POC-to-Production Sprint
The problem
Most GenAI pilots never reach production. The blocker is rarely the model: it's evaluation, the surrounding context, cost, latency, data boundaries and what happens when something fails. A pilot that dazzles in a demo falls over once real traffic, strange inputs and the question of who maintains it on Monday morning show up.
What production-ready actually means
Not that it "works once". That it holds up:
- Evaluated: an eval suite that measures whether it works, not a demo that passed by luck.
- Guardrailed: bounded behavior against strange inputs, failures and abuse.
- Observable: you know what it does in production and you find out when it degrades.
- Within budget: cost and latency under control at your real volume.
- Owned: your team understands it, operates it and evolves it.
How I work
- Diagnosis before prescription: I start by finding why your pilot isn't reaching production, not by arriving with a solution already in hand.
- Built for production, not for a demo: evaluable, observable and operable.
- Vendor- and framework-agnostic: I choose for your case, not for my favorite tool.
- The system stays with your team: documented and maintainable, with no dependency on me.
The process, in phases
A way of working, not a rigid script. It adapts to your case.
- Diagnosis. I find the real blocker across data, context, evaluation, cost and operations.
- Path to production. We agree on what "production-ready" means for your case and how it is measured.
- Build. I take the pilot there with whatever the case calls for: evals, guardrails, context retrieval, agents or simplifying what surrounds the model.
- Handoff. I hand over the system with its evals and its documentation, and transfer the knowledge to your team.
Deliverables
A system in production, not a demo: with its evaluation suite, its guardrails, a view of its architecture (C4 model), the key decisions on record (ADRs) and a runbook. Ready for your team to operate and evolve it without depending on me.
Who it is for
For teams whose GenAI pilot has already shown value and needs to reach production reliably.
No clear use case yet, or still finding fit? That's different work, and it comes first: the AI Discovery Sprint, to find where AI creates value and validate it is viable before building. Not sure which one you need? Email me and I'll point you the right way.
Frequently asked questions
What is an eval harness?
It is the infrastructure that puts an AI system through a battery of repeatable tests before every promotion to production and emits a verdict per category; it combines deterministic scorers (no model) and judge-based scorers. In the Sprint it is what decides whether a change can ship: no green verdict, no production.
What are guardrails?
They are the controls that bound the system behavior against strange inputs, failures and abuse: they validate the input, redact sensitive data, resist injection attempts and escalate risk signals. They are a production requirement, not decoration.
What does the handoff at the end of the Sprint include?
I hand over the system in production with its eval suite, its guardrails, a view of the architecture (C4 model), the decisions on record (ADRs) and a runbook, and I transfer the knowledge to your team so they operate and evolve it without depending on me.