From diagnosis to your teams' full autonomy: consulting, segmented training, workflows, and production deployment. We do not deliver a report on AI. We build what we recommend, then hand you the keys.
AI adoption in the workplace did not wait for a decision. It started in browsers, on personal accounts, on internal documents. The question is no longer whether to adopt AI. It is how to take back control of what is already happening.
Employees paste internal documents into consumer AI tools, from personal accounts. Nothing malicious: just the absence of a structured, available alternative.
A proof of concept gets demonstrated, applauded, then shelved. Between the demo and daily use, what is missing is integration with real data, reliability, and someone to maintain it.
One webinar for everyone, the same examples for finance and for support. Nobody leaves with something they can apply to their own work the next day.
A few employees pull ahead, others fall behind. Without structured AI upskilling, the skills gap becomes an organizational issue, then a morale issue.
Upskilling comes before tooling, tooling comes before scaling, and autonomy comes before we leave. Each pillar produces something you can verify: a real use case, a tool in production, a team that no longer needs us.
AI training paths segmented by role and by level: beginner, intermediate, advanced, built on your teams' real cases. Not a top-down session showing the same prompts to everyone.
What gets identified in the workshop gets built: AI assistants connected to your internal data, automation workflows, business tools. Then deployed to production, not left as a prototype.
The goal is explicit: your teams no longer need us. Internal champions identified and trained, documentation kept up to date, a handover planned from day one.
Consulting firms deliver reports. Training providers deliver slides. We deliver code in production, and we applied it to ourselves first.
We produce code roughly 3x faster than 18 months ago. At the scale of a full project, including review and iterations, the real gain is around 50%. This is the nuance we carry over to your teams: the acceleration is real, not magic.
Granit and Virgil manage their site without us. Côté Neuf runs on automation, with more than 3,000 pages produced and maintained through n8n. Autonomy is not a sales pitch. It is a measurable result.
Mistral or self-hosted models, OVH or Scaleway hosting, data processed in France. Sovereign AI is not a paid add-on. It is our default configuration, GDPR by design.
Three situations, including our own. None of them is a demo. These are systems used every day.
Self-hosted n8n workflow + Mistral: reading the incoming email, extracting order lines, qualification, structured delivery into the ERP. Under one minute per order, no manual re-entry.
Read the case studyProperty listings, local pages, and generated content kept up to date through n8n, on a catalog that changes constantly. The team steers, the machine produces.
Assistants connected to our codebase and content, internal automations, tooled code review. Roughly 3x faster on code production, around 50% gain on a full project.
We start small and verifiable. Nothing obliges you to move to the next format. And we say so when an AI project is not the right answer.
You describe your situation, we send back a written opinion: what looks automatable, what does not, and where to start.
Business interviews, inventory of existing usage, prioritization by value and feasibility. Output: a roadmap and scoped pilots, ready to be built.
The full format: segmented training, pilots built and deployed to production, a network of internal champions, documented handover. You come out of it with tools that run and teams that own them.
Four rules applied on every AI transformation engagement, including when they cost us revenue.
The number of accounts opened says nothing. We measure real usage and the time actually saved, before and after, on processes identified during scoping.
Models hallucinate, and some problems are better solved with a business rule, a script, or a better-designed form than with an AI project. We say so upfront, not afterward.
On anything with legal, financial, or human consequences, AI prepares and proposes: a person decides. This is a design principle, not a configuration option.
The code is delivered to you, the data is exportable, hosting is transferable, documentation is maintained. You can leave. That is precisely what makes the partnership healthy.
The questions that come up with executive committees, IT leadership, and HR before launching an AI transformation.