY LIVE priority\_case · 0.88 Healthcare Routes the urgent image to the top of the reading queue. - ![Logistics](https://dedicatted.com/wp-content/uploads/2026/08/logistics-1024x683.jpg) CAM 04 · DOCK B LIVE damaged\_box · 0.94 Logistics Catches the crushed carton before it leaves the dock. - ![Automotive](https://dedicatted.com/wp-content/uploads/2026/08/cam5-line-1024x684.jpg) CAM 05 · BODY LINE LIVE missing\_bolt · 0.97 Automotive Marks the body for rework while it's still on the line. - ![Safety & Security](https://dedicatted.com/wp-content/uploads/2026/08/cam6-a-1024x682.jpg) CAM 06 · SITE GATE LIVE no\_helmet · 0.93 Safety & Security Alerts the supervisor the moment PPE is missing. Your event not here? The same stack learns it — [describe your visual task](#contact-form). ## How It Works Six stages from first conversation to a model that keeps learning in production. 1. 1 Week 1### Strategic discovery An in-depth consultation to understand the visual task, the accuracy bar, and the business decision each detection should trigger — before any technology choices are made. 2. 2 Week 2### Data & feasibility audit We audit your footage, cameras, lighting, and data rights — and give you an honest feasibility verdict with a data-collection plan before you commit budget. 3. 3 Weeks 3–8### Model development Annotation, training, and validation against your acceptance criteria — not benchmark datasets. You see accuracy on your data at every iteration. 4. 4 Weeks 9–12### Production rollout We move the system from development to live — integrated with your ERP, MES, or alerting stack, deployed to cloud or edge devices. 5. 5 Every stage### Data protection & compliance Privacy by design throughout: anonymization, encryption, and role-based access — aligned with SOC 2, ISO 27001, and your industry's frameworks. 6. 6 Ongoing### Continuous maintenance Drift detection, accuracy monitoring, and scheduled retraining keep the model reliable as products, lighting, and environments change. [Start with a feasibility check](#contact-form) ## Computer Vision Success Stories [ All case studies ↗ ](https://dedicatted.com/insights) [ ![card 3](https://dedicatted.com/wp-content/uploads/2026/08/card-3.png) Case study Featured ↗ ### Custom Computer Vision on AWS: From 5 to 0.73-Second Recognition A third-party recognition vendor was slow and priced per scan at millions of scans. A custom model on AWS cut recognition from 5 seconds to 0.73 and took back the unit economics. - Computer Vision - AWS ](https://dedicatted.com/insights/custom-computer-vision-aws-case-study) [ ![Designing Platform for Instant Market Penetration – 2026-02-11T133414.933](https://dedicatted.com/wp-content/uploads/2026/02/Designing-Platform-for-Instant-Market-Penetration-2026-02-11T133414.933.png) Case study ↗ ### GenAI-Powered Video Intelligence Platform for a Global Television Network Decades of video archives became searchable by what is on screen — scenes, faces, and moments surfaced in seconds instead of days of manual review. - Media - Video Analytics ](https://dedicatted.com/insights/genai-powered-video-intelligence-platform-for-a-global-television-network) [ ![Two engineers review data on a tablet inside a large aircraft hangar with a plane in the background.](https://dedicatted.com/wp-content/uploads/2026/08/airline-2.png) Case study ↗ ### How an Aviation Tech Company Cut POC Time from a Month to Days Self-service environments with data and compute ready on day one — imagery-heavy proofs of concept that took a month now ship in days. - Aviation - AI Infrastructure ](https://dedicatted.com/insights/how-an-aviation-tech-company-cut-poc-time-from-a-month-to-days) ## Tech Stack We Bring Vision Into Production With Deep Learning Computer Vision Data & Annotation Deployment & MLOps Edge Optimization - ![pytorch](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/pytorch.svg) PyTorch - ![tensorflow](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/tensorflow.svg) TensorFlow - ![keras](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/keras.svg) Keras - ![hugging face](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/hugging-face.svg) Hugging Face - ![opencv](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/opencv.svg) OpenCV - ![yolo](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/yolo.svg) YOLO - ![detectron2](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/detectron2.svg) Detectron2 - ![roboflow](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/roboflow.svg) Roboflow - ![ground truth](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/ground-truth.svg) Ground Truth - ![aws sagemaker](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/aws-sagemaker.svg) AWS SageMaker - ![docker](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/docker.svg) Docker - ![kubernetes](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/kubernetes.svg) Kubernetes - ![mlflow](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/mlflow.svg) MLflow - ![onnx runtime](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/onnx-runtime.svg) ONNX Runtime - ![nvidia tensorrt](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/nvidia-tensorrt.svg) NVIDIA TensorRT - ![openvino](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/openvino.svg) OpenVINO - ![coreml](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/coreml.svg) CoreML - ![jetson](https://dedicatted.com/wp-content/themes/Dedicatted/public/img/tech/jetson.svg) Jetson ## featured technology partners ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) The Only Generative AI and MSP Partner in Canada As the only AWS Generative AI and MSP partner in Canada, we empower businesses to build scalable cloud solutions that drive innovation. [ Read More ](https://dedicatted.com/our-partners/aws) - ![The Only Generative AI and MSP Partner in Canada](https://dedicatted.com/wp-content/uploads/2025/01/name_amazon_color_color-300x125.webp) - ![Azure](https://dedicatted.com/wp-content/uploads/2025/01/azure.webp) - ![Oracle](https://dedicatted.com/wp-content/uploads/2025/01/oracle.webp) - ![Google Cloud](https://dedicatted.com/wp-content/uploads/2025/01/google_cloud.webp) - ![Databricks](https://dedicatted.com/wp-content/uploads/2025/01/name_databricks_color_color-300x125.webp) ## FAQ ### How much data do we need to start a computer vision project? It depends on task complexity: roughly 500–2,000 annotated images per class for simple detection, 5,000–10,000 for multi-object scenes, and 20,000+ for complex domains like medical imaging. Augmentation and synthetic data can reduce these needs — the feasibility check tells you your number. ### How long does a computer vision project take to reach production? A feasibility check takes about two weeks. A first production deployment typically lands in 2–3 months, depending on data readiness and integration complexity. Accuracy then improves continuously through retraining. ### Can computer vision run without constant cloud connectivity? Yes. We deploy optimized models directly to edge devices — cameras, industrial PCs, mobile hardware — so inference runs locally and only results sync to the cloud. Essential for factory floors and low-connectivity sites. ### Do you build models from scratch or use existing frameworks? Usually neither extreme: we start from proven architectures and pre-trained models, then fine-tune on your data. It’s faster and more accurate than training from scratch, and you own the resulting model. ### How is sensitive visual data protected during development? Data stays in your cloud account under your access controls. We apply anonymization (face and plate blurring), encryption, and role-based access — aligned with our SOC 2 and ISO 27001 certified practices. ### Can vision systems integrate with our existing infrastructure? Yes — models ship behind APIs and event streams that plug into your ERP, MES, WMS, or monitoring stack. Integration design is part of the discovery phase, not an afterthought. ## Computer Vision Development Services Computer vision development services help businesses automate visual tasks that previously required human review — quality inspection, object detection, optical character recognition, and video analytics. At Dedicatted, we build custom computer vision solutions on deep learning models trained on your data and deployed in your cloud environment or on edge devices. Our computer vision consulting covers the full lifecycle: use-case validation, data collection and annotation, model development and optimization, MLOps deployment, and continuous monitoring and retraining in production. We work across manufacturing, retail, healthcare, logistics, automotive, and security applications. As an AWS Premier Tier Partner, Dedicatted combines computer vision expertise with enterprise cloud engineering — so vision systems ship with the security, scalability, and cost controls production workloads require. ## Get a feasibility check on your vision use case Describe the visual task and what data you have. Our team responds within one business day with an honest read on feasibility, data needs, and a suggested first step. Please leave this field empty. By submitting this form, you agree with our [Terms & Conditions](https://dedicatted.com/privacy-policy) and [Privacy Policy](https://dedicatted.com/privacy-policy). Thanks, we have it. 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