Goals and Objectives
Impartial Analysis
Deliver technical feedback using pose analysis, biomechanics, and AI for actionable insights.
Progress Tracking
Track key metrics (bar path, velocity, RIR) and visualize trends over time.
Real-Time Feedback
Provide instant feedback during training and enable VAR-style meet verification.
Core Features
Pose Analysis
Get technical feedback on bar path, joint angles, velocity, symmetry, and more using advanced pose estimation.
AI-Powered Insights
Leverage custom LLMs and Google Gemini for actionable insights tailored to your technique, goals, and injury prevention.
Key Metrics
Pose Accuracy
Target: 95% accuracy for pose detection across diverse body types.
Real-Time Latency
Target: Feedback latency under 500ms for live sessions.
Open Questions
? What specific datasets will be used to train the custom LLM?
? Should Google Gemini AI be the default reasoning engine, or selectable?
? What additional features should be prioritized for the calorie counter?
Goals and Objectives
Impartial Analysis
Deliver technical feedback using pose analysis, biomechanics, and AI for actionable insights.
Progress Tracking
Track key metrics (bar path, velocity, RIR) and visualize trends over time.
Real-Time Feedback
Provide instant feedback during training and enable VAR-style meet verification.
Nutrition Integration
Include calorie counting with barcode scanning and AI food picture analysis.
Community Building
Foster collaboration between athletes and coaches through shared progress and tools.
Open Source
Build a transparent, collaborative platform encouraging contributions.
Core Features
Pose Analysis
Get technical feedback on bar path, joint angles, velocity, symmetry, and more using advanced pose estimation.
Progress Tracking
Visualize your improvements over time with detailed metrics and charts. Monitor trends and set performance goals.
AI-Powered Insights
Leverage custom LLMs and Google Gemini for actionable insights tailored to your technique, goals, and injury prevention.
Nutrition Tracking
Integrated calorie counter with barcode scanning and AI food picture analysis to monitor your diet effectively.
Key Metrics
Pose Accuracy
Target: 95% accuracy for pose detection across diverse body types.
Feedback Precision
Target: 90% alignment of AI feedback with biomechanical principles.
Real-Time Latency
Target: Feedback latency under 500ms for live sessions.
User Retention
Target: 70% monthly retention rate.
Community Engagement
Target: 50% monthly active users engaging with community features.
Platform Scalability
Target: Support 10,000+ concurrent users without degradation.
Open Questions
? What specific datasets will be used to train the custom LLM?
? Should Google Gemini AI be the default reasoning engine, or selectable?
? What additional features should be prioritized for the calorie counter?
? Should the dedicated camera be a standalone product or integrated accessory?
? Should community features be included in the MVP or added later?
? What pricing model should be used for the meet verification tier?
? Which open-source license (MIT or Apache 2.0) is most appropriate?
? What infrastructure is needed for scalable real-time analysis?
? What marketing and launch strategy should be employed?