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.
- 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 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.
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Grocery and convenience
High volume, thin margin, and a supply chain that cannot wait for a nightly batch.
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Specialty and apparel
Seasonal ranges, heavy returns, and personalization that has to work in store as well as online.
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Home improvement and trade
Mixed retail and trade accounts, deep catalogues and quoting that spans both.
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Marketplaces
Third-party sellers, data quality at scale and pricing that moves hourly.
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Direct-to-consumer brands
One system of record, small teams, and growth that outruns the first stack.
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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.
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 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.
Services behind this stage Data architecture 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 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 Managed services
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 AI-augmented development
Our Core Focus Area
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
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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
Managed retail platform
Monthly
- Data platform run, monitored and patched
- Peak-season readiness reviews before you need them
- Named engineers and agreed response times
Work in this industry
All case studies
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.
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.
Thanks, we have it.
Our team replies within one business day, with relevant experience and a first read on your problem.