Problem
A waitlist of more than 900,000 people put pressure on an AI video product's ability to provide access, deliver useful capabilities, and respond to issues as it grew.
Insight
Scaling access and improving the product experience had to move together. Prioritization needed to connect user needs, AI quality benchmarks, and commercial goals.
Execution
As founding Product Manager, I led AI engineering delivery, wrote PRDs, established benchmarks and KPIs, and used competitor research to prioritize work. We shipped six core features and four follow-up releases, alongside changes to pricing, credits, and packaging.
Alignment challenge
Infrastructure constraints favored fewer configurable choices for reliability, while users wanted control. I aligned engineering, design, and support around inferred defaults with an editable review step, giving the system room to optimize orchestration without removing user control.
Impact
- Scaled product access from 10k to 80k users per week. This measures access provision, not weekly active users.
- Releases supported ARR growth from $4.2M to $6M in three months as part of the wider team's work.
- Higher-tier adoption grew from 10% to 40%.
- Reduced major disruption duration from 24–48 hours to under six hours.
These results are drawn from my résumé; internal research and product documents are not published here.
Reflection
The highest tier initially had only two customers, so the packaging decision was informed by a small sample. I would instrument upgrade intent and price sensitivity earlier, then validate the revised tier with a broader cohort before scaling it.