The problem with generic AI
Language models like ChatGPT or Claude are powerful, but they don't know your company. If you ask them about your internal policies, your prices or your contracts, they don't have that information: they either give a generic answer or make one up (what's known as a hallucination). For a company, that's a risk.
What is RAG
RAG stands for Retrieval-Augmented Generation. Put simply: instead of letting the AI answer from memory, it first retrieves the relevant information from your own documents and then generates the answer using that information. It's like an employee who checks the company manual before answering.
How it works, in 3 steps
- 1. We index your documents (manuals, contracts, policies, knowledge bases) and turn them into data the AI can query.
- 2. For each question, the system searches for the most relevant fragments of your documents.
- 3. The AI generates the answer using only those fragments, citing the source.
Real use cases
- An assistant that answers questions about manuals, contracts and internal policies.
- Internal support so your team can find information instantly.
- Corporate knowledge lookup without depending on a single person.
- Analysis of long documents with well-founded answers.
Why it matters for your company
- Accurate answers based on your data.
- Fewer hallucinations and errors.
- Traceability: every answer cites its source.
- Your information stays private and under your control.
How to get started
You don't need to know anything about AI. We start with a diagnostic of your documents and processes, validate a use case with a pilot, and take it to production. If you want your team to have an assistant that truly knows your company, let's talk.