Most articles about building AI apps focus on the model. This one focuses on everything around the model — the architecture decisions that determined whether the product would actually ship, actually perform, and actually retain users. SportsReflector is an AI coaching app that analyzes athletic form across 22 sports and every common gym exercise. It uses on-device pose estimation to extract body landmarks from video, calculates biomechanical metrics against sport-specific benchmarks, and returns a 0-100 form score with corrective coaching feedback. I built it solo. Here are the architecture decisions that mattered most — and the ones I got wrong initially. Decision 1: On-Device vs Cloud Inference The first prototype sent video frames to a cloud GPU for pose estimation. It worked. It was also unusable. Round-trip latency for a single frame was 200-400ms depending on network conditions. For real-time AR overlay at 30fps, you need sub-33ms inference per frame.…