Local AI & RAG Systems – GDPR-compliant
Most companies cannot upload their contracts, quotes and files to someone else's cloud – and still don't want to miss out on AI. A RAG system solves this: it searches your documents, formulates answers and drafts from them and cites the source for every statement. Run on your hardware or an EU server, so nothing leaves the building.
What's included
RAG systems: answers from your documents – with sources
Questions like "What notice period applies with supplier X?" are answered in seconds – with a reference to the exact paragraph. No hallucinations, because the answer comes from your documents, not the internet.
Local AI models on your own hardware or EU servers
Open-source models run on a server in your network or in a German data centre. Nothing is sent to OpenAI, Google or Microsoft, nothing is trained, deleted documents are really gone.
Automated drafts for quotes, emails and reports
From past quotes, the service specification and the framework agreement, a draft quote with line items is created – as in the RAG system for BSU-Holding. Likewise replies to recurring emails or report drafts from minutes.
Integration with Microsoft 365, CRM, DMS and existing tools
The system reads the storage you already have: SharePoint, OneDrive, network drives, DMS or CRM. Access rights are inherited – anyone who can't open a file gets no answer from it.
GDPR concept, roles & permissions, human approval
A register of which documents may enter the index, logging of every query and human approval for anything that leaves the company. Coordinated with your data protection officer on request.
How it works
- 1
Pick the use case
We find the one case that eats the most time – quotes, contract questions or onboarding – and check your documents for it.
- 2
Pilot on real data
Within a few weeks a prototype runs on your documents. You test with real questions from daily work, not with examples.
- 3
Set up operation
Hardware or EU server, access rights, logging and the data protection concept are set up production-ready.
- 4
Brief the team & extend
A short training: asking good questions, knowing the limits. Only then the next use case.
Related references
- AI system · Holding
GDPR-compliant RAG system for BSU-Holding
Frequently asked questions
What is a RAG system?
Retrieval-Augmented Generation: the AI first finds the relevant passages in your documents and then formulates the answer from them – with citations instead of hallucinations. Your knowledge isn't trained into the model; it stays in your own index.
Does our data end up with OpenAI or Microsoft?
Only if you want it to. Depending on how sensitive the data is, I run open-source models entirely on your hardware, on an EU server, or use business APIs that don't train on your data.
What hardware does a local AI need?
For document search and text drafts, a server with a capable graphics card is usually enough. I size it by data volume and number of users – and happily start with a pilot on existing hardware.
How long does implementation take?
A first RAG system on your documents is usually up within a few weeks. You see the benefit on a real use case before investing further.
What happens when the AI gets something wrong?
Every answer shows its sources, and critical steps such as a quote keep a human approval. Review and logging steps keep everything traceable.
Do you train our staff to work with AI?
Yes, hands-on: how to ask good questions, where AI helps and where it doesn't – including clear rules for handling company data safely.
Does this fit your project?
Tell me briefly what it's about – you'll receive an assessment and a quote within two working days.