Marketplaces fail when supply and demand never meet — not when search is slightly smarter. Before AI, you need listings, bookings or transactions, and a way to pay out. No model fixes an empty catalog.
Once the loop runs, AI helps in predictable places. Search and discovery: natural-language filters, image-to-category tagging, ranking that learns from clicks. Trust and safety: flagging suspicious listings, summarizing disputes for ops. Operations: routing support tickets, drafting seller onboarding emails, predicting no-shows.
Each use case should reduce a cost you already feel — support hours, manual review, drop-off in search — not invent a new user story. We ship one AI touchpoint per milestone so founders can A/B it against the manual path.
The architecture stays boring on purpose: your orders, users, and wallets remain the source of truth. AI reads and suggests; humans approve until you trust the automation.