RAG for Internal Documents

A searchable knowledge base over your own documents — contracts, manuals, onboarding material, technical documentation. Employees ask questions in natural language; the system answers with citations.

What RAG technically is

Retrieval-Augmented Generation gives a language model relevant excerpts from your documents before it answers. This reduces reliance on training knowledge and makes answers easier to verify. It does not eliminate hallucinations: the model can miss sources, misread them, or add unsupported claims. We therefore build in citations, evaluation with real questions, and clear boundaries for answers that require human review.

Use cases

• New-hire onboarding: questions about processes, tools, and responsibilities without constantly bothering colleagues
• Compliance search: “Which of our contracts contain clause X?”
• Technical documentation: “How do I configure module Y of our software?”
• Contract search: locating relevant clauses across a contract portfolio

Stack options

• LangChain or LlamaIndex as orchestration
• Qdrant or Postgres with pgvector as the vector database
• An existing chat client, a small custom frontend, or an internal tool as the interface
• Model access through suitable APIs, EU providers, or local components when the project and data situation justify it

What’s included

• Document analysis (formats, volume, structure)
• Embedding pipeline with an appropriate chunking strategy for your document types
• Vector database setup matched to hosting, cost, and access model
• Frontend setup with authentication
• Access control model — not everyone should see everything
• Onboarding for end users (prompt examples, best practices)
• Written operations documentation

What’s not included

Document cleanup. We assume your sources are at least in a structured or searchable format (PDF, Markdown, Word, Confluence). For a pile of scanned faxes we need a separate OCR step first — let’s discuss separately.

Typical timeline

After a short discovery session, the usual planning range is 4–8 weeks. Document volume, access controls, integrations, and the agreed evaluation depth determine the actual schedule.

Best practices we ship with

• Citations for document-based answers and a visible fallback when no reliable source is found
• Hallucination reduction through context binding, evaluation questions, and human review for critical outputs
• Regular reindexing of new documents
• Logging to later analyse actual usage patterns