Building Enterprise Semantic Search with AI: Beyond Keyword Matching

Enterprise semantic search goes far beyond keyword matching to understand user intent. Architecture: 1) Dual retrieval: BM25 for exact keyword matches (fast, reliable for specific terms) + dense vector search for semantic similarity (finds conceptually related content). Combine with Reciprocal Rank Fusion (RRF) for merged result ranking. 2) Reranking: use CrossEncoder models (ms-marco-MiniLM-L-6-v2) or Cohere Rerank API to reorder top-50 results - improves precision significantly at small latency cost. 3) Query expansion: use LLM to generate alternative query formulations to improve recall. 4) Personalization layer: adjust ranking based on user role, department, and past interactions. 5) Chunking strategy matters: sentence-level chunks for Q&A, section-level for document retrieval, full documents for summarization. Production: index with HNSW for sub-millisecond vector search, implement query result caching for common searches, monitor click-through rates as implicit feedback.