Retail & digital commerce
Semantic Product Discovery
Understand shopper intent beyond exact keywords and rank relevant products using catalog, context, behavior, and availability.Semantic searchRankingCatalog intelligence
Learn more ← All industries
AI for retail & commerce
AI in Retail, Commerce & Consumer
Retail data is abundant but divided across commerce, stores, customer platforms, inventory, and supply systems. We engineer retail AI across the decision chain—from demand signals and product data to personalized discovery, visual experiences, and store or supply workflows.
The opportunity
AI in retail: where it creates advantage.
Retail runs on a chain of connected decisions—what to buy, where to place it, how to present it, what to recommend, how to serve the customer—and every link generates data. The opportunity in retail AI is not another isolated model; it is connecting those signals so each decision informs the next. A recommendation is only as good as the product data behind it; a forecast only matters if it changes replenishment.
We build that connected intelligence layer across commerce platforms, point of sale, customer systems, product information, and inventory. Discovery shifts from keyword matching to semantic and visual understanding. Demand planning moves from spreadsheet forecasts per channel to unified models that see promotions, local events, and substitution effects. Merchandising teams get copilots for enrichment, taxonomy, and assortment grounded in the live catalog.
Customer trust is the design constraint that matters most: consent and purpose rules govern personalization, visual experiences like virtual try-on are engineered with explicit privacy and retention controls, and every experience is measured against commercial outcomes—conversion, margin, availability, and returns—rather than engagement metrics alone.
The operating challenge
What makes Retail hard—and where AI pays off.
01
Discovery that misses intent
Keyword search fails shoppers who describe use cases, styles, or occasions—suppressing conversion and burying relevant catalog items.02
Forecast error cascades
Inaccurate demand signals cascade into stockouts, overstock, markdowns, and wasted working capital across channels.03
Catalog scale
Thousands of SKUs with inconsistent attributes, imagery, and enrichment drag down search relevance and merchandising speed.04
Service load
Routine order, product, and policy queries occupy agents while complex, high-value service moments wait.Representative outcomes
What production deployments deliver.
+23%discovery-to-product conversion from semantic search
31%forecast accuracy improvement on unified signals
-18%return rate on visually-assisted sessions
GAINX applications
AI applications for Retail.
Configurable solution patterns adapted to your systems, data, controls, and users—not generic software.
Retail & digital commerce
Virtual Try-On
Create a controlled visual exploration experience connected to garment assets, variants, availability, privacy, and analytics.Computer visionGenerative imagingCommerce integration
Learn more Customer experience & growth
Personalization & Next-Best-Action
Rank products, content, offers, and service actions using customer context, consented behavior, availability, and business rules.Machine learningDecision intelligenceExperimentation
Learn more Retail & digital commerce
Merchandising Copilot
Support product enrichment, taxonomy, assortment analysis, competitor research, campaign preparation, and catalog quality.Generative AIProduct dataWorkflow automation
Learn more Retail & digital commerce
Pricing & Promotion Intelligence
Analyze elasticity, inventory, competitor, margin, and campaign signals to support governed pricing and promotion decisions.Causal analysisOptimizationDecision support
Learn more Operations & supply chain
Demand Forecasting & Planning
Forecast demand across products, locations, channels, and horizons while explaining uncertainty and planning drivers.Time-series MLScenario planningDecision support
Learn more Operations & supply chain
Inventory Optimization
Recommend replenishment, allocation, and transfer actions using demand, lead time, service level, and stock constraints.OptimizationForecastingException workflows
Learn more Retail & digital commerce
Visual Catalog Quality
Detect missing, inconsistent, duplicate, low-quality, or non-compliant product imagery and route corrections at catalog scale.Computer visionClassificationContent workflow
Learn more Intended outcomes
The change we define before choosing technology.
01
More relevant discovery and conversion
02
Better availability with less excess stock
03
Faster merchandising operations
04
New visual shopping experiences
Frequently asked questions
Useful context before we begin.
01Can retail AI work with our existing commerce platform?
Yes. The intelligence layer integrates with your commerce, POS, PIM, CRM, order, and inventory systems through APIs and events—no re-platforming required. Architecture follows the systems you already run.
02How is personalization kept compliant?
Consent and purpose limitation are enforced at the data layer; models use only consented behavioral and transactional signals; and relevance is reviewed against business rules so recommendations respect margin, inventory, and brand decisions.
03What does virtual try-on require to launch?
Consistent garment imagery per variant, accurate product metadata, availability feeds, and clear privacy terms for customer images. The customer moment and quality threshold are defined before model evaluation begins.
04Where should a retailer start?
The fastest payback is usually semantic discovery on a high-traffic category—impact is visible in weeks. Demand forecasting delivers the largest margin effect but needs the data unification investment first.
Start with the business objective