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European headquarters
Oberallmendstrasse 18, 6300 Zug Switzerland
Netherlands
Zaandijkerweg 8, 1521 AX Wormerveer, Netherlands
United Kingdom
10 Midford Place, W1T 5AG, London, United Kingdom
Israel
Yigal Alon St 98, Tel Aviv, 6789141, Israel
AI development that fixes a specific retail problem, not a generic AI project
Most retail AI projects stall when nobody agreed in advance what "working" looks like for the business, regardless of whether the model itself performs well. At AI Engineering by Grid Dynamics, we build AI for retailers around one committed outcome, whether that is fewer stockouts during peak season, faster catalogue updates, or a support queue that does not grow with order volume, and we tell you honestly what it will cost and how long it will take before you commit.
Talk to a retail AI specialist
Why retailers choose AI Engineering by Grid Dynamics
A team that can start inside weeks, has already built on the platforms your stack runs on, and treats the pilot-to-production gap as the main risk to manage, not an afterthought.
Deployment in three to four weeks
Once scope is agreed, engineers and data scientists join your sprints rather than spending the first month on internal onboarding.
Delivery centres in European time zones
Your team works overlapping hours with your ops, merchandising, and engineering leads, not a nine-hour offset.
Built on the platforms retailers already run
Partnerships with Google Cloud, AWS, and Azure mean we integrate with your existing commerce stack rather than asking you to migrate to a new one first.
Strategy through to production, under one team
The same engineers who scope the use case build, ship, and hand over the model, so nothing gets lost translating a strategy deck into working code.
Retail & e-commerce challenges and how AI solves them
Five challenges account for most of the AI work retailers actually commission: fragmented systems, seasonal demand swings, inconsistent personalisation, slow catalogue operations, and rising support volume. Each shows up as a recurring line item in retail technology budgets, not a hypothetical, and below is how AI addresses each, and where it does not help on its own.
AI cannot fix disconnected systems, but a unified data layer can make the fragmentation invisible to the business. Pulling transaction, inventory, and customer data from POS, ERP, and e-commerce platforms into one model-ready source lets forecasting and personalisation tools work from a single version of the truth instead of five conflicting exports.
What we build for retailers
We scope every engagement around a business outcome first, then choose the AI capability that delivers it. Current delivery focus areas:
Demand forecasting and inventory intelligence
SKU-level demand prediction, safety stock optimisation, and allocation across warehouses and stores
Personalisation and recommendations
Unified customer profiles, product recommendations, and next-best-action models across web, app, and in-store channels.
Conversational AI for customer service
Support copilots and chat assistants trained on your policies, order data, and product catalogue.
Generative AI for catalogue and content
Product description generation, attribute tagging, and multi-market listing translation, with a human review step built in.
Computer vision for retail
Shelf and inventory monitoring, planogram compliance, and loss prevention, scoped to avoid individual customer identification unless a specific, compliant use case requires it.
Dynamic pricing
Pricing that responds to demand, inventory position, and competitor movement within rules your merchandising team sets and can audit.
Capabilities also within scope but scoped case by case rather than covered in depth here: BI and reporting layers, supply chain optimisation beyond inventory, and legacy commerce platform modernisation. If your project spans one of these alongside a listed capability, raise it during scoping rather than assuming it is out of reach.
How we deliver
Three engagement models, and cost and timeline ranges we can actually stand behind rather than a "contact us for a quote" deflection. Engagement models:
Fixed-scope project
A defined use case with a fixed deliverable and timeline. Best when you already know what you want built.
Team extension
Our engineers and data scientists join your existing team and roadmap. Best when the work is ongoing or the scope will evolve.
Consulting-led
We assess feasibility, data readiness, and ROI before recommending a build. Best when you are not yet sure AI is the right investment for this problem.
Where retail AI fails, and how we de-risk it
Most retail AI projects that stall do so for one of four repeatable reasons, and each has a concrete mitigation, not a vague reassurance.
Sales history exists, but it is split across systems, inconsistently labelled, or missing the granularity a model needs (SKU-level rather than category-level, for instance). We run a data readiness assessment before committing to a build timeline, so the timeline reflects the data you actually have.
Use cases
Concrete scenarios we build, not capability labels
Predicting stockouts before peak season
using SKU-level demand models trained on sales history, promotions, and seasonality, so replenishment orders go out before shelves empty rather than after.
Routing customer queries away from human agents
for order status, returns, and sizing questions, freeing support teams for cases that need judgment.
Cutting product listing time
for a new market by generating draft descriptions and attribute tags from existing product data, with a merchandising review step before publishing.
Detecting planogram compliance issues
from in-store camera feeds without identifying individual shoppers, flagging misplaced or out-of-stock items to store staff.
Adjusting price within merchandising-set rules
as inventory position and demand shift, rather than running static prices that ignore both.
Should you build, buy, or combine?
The right answer depends on your data maturity, timeline pressure, budget, and in-house team capacity, not on which option sounds more sophisticated
| Your situation | Standard use case (recommendations, basic forecasting), tight budget, need results fast | Use case is core to how you compete (a specific pricing model, a proprietary personalisation approach), or off-the-shelf tools do not fit your data or systems | You want the speed of a packaged platform with room to customise the parts that matter to your business | You are not sure yet which use case is worth pursuing, or whether your data can support it |
|---|---|---|---|---|
| Recommended path | Buy. Established platforms and packaged AI tools cover common retail use cases well; custom development adds cost without adding differentiation here. | Build. Custom development is worth the investment when the capability is genuinely differentiating or your systems are too particular for a packaged tool to fit. | Combine. Deploy a platform for the commodity capability and layer custom models on top of the specific piece that needs to be yours. This is the path most of our retail clients land on in practice. | Start with an assessment, not a build. A short feasibility and data readiness review costs a fraction of a full build and tells you which of the three paths actually fits before you commit budget. |
| Your situation | Recommended path |
|---|---|
| Standard use case (recommendations, basic forecasting), tight budget, need results fast | Buy. Established platforms and packaged AI tools cover common retail use cases well; custom development adds cost without adding differentiation here. |
| Use case is core to how you compete (a specific pricing model, a proprietary personalisation approach), or off-the-shelf tools do not fit your data or systems | Build. Custom development is worth the investment when the capability is genuinely differentiating or your systems are too particular for a packaged tool to fit. |
| You want the speed of a packaged platform with room to customise the parts that matter to your business | Combine. Deploy a platform for the commodity capability and layer custom models on top of the specific piece that needs to be yours. This is the path most of our retail clients land on in practice. |
| You are not sure yet which use case is worth pursuing, or whether your data can support it | Start with an assessment, not a build. A short feasibility and data readiness review costs a fraction of a full build and tells you which of the three paths actually fits before you commit budget. |
FAQs
Check for twelve to eighteen months of transaction history at the granularity your use case needs (SKU-level for demand forecasting, individual customer events for personalisation), stored in a system you can actually export from. If you are not sure, a short data readiness assessment before committing to a build timeline is far cheaper than discovering the gap three months into a project
Ready to scope your use case?
Tell us the problem you are trying to solve, not the AI capability you think you need, and we will tell you honestly whether it is a build, a buy, or a short assessment first.