Add AI Pose Estimation to
Your iOS App in Hours
QuickPose gives iOS developers a production-ready pose estimation engine — for fitness, yoga, sports biomechanics, health, and more. No ML expertise required. No cloud dependency. Ship faster.
import QuickPose
QuickPoseBasicView(
features: [
.fitness(.repCounter(.pushUp)),
.rangeOfMotion(.shoulderFlexion),
.yoga(.poseDetection)
]
)
Everything you need, nothing you don't
A focused, well-documented SDK built for developers who want to ship AI movement features without maintaining their own ML pipeline.
Accurate Pose Estimation
33-point full-body skeleton detection in real time. High accuracy across lighting conditions, camera angles, and body types — tuned for real-world apps, not lab demos.
High Performance
Optimised for iPhone's Neural Engine. Runs smoothly at high frame rates without draining battery — so your camera UX stays responsive and fluid.
Pre-Built Models
Ship immediately with ready-to-use models for rep counting, range of motion, yoga pose detection, and joint angle analysis. No training data required.
Customisable Output
Full control over what gets rendered and how. Overlay skeletons, joint angles, rep counts, and feedback text — or consume raw data and build your own UI entirely.
Open-Source Core
Built on MediaPipe, Google's open-source ML framework. Transparent, auditable, and backed by a well-maintained foundation — no black boxes.
SwiftUI & UIKit Ready
Native Swift API with SwiftUI components out of the box. Works with UIKit too. Integrate in minutes, not days — and fits naturally into your existing architecture.
One SDK, many verticals
QuickPose's pose estimation engine is designed to be domain-agnostic. These are the most common use cases — but the SDK can be shaped to fit your product.
Fitness & Exercise
Automated rep counting, real-time form feedback, and exercise recognition for gym, home workout, and personal training apps.
Yoga & Mindfulness
Detect and score yoga poses in real time, guide users into correct alignment, and track hold duration — without a human instructor.
Sports Biomechanics
Joint angle tracking, movement efficiency analysis, and technique coaching for athletes and sports performance platforms.
Health & Rehabilitation
Objective range of motion assessments for physiotherapy, post-surgery rehab, and clinical monitoring — replacing manual goniometry.
On-device AI your users can trust
Privacy isn't an afterthought — it's in the architecture. QuickPose processes everything locally on the device, making it naturally compliant with GDPR, HIPAA considerations, and App Store privacy requirements.
No Cloud Processing
All inference runs on the device's Neural Engine. No video frames are ever sent to a server — zero latency from network round-trips, zero data exposure.
No Video Storage
The SDK does not collect, store, or transmit any video or image data. You decide what movement metrics to persist, and where — on-device or your own infrastructure.
App Store Ready
Straightforward privacy nutrition label. No third-party data collection to declare. Passes App Store review without surprises, even for health and medical categories.
Two ways to integrate
Start with the GitHub repo and our docs, or work directly with our team for a faster, customised integration.
Build it yourself
Add QuickPose via Swift Package Manager and follow our step-by-step documentation. Most developers have a working prototype within a day.
- Swift Package Manager install
- Full API documentation
- Sample projects & code snippets
- Active GitHub community
Work with our team
Book a consultation and our engineers will integrate QuickPose directly into your codebase — configured exactly for your use case, UI, and brand.
- Dedicated integration engineer
- Custom exercise & model config
- Code review & handover
- Post-launch support included
Common developer questions
QuickPose supports iOS 14 and above. It runs on all iPhones from iPhone 8 onwards. Performance is best on devices with Apple Neural Engine (A12 Bionic and later), but the SDK is optimised to run well across the supported range.
QuickPose is distributed via Swift Package Manager. Add the package URL from our GitHub repo directly in Xcode — File → Add Package Dependencies — and you're ready to import. Full setup instructions are in our documentation.
Yes. QuickPose provides native SwiftUI view components for the fastest integration path, as well as a UIKit-compatible API for existing codebases. Both are fully supported and documented.
Yes. The SDK includes a wide library of pre-built models covering fitness exercises, yoga poses, range of motion metrics, and joint angle tracking. You configure which features are active at runtime. For entirely custom exercises or new AI models, our team can work with you to build and train those specifically.
You can access the SDK and start building for free via our GitHub repo. The first 100 devices per month are free. See our pricing page for more details.
No. All processing happens entirely on the device. No video, images, or movement data are sent to QuickPose or any third-party server. You retain full control over any output data generated by the SDK.
Absolutely. Our integration team can work directly with you to embed QuickPose into your existing iOS codebase, configure it for your specific use case, and review the implementation before handover. Book a free consultation to discuss your project.
Ready to Add AI Movement to Your App?
Get started with our GitHub repo and docs today, or talk to our team about a guided integration tailored to your use case.
View on GitHub →Want help? Book a free consultation or email info@quickpose.ai
