When we built a real-time AI system for baseball training, the model wasn’t the hardest part. Human behavior was.
Lighting changed constantly.
Camera placement was inconsistent.
Movements were fast and unpredictable.
Hardware drifted over time.
Early versions of the system were highly sensitive.
Frame-by-frame precision looked great in tests.
In real sessions, it created noise.
False corrections.
Jittery feedback.
Loss of trust from coaches.
So we made a decision most teams avoid.
We reduced sensitivity.
We smoothed signals.
We raised confidence thresholds.
The model became less ACCURATE on paper.
The system became more usable in practice.
The key insight was simple.
Real-time training systems don’t need perfect measurements.
They need consistent behavior under bad conditions.
Most AI failures we see today aren’t model failures.
They’re system design failures.
If you’re building AI that interacts with humans, optimize for:
latency tolerance
consistency
trust over precision
Benchmarks won’t save you in production.
If you’re solving similar problems at scale, we’ve already lived through these tradeoffs.
https://bit.ly/MeetSiddharth
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