QuickPose provides iOS and Android SDKs that let developers add AI-powered pose estimation to mobile apps in hours, not months. The SDK handles rep counting, form feedback, range of motion, and yoga pose detection entirely on-device, with no cloud processing or video leaving the device. Built on Google's open-source MediaPipe framework, it has powered over 3 million sessions across fitness, sports, health, and yoga applications.
Funding
Funding not disclosed
Founders
Product
Problem
Building accurate body tracking and pose estimation from scratch requires a dedicated machine learning team, months of iteration, and ongoing maintenance. Mobile app developers who want to add movement-based features like rep counting or form feedback face significant technical complexity, platform differences, and performance trade-offs that delay product launches and consume engineering resources.
Solution
QuickPose provides a production-ready pose estimation SDK for iOS and Android that developers can integrate in as little as two hours. The SDK includes pre-built features such as rep counters, form feedback, range of motion tracking, and yoga pose detection, all running fully on-device with no cloud processing or network round-trips. Built on Google's open-source MediaPipe framework, QuickPose ensures transparency and avoids vendor lock-in while delivering real-world accuracy tuned for imperfect lighting, odd angles, and mid-range phones. The SDK is designed with a developer-first approach, featuring comprehensive documentation, sample projects, and native support for Kotlin, Jetpack Compose, and React Native.
Target Audience
Primary customers are mobile app developers and product teams building fitness, yoga, sports performance, health, and health insurance applications that require AI-powered movement tracking and analysis.
Features
- Fully on-device processing with no video leaving the app, no network latency, and GDPR-compliant architecture by design
- Pre-built movement features including rep counters, form feedback, range of motion, and yoga pose detection
- Built on MediaPipe, Google's open-source ML framework, ensuring transparent and auditable technology
- Native SDKs for iOS and Android with Kotlin and Jetpack Compose support, plus a React Native plugin
- Real-world accuracy tuned for imperfect lighting, odd angles, and mid-range phone hardware
- Average integration time of two hours, supported by extensive documentation and sample projects