Embeddings transform text, documents, and structured data into dense numerical representations that capture semantic meaning. When your organization's knowledge is embedded, similar concepts cluster together regardless of the words used to describe them. This enables semantic search, relationship discovery, and intelligent matching that goes far beyond keyword matching — understanding intent and meaning rather than just text.
Text documents, structured data, and metadata are all embedded into a unified vector space. A search query can find relevant results across document types and formats.
Embedding models are optimized for your industry's terminology and concepts. Medical terms, financial instruments, manufacturing specifications — each domain gets appropriate semantic representation.
As new knowledge enters the repository, embeddings are generated incrementally. The vector space grows and refines without expensive full reprocessing.
Embeddings reveal hidden connections between documents, processes, and decisions that share semantic similarity — even when they use completely different terminology.
Embeddings works in concert with other layers in the intelligence stack — each connection amplifying the capability of both components.
Unlock semantic understanding of your entire knowledge base. Enable AI agents and human teams to find relevant information based on meaning — not just keywords — dramatically improving knowledge discovery and reducing duplicate effort.
Discover how Embeddings fits into your enterprise intelligence strategy.
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