Uplift Labs has developed a new AI-driven analysis tool, Signals, to evaluate an athlete’s biomechanics featuring specific recommendations for improvement.
Used by MLB in pre-draft scouting and by pro and college teams -- including the Blue Jays and defending College World Series champion LSU -- Uplift’s hitting and pitching assessments will now feature a 360-degree view of a 3D avatar synced with the video as well as an automated list of strengths and weaknesses (called “Focus Areas” in the portal). The latter includes links to helpful YouTube videos with resources on drills and explanations.
“What we’re really excited about this launch is it’s moving us from descriptive analytics to prescriptive analytics,” Uplift CEO Sukemasa Kabayama said.
Kabayama added that a natural language tool using agentic AI is in development and expected to be launched later this year. Signals is currently operational with baseball and countermovement jumps, a standard exercise for functional movement readiness, with more motions planned soon.
For now, the goal of Signals is to replace the static PDF reports most coaches were sharing with athletes.
“The folks that we’ve had beta test this view have reported they’re sitting down with their athletes much more, especially the new 3D view showing the swing plane and the bat path,” Uplift VP of product Matt Kowalski said.
Part of Uplift’s value proposition is that it doesn’t require expensive multi-camera enterprise installations for motion capture and instead uses a pair of iPhones or iPads. Signals prioritizes feedback related to kinematic sequencing, rotational efficiency and energy retention -- essentially, how well an athlete moves and applies the force he or she generates into the swing or pitch.
Uplift Labs will continue operating primarily under its B2B, SaaS model in which subscribers are teams, leagues and academies, but Signals will ensure that more of the collected data is understood and used by coaches and athletes.
“Our whole vision is to democratize sports medicine, sports science for the everyday athlete, meaning both amateur and youth,” Kabayama said. “We want to make sure that even amateur athletes -- who don’t have any mechanical background or sports science background or even data literacy -- are able to use our platform without a problem.”
