The Challenge
WorkPlay Sports had to overcome the technical hurdle of tracking high-speed lacrosse movements in unpredictable outdoor environments, where lighting and motion blur often cause generic AI models to fail. Beyond the technical complexity, the platform had to compete with video games for the attention of athletes as young as six years old, requiring a frictionless and highly motivating user experience. As an early-stage startup, they also faced the “infrastructure challenge” of supporting real-time ML inference and global scalability on a limited budget.
Solution Implemented
Valere delivered a cross-platform mobile application built with Flutter, powered by a custom YOLOv8 computer vision model optimized for real-time, on-device inference. The solution utilizes a robust AWS backend, including SageMaker for model hosting and an asynchronous processing architecture to handle real-time leaderboards and reward systems. By implementing an iterative AI training methodology across five cycles, it delivers accurate rep counting using custom AI models despite outdoor conditions, keeping athletes engaged while encouraging consistent practice.
About the Company
Mission
WorkPlay Sports is transforming youth athletic development by making individual practice measurable, engaging, and rewarding. The company addresses a critical gap in sports training: while athletes spend hours at organized practices, fundamental skill-building at home often gets neglected due to lack of motivation, feedback, and accountability.
Focused initially on lacrosse players aged 6-17, WorkPlay combines cutting-edge computer vision technology with proven gamification principles to make wallball practice addictive rather than repetitive. The platform serves three key stakeholders: young players who need motivation and recognition, coaches who want visibility into off-field effort, and parents seeking return on their club sports investment.
By tracking every rep, awarding progress coins, maintaining competitive leaderboards, and providing transparent reporting, WorkPlay creates a virtuous cycle where practice becomes its own reward while strengthening team culture and individual accountability.
The Challenge: Achieving 92% Computer Vision Accuracy in Unpredictable Outdoor Environments
WorkPlay Sports is transforming youth athletic development by making individual practice measurable, engaging, and rewarding. The company addresses a critical gap in sports training: while athletes spend hours at organized practices, fundamental skill-building at home often gets neglected due to lack of motivation, feedback, and accountability.
Focused initially on lacrosse players aged 6-17, WorkPlay combines cutting-edge computer vision technology with proven gamification principles to make wallball practice addictive rather than repetitive. The platform serves three key stakeholders: young players who need motivation and recognition, coaches who want visibility into off-field effort, and parents seeking return on their club sports investment.
By tracking every rep, awarding progress coins, maintaining competitive leaderboards, and providing transparent reporting, WorkPlay creates a virtuous cycle where practice becomes its own reward while strengthening team culture and individual accountability.
Unlike controlled indoor environments, youth athletes practice in backyards, driveways, and parks with unpredictable conditions. Computer vision models must handle varying lighting from dawn to dusk, motion blur from fast-moving equipment, changing camera angles as phones shift position, and environmental interference from shadows, weather, and background activity.
The accuracy requirements are unforgiving. Players lose trust immediately if the system miscounts reps or fails to detect legitimate catches. Parents question the value if progress metrics seem arbitrary. Coaches dismiss tools that don’t reflect actual skill improvement.
Beyond technical accuracy, the platform must compete with video games and social media for young athletes’ attention. The user experience needs to be intuitive enough for 6-year-olds while providing depth for teenagers. Onboarding must be instant; any friction means abandoned downloads.
For early-stage companies like WorkPlay Sports, the infrastructure challenge compounds these difficulties. The platform must support:
- Real-time ML inference on every training session without latency
- Cross-platform mobile delivery on iOS and Android
- Scalable storage for user-generated content and training data
- Asynchronous processing for leaderboards and competitions
- Multi-environment deployment for testing and iteration
- Cost-effective infrastructure that scales with user growth
Why WorkPlay Sports Partnered with Valere for AI Native Solution Development
WorkPlay Sports selected Valere based on a proven track record of bridging the gap between complex AI and user-centric mobile experiences.
WorkPlay Sports selected Valere based on three critical factors:
- Deep AI/ML and Computer Vision Expertise: Proven experience building and deploying custom machine learning models for real-world applications, specifically expertise with object detection and motion tracking in challenging conditions
- Cross-Platform Mobile Development: Track record delivering Flutter applications with complex backend integrations and offline-first architectures
- Scalable AWS Infrastructure: Demonstrated ability to architect cost-effective, production-ready cloud solutions that grow with startups
The team appreciated Valere’s iterative approach to AI model development. Rather than promising perfect accuracy immediately, Valere outlined a realistic path: build a baseline model, test in real conditions, identify failure modes, retrain with augmented data, and iterate until production-ready. This transparency built trust.
Valere didn’t just build what was requested, they educated the WorkPlay team on tradeoffs between model accuracy and inference speed, the importance of representative training data, and architectural decisions that would impact future feature development. This consultative approach ensured the platform was built on solid technical foundations.
The Solution: Engineering an AI-Powered Mobile Experience with custom Computer Vision to increase player practice consistency
Valere architected a comprehensive platform that transforms the solitary chore of practice into a competitive, social, and measurable game.
Infrastructure & Architecture
The platform is built on a scalable AWS foundation designed for low-latency feedback and rapid user growth:
- Compute & Backend: A Node.js/NestJS backend deployed via AWS Elastic Beanstalk for multi-environment stability.
- AI Model Hosting: Amazon SageMaker handles computationally intensive tasks and model hosting, while CoreML enables <100ms on-device inference.
- Asynchronous Processing: Amazon EventBridge and SQS manage leaderboard updates and background tasks without affecting app performance.
- Content Delivery: Amazon CloudFront and S3 ensure fast media delivery and secure client-side uploads using pre-signed URLs.
Specialized Workflow: Iterative AI Refinement
The core tracking engine was developed through five rigorous training cycles:
- Baseline: General sports datasets (60% accuracy).
- Custom Data: 500+ practice clips (75% accuracy).
- Augmentation: Expanding the dataset (85% accuracy).
- Edge Cases: Solving for sunset lighting and shadows (92% accuracy).
- Optimization: Real-time on-device processing.
Phased Implementation
The project moved from Product Discovery and requirements gathering to parallel AI Model Training and Flutter Mobile Development, followed by a multi-environment AWS deployment and comprehensive beta testing with youth teams.
The Results
The launch of the WorkPlay platform validated that young athletes will practice consistently when provided with accurate tracking and immediate rewards, delivering a polished experience months faster than traditional development cycles.
Key Outcomes
User Adoption & Engagement:
- 300+ users acquired within the first week.
- High daily engagement with players maintaining multi-day practice streaks.
- Positive organic referrals from coaches and parents.
Technical Performance:
- 92% AI model accuracy achieved in real-world outdoor conditions.
- <100ms inference latency for real-time player feedback.
- Zero critical production bugs since launch.
Business & Operational Impact:
- Established 10x growth potential without requiring architectural changes.
- Achieved competitive differentiation through proprietary AI and behavior design.
- Accelerated time-to-market, capturing share before competitors could respond.
