Semantic Search

Semantic Search retrieves information based on meaning rather than exact keyword match, using embeddings and vector similarity. Unlike traditional keyword search where you must guess the exact terms, semantic search finds conceptually related content — a search for "how to reduce AI hallucination" might return documents about "grounding," "RAG," or "retrieval-augmented generation" even though those exact words never appear in the query. Examples include the search inside Notion, Slack's AI search, the discover features in Spotify and Netflix, Algolia's vector search, and the question-answering capabilities of modern AI assistants. The technology relies on embedding models, vector databases, and approximate nearest-neighbor algorithms. Modern systems typically combine semantic search with lexical (keyword) search in hybrid configurations for best results. AI governance, AI compliance, and AI risk management programs review semantic-search systems to ensure responsible AI, particularly around access controls (who can search what?), bias in retrieval (whose content surfaces first?), and citation transparency in user-facing applications.

Centralpoint Powers Semantic Search Without Sending Data Outside: Oxcyon's Centralpoint AI Governance Platform handles semantic search inside your firewall and pairs it with model-agnostic LLM access — ChatGPT, Gemini, Llama, embedded. Centralpoint meters consumption and embeds search-powered chatbots into your portals with one JavaScript line.


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