Envorso Sports
Envorso Sports is the AI-first fan experience platform for professional sports teams. We replace the fragmented stack of websites, apps, ticketing, and CRM tools with a single intelligent system — powering personalized fan experiences, smarter marketing, and more revenue. From content and commerce to in-stadium engagement, Envorso runs the full fan journey. Already live in Major League Rugby and expanding fast!
Open Opportunities
Available Challenges
Ship a Fan Offer Engine Before Marketing Has a CRM
The Challenge Envorso Sports runs the fan-facing platform (ticketing, website, and mobile app) for the Seattle Seawolves of Major League Rugby. The league itself has just come onto the platform, and other teams will onboard through the season. Ticketing, website, and mobile app are live with paying customers. CRM, marketing automation, personalization, and revenue operations (pricing and yield) are what gets built next, AI-native from the start, by you. Here is the front office today. Each club and the league hold a large fan database: ticket transactions, web and app activity, shop purchases, and email engagement. The league's database is many times larger than any single club's, and almost none of it is used today. One salesperson calls season-pass holders. One marketer sends a broadcast email and sees who comes through. Nobody follows up on the fans who start a ticket purchase and walk away. Your first feature: a fan offer engine, starting with cart win-back as its first trigger. Every day, some fans start a ticket purchase and abandon it, a stale cart. The team wants something that looks at stale carts, decides which fans are worth a win-back offer, picks the offer and the channel, and puts it in front of a marketer to approve, edit, or reject, instead of a dashboard that reports how many carts went stale. Here is a sample of stale carts from the last 7 days. Read it carefully, it reflects the real data, including its rough edges. | Cart ID | Fan ID | Team | Source | Match | Seats | Section | Section Sold % | Cart Value | Abandoned | Lifetime Tickets | Last Purchase | Shop Spend (12 mo) | Season Pass | Email Opt-In | Push Opt-In | |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| | C-1001 | F-204 | Seawolves | Web | Next home match (in 4 days) | 2 | Lower Bowl | 88% | $96 | 3 hrs ago | 14 | 21 days ago | $120 | No | Yes | Yes | | C-1002 | F-511 | Seawolves | Web | Home match (in 18 days) | 4 | Upper Deck | 41% | $140 | 26 hrs ago | 0 | Never | $0 | No | Yes | No | | C-1003 | F-092 | Seawolves | App | Next home match (in 4 days) | 1 | Lower Bowl | 88% | $58 | 70 hrs ago | 3 | 180 days ago | $35 | No | No | Yes | | C-1004 | F-333 | Seawolves | Web | Next home match (in 4 days) | 6 | Club | 97% | $540 | 1 hr ago | 40 | 9 days ago | $410 | Yes | Yes | Yes | | C-1005 | F-777 | Seawolves | Web | Home match (played 2 days ago) | 2 | Upper Deck | 63% | $70 | 96 hrs ago | 1 | 300 days ago | $0 | No | Yes | No | | C-1006 | F-204 | Seawolves | App | Next home match (in 4 days) | 2 | Lower Bowl | 88% | $96 | 2 hrs ago | 14 | 21 days ago | $120 | No | Yes | Yes | | C-1007 | F-618 | League team (onboarding) | Web | Their home match (in 9 days) | 3 | Sideline | 55% | $105 | 30 hrs ago | 2 | 60 days ago | Not synced | No | Yes | No | | C-1008 | Guest (no Fan ID) | Seawolves | Web | Home match (in 18 days) | 4 | Upper Deck | 41% | $140 | 5 hrs ago | Unknown | Unknown | Unknown | No | Yes | No | Data notes: Match is the match the cart was for, relative to today. Section Sold % is that section's sell-through for that match. C-1008 is a guest checkout, its email address matches the account for F-511. What to Deliver The engine: a proof-of-concept that processes the stale carts and, for each fan worth acting on, proposes a specific offer (for example a promo code, a season or match pass, a merch add-on, a seat upgrade, or a reminder-only nudge), the channel (email or in-app/push), and a stated reason. Not every cart should produce an offer. Use AI where it earns its place, deciding what should be rules and what should be a model call is part of the task. The surface: a web UI in TypeScript (React/Next.js patterns, a single component or small app is fine) where a marketer sees each proposed offer and approves, edits, or rejects it before anything reaches a fan. A JSON dump is not sufficient. The README and video walkthrough, see File Upload Requirements below. Constraints to Consider No CRM or marketing automation exists. Delivery is a transactional email provider and our mobile app's in-app/push messaging. You can stub both, but your design has to work with nothing else, no Salesforce, HubSpot, or Braze. TypeScript is the stack. The web app is Next.js and the services are TypeScript. Explore in anything you like, but what you'd ship runs in TypeScript. You are the first in-house engineer. A five-engineer team in Argentina works overlap hours on the rest of the platform. There is no ML or ops team, whatever monitoring this needs, you design. First step ships in one sprint for the Seawolves. Other teams in the league come onto the platform this season. Scope accordingly. A wrong send costs real money and fan trust. The fan base is small and loyal. An offer to the wrong fan, through a channel they opted out of, or twice, is a bigger cost here than at a large e-commerce site. This cannot fail silently. Offer guardrails: promo codes are single-use and capped at 15% off. On Using AI 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. This includes agentic development. If you use coding agents, show how you scoped each task, the guardrails you gave them (for example a spec or tests), and what you rejected or changed on review. 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 for your video: 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. Speak naturally. Communication is assessed on clarity of technical ideas and logical structure, not verbal polish, accent, or filler words. This role works closely with team front offices, so assume a team's marketer is watching alongside an engineer. Submission Upload each deliverable as a separate file directly on the Provn platform: your code, your README document (Sections A, B, and C), and your video walkthrough (MP4 or MOV).
Ship a Cart Win-Back Agent Before Marketing Has a CRM
Envorso Sports runs the fan-facing platform — ticketing, website, and mobile app — for the Seattle Seawolves of Major League Rugby. Ticketing, website, and mobile app are live with a real paying customer. CRM, marketing automation, and AI-driven personalization are on the near-term roadmap but not yet built. Your product team wants a first agentic feature: a cart win-back agent. Every day, some fans start a ticket purchase and abandon it before checkout — a stale cart. Today, nobody follows up. The team wants an agent that looks at stale carts, decides which ones are worth a win-back offer, and proposes a specific offer a marketer can approve or reject — instead of a dashboard that just reports how many carts went stale. You've been given a small sample dataset representing stale carts from the last 7 days. Build a first version of this feature. | Cart ID | Fan ID | Seats | Section | Cart Value | Abandoned | Lifetime Tickets | Last Purchase | Email Opt-In | |---|---|---|---|---|---|---|---|---| | C-1001 | F-204 | 2 | Lower Bowl | $96 | 3 hrs ago | 14 | 21 days ago | Yes | | C-1002 | F-511 | 4 | Upper Deck | $140 | 26 hrs ago | 0 | Never | Yes | | C-1003 | F-092 | 1 | Lower Bowl | $58 | 70 hrs ago | 3 | 180 days ago | No | | C-1004 | F-333 | 6 | Club | $540 | 1 hr ago | 40 | 9 days ago | Yes | | C-1005 | F-777 | 2 | Upper Deck | $70 | 96 hrs ago | 1 | 300 days ago | Yes | What to build The agent(s): Design and build (proof-of-concept level, not production-hardened) an agentic system — more than one prompt-and-response wrapper — that processes the stale cart data and, for each cart worth acting on, proposes a specific win-back offer (e.g., a discount, a section upgrade, a reminder-only nudge) with a stated reason. Not every cart needs to result in an offer — deciding which carts are worth acting on is part of the task. The surface: Build a UI (web, using React/Next.js patterns — a single component or small app is fine) where a marketer would actually see the agent's proposed offers and approve, edit, or reject each one before anything goes to a fan. A JSON dump is not sufficient — this must be something a non-technical marketer could use. The README and video walkthrough — see File Upload Requirements below. Constraints to Consider No CRM or marketing automation platform exists yet. You cannot assume Salesforce, HubSpot, or similar — your agent's output has to be usable by a marketer with nothing but this UI and, at most, a manual email/SMS send. You are the only engineer on this feature. There is no ML platform team to hand this off to — whatever monitoring or evaluation your agent needs, you have to design it yourself, not "the ops team will handle it." First step must be shippable in one sprint. Envorso Sports has one live customer today. Do not design a six-team, cross-league personalization engine — scope this to something a real marketer could use on Monday. A wrong offer costs real money and fan trust. The Seawolves are a real team with a real (and small) fan base — a bad or tone-deaf offer to a loyal fan is a bigger cost here than at a large e-commerce platform. Your design should reflect that this can't just "fail silently." 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. 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 for your video: 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. Speak naturally. Communication is assessed on clarity of technical ideas and logical structure — not verbal polish, accent, or filler words. Because this role involves explaining agent behavior to non-ML people, also assess yourself on whether a product person (not just an engineer) could follow your explanation.
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, 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.