Semantic Search transforms how teams and AI agents access organizational knowledge. Instead of constructing precise keyword queries and hoping for results, users ask questions in natural language and receive answers synthesized from across the entire knowledge base — with full source attribution. Every answer traces back to specific documents, decisions, or conversations, ensuring trustworthiness and verifiability.
Ask 'Why did we change the pricing model last quarter?' instead of searching for 'pricing model change Q3 2025'. The system understands intent and retrieves relevant context.
Every answer includes references to the specific documents, meetings, or decisions that informed it. Users can verify, explore deeper, and trace the knowledge chain.
Search results are influenced by who's asking, what they're working on, and what their role requires. A support agent gets different emphasis than a product manager for the same query.
When an answer requires information from multiple sources, the system synthesizes a coherent response — not just a list of document links but an actual answer.
Semantic Search works in concert with other layers in the intelligence stack — each connection amplifying the capability of both components.
Reduce time-to-answer for organizational questions from hours to seconds. Enable self-service knowledge access that scales to entire organizations, and give AI agents the retrieval capabilities they need to make informed decisions.
Discover how Semantic Search fits into your enterprise intelligence strategy.
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