Semantic Search

Semantic search uses AI embeddings to understand the conceptual meaning of a query and return results that are semantically similar, even if they use different words. Traditional keyword search requires exact term overlap; semantic search understands that "ML model deployment" and "putting AI into production" are asking about the same thing.

The technical foundation is vector embeddings: text is converted into dense numerical vectors that capture semantic meaning, and search becomes a nearest-neighbor lookup in that vector space. This is why vector databases like Pinecone, Weaviate, and pgvector are central to modern search and RAG architectures.

Semantic search is a foundational component of enterprise knowledge bases, customer support AI, and internal documentation tools. Hybrid search — combining semantic similarity with keyword matching — is increasingly the standard approach, as it balances the conceptual power of embeddings with the precision of exact-match retrieval.