RAG-powered conversations backed by your knowledge base. Every answer cites its source — no hallucinations, no guessing.
Conversations
Under the hood
Retrieval-Augmented Generation keeps AI answers accurate by grounding them in your actual documents.
Your question is converted to a vector embedding using the same model that indexed your KB.
The nearest matching chunks are retrieved from your knowledge base using cosine similarity.
The top chunks are bundled with your question into a prompt with full source attribution.
The LLM synthesises a precise answer from the retrieved context — not from training data alone.
Features
Every response includes citations linking back to the exact KB chunk used.
Full conversation context is retained so follow-up questions stay coherent.
Target specific collections so answers stay within the right domain.
Responses stream in real time — no waiting for the full answer to generate.
Works with any model via LiteLLM: OpenAI, Anthropic, Mistral, local models.
Chat insights flow directly into helpdesk tickets for seamless agent handoff.
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