Retail&Ecommerce
Retail margins are won or lost in inventory planning and customer experience. We build the demand forecasting models, personalization systems, and computer vision tools that help retailers stock the right products, serve the right recommendations, and keep customer data working across every channel instead of trapped in silos.
Forecast refresh cadence
RetailLoop stockout reduction, pilot categories
Personalization latency target
WhatmakesAIhardhere
Demand forecasting misses cost real money in both directions
Overstock ties up capital and ends in markdowns; understock means lost sales and disappointed customers. Traditional forecasting methods, often built on last year's seasonal averages, struggle to react to shifting demand, promotions, or supply disruptions in time.
Personalization at scale is hard to do well
Customers expect recommendations and offers that feel relevant, but building that experience across millions of SKUs and customer segments, in real time, at every touchpoint, is a genuinely hard engineering problem that most retail teams don't have the in-house AI expertise to solve alone.
Returns and payment fraud erode margin
Return fraud and friendly fraud (legitimate-looking transactions that are later disputed) cost retailers real margin every quarter, and manual review processes are too slow and too inconsistent to catch the patterns that distinguish fraud from ordinary returns.
Customer data is fragmented across channels
In-store POS, ecommerce, mobile app, and loyalty program data frequently live in separate systems that don't reconcile customer identity consistently. That fragmentation makes it hard to build a single view of a customer or measure marketing performance accurately.
Whatwebuild
SKU-level demand forecasting models
We build forecasting models trained on historical sales, promotional calendars, seasonality, and external signals like local events or weather, refreshed daily so inventory teams are planning against a current picture of demand rather than a static seasonal template.
Computer vision for merchandising and inventory
We build computer vision systems that count shelf inventory from store camera feeds or associate photos, detect out-of-stock conditions, and verify planogram compliance, giving operations teams visibility without manual shelf audits.
Personalization and recommendation engines
We build real-time recommendation systems that combine purchase history, browsing behavior, and inventory availability to serve personalized product suggestions across web, app, and email, tuned to your catalog rather than a generic recommendation template.
Unified customer data pipelines
We design data pipelines that reconcile customer identity across POS, ecommerce, and loyalty systems into a single customer record, giving marketing and merchandising teams one consistent source of truth for segmentation and measurement.
Conversational shopping agents
We build AI agents that handle product questions, order status, and return initiation directly in chat, escalating to a human agent when a request falls outside what the agent can confidently resolve, reducing support volume during peak seasons.
Howwe'dapproachthis
Commonquestions
Accuracy varies significantly by category and how volatile demand naturally is, so we don't quote a single universal number. What we do is benchmark a new forecasting model against your current forecasting method on historical data before go-live, so you can see the actual improvement for your specific catalog before committing to a rollout.