Building an AI-Powered Personal Trainer
A smart fitness app with workout generation, form feedback and coach inbox.
Key Details
| Challenge | The product needed plans, form feedback and retention without a huge coaching staff. |
|---|---|
| Solution | A mobile app with workout generation, computer-vision form hints and a coach inbox. |
| Technologies | React Native, Python, on-device pose models |
Technologies used
React
Python
TensorFlow
PostgreSQL
Docker
Client background
A consumer fitness startup wanted personalized plans and form feedback without staffing a large coaching bench. Retention dropped after week four when users lost guidance and coaches could not scale 1:1 video review.
Key challenges
- Workout plans were static and ignored equipment, goals and recovery.
- Form feedback required expensive human video review.
- Coaches lacked a prioritized inbox of sessions that needed attention.
- On-device constraints ruled out heavy cloud vision for every rep.
What we built
- React Native app with adaptive workout generation and progress tracking.
- On-device pose models for form cues without sending every frame to the cloud.
- Coach inbox that surfaces flagged sessions and user questions.
- Backend for plans, subscriptions and privacy-safe telemetry.
Project team: 7 engineers across AI/ML, backend and domain specialists — delivery over 18 weeks.
How we delivered
01
Product
Defined the thin coach loop: generate, cue, escalate only when needed.
02
Mobile
Shipped RN app with offline-friendly workout flows.
03
Vision
Tuned on-device pose hints for a small set of high-value movements.
04
Coach ops
Built triage so human coaches spend time where AI is unsure.
Business impact
On-deviceForm cues
SaferPlans
BetterWeek-4 retention