Relevant, controlled, ethical. AI is only useful if it genuinely improves usage. We start with strategic framing: concrete goals, success criteria, constraints (security, data, governance), budget, and maintenance. We audit your existing data and flows (sources, quality, rights), then propose targeted use cases: internal assistants, document search, summarisation and classification, decision support, automations. If uncertainty is high, we run a POC or prototype-testing to quickly measure value. Tech-wise, AI integration is treated like any other API: clear contracts, versioning, monitoring, safeguards. We connect AI to your systems via API automation, respect performance requirements (latency, cost), log for visibility, and set thresholds to maintain control. When needed, we use contextualisation techniques (retrieval, tools, agents) — never trendy gimmicks. Autonomy is a goal, not an excuse: human validation loops remain for sensitive actions. Connections with other hubs are structural. With Design, we plan UX for assistants (prompts, feedback, error explanations) and ensure accessibility (keyboard, screen readers, clear language). With Consulting, we define product governance (who supervises, which decisions are automated, revision process), measure impact, and plan skill-building via training-sensitisation. If the solution proves useful, we industrialise it in a lean custom web development, linked to CI/CD pipelines and existing security policies. Our compass: usefulness, resilience, responsibility. No “magical” AI; a human-scale, understandable, sustainable AI. We prefer to deliver small and reliable rather than large and fragile. Deliverables: framed use cases, integration architecture, impact measurement, POC/prototype, UX Design guidelines for assistants, product governance plan, accessibility checklist, industrialisation roadmap. You get evidence, decisions, and a realistic roadmap.