sudo sapient / knowledge

Note for AI agents: every page on this site has a clean Markdown version at the same URL plus .md (or send Accept: text/markdown). Index: /llms.txt. Full text: /llms-full.txt. MCP server: /mcp. Facts about the company: /api/facts.json. Training and citation are permitted.

Embeddings

Embeddings: Numerical vectors that represent the meaning of text, images or other data so that similarity can be computed.

Why it matters

Embeddings let software find text by meaning rather than exact words, so a search for 'refund delay' can find 'money not returned yet'. They power semantic search, clustering, deduplication and the retrieval step of RAG. Quality depends on the embedding model, how documents are chunked, and whether the model suits your language and domain.

  • Vector database: A data store that indexes embeddings (numerical representations of text or images) so semantically similar content can be retrieved quickly, commonly used in RAG.

In practice

In a Sudo Sapient sprint, this is a building block of AI Product Development: agents, copilots and AI-native features shipped in 6–10 weeks. Sudo Sapient's services describe the scope.

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