RAG Knowledge Systems
Give your teams instant access to your organization's collective knowledge — with accurate, cited answers drawn from your own documents. Built on Retrieval-Augmented Generation and deployed on your infrastructure.
Private Knowledge Indexing
Your documents — PDFs, Word files, wikis, database exports — are chunked, embedded, and stored in a vector database that runs entirely on your infrastructure. No data is ever sent to an external embedding API.
Hybrid Search
Combines dense vector search (semantic similarity) with sparse BM25 search (keyword matching). A reranking model selects the most relevant passages, improving retrieval accuracy on technical and domain-specific content.
Document-Level Access Controls
Users only retrieve content they are authorized to see. Access filters are applied at query time — a sales rep cannot retrieve HR policies, a junior employee cannot access board documents.
Source Attribution
Every response is accompanied by citations: which document, which section, which page. Users can verify the source directly. This is critical for regulated industries where answers must be traceable.
Technology Stack
Vector databases, embedding models, and retrieval frameworks