An AI assistant that truly knows your business 

A language model on its own knows nothing about your procedures, prices or contracts. Ask it about your business and it improvises, which is where "hallucinations" come from.

How RAG works

Retrieval-Augmented Generation first searches your documents for relevant passages, then asks the model to answer only from those excerpts, citing its sources.

• Your documents are split, indexed and embedded in a database (for example PostgreSQL + pgvector).
• For each question, the closest excerpts are retrieved and passed to the model.
• The answer comes with clickable references to the original documents.

What makes the difference in production

Indexing quality

Good chunking (by section, keeping headings) matters more than the choice of model. We clean PDFs, preserve structure and enrich every chunk with metadata.

Continuous evaluation

We build a reference question set with your teams. Every change is tested automatically: accuracy, citations, and refusal when the information isn't there.

Cost and data control

Right model for each task, caching, logging without sensitive data: the assistant stays fast, affordable and GDPR-compliant.

Where to start?

A narrow scope (a support knowledge base, a legal corpus) lets us ship a first version in a few weeks, then expand. Describe your case in our simulator to receive a first specification.