Our Method in Three Steps
Scope
We clarify where you stand and where you need to go: a diagnostic of your processes, IT, and data/AI maturity, prioritization of key issues, and definition of a realistic target, architecture included.
Deliver
We prioritize based on real impact: ROI, effort, and risk. We then build the agents, orchestration, automation, and data platforms, and industrialize the solution so it holds up in production, not just in a demo.
Anchor
The best technology is worthless if no one uses it. We support change management by training teams and measuring results to continuously adjust.
Discover Elio
Before we design a solution for you, we put it to the test ourselves. Elio, our internal agentic workspace, already structures our consultants’ daily work: engagement scoping, presales, and collaboration on assignments. We apply our own principles to ourselves.
FAQ
Classic machine learning predicts or classifies based on historical data (for example, fraud detection or demand forecasting). Generative AI, on the other hand, produces new content (text, code, analysis) from a model trained on large corpora.
It depends on your data maturity and the complexity of the use case, but our three-step approach (Scope, Deliver, Anchor) is specifically designed to deliver a first result quickly rather than waiting for the perfect project.
Through governance built in from the scoping stage (data traceability, performance monitoring, feedback loops) rather than controls added as an afterthought.
No. We work with the platforms our clients already have in place (Microsoft, AWS, Google Cloud, Databricks, Snowflake, among others) rather than requiring a technology overhaul first.
Have an AI project in mind? Let’s talk.
Contact
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Philippe Harel
Leader Data & IA