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.
- 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
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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.
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B2B SaaS
Multi-tenant from day one, and a cost per tenant somebody will ask about.
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Platform and infrastructure
Products whose customers are engineers, so the bar is the API.
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AI-native products
Inference cost, evaluation and a model that changes under you.
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Media and streaming
Volume, latency and content pipelines that never stop.
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Marketplaces
Two sides to keep honest and data quality that decides trust.
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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.
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 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.
Services behind this stage Cloud and DevOps Managed services
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 Cloud and 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 Managed services
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 Generative AI
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
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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
Managed platform
Monthly
- Platform run, monitored and patched
- On-call support with agreed response times
- Named engineers who know your stack
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.
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.
Thanks, we have it.
Our team replies within one business day, with relevant experience and a first read on your problem.