Building AI-native products that drive measurable ROI.

Problem

Grassroots football clubs run on spreadsheets and WhatsApp. Subscriptions are chased by hand, coaches can't easily track player stats so decisions go on gut feel, and player development lives in coaches' heads. Off-the-shelf tools are built for academies and force the club to fit the software.

Discovery

I built the first version as an internal tool for my own club, where I sit on the committee — so I was the user. Watching what volunteers actually used (and ignored) turned a weekend prototype into a product decision: build the whole club in one place, or don't bother.

What I Designed

• An eight-role permission model (Chairman, Secretary, Treasurer, Gaffer, Coach, Helper, Player, Guardian) — designed before any code, so every screen and action is scoped to the right person.
• A payments model built on Stripe Connect: money flows direct to each club's own account and the platform never holds it — the decision that removed the biggest trust blocker for volunteer treasurers.
• Offline read-caching, so the app still works on a touchline with one bar of 4G.
• A revenue engine — subscriptions, a club shop and fundraising — so the platform pays for itself.
• Spec-first delivery: every feature is documented and agreed before it's built, tracked as an issue, and verified on a separate dev environment before it goes live.

How It Was Built

I designed the system; AI wrote the code. Claude Code was the engineering team — I specified the roles, data model, payment flow and edge cases, and directed it to build them safely. That's the skill I'd bring to a Senior AI PM role: knowing exactly what to build and why, and getting AI to deliver it — not writing every line myself.

Result

Launched April 2026. Two clubs are using it today, with the first paying customer secured inside the first month — a live, revenue-generating product designed and shipped solo alongside a full-time role. Live at cheersgaffer.com.

Problem

In the employee benefits industry, renewals in year two and beyond meant manually recreating each customer's current-year data — painstakingly "impersonating" customers and adding items to their basket. Around 5 days of labour per customer, times thousands of employees.

Solution

Worked alongside the Database Engineer to script the entire process, replacing the manual impersonation workflow with a single automated run.

Result

Renewals cut from ~5 days of manual work per customer to one click.

Problem

Sales and pre-sales teams spent days building demo data ahead of live SaaS demonstrations — every demo needed enough realistic, scenario-specific data to make the product shine.

Solution

Designed a Claude Skill that turns an exported product JSON config into rich demo data. It quizzes the user on the industry they're demoing to and the story they want to tell, so the data is specific enough — demoing Incident Remediation to an insurance company needs enough bad incidents to show triage and reporting.

Result

Better-quality demo data in a fraction of the time — from a full day down to 5 minutes, generated while they make a coffee. Faster, sharper demos helped win more deals, increasing revenue.