We label key moments, player actions and object tracks, giving sports analytics and gaming teams the data for real-time performance breakdowns.
Use cases
Training data built around the use cases our clients take to production.
Player tracking
Utilizing advanced data annotation techniques, player tracking provides real-time insights into athletes' movements and performance metrics. By analyzing position data, coaches can tailor training programs to individual needs, enhancing overall team performance and strategic decision-making.
Pose estimation
Pose estimation leverages AI to analyze athletes' postures during training and competition. This data-driven approach enables coaches to provide targeted feedback, ensuring athletes maintain optimal form and reduce the risk of injury.
Crowd monitoring
Crowd monitoring systems use data annotations to analyze audience behavior and density during events. Effective monitoring not only enhances safety but also improves the overall spectator experience, ensuring timely responses to crowd dynamics.
Fitness analysis
Fitness analysis tools utilize data annotation to track athletes physical metrics and performance over time. These insights enable personalized training programs, empowering athletes to achieve their peak performance efficiently.
Advertisement monitoring
Advertisement monitoring involves annotating media content to assess the effectiveness of promotional campaigns in sports. By analyzing viewer engagement and ad placements, brands can optimize their marketing strategies and maximize return on investment.
Annotation types
Pick an annotation type to see what it's for and how it looks on real data.
Bounding box annotation is our go-to technique for tracking players during matches. By drawing rectangles around athletes in video footage, we help analytics platforms monitor player movements and interactions, enabling teams to strategize more effectively and gain insights into their gameplay.
What makes it hard
These are the problems that quietly break models in this domain, and how our guidelines handle them.
Edge cases we label
Balls and limbs move fast; keyframes are placed where motion changes, as in our 98.7%-accuracy volleyball project.
Players in identical kits keep consistent IDs through crossings, huddles and camera cuts.
Players block each other constantly; hidden joints are inferred and flagged, not dropped.
Scoreboards and graphics are labeled or masked so they don't confuse your model.
How we work
Every project follows the same five steps, with multi-layer QA backing our 98%+ accuracy guarantee.
We review your data, edge cases and success criteria, then write annotation guidelines with you.
A trained team labels a pilot batch, following the agreed guidelines to the letter.
You review the pilot. We fold your feedback into the guidelines before scaling up.
Every batch passes peer review, expert QA and automated checks before it leaves us.
Labels ship in your format on your schedule, ready to drop into training.
Proof
“For our ADAS project, Intellisane AI delivered precise vehicle annotations across diverse traffic scenes, including multiple object classes and occlusion scenarios. Their expertise in automotive data workflows and quality-first mindset helped us pass all validation checks, with timely delivery and professional communication throughout.”
Get started
Tell us what your model needs to learn. We'll scope the work and label a free pilot batch.
