RAG or fine-tuning: what is the difference?

RAG connects the model to your up-to-date documents, without retraining; fine-tuning adapts the model to a style or a task. RAG is often the most pragmatic starting point; see our detailed comparison.

Does our data leave the company?

Not if the architecture is sovereign or on-premise: it is a design choice, not a constraint.

Can a RAG system hallucinate?

The risk is greatly reduced because the answer is grounded in your sources and referenced, but continuous evaluation remains necessary, and we build it in.

How long does it take to get a first RAG into production?

Usually 8 to 12 weeks for a well-defined scope.

Making your internal knowledge searchable, with confidence

A RAG (Retrieval-Augmented Generation) system connects a language model to your own documents so that it answers from your internal knowledge (procedures, contracts, technical documentation) rather than from generic knowledge. Kolibro builds secure RAG systems, with role-based access and sovereign hosting, and puts them into production in your operations.

The problem: the knowledge exists, but cannot be found

Your answers are in your documents, but scattered across servers, intranets, emails and experts' heads. Searching takes time: according to the McKinsey Global Institute, knowledge workers spend about a fifth of their time, close to one day a week, searching for and gathering information. A conventional search engine looks for words; a RAG system looks for meaning and answers, with sources to back it up.

How RAG works, in plain terms

Your documents are split up and indexed by meaning, not just by keywords. For each question asked in natural language, the system retrieves the most relevant passages and passes them to the language model, which writes an answer grounded in those sources, with the references. The result: fewer hallucinations, a verifiable answer, and data that never leaves your perimeter.

Security and sovereignty by default

In regulated sectors, trust is the condition for use. Our RAG deployments include sovereign hosting in France or on-premise, secure role-based access to sensitive data, full ownership of the code and data, and reversibility built in from the design stage.

Use cases

A searchable business knowledge base, a document copilot connected to your tools, analysis and search across thousands of contracts or technical files, a compliant assistant on sensitive data. RAG is rarely an end in itself: it answers a concrete problem and fits into a workflow (support, compliance, production) that is measured on a real gain. It is often combined with document analysis, which extracts and structures the information before making it searchable.

Frequently asked questions

RAG or fine-tuning: what is the difference?: RAG connects the model to your up-to-date documents, without retraining; fine-tuning adapts the model to a style or a task. RAG is often the most pragmatic starting point; see our detailed comparison.
Does our data leave the company?: Not if the architecture is sovereign or on-premise: it is a design choice, not a constraint.
Can a RAG system hallucinate?: The risk is greatly reduced because the answer is grounded in your sources and referenced, but continuous evaluation remains necessary, and we build it in.
How long does it take to get a first RAG into production?: Usually 8 to 12 weeks for a well-defined scope.

Book 30 minutes →

Let's start with a single workflow

The entry point is simple, bounded, and you walk away with something useful either way.

You pick a workflow that costs you time or margin.

Within a few weeks, we diagnose it with your teams.

You leave with a costed plan and a first proof of value.