Retail AI solutions that connect the web order to the shelf
We build AI solutions for retail and e-commerce operators whose demand arrives through an app, a marketplace and 40 stores at once. Forecasts that set replenishment, cameras that count queues and shelf gaps, assistants that answer order questions, and automation that turns a B2B purchase order email into an ERP sales order. Measured in stockouts, queue minutes and orders processed without a keystroke.
Retail runs on five systems that rarely agree
The e-commerce platform knows what sold online, the POS knows what sold in store, the ERP knows what was ordered, the warehouse knows what shipped and the marketplace knows what was returned. Promotions, Ramadan, Diwali and a hot week in July all land on top. Demand forecasting retail AI only works when those five sources are reconciled first, so that is where we begin.
Where we deliver: in India, for omnichannel retailers reconciling GST e-invoicing with marketplace settlements under the DPDP Act 2023; in UAE, for retailers preparing for the 2026 e-invoicing rollout and handling customer data under UAE PDPL; in Saudi Arabia, for operators already on ZATCA Fatoora e-invoicing with controls aligned to the SDAIA PDPL and NCA ECC-2:2024; and in United States, for retailers mapping customer data practices to state privacy laws such as the CCPA. Our offices are listed on the locations page.
Six retail AI use cases we have engineered
Pilots start with one category or one store cluster.
Demand forecasting
SKU-by-store forecasts that account for promotions, seasonality and marketplace demand, with error reported in units and days of cover so a planner can act. Models retrain on a schedule and flag when a category drifts. See predictive analytics and optimization.
Customer segmentation
Purchase, browse and loyalty data combined into segments your marketing team can name and use, built on consented data in a governed platform rather than a vendor black box.
Learn more →Store traffic and queue analysis
Existing store cameras count footfall, queue length and dwell by zone, processed on site with no face data retained. Store managers get a queue alert on their phone, not a weekly report. Retail video analytics built on the computer vision pattern.
Recommendation and personalization workflows
Next-best-offer and product recommendation models served into your app and email tools through APIs, with holdout tests so uplift is measured rather than assumed. Retail personalisation AI with the test built in.
Inventory and replenishment analytics
Forecasts, open orders and supplier lead times from Dynamics 365 Business Central or your ERP produce replenishment proposals a buyer approves. AI inventory optimisation that respects minimum order quantities and shelf capacity.
Learn more →06Customer service assistants
Order status, returns and product questions answered by an assistant that reads your order system and policies, in English, Arabic and Indian languages, and hands off to an agent with the full context when it cannot resolve the case.
Learn more →Predictive analytics, edge vision and commerce integration from one team
Retail AI fails when the forecast lives in a notebook and the ERP never hears about it. Our data engineering, forecasting, edge vision and application teams build one governed data layer, then put models and agents on top of it with the integrations to POS, e-commerce, ERP and warehouse systems already designed.
For B2B retailers and distributors, AI Order Automation reads purchase orders from email, PDF and portals, validates them against price lists and stock, and posts sales orders to the ERP with exceptions routed to a person.
A worked example: from recurring stockouts to a forecast the ERP acts on
A grocery chain keeps running out of fast movers in its busiest stores while slow movers pile up elsewhere. Here is how that becomes a store-level forecast feeding replenishment in Business Central.
- 01
Quantify the stockouts
Lost sales are estimated from POS gaps and shelf scans for the top 200 SKUs in 10 stores. That category and cluster become the pilot scope.
- 02
Reconcile the five systems
POS, e-commerce, ERP, warehouse and marketplace data are joined on SKU and store, with mismatches in units of measure and promo codes fixed before any model runs.
- 03
Define forecast and service targets
Forecast error in units, target days of cover per SKU class and an agreed stockout rate, with the baseline from the previous two seasons.
- 04
Prototype the forecast
Store-level models tested against last year's actuals including Ramadan and Diwali peaks, with a simple seasonal method as the baseline to beat.
- 05
Validate with buyers and IT
Buyers review proposals for a month in parallel with their own orders. IT reviews data access, retention and the ERP integration design.
- 06
Integrate with replenishment
Approved proposals create purchase lines in Business Central. Minimum order quantities, supplier calendars and shelf capacity constrain the output.
- 07
Monitor and widen
Stockout rate, forecast error and buyer override rate are reviewed weekly. The next category joins when the first holds its numbers through a promotion cycle.
How value is measured across channels
Success is counted in forecast error, stockout rate, queue minutes above threshold, assistant resolution rate and orders processed without manual entry. The baseline comes from your own POS, e-commerce and ERP history before the first model runs.
We test under the conditions that break retail systems: promotion weeks, festival peaks, new store openings and marketplace returns. If a category cannot beat the seasonal baseline after two cycles, we say so.
Governance for customer data, cameras and e-invoicing
Retail AI touches customer identities, in-store video and tax documents, so three sets of rules apply at once. We build on consented customer data with purpose limits, keep video processing on site with no biometric retention unless you have a lawful basis and notice in place, and design order and invoice automation to work alongside the e-invoicing regime in each market.
By market: the DPDP Act 2023 and GST e-invoicing in India; UAE PDPL and the 2026 e-invoicing rollout in UAE; the SDAIA PDPL, NCA ECC-2:2024 and ZATCA Fatoora in Saudi Arabia; and state privacy laws such as the CCPA plus PCI DSS for payment data in United States. Your legal and security teams review every proposal before it goes live.
Why retailers work with Constient
Because we build the data layer, the model and the ERP integration as one piece of work, so the forecast changes an order rather than a slide. Our Dynamics 365 Business Central practice, order automation product and edge vision team mean a retailer gets one accountable group from camera to purchase order.
We do not quote uplift percentages before the pilot. Forecast error, stockout reduction and assistant resolution are measured on your data, against your baseline, and reported whether or not they flatter us.
Frequently asked questions
How does a retail AI engagement with Constient begin?
With a discovery workshop on one category, store cluster or channel where the pain is measurable: stockouts, queue complaints or manual order entry. We check data access across POS, e-commerce and ERP, agree a baseline from your history and scope a fixed-price pilot. Forecasting pilots are planned around one full promotion or festival cycle.
Can you work with our e-commerce platform and ERP?
Yes. We integrate with common e-commerce platforms and marketplaces through their APIs and with Dynamics 365 Business Central, SAP and Oracle for the ERP side. For Business Central we build AL extensions directly. Where an API is rate-limited or missing, we use scheduled exports. Interface availability is confirmed in discovery so it does not block the pilot.
Do store cameras need to be replaced for queue and footfall analytics?
Usually not. Existing IP cameras work if placement covers the queue and entrance zones, which we check during a site survey. Processing runs on a small on-site device and only counts and alerts leave the store. No face images are stored, and the configuration is documented so your privacy team can confirm it against the DPDP Act, UAE PDPL or the SDAIA PDPL.
How is customer data handled for segmentation and personalization?
Only consented data with a documented purpose is used, inside your tenant and region. We build the segmentation and recommendation models on a governed data platform where access, lineage and retention are enforced, and we run holdout tests so uplift is measured honestly. Marketing sees segments and scores, not raw records.
What does a retail AI project cost?
Discovery and pilot are fixed price. Production rollout is a fixed-price build with a support agreement or a dedicated engineering team billed monthly, depending on how many categories, stores and systems are in scope. AI Order Automation is licensed as a product with implementation priced separately once order volumes and ERP are known.
Build a retail technology roadmap around one category and one number
Bring the stockout, the queue or the order backlog. We will map the systems and propose a bounded pilot measured against your own history.