Semantic Search

Traditional keyword search (BM25, TF-IDF) matches query terms against document terms — effective when vocabulary is known and consistent, but brittle when users paraphrase, use synonyms, or search across multilingual content. Semantic search eliminates this vocabulary mismatch by encoding both the query and each document into dense vectors using an embedding model and then retrieving documents whose vectors are closest to the query vector. A search for "car insurance claim process" surfaces documents discussing "vehicle coverage reimbursement procedures" even if neither exact phrase appears in the other.

The architecture of a semantic search system involves three phases: offline indexing (embedding all documents and storing vectors in a vector database), online query processing (embedding the user's query and performing ANN search against the index), and optional reranking (applying a more precise cross-encoder model to re-score the top candidates). Latency budgets for enterprise search are typically tight — users expect sub-second responses — which means the embedding inference and ANN search steps must both be highly optimized, often involving dedicated GPU inference endpoints and carefully tuned index parameters.

Enterprise deployment considerations include handling domain-specific vocabulary (legal, medical, financial), supporting multilingual corpora, scaling to internal knowledge bases of millions of documents, and integrating semantic search into existing enterprise search platforms (SharePoint, Confluence, ServiceNow). Organizations also face the challenge of result explainability: unlike keyword search, where highlighted matching terms explain relevance, semantic search results require new UX patterns such as passage extraction and similarity score display to help users trust and navigate results.