Hallucinations, weak retrieval, no evaluation, no path to production? We diagnose architecture and retrieval quality and take your RAG system to production, GDPR-compliant and on-premise capable.
Why RAG prototypes stall.
From concept to a production-ready RAG pipeline in three days, quantitatively validated.
Three days, one common thread.
Monitoring, evaluation and ongoing development of your RAG system in production.
What we take care of.
Many AI projects stay a demo. We build AI systems that run in production, with a focus on RAG, GDPR-compliant and on-premise capable.
Microsoft, open source, or something of your own.
We make your corporate knowledge searchable for customers, employees, and AI agents.
RAG stands for Retrieval Augmented Generation. In plain terms: an AI that does not answer off the cuff, but first looks things up in your own documents and then gives a sourced answer. Instead of guessing, it names the source.
A question in plain language, the way you would ask a colleague.
It searches your own documents, such as contracts, policies or files, for the relevant passages.
A plain-language answer with a reference to the source. Traceable instead of guessed.
What this looks like in practice
What RAG looks like concretely depends on your industry:
Contracts, policies, tickets, emails: it all exists, but spread across SharePoint, file servers, CRM, and half a dozen tools. What you need right now, you still can't find.
ChatGPT barely helps: it doesn't know your internal documents and often isn't allowed to see them for compliance reasons. And low-code builders break the moment things get serious.
The answer is Retrieval Augmented Generation (short: RAG): an AI-based method that looks things up in your own documents and gives sourced answers.
But few RAG prototypes make it from test to real operation. That's exactly our work.
Many RAG systems shine in the demo and fail in real operation. In a RAG review we analyze architecture and retrieval quality, find the weak spots and show the concrete path to production.
The result: a clear diagnosis with prioritized actions.
See RAG Review & AuditFrom zero to RAG prototype in three days.
Evolutionary, from naive to modular RAG: hybrid search, reranking, evaluation with Ragas. You build it yourself and learn to weigh stacks neutrally.
Stalled prototype? We take it to production.
We diagnose architecture, retrieval quality and evaluation, find the weak spots and show the concrete path to production.
We built our own RAG architecture.
That's why we master any stack: Microsoft (Copilot, Azure AI Search, Fabric), ready-made platforms (Vespa.ai, Qdrant …) or fully tailored. Connector system, chunking, RBAC, model- and infrastructure-agnostic.
Search claims files and policies with AI, assess cases faster, BaFin-compliant.
Search case files and contracts in seconds, deadlines in view, with confidentiality.
Listings, contracts and property documents on demand, inbox automated.
Clinical knowledge, guidelines and documentation instantly within reach, on-premise.
Research, fund data and reporting on demand, investment decisions sourced, BaFin-aligned.
Studies, SOPs and regulatory docs instantly searchable, GxP-aligned and on-premise.
Policies, contracts and terms in seconds, sourced and BaFin-aligned.
Technical docs and maintenance knowledge right at the machine, on-premise.
A knowledge layer for production-grade RAG systems, and where it actually reduces hallucination.
Why chunking strategy decides between production-grade and demo toy.
Getting DATEV data into AI agents safely, with audit trail and client separation.
A free initial call: we'll look at your use case, assess feasibility honestly, and tell you whether and how RAG pays off for you.