Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.
Semantic search across a project's knowledge base. Use natural language queries to retrieve top matching chunks with source and score, with optional metadata filtering.
Search documents using semantic understanding to find relevant content based on meaning rather than keywords. Understands natural language queries and returns ranked passages with source information.
Process natural language queries to obtain structured data on leads, contacts, and opportunities. Supports table, natural, or combined response formats.