Most retail AI is a recommendation widget. We build more.
If we had a rupee for every “AI-powered recommendation widget” sold to a retailer, we could buy one. Artificial intelligence in retail works when it touches the whole funnel — search relevance, product discovery, dynamic pricing, customer service, inventory forecasting, and lifecycle marketing.
What we build for retail and ecommerce
- Vector search — natural-language and image search that actually finds what the shopper meant.
- Personalisation engine beyond “customers also bought” — cohort-level and journey-stage personalisation.
- Dynamic pricing with elasticity modelling, competitive intelligence, and guardrails.
- Customer service automation — voice and chat via TalkTaro for order status, returns, exchanges, and pre-purchase questions.
- Inventory and demand forecasting at the SKU-location-week granularity that planners actually need.
- Catalog enrichment — auto-tagging, attribute extraction, image cleanup at scale.
Ai in ecommerce — what to do first
If you're starting out, fix search first. Bad search kills more revenue silently than any other backend problem in ecommerce. After search, the highest-ROI moves are usually customer service automation (because the cost-per-ticket is enormous) and inventory forecasting (because dead stock is also enormous).