Provides read-only, citation-backed semantic search and retrieval-augmented generation over enterprise documents via standardized MCP tools, with local embeddings for privacy.
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.
Provides 11 MCP tools for deterministic, local semantic search over your documents, including indexing, retrieval, exact-match facets, temporal truth, semantic diff, and agent-first JSON output. Enables LLMs and agents to search, retrieve, and analyze documents without cloud dependencies or per-query costs.
Enables document ingestion, semantic search, and retrieval-augmented generation via MCP tools and REST API, using vector embeddings and intelligent chunking.
Enables local knowledge base management with retrieval-augmented generation (RAG), providing semantic search, document reading, listing, and Q&A via MCP tools and REST endpoints, all running locally without cloud dependencies.