Attack Tee was not a “computer vision app.”
It was a real-time training system operating under tight latency and reliability constraints.
Key challenges we faced:
- Sub-second feedback requirements
- High-speed motion blur
- Inconsistent lighting and hardware noise
- Coach-facing interpretability
Architecture decisions that mattered:
- Edge-first processing to reduce latency
- Conservative model thresholds to avoid false confidence
- Explicit fallbacks when confidence dropped
- Feedback timing designed around athlete perception, not frame accuracy
Biggest lesson:
Production AI is a systems problem. Models are only one component.
Most failures we see today happen because teams treat AI as a plug-in
instead of an operational system.
If you’re designing AI for real-world interaction, optimize for:
- Consistency
- Latency
- Trust
Not leaderboard metrics.
👉 If you’re building production-grade AI systems, we’ve solved these problems before: https://bit.ly/MeetSiddharth
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