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RAG Systems & LLM Integration for Companies

AI that answers based on your own data and documents, transparent, secure and integrated into your systems.

In short

A RAG system (Retrieval-Augmented Generation) connects a language model with your own data, so it answers questions based on your documents, with source references instead of made-up answers. Soneo from Vienna builds such systems and integrates LLMs into your processes in a GDPR-compliant way, from concept to operation.

What is a RAG system?

RAG stands for Retrieval-Augmented Generation. Instead of relying solely on a language model's training, a RAG system first retrieves relevant passages from your own documents and lets the LLM formulate the answer from them, including source references. This makes answers current, traceable and far more reliable.

RAG or fine-tuning, which is better?

Fine-tuning changes the model itself and suits style or narrowly defined tasks, but it is expensive and quickly outdated. RAG keeps knowledge outside the model up to date, is cheaper to maintain and provides sources. For most enterprise use cases with proprietary knowledge, RAG is the better start, and the two can be combined.

What does a RAG system cost?

The intro call is free. A working prototype on your documents is often possible within a few weeks. Cost drivers are data volume, data quality and depth of integration. You receive a transparent estimate upfront.

Is it data-protection compliant and secure?

Yes. We work GDPR- and AI-Act-compliant, rely on EU hosting and enable on-premise. Your data stays under your control; access can be controlled via role-based permissions, and answers remain verifiable through source references.

How does a project work?

We start with a clearly defined use case and your documents, build a prototype, integrate the system into your environment and then move to operation with monitoring and continuous improvement.

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Frequently Asked Questions

Answers about RAG systems and LLM integration

Fine-tuning changes the model itself (good for style or narrow tasks, expensive, quickly outdated). RAG keeps your knowledge outside the model up to date, is cheaper to maintain and provides sources. The two can be combined.

The intro call is free. A prototype on your documents is often possible within a few weeks. Cost drivers are data volume, quality and depth of integration, with a transparent estimate upfront.

Yes. EU hosting is standard, on-premise is possible. Your data stays under your control, access is controlled via role-based permissions, and answers are verifiable through sources.

The risk drops significantly because answers come from your documents and include sources. We add further controls so results remain traceable.

Yes. We integrate LLMs and RAG into your existing tools and data sources, from SharePoint and wikis to line-of-business applications.