AI at the service of your business

No! Modern LLMs (GPT-5, Claude) are pre-trained on huge corpora. For a chatbot or content generation, your business data (documentation, FAQ, processes) is enough. For prediction, you need more historical data, but we assess that together during the audit.

A chatbot POC starts from €5,000. A complete integration with RAG and automation ranges from €10,000 to €30,000 depending on complexity. We always define a precise budget after the initial audit.

This is a real issue! We use RAG (Retrieval Augmented Generation) to anchor responses in your real data. Add to that well-designed prompts, automatic validation, and safeguards for sensitive cases.

Absolutely. We can use models with data processing agreements (OpenAI, Azure), open source models hosted on your premises, or European solutions like Mistral. Your data is never used to train third-party models.

A functional POC in 2-4 weeks. A production-ready solution in 6-10 weeks. We favor short iterations: you see results quickly and we adjust continuously.

Yes, that's our specialty! REST API, webhooks, SDK — we adapt to your technical stack. Nuxt, React, Laravel, Django... we interface with everything.

A chatbot answers from what it knows or from what you give it to read. An agent holds tools and can act: query a database, open a ticket, send a document, trigger a workflow. The difference is not cosmetic, it changes the architecture. An agent that writes into your systems needs explicit per-action permissions, human validation on committing operations, and full traceability.

Yes, and it is an architecture decision we make during framing. Three options: a European model such as Mistral, a US provider with a data processing agreement and European hosting, or a self-hosted open weight model on your infrastructure. The last option costs more to run but guarantees nothing leaves your premises. We help you weigh this against your sector and the real sensitivity of the data involved.

Through four levers, applied from the design stage. Pick the right model per task rather than the most powerful everywhere, since simple classification does not need a reasoning model. Cache what repeats. Limit the context size you send, which is the single largest cost driver. Set per-user quotas and overage alerts. We instrument cost per request from day one of production, so you see it before the invoice.

By measuring it, against criteria defined before you start. We build an evaluation set from real cases in your business, with explicit success criteria and automated scoring that runs on every change. That makes it possible to compare two prompt versions or two models objectively. In production we track escalation rate to a human, flagged answers and latency.

Regularly. We start with a technical audit of what was built: retrieval quality, prompt structure, error handling, real cost per request. Sometimes the conclusion is that part of it is salvageable and the surrounding architecture needs rebuilding, sometimes that starting fresh is faster. We tell you plainly which of the two, with numbers.