Knowledge & search
Corporate Knowledge RAG Agent
A RAG agent answers staff and customer questions from your documents and policies — grounded in the source, with a citation. Not in the base — it honestly says so.
“Staff ask the same thing a hundred times, let’s put an AI on our knowledge base” is a reasonable request. But “give the model the documents and ask” breeds confident inventions: the model will fabricate an answer that isn’t in the policy, and do it convincingly. A RAG agent is built differently: first retrieval over your base, an answer strictly from what was found with a link to the source, and an honest “not found” where there are no sources.
What it does
It takes a question from staff or a customer in any channel, retrieves relevant fragments from the vector index of your documents, policies and ticket history, and composes an answer strictly from what it found. Each answer carries a link to the document and section so the person sees the original. If nothing relevant is in the base, it answers “not found in the sources” and hands off to a human when needed. This isn’t a chat “inspired by” corporate knowledge but an auditable answer traced to a specific document.
Where the line is
The main RAG risk isn’t “no answer found” but a confidently issued invention indistinguishable from fact. So refusal is part of the agent’s contract: a relevance threshold, an explicit “not in the base”, a source link under every answer. The knowledge retrieval inside the agent is a full RAG system, and its quality depends directly on how the vector knowledge base is built: without a clean index, even a perfect model answers from garbage.
How the chain works
- 01Knowledge-base retrieval · embedder
For a question it finds relevant fragments in the vector index of documents, policies and history — by meaning, not word overlap.
- 02Source-grounded answer · RAG + mid model
Composes an answer strictly from the retrieved fragments and attaches a link to the document and section. Grounded in the source, not the model's memory.
- 03Coverage check · rule + model
If there are no relevant fragments or they're weak — it answers 'not found in the base' rather than inventing. A boundary instead of a hallucination.
Integrations
OpenAI
YandexGPT
Google Sheets
Telegram+ any external API
Cost calculator
Estimate at a blended per-token rate (input+output). Exact cost depends on context length, number of calls and the share of manual review — we scope it to your process.
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