RAG Knowledge Assistant

An assistant that answers staff questions grounded in the company's own documents — retrieval first, then a guard-railed LLM, always with citations.

Step by step, in pictures

See the automation in action — these are demonstration captures of the working project, so you know exactly what you'd get.

1. 1

   The workflow

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   Question → retrieval of the relevant passages → grounded answer with citations.

2. 2

   The customer's question

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   The customer asks a plain-language question (French or English).

3. 3

   The sourced answer

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   The assistant answers strictly from your documents, with the sources cited — and admits it when it doesn't know.

How it works

The problem

LLMs are confident but don't know a company's private, ever-changing knowledge — so they hallucinate, and teams keep losing time digging through scattered docs, wikis, and PDFs.

The approach

1. 1

   Documents are ingested, chunked, embedded, and stored in a vector database; a scheduled sync keeps the index fresh as content changes.

2. 2

   On each question, the system retrieves the most relevant chunks and passes only that grounded context to the LLM.

3. 3

   The model answers strictly from the retrieved context and returns citations; guardrails make it refuse — rather than guess — when nothing relevant is found.

4. 4

   Unanswered questions are logged to reveal knowledge gaps, and the assistant is delivered where people already are: Slack and an embeddable web widget.

Highlights

• Grounded, cited answers from your own knowledge — no model fine-tuning required.
• Guardrails prevent hallucination: no relevant context means an honest 'I don't know'.
• Pluggable: swap the model, vector store, or sources without touching the flow.

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