Connecting AI to your internal documents: the guide to reliable answers

"Can you summarise our leave policy?" Ask ChatGPT and it will answer confidently… with a generic answer unrelated to your company. Of course — it has never seen your documents. Here's how to give it access, the right way.

Why connect your AI to your documents

An AI wired into your document base becomes a different tool. It can:

• answer employee questions (HR, IT, process) from your real procedures;
• assist customer support with your product documentation;
• surface a contract clause or policy in seconds;
• act as an internal semantic search engine that understands intent, not just keywords.

The common thread: answers grounded in your knowledge, not in a general model's statistics.

Three approaches (and why RAG wins)

1. Copy-paste into the prompt

You paste the document into the chat. Simple, but: limited by context size, repeated for every question, unmanageable beyond a few pages, and your documents go to the model vendor.

2. Fine-tuning

You retrain the model on your data. Expensive, slow to update (every change = a new training run), and the model blends its knowledge: it can't cite a source and can still hallucinate.

3. RAG — the right answer

RAG (Retrieval-Augmented Generation) retrains nothing. It retrieves the relevant passages from your documents at question time, then asks the model to answer from those excerpts. The result: instant updates, cited answers, far fewer hallucinations. It's the enterprise standard.

How it works, concretely (5 steps)

1. Connect sources. Documents flow in from your tools: Notion, Google Drive, SharePoint, Confluence, Slack, or plain PDF/Word.
2. Clean. Remove noise (headers, navigation) and standardise the text.
3. Chunking. Documents are split into coherent passages — the most underrated step, which drives answer quality.
4. Vectorisation. Each passage becomes an embedding (a vector) capturing its meaning, stored in a vector database (e.g. pgvector).
5. Retrieve + cited answer. For each question, the system finds the most semantically similar passages, hands them to the model, and returns an answer with its sources.

Which sources to connect

Value comes from coverage. The most useful sources to wire first:

• Wikis & docs: Notion, Confluence, Google Drive, SharePoint, OneDrive.
• Conversations: Slack, Teams (your teams' tacit knowledge).
• Files: PDF, Word, Excel, slide decks.
• Business bases: catalogue, FAQ, support knowledge base.

Security, permissions and sovereignty

Connecting AI to your documents means trusting it with your most sensitive data. Three non-negotiables:

• Permissions enforced: the AI must only expose to each person what they're allowed to see.
• Isolation: your data stays partitioned, encrypted, per organisation.
• Sovereignty: EU-hosted, GDPR-compliant infrastructure so your contracts and customer data never leave the EU.

Where to start

1. Pick one precise use case — customer support, HR or sales.
2. Connect the 2–3 sources that cover it.
3. Test on real questions, check the citations.
4. Expand sources and teams gradually.

FAQ

Do I need to retrain the model? No. RAG works with existing models (ChatGPT, Claude, Mistral, or open models) without fine-tuning.

Are my documents sent to the LLM vendor? With sovereign infrastructure, indexing and storage stay in Europe; only the strict minimum is passed at answer time — and you keep the choice of model.

How long to get started? A few minutes for a first corpus: connect a source, index, query.

Ragnight is the knowledge infrastructure that connects your documents to your AI assistants: indexing, semantic search (pgvector) and cited answers, EU-hosted and GDPR-compliant. Get started.