POC to Production

The problem

An AI POC proves that an idea works: on a sample of data, in an isolated environment, in the hands of the people who built it. Production raises different questions. How does the system integrate with your information system? Who runs it day to day? What does it cost to use? What happens when it gets things wrong?

These are not data-science questions. They are architecture questions — and that is where most AI projects stall.

How I work

1. Maturity assessment

I assess your data, systems, teams and governance, and review existing POCs. You come away with a clear map: what deserves to be industrialised, what is better abandoned, and in which order to move.

2. Use-case scoping

We pick one use case, just one, and set its success criteria before building: expected gain, acceptable quality threshold, time, data and compliance constraints.

3. Target architecture

Together with your teams, I design the architecture that will hold over time:

• Integration with your existing systems: APIs, data flows, identity management;
• Security: data access, protection against prompt injection, logging;
• Sovereignty: hosting choices, open models or managed services;
• Costs: sizing and control of inference costs.

4. Industrialisation

Go-live, deployment pipeline, observability, continuous evaluation of answer quality, operating procedures. And above all: skills transfer, so that your teams become autonomous.

Why an architect

Thirty years of information systems architecture, including six leading architecture and then technology at a SaaS software vendor: I know what it takes for a system to hold in production, not just to work in a demo.

What this is not

• An agency delivering yet another prototype.
• A long commitment imposed upfront: each step produces a usable deliverable, and each one can be the last.