How I work
Head of AI at Crème de la Crème, engaged through my own company. I own the technical side end to end: discovery with the people who decide, the architecture, the code, the path to production, and the adoption that follows. Evals, guardrails and cost control are first-class concerns, not afterthoughts.
Method
From a business problem to a system people use
Discovery with the people who will use the system, architecture with the trade-offs written down, the code itself, a production path that holds up when the system gets it wrong, and adoption tracked after go-live. Evals, guardrails and cost control are first-class concerns, not afterthoughts.
Discovery
Discovery with the leadership and with the people who will live with the result, turned into a short use-case portfolio: an owner, a success measure, a blocker list. Including the cases worth killing.
Tested against six business teams with different risk tolerances.
Architecture
Models, data, integration, identity, privacy, governance, evaluation, deployment. Every significant call is written down with what it costs.
32 trade-offs recorded before a line of the MCP server was written.
Proof of value
A prototype on real data early, so the decision to go further rests on evidence rather than on a demo that only works on the happy path.
An eval harness that had to beat the production baseline, brief by brief.
Build
I write the code, front and back, working through coding agents, and what ships goes through the client's engineering review. When a piece needs expertise I do not have, I bring in someone who does and write down who owns it once they leave.
Six systems built this way, front and back, on the same engagement.
Production and governance
Evals against whatever is already running, guardrails wired in rather than suggested, authorization and audit designed in. The question of what happens when the system gets it wrong is answered before it ships.
Isolation predictions written down before each role switch, then checked.
Adoption
Usage tracked after go-live, the build documented so another engineer can take it over, and systems retired when a vendor closes the gap.
Rolled out to Sales ahead of general availability.
- 01
Discovery
Discovery with the leadership and with the people who will live with the result, turned into a short use-case portfolio: an owner, a success measure, a blocker list. Including the cases worth killing.
Tested against six business teams with different risk tolerances.
- 02
Architecture
Models, data, integration, identity, privacy, governance, evaluation, deployment. Every significant call is written down with what it costs.
32 trade-offs recorded before a line of the MCP server was written.
- 03
Proof of value
A prototype on real data early, so the decision to go further rests on evidence rather than on a demo that only works on the happy path.
An eval harness that had to beat the production baseline, brief by brief.
- 04
Build
I write the code, front and back, working through coding agents, and what ships goes through the client's engineering review. When a piece needs expertise I do not have, I bring in someone who does and write down who owns it once they leave.
Six systems built this way, front and back, on the same engagement.
- 05
Production and governance
Evals against whatever is already running, guardrails wired in rather than suggested, authorization and audit designed in. The question of what happens when the system gets it wrong is answered before it ships.
Isolation predictions written down before each role switch, then checked.
- 06
Adoption
Usage tracked after go-live, the build documented so another engineer can take it over, and systems retired when a vendor closes the gap.
Rolled out to Sales ahead of general availability.
Applied AI, from the business problem to production
I work as the technical owner of an AI portfolio: discovery with the people who will use the system, architecture with the trade-offs written down, the code itself, production, and then adoption. Three ways to work together.