Case Study
Athlix AI
Risk terminal for athlete finances. Forecasts career collapse, contract instability, and retirement liquidity. Built at the Cursor Boston hackathon.
Executive Summary
Athlix AI is a risk terminal for athlete finances. It forecasts career collapse, injury-linked earning decline, retirement liquidity failure, and contract instability. Built for the Cursor Boston × AIC × Hult International sports hackathon, it targets agents, advisors, and front-office officers, not fantasy or betting.
Problem & Constraints
Pro athletes face compressed earning windows, volatile income, and complex contracts that call for accessible simulation tools.
Architecture
Landing (ticker + search) → /dashboard/[slug] player terminal → deterministic mock risk engine → scenario simulator sliders → OpenRouter/DeepSeek AI analyst chat panel.
Methodology
- Built cinematic Next.js 16 frontend with Framer Motion and Recharts
- Deterministic mock engine produces stable, demo-safe risk numbers
- Scenario simulator recomputes wealth charts, radar, and exposure bars in real time
- Integrated Vercel AI SDK v6 with OpenRouter/DeepSeek for streaming analyst output
Results & Metrics
| Metric | Result |
|---|---|
| Hackathon | Cursor Boston × AIC |
| Stack | Next.js 16 + Tailwind v4 |
| Profiles | 5 hand-crafted analyst profiles |
| AI | OpenRouter + DeepSeek |
Tech Stack
Next.js 16, TypeScript, Tailwind CSS v4, Framer Motion, Recharts, Vercel AI SDK, OpenRouter
Future Work
Real contract data integration, agent/advisor matching, mobile companion app.