SPEC.CASES / 01
The work, in depth.
Long-form breakdowns of the product and growth work I'm proudest of: what the job was, how I ran it, and what actually happened. Each one is a real project, with the numbers and the lessons attached.
Every case study.
Key impact
Whole team adopted · used daily
I built Drops an internal AI agent that turns scattered product context into answers the team can trust.
How I built Drops one AI agent wired to every source our product context lives in: four modes, 17 on-demand skills, and a citation for every answer.
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+200% user visits referred by AI · 8 weeks
An AI-readable mirror of my site that widened citation coverage, lifted positions and grew user visits referred by AI by 200% in eight weeks.
An 8-week experiment shipping an AI-readable mirror of StillMind alongside the human site. Coverage up 38%, user visits referred by AI up 200% as the new sustained baseline, and citations that finally describe the product accurately.
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+15.6% engagement
A spaced-repetition engine that surfaces each word just as you start to forget it.
I rebuilt Drops’ spaced-repetition review around a memory-fading model that surfaces each word as it starts to slip: +15.6% engagement, +37% for new users.
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+20% ARPU
A push engine that stays relevant as users change, and fresh as the months pass.
Rebuilt Drops push as a lifecycle engine: 18 scenarios × 5 user stages, with copy that rotates monthly. +24% reactivation, +22% conversion, +20% ARPU.
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Validated growth upside
Replacing the 5-minute limit that told most of our users to leave.
A high-risk bet to replace Drops’ 5-minute paywall with a freemium model that grows engagement, rankings and revenue. De-risked in stages, then parked.
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+11.5% ARPU YoY
A pricing platform that finds the revenue-optimal price in every market, not one global compromise.
I took over monetisation at Drops and tested a price left unchanged for years. The elasticity data found the real win: each market has its own best price.
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