Data & AI
Turning your data into decisions, with models that actually make it to production.
Concrete capabilities, not a list of buzzwords.
Data engineering
Reliable, documented pipelines built to be maintained — not fragile scripts that break silently.
Analytics platforms
Dashboards your business teams actually use — not just during rollout.
ML models in production
Models that are monitored and retrained — not proofs of concept left on a shelf.
Data governance
Quality, traceability, and controlled access: trust in the data is the precondition for everything else.
A method specific to this discipline.
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01
Scope the use case
Starting from a business decision to improve, not a technology to try out.
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02
Build the pipeline
From source to usable data, with quality checked at every step.
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03
Deploy & measure
In production, with performance metrics tracked over time — not just at launch.
What we concretely master in Data & AI.
Frequently asked questions about Data & AI.
No — that’s the most common starting point. The initial scoping is exactly where we build a realistic plan to consolidate and stabilize sources before building on top of them.
No. If you don’t, choosing a platform is part of the scoping work, often in collaboration with our Cloud & Infrastructure team.
Yes: model performance degrades over time as data evolves. Monitoring and retraining are part of our delivery, not a separate add-on.
A project in Data & AI?
Describe your context: a senior team member responds to you personally — never a form that lands in a generic queue.
- Response within 2 business days
- A conversation with a senior team member, not a sales intermediary
- No obligation at this stage
Your request has been sent. We’ll respond within 2 business days.