LLM & RAG Entwicklung
LLM & RAG systems that give reliable, sourced answers
We build AI assistants that work on your own data — with reliable answers, source citations, and proper access control.
Assistants that know your content
Using retrieval-augmented generation (RAG), assistants access your documents, wikis, and databases — with traceable sources instead of hallucinations.
- RAG over internal documents, manuals, and tickets
- Source citations and hallucination control
- Retrieval evaluation and quality benchmarks
Secure and integrated
We handle privacy, permissions, and operations: access only to permitted content, EU hosting, and integration into your existing channels.
- Role and permission model for content
- Integration into website, Teams, Slack, or intranet
- GDPR-native, EU-hosted, optionally on-premise
FAQ
How do you prevent the model from making things up?
We ground answers in your data with RAG, enforce source citations, add guardrails, and run retrieval evaluation so quality is measurable.
Can it run without sending data to the US?
Yes. We support EU-hosted models and on-premise deployments where data residency matters.
Build an AI assistant on your own data
Book a call and we'll scope a production-grade RAG assistant for your use case.
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