General AI models know a lot about the world but nothing about your internal processes, products or contracts. Retrieval-augmented generation (RAG) connects AI to your own documents so it can answer accurately – and show its sources.
How RAG works
- Ingestion: documents (PDFs, Word files, wiki pages, tickets) are split into sections and indexed, often using embeddings for semantic search.
- Retrieval: when a user asks a question, the system finds the most relevant sections.
- Generation: the AI model writes an answer based only on those sections and cites them.
Use cases
- Internal knowledge assistant for employees ("How do I request a company car?")
- Customer support based on manuals and FAQs
- Technical documentation search for service technicians
- Contract and policy questions for legal and HR teams
- Sales enablement with product information
What makes RAG good
- Clean source documents: outdated or contradictory content leads to wrong answers
- Good chunking: sections that keep context together
- Hybrid search: combining keyword and semantic search
- Permissions: users only see answers from documents they are allowed to access
- Citations: every answer links to its source
- Evaluation: test questions with expected answers to measure quality
RAG vs. fine-tuning
Fine-tuning changes how a model behaves; RAG gives it knowledge. For company knowledge that changes often, RAG is usually the better and cheaper choice.
Privacy and security
Keep documents in your controlled environment, use EU hosting where needed, log access and choose AI providers with appropriate data terms.
Getting started
Pick one well-defined knowledge area, collect and clean the documents, build a prototype and test it with real users. Expand once quality is proven.
We build RAG systems that integrate with your document storage and respect access rights.