Define an AI-Native Fan Platform Before the League Owner Dinner
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,…
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What You'll Be Doing
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 v1 product roadmap for the next 6 months — clear, opinionated, shippable.
- A point of view on one AI-native fan experience capability that would be the headline differentiator.
- A point of view on how your engineering team will operate differently to ship faster. The CEO keeps hearing "AI-native engineering" from competitors and wants to know what that actually means in practice for a 4-person junior remote team.
A few things about your customers — assume these are accurate:
- The Eastside Otters' average fan is 52 years old.
- Most fans buy tickets before the day of the game — roughly an 80/20 split (before game day / day-of).
- About 70% of the team's revenue is gameday (tickets, add-ons like club passes or high-fives with the team, merch). 30% is season tickets and sponsorships.
- The team owners think in terms of butts in seats and revenue per fan, not DAUs or NPS. Most are not technical. They make decisions partly on personal trust.
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.
Constraints to Consider
- Engineering team is what it is. You can't hire a Staff Engineer this quarter. Your operating point of view must work for the 4 mid-junior remote engineers and one part-time designer you have today.
- v1 must ship within 8 weeks of the dinner — i.e., roughly 14 weeks from today. Anything you can't deliver in that window must be explicitly cut and named.
- The fan you're designing for is 52, not 25. If your v1 assumes a tech-native urban user, it won't work for the actual customer base. Most fans buy tickets before game day — many on their phones or via the team website in the days leading up — though a meaningful share still buys day-of at the gate.
- The audience for your video is the team owners, not your engineering team. Eight non-technical sports executives. They care about revenue per fan, fan retention, and league differentiation. They make decisions partly on trust. Frame accordingly.
- Curtain Call already has a unified data backbone. Identity, ticketing events, merch transactions, and mobile app behavior are already flowing into one warehouse. Don't pitch building a CDP from scratch — the platform exists. The question is what you do with it.
AI Usage Guidance
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.
What You'll Accomplish
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.
How Your Work Will Be Scored
What to Submit
Founding PM Brief
Format: .pdf, .doc, .docx, .rtf, .txt, .md
A single-page document or short slide deck (≤5 slides) that you'd actually walk a CEO through before the dinner. Cover:
- The single most important fan or customer outcome you're optimizing v1 around, and what you're explicitly not optimizing for.
- Your 6-month roadmap with the v1 cuts named — for each cut, name the competing user or business goal you sacrificed and why.
- Your AI-native fan experience capability, tied to a specific fan moment and a specific failure mode for a 52-year-old fan with varying tech literacy.
- Your data thesis — what data, used for what, that a single-product competitor (a standalone ticketing vendor, a standalone mobile app vendor) cannot replicate.
Format and style are your call — this is what you'd actually hand the CEO.
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README Document
Format: .pdf, .doc, .docx, .rtf, .txt, .md
Three required sections:
Section A: Written Analysis (300–500 words). Explain the strategic logic behind your v1. Why this scope and not the obvious alternative? What hypothesis does v1 test, and what measurable signal would tell you if you were right? Name the assumption you made under ambiguity and what would make you revisit it.
Section B: Engineering Team Operating POV (200–300 words). How will your 4 mid-junior remote engineers and one part-time designer operate differently with AI to ship the v1 in 8 weeks? Be specific — name tools, workflow shifts, evaluation practices, or architectural defaults. Do not pitch hiring, do not pitch a methodology white paper, and "we'll be AI-native" is not an answer.
Section C: AI Usage Log (Mandatory). This is not a trick. We want to see how you work with AI — not whether you used it. In a short section of your README, document your AI collaboration process. For each significant interaction with an AI tool, briefly note: what you asked the AI to help with / what it gave you / what you kept, changed, or rejected — and why. Three interactions documented is sufficient.
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Video Walkthrough
Format: .mp4, .mov, .webm
Record as MP4 or MOV and upload directly on the Provn platform as a separate file. Your audience is the eight team owners — non-technical sports executives who decide partly on trust. Cover:
- 60-second summary of what you'd bring to the dinner and why.
- 2–3 minutes walking through the brief in business terms the owners actually care about (revenue, retention, differentiation), naming the cuts and the specific concern or objection an owner would raise about your proposal.
- 1–2 minutes on your engineering team operating POV.
- 1 minute answering the mandatory AI question below.
- 30 seconds on what you'd do differently with more time.
Speak in plain language — not PM jargon. Filler words and pauses are not scored against you.
The single highest-signal indicator: your video answer to the mandatory AI question. If you cannot name a specific moment where you redirected AI output, evaluators will assume you did not.
Mandatory AI question: Walk me through one moment where you disagreed with, pushed back on, or redirected what the AI gave you — and what you did instead. Name the specific moment. Explain what the AI produced that didn't meet the bar, what you did differently, and why.
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