AI Fitness & Wellness App
Adaptive Training Plans, Wearable Integration & Progress Analytics
Project Overview
A personal training business wanted to scale beyond one-on-one sessions without losing the personalisation that made clients love them. They needed a mobile app that could deliver their methodology to hundreds of users simultaneously — with AI adapting each plan as the user progressed.
The Challenges
- 1
Personalisation at scale without requiring human trainer input each session
- 2
Wearable integration across Apple Watch, Fitbit, and Garmin ecosystems
- 3
Progressive overload logic that avoids driving users into injury patterns
- 4
High day-7 retention in a category with notoriously poor long-term engagement
Our Approach
We built a Flutter app backed by a Python ML pipeline. Collaborative filtering matches new users to workout templates based on profile similarity. A TensorFlow model trained on workout-progression data adjusts sets and reps weekly based on actual performance. Apple HealthKit and the Fitbit API feed live biometric data into the adaptation loop.
Key Features & Metrics
AI-generated weekly workout plans that adapt to actual performance each week
Wearable integration: Apple Watch, Fitbit, and Garmin via HealthKit and native APIs
Meal tracking with macro targets auto-adjusted to match weekly training load
Progress analytics with streak tracking, milestones, and coach commentary
Video exercise library with in-app form cue overlays for every movement
Day-30 retention of 61% vs. 23% fitness app industry average
Results & Business Outcome
The trainer scaled from 20 active clients to 340 app users without adding headcount. Day-30 retention hit 61% vs. the 23% industry average. The app received a 4.8★ rating from its first 200 App Store reviews.
The future of personal training is not one trainer talking to one client — it is one methodology that adapts intelligently to every single person who uses it.
Let's Build Something Intelligent Together
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