Questions we hear most.

Sovereign AI refers to AI systems whose data, models, and hosting remain under the control of the using organization and its legal framework. In practice for a French or European company: models hosted in the EU, data that doesn't transit to providers outside GDPR jurisdiction, code and architecture documented and auditable. It's not a brand, it's an architectural stance - and a strong requirement of the EU AI Act for high-risk uses.

Our diagnostic runs 2 to 4 weeks. Pricing depends on scope (number of departments involved, data volume, depth of the mapping) and is quoted after a first one-hour conversation - free and no commitment. Typically a few thousand to a few tens of thousands of euros depending on organization size. The deliverable is a 6-18 month roadmap that leadership can act on directly.

BI is the infrastructure that industrializes the production of regular, trustworthy indicators - executive dashboards, financial reporting, consolidated KPIs. Data analytics covers ad-hoc and exploratory analysis on the same data - hypothesis tests, segmentation, modeling. BI provides the stable foundation, analytics produces the punctual insight. A mature organization needs both, in that order.

The European AI Regulation (Regulation (EU) 2024/1689) governs the placement on the market and use of AI systems in the EU. It classifies systems by risk level (prohibited, high-risk, limited, minimal) with differentiated obligations: documentation, transparency, human oversight, conformity assessment. Any organization that develops, deploys, or uses an AI system touching the EU market is concerned - publishers, integrators, employers, public administrations.

Three simple signals: (1) you can explain in one sentence what an AI project would change for a specific business department, (2) you know where the needed data lives and who owns it, (3) you accept killing an AI project if it has no measurable value. If any of the three is fuzzy, start with a short diagnostic before launching any POC. Less glamorous but avoids the pitfall shared by 80% of organizations that report no tangible GenAI impact (McKinsey, State of AI 2024).

Depends on the use case and scope. Well-scoped document copilots generate 15-30% time saved on the affected tasks. Document automation can divide processing time by 2 to 5. Executive dashboards cut budget cycles by 30-50%. But these numbers only hold if the case is scoped AND measured from the start - otherwise you have neither usage proof nor a baseline. ROI is instrumented, not guessed.

Default architecture: models hosted in France or the EU, data that does not leave the client infrastructure without explicit agreement, code and configuration documented and transferred. When the case justifies it, we build on fully open-source or on-premise stacks. No client data is sent to OpenAI, Anthropic, or any third-party model without a written contract and leadership approval. The same stance applies to internal tools (notably domain copilots).

A copilot assists a human in their workflow - it suggests, the human validates (e.g., drafting an email, suggesting a contractual answer). An AI agent executes actions autonomously within a defined scope - it decides and acts (e.g., automatic ticket routing, end-to-end processing of standard documents). Copilots fit when human judgment remains critical. Agents fit when scope is well-bounded and controls are automatable.

Count 2 to 6 months depending on the complexity of the existing landscape. A small organization (1-2 sources, 1 department) can converge in 6-8 weeks. A multi-entity organization with a stack of piled-up tools requires 4-6 months to rationalize, consolidate, and harden. We deliver in measurable increments: a usable executive dashboard at 4-6 weeks, then successive enrichments. No tunnel.

No - but you need to know where the data lives, who produces it, and accept to harden what needs to be hardened for the targeted use case. The pursuit of perfect data in absolute terms is a trap: it prevents progress. The right discipline: pick the case, identify the 2-3 critical sources for that case, harden those sources, then deploy. Everything else can wait for the next iteration.