Services / AI and analytics
The pilot-to-production gap — where it really is, and how to cross it.
AI and analytics aren't solved by model performance. They're solved by ownership, lifecycle, data protection, production environment, procurement and governance. In that order.
Six requirements for production use
Regulatory understanding
AI Act, MDR/IVDR, GDPR and sectoral regulation — what applies, when, with which obligations.
Data protection & role clarity
Purposes, legal bases and roles are clear before a model touches production.
Production environment capability
An environment that holds production load, monitoring and the release cycle — not just a pilot.
Lifecycle ownership
Someone owns the model after it's in production. Models age, data shifts, conditions change.
Security
Access control, logging, model protection and data integrity are part of the solution, not bolt-on.
Procurement & governance fit
The solution fits inside the organisation's procurement model and governance — it doesn't bypass them.
Experimental vs. production-ready
The distinction sounds abstract, but in practice it's the difference between two worlds:
- Isolated pilot, unclear ownership, manual data → governed inputs, defined release logic.
- Weak monitoring, uncertain deployment → monitoring and drift detection, controlled environments.
- Single-expert dependency → named owner, shared responsibility.
- No maintenance plan → model lifecycle and maintenance defined.
How the AI Act shows up — short take
The AI Act is the EU's risk-based framework for artificial intelligence. It defines risk categories and places elevated obligations in particular on high-risk AI systems. It doesn't override MDR, GDPR or sectoral regulation — it sits alongside them. Invinite's starting point: the AI Act doesn't change why production readiness, ownership and quality matter. It makes them visible and auditable.
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