Case Study · Zero-to-One · Founder & Product Manager
Buffalo Bills fans had no reliable way to find, compare, or trust private tailgate lots near the new Highmark Stadium — so I scoped, built, and launched a solution myself, solo, end-to-end.
With a new stadium opening, fans had no dependable way to find private tailgate lots, compare pricing, or know what payment methods lots accepted — just scattered word-of-mouth and guesswork.
Authored a full PRD, scoped an MVP across 6 Jira epics, and built solo using Claude Code as an AI-assisted engineering partner — practicing the exact workflow modern PM teams use.
Fans didn't just want to browse lot data — they wanted to contribute and correct it themselves, since they know their lots better than any dataset I could build alone.
An interactive satellite map with tappable lot overlays, filtering by price and amenities, plus a unified intake form so fans and lot owners could submit new lots or correct existing data.
The launch weekend proved the concept — and fans took it from there.
Background
With Highmark Stadium opening as the Bills' new home, thousands of fans faced a familiar but unsolved problem every game day: where do you park? Private tailgate lots near the stadium exist in abundance, but information about them was scattered across Facebook groups, word-of-mouth, and guesswork. There was no single place to compare price, distance to the gate, amenities, or which payment methods a lot accepted.
For a fanbase as passionate as Buffalo's, this wasn't a minor inconvenience — tailgating is part of the identity of a Bills game day. I'd experienced this problem myself for years, and with a new stadium creating a natural reset in how fans would find parking, the timing felt right to solve it properly.
I also saw this as a chance to build something real, on my own initiative, using the exact tools and workflows referenced across modern PM job descriptions — Jira, GitHub, and AI-assisted development — rather than waiting for a company to hand me that experience.
Discovery & Strategy
I started by authoring a full PRD — defining user personas (fans looking to park, and lot owners looking to be found), MVP feature scope, a data model for lot attributes, success criteria, and a phased v2 roadmap. Rather than trying to build everything at once, I intentionally pushed non-essential features (like an admin dashboard and a smart lot recommender) to v2, keeping launch scope tight.
I broke the work into 6 epics in Jira — satellite map view, interactive lot info cards, lot data & schema, a submission and moderation flow, launch readiness, and lot filtering by price and distance — with acceptance criteria written in Given/When/Then format for every story.
Working With AI as an Engineering Partner
I built the entire app using Claude Code, treating it as my development team. Every story got its own branch and PR with ticket-prefixed commits — the same git discipline a real engineering team would follow — which let me practice a modern AI-assisted workflow that's now standard on many product teams.
Once the core map was functional, I shifted focus to a question I hadn't fully answered up front: how would lot data actually stay accurate over time? That question shaped the entire submission and moderation epic that followed.
What I Built
The final product is an interactive satellite map of the area around Highmark Stadium, with tappable overlays for each private tailgate lot. Tapping a lot surfaces price, distance, amenities, and accepted payment methods in an info card, and fans can filter the map by price range and distance to find a lot that fits their game day.
The feature I'm most proud of is the submission system: a single, unified intake form that lets fans and lot owners either submit a brand-new lot or correct information on an existing one — amenities, pricing, anything that had changed. I designed it as one form rather than two separate flows specifically to reduce friction for users who might not know upfront which one they needed.
Every submission routes to me for manual review before going live, which let me maintain data accuracy as the map scaled well beyond what I could have populated alone.
Results
The launch post trended on r/buffalobills and crossed 100K views on Twitter/X, driving a wave of organic traffic and, more importantly, real engagement — I personally reviewed 35+ fan submissions via the intake form, while also monitoring Reddit and Twitter comments for additional feedback and corrections. Every one of the 34 lots added since launch came from a fan or lot owner submission, not from me manually seeding the map.
Looking Back
This project taught me that launching is the beginning of the real product work, not the end of it. The map I shipped on day one looked very different from what fans actually needed once real usage started — the submission system in particular became far more central to the product than I'd originally scoped it to be.
I also learned firsthand what it means to be both the builder and the community manager for something you ship. Reviewing every submission myself, and watching fans correct and add to the map in real time, gave me a much deeper appreciation for how much trust matters in a community-driven product — data quality isn't just a technical concern, it's the whole value proposition.
Working solo with Claude Code as my engineering partner also reshaped how I think about the PM role itself. Writing acceptance criteria precise enough for an AI collaborator to execute against forced a level of clarity I hadn't needed writing specs for human engineers who could ask clarifying questions in a standup. That discipline made me a sharper PM, not just a faster builder.