Document search platform converting unstructured text into vector embeddings with contextually relevant retrieval. Multi-tenant indexing, hybrid keyword + semantic search, and relevance tuning.
100K+ documents indexed
<500ms p95 retrieval latency
30% relevance lift over keyword-only
Traditional keyword search fails when users don't know the exact terms in a document. Organizations need search that understands meaning, not just keywords.
Document ingestion with automatic text extraction and cleaning
OpenAI embedding generation with batch processing
Pinecone vector store with namespace-based multi-tenancy
Hybrid retrieval: BM25 keyword scoring + cosine similarity fusion
Relevance tuning API for per-tenant search customization
PostgreSQL for document metadata and access control
Balanced keyword vs semantic scoring weights - built an A/B testing framework to tune relevance per tenant
Designed namespace isolation in Pinecone for multi-tenant data separation without performance degradation
Implemented incremental indexing to avoid full re-embedding when documents are updated