AI-Powered Marketplace Content & Search Platform
Owning a platform indexing 517K+ products across 29 marketplaces, with semantic search built for how creators actually search.
Problem & industry context
Creator-commerce platforms sit between influencers and marketplace catalogs spanning dozens of regional and international storefronts. Product data arrives inconsistent — mismatched categories, duplicate listings, multilingual titles — and creators need to find relevant items by intent, not just keyword match. Systems that can't reconcile catalog scale with search relevance either drown in stale data or return irrelevant results.
Insight
At this scale, the hard problem isn't scraping — it's reconciliation and retrieval. A category taxonomy that's 80% automated still needs the remaining 20% to be right, because a wrong mapping breaks discovery for every product under it. Vector search earns its complexity only when keyword search demonstrably fails on real creator queries — semantic search on top of a shaky data foundation just returns confidently wrong results faster.
What I built
Own an AI-powered marketplace content and search platform end-to-end: architected a pipeline indexing 517K+ products across 29 marketplaces (EN + AR), built a PostgreSQL→MongoDB sync layer to bridge relational category logic with document-store product data, and shipped a gRPC semantic search API backed by Pinecone across 5,109 category mappings. Engineered Cloudflare R2 image processing to keep listing assets performant at this scale.
Technical approach
Stack and tooling for this work: Python, FastAPI, PostgreSQL, MongoDB, Pinecone, gRPC, Cloudflare R2. Topics covered: Vector Search, Data Engineering, gRPC, E-commerce.
Topics
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