AI inside real systems.Not on top of them.
Most "AI integrations" are demo code wrapped around an API. We build the layer underneath: the data flow, the retrieval, the validation, the part that has to stay correct under real load, on real customer data.
Inside an ERP that serves companies, moving a week. Stable, in production.
Follow one questionthrough the system.
The question enters the system.
Scroll to follow it, layer by layer.
We architected and shipped the AI layer inside an ERP serving 70+ companies. $5M+ a week of their operations runs through it.
We didn't build the ERP itself. The team that did brought us in because their customers needed AI that holds up on real production data, and they trusted us to ship it end to end.
Reports are generated through chat, per company, on real data, in production. They aren't summaries of summaries: they're computed, validated and reconciled before anyone sees them.
It's been running. Stable. That's the bar.
In our own product too
Agents wrote the exam bank.A program decided what shipped.
For Simulation Concours, our exam-prep platform, a fleet of agents wrote 4,000 questions and 379 PDFs in seven weeks. None of it went live on the model's word.
questions written by agents. exam papers sat blind before release, out of 80 on the last. Not one answer key was challenged.
Follow one questionthrough the factory.
An agent is about to write it.
Scroll to follow it, layer by layer.
Three more places we put a model to work, and what we put around it.
- 01Demo live, production in November
A recruiting agent
For a Brussels advisory firm. The model picks a level per criterion and quotes the profile; code adds up the score. Nobody is rejected automatically, and age and nationality are left out by design.
- 02Production
Articles for a healthcare company
The model drafts articles in Belgian French; the output is structured and cleaned; a person publishes.
- 03Prototype
Quotes from the inbox
For an office-supplies reseller. An agent reads requests, prices them against the catalogue, fills the PDF quote and drafts the reply. Nothing leaves without a person's approval.
The model is never the last step. That's the bar.
The bar that keeps usout of some projects.
- 01Chatbots whose only job is to look modern.
- 02AI features with no path to production.
- 03Pipelines that work on sample data and quietly fail on the real thing.
- 04Anything where hallucinations are a problem for later.
If AI goes inside a system that matters, it has to behave like the rest of that system: predictable, monitored, debuggable, correct.
Four things that are usually truewhen it's a fit.
- 01
You have a real ERP, platform or product, and you want AI built into it, not next to it.
- 02
The outputs have to be right: financially, operationally, contractually.
- 03
You want one team owning the data, the model and the integration, not three vendors arguing about whose fault it is.
- 04
You can show us real production data, real customers and the real decisions the AI has to support.