Inspiration
Most sports AI tools stop at analysis. They show stats, heatmaps, and shot placement, but during a match, players don’t need more data. They need answers.
What should I fix right now? Where should I hit next? How do I beat this opponent?
As players ourselves, we realized: The gap is not better analysis, it’s better decisions. So we set out to build something fundamentally different: An AI that doesn’t just analyze your game, but coaches you in real time.
What it does
TennisMind is an autonomous coaching agent. It transforms tennis from: passive analysis to active decision-making Instead of dashboards, it gives executable actions:
“Push deep crosscourt to opponent backhand” “Stop attacking middle balls” “Recover faster after contact”
Core Agent Loop
Observe → Reason → Decide → Coach → Improve Key Capabilities 🧠 Decision-Making AI Not just what happened, but what to do next 🎤 Real-Time Voice Coach Powered by Vapi → gives live tactical feedback like a real coach 🧠 Persistent Memory Powered by Redis → remembers weaknesses, patterns, and improvement over time 🎯 Opponent Intelligence Using Browserbase → generates pre-match strategy 🏋️ AI Training Generation → creates adaptive drills (virtual ball machine)
How we built it
We combined LLM reasoning, memory systems, and real-time interaction to create a full agent loop. Tech Stack Meta LLaMA → reasoning + tactical decisions Redis → short-term + long-term memory Vapi → real-time voice coaching Browserbase → opponent data scraping System Flow Input (swing / scenario) LLM analyzes and reasons Agent generates decision Voice coach delivers feedback Redis stores memory Next interaction adapts
Challenges we ran into
- Turning analysis into decisions LLMs tend to produce vague explanations. We had to design prompts that generate clear, tactical, actionable outputs.
- Real-time experience Voice feedback must feel: fast natural helpful We optimized for low-latency + concise coaching phrases.
- Memory design We needed: fast access (real-time coaching) persistent tracking (long-term improvement) We used Redis to unify both short-term and long-term memory.
- Building a “real product” in hours We focused on: Product illusion + working agent loop So it feels complete — and actually works.
Accomplishments that we're proud of
✅ Built a fully autonomous coaching loop end-to-end ✅ Delivered real-time voice coaching (wow moment) ✅ Implemented persistent player memory ✅ Generated actionable tactical decisions (not metrics) ✅ Extended to opponent-aware strategy What we learned The biggest shift in AI is from analysis → action Memory is what turns a model into an agent Users don’t want insights — they want decisions Real-time interaction dramatically increases perceived value What's next for TennisMind
We plan to expand into a full AI training system:
🎥 Real-time camera-based coaching 🤖 AI opponent simulation 🧠 Advanced player modeling 📈 Fully automated training plans
Final Thought
Most tools help you understand your last shot. We help you win your next one.
