Retail’s AI moment is not a chatbot on a product detail page. It is the merchandiser who replans the buy in an hour instead of a week.

Where we work

Modern-trade retail chains, D2C and marketplace brands, quick-commerce operators, and CPG manufacturers selling into both organised and general trade. India work spans multilingual product catalogues, kirana-tier logistics, and ONDC-integrated commerce flows.

Where AI changes the economics

• Search and discovery. LLM-augmented relevance over your catalogue — synonyms, intent, comparative queries, vernacular search — measured against conversion, not click-through.
• Demand forecasting. SKU-store-week models that handle long-tail sparsity, promotions, weather, and event calendars. Output that the planner can override and the system learns from.
• Personalised merchandising. Recommendations and ranking that respect business rules (margin, inventory, brand mandates) — not pure collaborative-filtering optimisation against engagement.
• Marketing content at scale. Product copy, localised creative, lifecycle email and CRM journeys generated against brand-voice guardrails and translated into the languages your customers actually shop in.
• Conversational commerce. Storefront and post-purchase assistants grounded in your catalogue, policies, and order data — not generic LLM responses that hallucinate the return policy.

What we will not do

Ship dark-pattern personalisation. Sell price discrimination under another name. Deploy synthetic influencer content without the disclosures ASCI and the FTC now require.

What you get

Personalisation and planning systems integrated with your commerce stack — Shopify, Magento, SAP CAR, or custom — and measured against the merchandising KPIs your team is already running. Conversion, basket size, sell-through, markdown rate. The numbers move; the planner stays in the driver’s seat.