HPE automates stroke analysis, drill tracking, and progress measurement, letting coaches scale beyond their physical capacity and enabling founders to launch differentiating AI-powered features without wearables or sensors. See the use case for real-world examples.

According to industry data, tennis participation in the U.S. is at an all-time high, with massive opportunity among the 22.6M people very interested but not yet playing. HPE helps clubs, apps, and platforms capture this demand by offering automated, scalable training enhancements.

General-purpose models miss critical racket, ball, and rotational biomechanics unique to tennis. The use case explains where generic models fail, and how custom pipelines solve accuracy, fairness, and environment variability.

Yes. The use case outlines how we achieved smooth on-device performance using lightweight models and hybrid processing. This approach is ideal for founders aiming to reach mass adoption without requiring premium hardware.

HPE unlocks new subscription models, reduces coach workload, and increases player retention. The use case includes a sample revenue scenario and operational gains seen in real deployments.