Generative AI & sovereign RAG.
An internal copilot that answers from your own documents, hosts your data in Europe and cites its sources. We design bespoke generative-AI solutions — useful, compliant and sovereign — not demos that sleep in a notebook.
How do you deploy a private, compliant generative-AI copilot on your own data?
The safest way to deploy generative AI in a business is the RAG architecture (retrieval-augmented generation): instead of sending your data to a public model, you index your own documents in a vector store, and the model generates its answers from the relevant retrieved passages — citing its sources. To stay sovereign and compliant, the system is hosted in European data centres, the language model is itself operated in Europe (or self-hosted as open-weight), and no data leaves the Union. The result: a copilot that answers with the organisation's context, where every answer is traceable to its source document, and which satisfies the GDPR and the AI Act. This is the approach that turns a proof of concept into a reliable production tool.
RAG (retrieval-augmented generation) — A generative-AI architecture that enriches a language model's answers with information retrieved in real time from an organisation's document base. It reduces hallucinations, allows sources to be cited, and keeps data under control.
- Self-hostable
- You keep your data
- Sources cited
- Every answer traceable
- Step by step
- From prototype to production
- GDPR + AI Act
- Good practices built in
Why RAG rather than a public model
Pasting your documents into a public chatbot raises three problems: data leakage, no traceability and hallucinations. RAG solves them. Your documents stay in a store you control; the model answers only from the relevant passages and cites its sources; and every answer can be audited. That is the difference between a gadget and a tool you can rely on to decide.
Sovereign by default
- Hosting in European data centres (Belgium, France, Germany, the Netherlands).
- Models operated in Europe or self-hosted open-weight models — no dependency on a non-EU API.
- Strict isolation: your data is never used to train a third-party model.
- Full traceability: request logging, citations, and access control.
From prototype to production
We start with a prototype on a concrete case, then industrialise progressively: document-ingestion pipeline, vector store, orchestration, guardrails (filtering, rate-limiting, oversight) and answer-quality monitoring. The goal is simple — to go from a demo to a reliable tool you can lean on day to day.
What you get
- Use-case scoping and costed feasibility study
- Ingestion pipeline and vector store on your documents
- RAG copilot with source citation and guardrails
- Sovereign European hosting, data isolation
- Quality monitoring (relevance, hallucinations, latency)
- GDPR- and AI-Act-compliant documentation
Frequently asked
Answers to the most common questions — timelines, sectors, compliance, hosting, methodology.
What is the difference between RAG and fine-tuning?
RAG retrieves information from your documents at question time — ideal when knowledge evolves and sources must be cited. Fine-tuning re-trains the model on your data — useful to adapt style or specific tasks. The two are often combined; we choose based on your real need, not on fashion.
Is my data used to train a third-party model?
Never. In our architectures, your documents stay in a store you control and are not sent to train an external model. That is a condition of sovereignty and compliance.
Does RAG eliminate hallucinations entirely?
It strongly reduces them by grounding answers in real, cited sources, but no approach removes them 100%. That is why we add guardrails and oversight, and measure the error rate continuously rather than assuming it is zero.
Ready to unlock
your data?
Let’s discuss your transformation challenges. An initial conversation, no commitment, to frame your need and see how to move forward.