You have just joined Curtain Call, a 9-person fan-experience startup, as the founding PM.
Curtain Call is building a unified fan-experience platform for mid-tier professional sports leagues — minor league baseball, second-division soccer, regional rugby — leagues that can't afford to stitch together separate ticketing, mobile app, CRM, marketing automation, and merch vendors, but desperately want what the big leagues have: a unified, AI-personalized fan experience.
Curtain Call is currently live with one team — the Eastside Otters of the Pacific Northwest Hockey League — with 18 months of runway. The CEO's plan is to expand to all 8 PNHL teams within a year, then jump to an adjacent league.
You report to the CEO. You have 4 engineers (mid-junior, all remote, all in São Paulo) and one part-time UX designer.
In 6 weeks, the CEO is hosting a private dinner with all 8 PNHL team owners. He wants you to walk into that room with three things ready to defend:
A few things about your customers — assume these are accurate:
You have 50 minutes — including a short video — to put together what you'd actually bring to that dinner. This is not a complete product spec. It's the pitch.
We expect you to use AI tools. We evaluate how you use them — not whether you use them. Evidence of iteration, redirection, and critical evaluation scores higher than a polished output with no process documentation.
Demonstrate founding-PM scope discipline by shipping a defensible v1 in weeks not months, with explicit cuts and the business goal each cut sacrifices
Build an AI-native product point of view tied to a real customer moment in sports/fan engagement — not a generic "add AI" pitch.
Articulate a data thesis that explains why a unified platform creates a moat single-feature competitors can't replicate.
Communicate strategic trade-offs in terms a non-technical executive audience would care about (revenue, retention, differentiation) — not PM jargon.
Show how a junior remote engineering team should change how it works with AI tooling — credible specifics, not platitudes
Demonstrate critical engagement with AI tools — iteration, redirection, and ability to identify what the AI got wrong.
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