Audit and framing
Clarify the problem, constraints, data and success criteria before committing to a build.
AI implementation consulting
Most AI projects stall between the convincing demo and a system the business can actually run. I take one use case from a business problem to a monitored production system.
The engagement covers scoping, architecture, build, evaluation and handover, with acceptance criteria agreed before writing code.
The situations this answers
- A prototype that never reached production.
- No clear boundary between models, data and business tools.
- No shared definition of when the system is good enough.
- Operating costs and failure modes discovered after launch.
How it fits together
Five steps from framing to hand-over
Frame, design, build, evaluate and hand over, with operational feedback feeding back into design, on a shared Python fullstack foundation.
What you get
- A scoped use case with acceptance criteria agreed before the build.
- An architecture that separates data, models, tools and business rules.
- An evaluated system with a documented failure analysis.
- Observability, cost measurement and a written operating procedure.
- Documentation and a handover session for your team.
How an engagement runs
Design and delivery
Architecture, build and evaluation of a new system, from prototype to production.
Industrialisation and takeover
Harden, monitor and take over an existing system, with documentation and handover.
Proof and related reading
Frequently asked questions
How long does an AI implementation take?
It depends on scope. A focused evaluation takes a few weeks; a production system with integrations and monitoring usually runs over several months. The framing phase fixes the sequence before the build.
Do you work with our existing stack?
Yes. I work with your cloud, data platform and model providers, and recommend a change only when a constraint makes it necessary.
Can you take over an existing AI system?
Yes. The industrialisation mode starts with an audit of the current architecture, evaluation and costs, then a hardening plan.
Who owns the code and the data?
You do. The system is built in your repositories and infrastructure, and access is granted only for the duration of the mission.