In-Store Checkout Modernization
Move in-store checkout off legacy code onto a modern stack your team can staff, without changing totals, change or records.
AI coding agents rebuild the checkout through to the card reader and payment register, and an AI testing agent runs every sale against a digital twin of a store setup, so payment changes are tested without store hardware.
Our work: Rebuilt a global retailer’s in-store checkout from C++ to a modern mobile stack with AI coding agents. Read the case study
Personalization and Retention
Spot customers drifting away category by category, before they leave, and offer only what changes their behavior.
Models find customers whose spending is declining, predict lifetime value and choose the next best offer for each one, tested against a holdout. Generative AI writes personal messages within brand and offer rules, and summarizes each customer for service teams.
Assortment by Store
Give each store the range its customers want, instead of one national range.
Models cluster stores by demand, predict each item’s sales and what it takes from similar items, and suggest adds and cuts. Generative AI reads product attributes and reviews to find true substitutes, so removing an item doesn’t lose its buyers, and explains range changes to buyers.
Returns and Fraud
Predict return risk at checkout, and catch abuse such as wardrobing and receipt fraud early.
Models flag unusual return patterns by customer, store and item and send them to people for review, so honest customers aren’t treated as suspects. Generative AI reads return reasons and reviews to find fit and quality problems, and drafts case notes for investigators.
Site Selection
Score a new store by the sales it adds after you subtract what it takes from nearby stores and the site.
Models predict sales for candidate sites and how much they take from nearby stores and online. Generative AI summarizes site reports, zoning documents and local news for each candidate.
Marketing Mix
Shift budget toward the channels with measured return, and check the shift with a live test before a big move.
Marketing mix models estimate the return on each channel, including the offline and brand spend that last-click attribution misses, and recommend budget shifts that are checked with live tests before big moves. Generative AI drafts campaign copy variants and summarizes results for the weekly review.
Product Content and Search
Fill in missing attributes and thin product pages so customers can find items in search.
Generative AI extracts attributes from images, specs and supplier files, writes on-brand descriptions and fills search tags. Facts come only from supplier data and images, claims are checked against rules, and merchants approve before anything is published.
Shopping Assistant and Associate Copilot
One assistant for customers online and associates in store, answering from the same approved data.
A shopping assistant answers questions from the catalog, stock and policies; an associate copilot finds stock, orders and procedures. Prices and offers follow the rules, and anything unusual goes to a person.
