Case Study

Semantic Search Engine

Document search platform converting unstructured text into vector embeddings with contextually relevant retrieval. Multi-tenant indexing, hybrid keyword + semantic search, and relevance tuning.

OpenAI EmbeddingsPineconeNext.jsNode.jsPostgreSQL

100K+ documents indexed

<500ms p95 retrieval latency

30% relevance lift over keyword-only

The Problem.

Traditional keyword search fails when users don't know the exact terms in a document. Organizations need search that understands meaning, not just keywords.

Architecture.

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

Technical Challenges.

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

Results.

Sub-second retrieval across large document collections
Multi-tenant architecture serving isolated search indexes
Hybrid search outperforms pure keyword or pure semantic by 30%+

Interested in building something similar?

Let's Talk