Audit and framing
Interviews, architecture review and data assessment, consolidated into one readiness picture and a first business case.
AI consulting for production-grade AI systems
Most AI programmes stall between a convincing prototype and a system the business can rely on. The gap is engineering: data, architecture, evaluation, permissions and monitoring.
I work as an AI consultant and cloud architect: I audit what exists, design the target architecture and stay until the system runs in production.
The situations this answers
- AI pilots that never reach production.
- A vendor roadmap that is hard to challenge on technical grounds.
- No shared priority order across competing AI initiatives.
- Architecture and data that cannot support the intended models.
How it fits together
From readiness to a sequenced roadmap
Readiness across data, architecture and compliance drives a prioritised use-case portfolio and a phased roadmap.
What you get
- A readiness assessment of data, architecture and operating constraints.
- A ranked use-case portfolio with effort, risk and expected impact.
- A target architecture and the gap to close.
- A production plan with evaluation, observability and cost controls.
- A written report your team and your board can both use.
Production figures
- AI systems delivered to production
- 3 platforms Energy · Research · Health — EDF, CNRS, startup (public background samalgo.com)
How an engagement runs
Architecture and plan
Target architecture, model and provider trade-offs, security constraints and a sequenced plan with explicit decision points.
Production and handover
Delivery with your team, evaluation and observability in place, then a documented runbook and a clean handover.
Proof and related reading
Frequently asked questions
What does an AI consultant actually do?
Assess what your data and architecture can support, rank the use cases that are worth building, design the target system and help deliver it. The output is a technical decision, not a maturity score.
How do you avoid recommending the wrong stack?
Options are compared against your constraints: data volumes, latency, security, operating cost and the skills of your team. The report documents why each option was kept or rejected.
Can you work with our existing team?
Yes. The engagement is designed to make your team autonomous: pairing, architecture reviews and a written runbook are part of the delivery.
Do you cover strategy and delivery?
Both, but as separate decisions. You can stop after the audit and the roadmap, or continue with a scoped build.
How do you handle data residency and compliance?
Region, model hosting and data flows are part of the architecture. Sovereign cloud and EU-hosted models are considered when residency constraints require them.