Lead with vision, build with teams.
Direction first, then the people, architecture, and rituals that turn it into delivery. The strongest AI work is a team sport.
AI leadership · Mathematics · Governed agentic AI
Building AI that makes humans more capable, curious, and in control.
I lead AI strategy and delivery in insurance as Director of AI in a Chief Data Office. Independently, I build and write about agentic systems that combine model reasoning with deterministic controls, evaluation, and human judgment. This site is the public record of that personal work.
- Enterprise AI leadership
- Evidence-led research
- Human control at the boundary
Public work, built to inspect
Open systems, testable ideas, and honest caveats.
The open-source systems and research briefs publish code, architecture, safety posture, and lessons instead of hiding them behind product claims.
How the work happens
Three habits carry ideas into working systems.
Mathematical rigor, capable teams, and clear teaching keep ambitious AI work useful and accountable.
Apply mathematical rigor to AI and data.
Models that earn their conclusions and systems that stay legible as they grow. Data science, agentic design, and evaluation grounded in clear reasoning.
Teach what we learn.
Writing, lecturing, and public work that turn experience into something others can use. Education and transformation are the same skill in different rooms.
Featured project
ZenInvest is the flagship open project on the site.
Six months of live evidence, published including the negative results, and a redesign built to measure where LLMs genuinely add value to investment decisions.
featured
ZenInvest
An open, audited experiment in where LLMs actually add value to investing. The first design, a multi-LLM committee, ran live for six months on a Trading 212 Practice (paper) account and lost money; the evidence is published, and the redesign, ZEN-2, is being built against control arms under a decision rule frozen in advance.
Why it stands out
- Negative results published, not buried
- Measured against control arms
- Deterministic rules keep the veto
- Research posture over return promises
From the lab
R&D briefs: agentic AI put under real constraints.
Short write-ups of the systems built in the lab — each one a testable idea, the engineering behind it, and the pattern worth reusing.
Can an agent get better without changing its weights?
ZenArena puts one of agentic AI's favourite claims — that memory makes agents better — under a falsifiable test, using chess and a Stockfish truth signal to measure whether governed memory beats remembering everything.
Agents advise, actuaries decide: an AI pricing engine a regulator could read
ZenRate wraps a deterministic actuarial core in a layer of collaborating AI agents — where exactly one agent holds a veto, every decision is recorded bi-temporally, and the compliance paperwork falls out of the architecture.
Forecasting as a loop, not a number on a slide
ZenForecast is an open-source, governance-first Python framework that treats forecasting as a continuous predict → decide → act → learn loop — with interchangeable models, leakage-free backtesting, and an audit trail behind a single API.