What Is Signals and What Problem Does It Solve?
Most coaches have been sharing static PDF reports with athletes. A flat document. A few numbers. Maybe some annotated screenshots. Athletes glance at it, nod, and move on.
Signals replaces that workflow entirely.
Instead of a static PDF, coaches and athletes get an automated report that identifies specific strengths and focus areas, links directly to drill videos on YouTube, and renders a 3D avatar of the athlete’s swing synced with actual video footage. It’s the difference between handing someone a weather report and giving them a live radar map.
The shift Uplift CEO Sukemasa Kabayama describes is precise: “It’s moving us from descriptive analytics to prescriptive analytics.”
That distinction matters enormously in practice. Descriptive analytics tells you what happened. Prescriptive analytics tells you what to do about it.
How Signals Actually Works
Here’s the workflow in plain terms.
A coach or athlete records a swing using two iPhones or iPads — no expensive multi-camera enterprise setup required. Uplift’s AI processes the footage, reconstructs a 3D model of the movement, and generates a Signals report automatically.
The report breaks down into two buckets: Strengths and Focus Areas.
A sample report from April 2026 shows exactly how granular this gets:
Sequencing Analysis
The system tracks whether the pelvis leads the trunk, which leads the arms — the correct kinematic sequence for efficient energy transfer into the swing.
In the sample report, the athlete hit 87% correct sequence order across 31 captures, with a consistency rating of ±13%. That’s not a vague observation. That’s a repeatable, measurable baseline coaches can track over time.
Arm Speed Metrics
Peak arm angular velocity is tracked across every capture. The sample athlete averaged 1,005 degrees per second, with a range from 700 to 1,346 deg/s. That spread tells a coach something important: the movement is there, but consistency is the limiting factor.
Focus Areas
The same report flagged three specific issues holding the athlete back: forward drift, front-side stability, and staying loaded over the back hip. Each of these reduces how efficiently the sequencing translates into usable bat speed.
Critically, each focus area comes with linked resources — not just a label, but a path forward.
Who Is Already Using It?
Signals isn’t a prototype. It’s operational and already embedded in serious competitive environments.
MLB pre-draft scouting uses Uplift’s assessments. The Toronto Blue Jays are on the platform. So is LSU, the defending College World Series champion. These aren’t early adopters taking a risk — they’re organizations with real stakes in getting biomechanics right.
Uplift VP of Product Matt Kowalski noted a behavioral shift among beta testers: “They’re sitting down with their athletes much more, especially the new 3D view showing the swing plane and the bat path.”
That’s the real signal here. When the data becomes visual, interactive, and prescriptive, coaches actually use it.
The Hardware Advantage: No Enterprise Setup Required
One of the biggest barriers to biomechanics analysis has always been cost. Traditional motion capture systems require controlled environments, multiple calibrated cameras, and significant technical overhead. That puts the technology out of reach for most college programs, academies, and youth organizations.
Uplift’s approach flips that model. Two iPhones. That’s the hardware requirement.
The AI does the heavy lifting — reconstructing 3D movement from standard smartphone video using computer vision and machine learning. This isn’t a compromise on quality; it’s a deliberate architectural choice to make the technology scalable.
For B2B buyers — teams, leagues, academies — this dramatically lowers the barrier to deployment. No installation contracts. No specialized hardware procurement. Just a SaaS subscription and the devices coaches already carry.
What’s Coming Next
Signals currently covers baseball swings and countermovement jumps, with more motion types planned. The countermovement jump is a standard functional movement readiness test, which signals Uplift’s intent to expand beyond sport-specific mechanics into broader athletic performance monitoring.
More significantly, Kabayama confirmed that a natural language interface using agentic AI is in development and expected to launch later this year. That would allow coaches and athletes to query their biomechanics data conversationally — asking follow-up questions, requesting comparisons, or drilling into specific metrics without needing data literacy.
That’s the logical next step: moving from automated reports to an AI coaching assistant that can hold a conversation about your swing.
The Bigger Vision: Democratizing Sports Science
Uplift operates primarily as a B2B SaaS platform — its customers are organizations, not individual athletes. But Signals is designed to ensure the data actually reaches the people it’s about.
Kabayama frames the mission clearly: “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.”
That’s a meaningful design constraint. Building for the MLB scout and the youth travel ball coach simultaneously requires stripping away jargon, surfacing the right insights automatically, and making the interface intuitive enough that no manual is needed.
Signals appears to be built with that constraint in mind. The report format is clean. The recommendations are direct. The YouTube drill links remove the gap between diagnosis and action.
What This Means for Coaches, Scouts, and AI Adopters
If you’re a baseball coach or academy operator, Signals offers a concrete upgrade to your athlete development workflow. The shift from static PDFs to interactive, prescriptive reports isn’t incremental — it changes how conversations with athletes happen.
If you’re a scout, the ability to capture standardized biomechanics data with two iPhones during a workout or showcase event is a significant operational advantage. Consistent data collection at scale, without logistics overhead.
If you’re tracking AI tools in sports tech, Uplift Labs is demonstrating what the next layer of sports analytics looks like: not just dashboards and stats, but AI that observes movement, interprets it, and tells you what to do next.
The gap between elite sports science and everyday athletic development has always been resources. Uplift Labs is betting that AI and smartphones can close that gap — and Signals is the clearest evidence yet that the bet is paying off.
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