About
QuickPose is a developer-focused AI pose estimation platform that provides production-ready SDKs for iOS and Android, allowing teams to ship movement intelligence features in a single sprint rather than spending months building ML pipelines from scratch. Built on top of Google's MediaPipe framework, the SDK runs entirely on-device — meaning no video ever leaves the user's phone, no network round-trips are required, and GDPR compliance is built into the architecture by design. Developers can integrate QuickPose via Swift Package Manager or Gradle with full documentation, sample projects, and working code snippets. Once installed, teams can choose from a rich catalog of pre-built features: automated rep counting, real-time exercise form feedback, yoga pose detection and scoring, joint angle analysis for sports biomechanics, and objective range-of-motion assessments for health and rehabilitation use cases. QuickPose serves four primary verticals: fitness and exercise apps, yoga and mindfulness platforms, sports performance coaching tools, and health and physiotherapy applications. Beyond the SDK, QuickPose also offers the JointTrack API for web integration, as well as professional services including custom AI model development, startup and MVP support, and full end-to-end app development. The platform is suitable for indie developers, health startups, and enterprise teams looking to add measurable AI-driven retention and premium upsell features.
Key Features
- iOS & Android SDKs: Install via Swift Package Manager or Gradle and integrate pose estimation into your mobile app within a single development sprint.
- Fully On-Device Processing: All AI inference runs locally on the user's device — no video is sent to the cloud, eliminating latency and ensuring GDPR-compliant privacy by architecture.
- Pre-Built Movement Features: Choose from a catalog of ready-to-use features including automated rep counting, real-time form feedback, yoga pose scoring, joint angle analysis, and range-of-motion assessments.
- JointTrack API: A web-accessible API for joint tracking that extends pose estimation capabilities beyond native mobile apps to web-based platforms.
- Custom AI & Professional Services: Beyond the SDK, QuickPose offers bespoke model development, MVP build support, and full end-to-end app development for teams that need more than a self-serve integration.
Use Cases
- Fitness apps adding automated rep counting and real-time form correction to act as an always-on personal trainer
- Yoga and mindfulness platforms detecting and scoring user poses in real time to guide alignment without a human instructor
- Sports performance tools analyzing joint angles and movement efficiency to coach athletes on technique
- Physiotherapy and rehabilitation apps delivering objective range-of-motion assessments for post-surgery recovery monitoring
- Health insurance or corporate wellness platforms measuring physical activity and movement quality at scale without invasive hardware
Pros
- Fast Integration: Developers report an average integration time of 2 hours, dramatically reducing time-to-market compared to building pose estimation from scratch.
- Privacy-First Architecture: On-device processing means no user video or biometric data ever leaves the device, making compliance straightforward and user trust easier to build.
- Battle-Tested at Scale: The SDK has powered over 2 million sessions across 500+ apps, demonstrating production reliability across diverse use cases.
- Open-Source Foundation: Built on Google's MediaPipe, so developers are never locked into a proprietary black box and can inspect or extend the underlying framework.
Cons
- Paid Pricing Model: QuickPose is a commercial SDK with per-app or usage-based pricing, which may be a barrier for solo developers or very early-stage projects with no budget.
- Mobile-First Focus: The primary SDKs target iOS and Android; web use cases require a separate JointTrack API integration, which may involve additional setup and cost.
- Limited to Pose/Movement Use Cases: QuickPose is purpose-built for body tracking and movement analysis — teams needing broader computer vision capabilities (e.g., object detection, facial recognition) will need additional tools.
Frequently Asked Questions
What platforms does QuickPose support?
QuickPose offers native SDKs for iOS (via Swift Package Manager) and Android (via Gradle), as well as a JointTrack API for web-based integrations.
Does QuickPose require sending video to the cloud?
No. All AI processing happens entirely on-device. No video or biometric data ever leaves the user's phone, making the SDK inherently GDPR-compliant.
What movement features are available out of the box?
Pre-built features include automated rep counting, real-time exercise form feedback, yoga pose detection and scoring, joint angle analysis, and range-of-motion assessments for rehabilitation.
What ML framework does QuickPose use under the hood?
QuickPose is built on MediaPipe, Google's open-source ML framework for live perception tasks, so developers are not locked into a proprietary pipeline.
Can QuickPose build a custom solution for my specific use case?
Yes. In addition to the self-serve SDK, QuickPose offers professional services including custom AI model development, startup MVP support, and full end-to-end app development.
