Services
AI prototype to production
One use case, from the first commit to production, with the monitoring that goes with it.
The problem
The prototype works in the notebook. Six months later, it is still in the notebook.
The gap between “it answers well on ten examples” and “it runs every day for fifty users” is where most AI projects die: no evaluation set, no cost control, no guardrails, and nobody able to pick up the code.
What I do
I take one use case — document extraction, an internal assistant over your knowledge base, classification, scoring, assisted generation — and take it all the way to production.
In practice:
- Week 1 — precise scoping, acceptance criteria, and an evaluation set built from your real cases.
- Weeks 2 to 4 — iterative implementation. A weekly checkpoint with a running demo, never a slide.
- Following weeks — hardening: error handling, cost, latency, guardrails, tests, CI/CD.
- End of engagement — deployment on your infrastructure, documentation, and two handover sessions with your developers.
How success is measured
Before a single line is written, we agree on what success means: an accuracy figure on the evaluation set, a processing time, a cost per request. Those numbers are tracked continuously and shown on a dashboard you keep.
What you keep
All the code, in your repository, yours to license as you wish. No proprietary tool locked in by me, no subscription to pay me after the engagement ends.
Got a project in mind?
30 minutes is enough to know whether it is feasible, at what cost and by when.