Think of this as a super talent scout that watches athletes’ movements and stats and quietly flags the kids whose patterns look like today’s Olympians at an early age.
Traditional talent identification in sport is subjective, slow, and limited to where expert scouts can physically be. This AI system uses data and video to systematically spot high‑potential athletes earlier and more broadly than human scouts alone.
Access to large, labeled athlete performance datasets and biomechanical video; partnerships with federations and training centers; and integration into existing talent ID and development workflows create stickiness and data-network effects over time.
Hybrid
Vector Search
High (Custom Models/Infra)
Collecting and labeling enough high-quality, longitudinal athlete data across sports, ages, and geographies; and ensuring model fairness across demographics.
Early Adopters
Focus on Olympic‑level potential detection, likely leveraging Intel’s silicon, toolchains, and partnerships with sporting bodies, rather than generic fitness or performance analytics.