About Gabriella Systems
Every athlete deserves structured evidence, not just impressions
Gabriella Systems builds computer vision infrastructure for bat-and-ball sports — making movement intelligence accessible, measurable, and trustworthy.
Who we are
Gabriella Systems builds computer vision infrastructure for cricket — and, in time, for baseball and softball. We combine computer vision and trustworthy AI so that coaches and athletes have objective performance evidence from ordinary practice video.
Mission
Make movement intelligence accessible and trustworthy for every cricket team, academy, and athlete
We build AI systems that turn ordinary practice video into structured, honest performance evidence — so coaches can measure what actually changed, and athletes can understand how.
Vision
A future where every athlete is measured accurately, fairly, and with full transparency
AI in sport earns trust by being explicit about what it can measure, what it cannot, and why — across every camera view, every skill level, and every stage of athletic development.
Why we exist
The problem we set out to solve
Technical changes in batting and bowling are often small — a shoulder opening a fraction earlier, a knee angle shifting by a few degrees, a release timing changing by a handful of frames. These details are difficult to see consistently in real time and difficult to recall reliably from session to session.
Gabriella was built so coaches can place the camera where the take needs it — including side-on, so Bat and Head come from a view that can see them — and get evidence that matches that view. We do not invent a number the camera cannot see.
Accessible capture
Analysis should work from ordinary video at the nets — no specialist hardware required to start.
Honest metrics
Metrics follow the camera view. We do not invent a number when that view cannot support it.
Progress, not snapshots
Single-session impressions are not useful. Reproducible trends across sessions are.
Coaches stay in control
Our system surfaces evidence and supports coaching judgment — it does not replace it.
What we stand for
Our values
Place the camera for the view
The camera can sit where the coach needs it, including side-on so Bat and Head come from a view that can see them. Front-on batting is for pitch map, wagon wheel, and ball-path charts — planned adds, not live yet.
Evidence, not impressions
Coaches deserve structured data they can compare across sessions, not one-off visual impressions that are hard to recall or repeat.
Clear about each view
We say what a camera view can show. When the view cannot support a metric, we do not invent a number.
Human authority matters
AI should support coaches, not replace them. Our system surfaces evidence and priorities — the coaching decision remains with the coach.
Progress over ranking
The most useful comparison is an athlete against their own baseline, not against others. Individual progress is the goal, not league tables.
Research-grounded practice
Our measurement system is built on active research in computer vision, movement representation, and trustworthy AI — not assumptions.
Research
Product and research, together
Gabriella's product is built on active research — in visual AI, movement representation, uncertainty-aware measurement, and responsible AI for sport. Every improvement to how we find, track, and measure feeds back into the system athletes and coaches use.
We also study harder questions — responsible AI for sport, trustworthy measurement, and what good AI design looks like when the athlete is still developing.
The team
Who builds Gabriella
Gabriella Systems is an early-stage company working at the intersection of computer vision research and cricket coaching practice. The founding team combines technical and domain expertise across sports AI, software engineering, and athlete development.
Scientific Advisory
Prof. Mubarak Shah — Scientific Advisor, Computer Vision.
Distinguished Professor of Computer Science at UCF and founding director of the Center for Research in Computer Vision. Fellow of IEEE, AAAI, IAPR, and ACM.
See full advisory profile →
UCF does not sponsor or endorse Gabriella Systems.
Where we are going
Building toward a full movement intelligence platform
Today Gabriella analyzes cricket batting and bowling from a single camera. Each phase of our roadmap extends both the depth of analysis and the consistency of capture — until every training session becomes a structured record that grows over time.
Live today
Batting & bowling video analysis
Pose, bat, and ball tracking with view-aware metrics and overlays from cricket clips in the Showcase. The camera view decides what we can show; we do not invent a number when it cannot.
Next
Session workflows and academy pilots
Cleaner delivery-by-delivery review flows, session-level analysis from long match video, and structured reporting for coaching staff.
Hardware
Portable capture system
Purpose-built nets capture to improve placement consistency, frame rate, and calibration beyond phone video.
Platform
Player histories and longitudinal intelligence
Persistent player profiles, session-to-session progress tracking, and trend analysis across weeks and seasons.
Future
Baseball, softball, and broader bat-and-ball sports
The movement intelligence platform is designed to extend to baseball and softball. Research is already underway to support this transition.
Work with us
We work with cricket academies, coaches, and performance teams who want structured, reproducible measurement — not AI that invents numbers.