RAG development for private knowledge systems
A RAG system over your own documents that answers with the exact passage it used — so every answer can be checked, and the system says so when the files do not cover a question. That is what we build: retrieval-augmented generation that survives compliance review.
Public chatbots invent answers. A private RAG system does not have to: it retrieves the relevant passages from your documents first and answers with references to them. Your team gets answers they can trace to the source — decisive in legal, finance, HR and every knowledge-heavy workflow.
We provide RAG development for companies in Germany and the EU: document ingestion and versioning, semantic and keyword indexing, role-based access, answer generation with citations, and an evaluation set that proves answer quality on your questions before anyone uses it. Hosting in the EU cloud, in your environment or fully on-premise.
What is included
Source-linked answers
Every answer cites the document and passage it came from, so users can verify before they trust.
Honest refusals
When your files do not cover a question, the system says so instead of guessing.
Role-based access
Who may ask what: permissions per document and per user group, versioned documents included.
Your environment
EU cloud, your own infrastructure or on-premise. Your files stay where they belong.
Measured quality
A held-out evaluation set on your real questions shows how good the system is before it ships.
How we work
- 1
Scope the corpus
Which documents, which quality, which access rules. We start with the set where a good answer matters most.
- 2
Build and evaluate
Ingestion, indexing, generation with citations — evaluated against agreed questions with agreed grading.
- 3
Roll out and maintain
Handover to your team, document update pipelines, and re-evaluation when the corpus or models change.
Frequently asked questions
Where do our documents stay?
Where you want them: EU cloud, your own infrastructure or fully on-premise. We routinely build RAG systems that never let a single document leave the customer network.
Which languages does the RAG system understand?
German and English out of the box, including mixed-language corpora — policies written in German, questions asked in English and vice versa.
How is RAG different from ChatGPT with our documents?
Permissions, traceability and evaluation. A private RAG system enforces role-based access, cites the exact passage per answer, refuses when sources are missing, and is tested against held-out questions before release.