On-prem RAG search aims to cut token costs for company knowledge
An enterprise on-prem RAG is being built to help teams find answers inside their own company documents, with cited sources shown next to the answer. The system emphasizes that data stays inside the organization, there are no , and the company is not locked into one vendor.
The problem is that teams create many useful documents but still struggle to find what they need later. Engineering specs, decisions, postmortems, and runbooks pile up in Confluence, while often fails when people ask meaning-based questions.
Large document collections also lose context, so new hires keep asking the same questions and senior engineers become the people others rely on for searching. The current MVP includes a working Confluence connector, using and BM25, answers with , and a setup where data remains within the organization’s own .
Key points
- The product is an enterprise on-prem RAG for internal company knowledge.
- It promises that data stays inside the organization and avoids and .
- The first working connector is for Confluence.
- The MVP combines and BM25 in a setup.
- The main use case is reducing repeated questions, slow onboarding, and reliance on senior engineers as human s.