From the business need to production.
We scope the use case, build the data, train the models, design the agents and run the whole thing once live. One team, no hand-off.
How we work
We always begin the same way, sitting down with your teams to understand the business before anything else. After that you can join us at any step, or hand us the whole chain.
- 01
Scoping
We start from the business need, not from a technology.
- 02
Data pipelines
Pipelines that make your data dependable, documented and reproducible.
- 03
Models & agents
Selected, trained, evaluated, packaged for production.
- 04
Integration
Delivered inside your applications, triggered by your events.
- 05
Supervision
Monitored, tuned and scaled as usage grows. We stay for the long run.
What we do
Data pipelines and platforms
We turn what you already have into a reliable, governed source for AI, on a platform you can run yourself.
Custom AI models
Classification, extraction, prediction and retrieval. We select, fine-tune and evaluate what fits the task, small models included, and deploy them on your data.
AI agents
Systems that read your data, decide, act and report back, with every run traced and a supervision your teams can audit.
Integration and operation
Delivered inside the applications your teams already use, then monitored, tuned and scaled once live.
Working with your teams
We work alongside your teams on what has to change around the system: the process, the ownership and the decisions it feeds.
Training
Sessions for the people who will use and run what we build, from the business teams to the engineers who inherit it.
From prototype to production
Many organisations already have something that works: a notebook that answers the question, an agent a team built on the side, a model that scores well on last year’s data. What they don’t have is a version the company can depend on. The distance between the two is mostly engineering, and it comes down to three things.
The data it reads
A prototype runs on an export. A production system runs on live data: connected, governed, refreshed, with provenance on every row. We build that source, or plug into Atlas.
How it is evaluated
“It looks right” is not a metric. We build test sets from real cases, score every version, and re-run them whenever a prompt, a tool or a model changes.
What happens when it is wrong
Every system fails sometimes. Ours fail visibly: every run traced, approval gates where the stakes justify them, a full history when someone asks why, and a way to roll back.
Bring us the prototype. We’ll tell you what it takes to make it something the company can rely on.
Your data stays yours
Sovereignty isn’t a feature we add at the end. It shapes the architecture from the first day: where the models run, who can reach the data, and what can be proven afterwards.
Deployed in your environment
Models and compute run inside your infrastructure, or on a sovereign cloud of your choosing.
You own what we build
Models, pipelines and code belong to you, in open formats, with no provider lock-in.
Security
Access stays limited to the people on your project, and your data never trains anyone else’s model.
Traceability
Where each piece of data came from, what transformed it, and which model used it.
Let’s talk about your project
Tell us what you are trying to solve. We will tell you what it takes, what it does not take, and where AI is genuinely the right answer.