RegimenAI — AI Health Platform
AI-powered fitness and health platform delivering personalized workout and nutrition recommendations based on user goals and progress data.
Case study summary
- Client type
- Fitness and health product
- Industry
- Health and fitness
- Duration
- TODO: Confirm project dates and duration
- My role
- AI integration and backend development
- Team size
- TODO: Confirm team size and collaborators
- Stack
- Python, FastAPI, LLM Integration, PostgreSQL, React
- Platforms
- Application APIs
Challenge
Connect user goals and progress information to useful model-backed fitness workflows without treating free-form generation as validated application state.
Approach
Build a bounded integration between product inputs, model-backed services and application responses. Confirm any personalization or outcome claims against owner-provided evidence.
Architecture
Key features
- AI-backed product workflows
- Goal and progress context
- Backend integration
- Structured application responses
Results and measurement
The delivered scope is described in this case study. Numeric performance or business results require a measurement source and owner approval.
TODO: Add the verified baseline, final value, measurement period and evidence link. API-count claim: TODO — verify before publication.
Client feedback
TODO: Add an approved client quote for this project, with the client's attribution and source. No invented testimonial is displayed.
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Technical concept illustration. The case study below describes the project implementation.
Generic fitness plans ignore the fact that user progress isn't linear. RegimenAI is an AI-powered fitness platform generating personalized workout and nutrition plans based on a user's goals, history, and progress, built around a feedback loop rather than a one-time generation step — plans adapt based on what a user actually completes and reports back, moving beyond static, one-size-fits-all templates.
What I Built
- Personalization engine generating recommendations from goals, history, and constraints
- Progress tracking that logs activity and adapts plans over time
- LLM-assisted plan generation for both workouts and meal plans
- Feedback loop where plans adjust based on completion rates and explicit user feedback
Tech Stack
- Backend: Python, FastAPI
- AI: LLM-based plan generation
- Database: PostgreSQL
- Frontend: React
Key Decisions
- Built a feedback loop where plans adapt based on completion rates and explicit user input, rather than a one-time generation step, since static plans ignore the reality that user progress isn't linear
- Used FastAPI for the backend to keep the personalization and plan-generation endpoints fast and typed, appropriate for a service making frequent small requests as users log progress
Outcome / Impact
Delivered a platform that produces genuinely personalized fitness plans that evolve with the user, rather than static templates handed out once and forgotten.
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