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: a language model is fed relevant excerpts from your documents before it answers. The model does not hallucinate from its training knowledge — it answers based on the documents you provide it, and cites the source of every statement.

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
• OpenWebUI as the frontend for most cases, or a custom frontend for special requirements
• Optional: the language model runs on-premise (see On-Premise LLM Deployment), so that the RAG process never leaves your network either

What’s included

• Document analysis (formats, volume, structure)
• Embedding pipeline with an appropriate chunking strategy for your document types
• Vector database setup on your infrastructure
• 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.

Delivery timeline

4–6 weeks depending on volume and complexity.

Best practices we ship with

• Citations mandatory on every answer
• Hallucination mitigation through strict context binding
• Regular reindexing of new documents
• Logging to later analyse actual usage patterns