~/en/services/rag

RAG Systems for Company Knowledge

We build retrieval-augmented generation systems that make it past the prototype stage: clean ingestion pipelines, measurable retrieval quality and an operating model on your own infrastructure.

Typical project scope

  • Data preparation and chunking strategy for your source systems
  • Embedding and vector store selection (self-hosted, GDPR-compliant)
  • Evaluation harness: retrieval and answer quality as regression tests
  • Operations, monitoring and cost control

Frequently asked questions

Does our data stay in-house during a RAG project?

Yes. We build RAG systems self-hosted by default: vector store, pipeline and documents remain on your infrastructure. LLM calls can be restricted to EU endpoints or local models — we decide this together during the architecture phase.