AI Health Coach
The AI Health Coach Web Application is an intelligent health guidance platform designed to provide personalized wellness advice using AI-driven recommendations and voice responses.
The Situation
What needed fixing.
Traditional health advice platforms often provide generic recommendations without personalization or engaging user experiences, leading to low user motivation and reduced effectiveness in maintaining consistent wellness routines.
What we built
The system we shipped.
The solution involved designing a mood-based AI health coaching system that combines personalized logic, voice synthesis, and data tracking to deliver customized wellness advice.
Frontend
- Next.js
- Responsive UI with Scottish tartan-inspired theme
- Dark-mode visual design
Backend
- Flask (Python)
- AI processing logic
- REST API integration
- Mood-based recommendation engine
Database
- SQLite
- Stores user data, authentication records, and mood history
Authentication
- Flask-Login
- JWT-based authentication
Voice Integration
- gTTS (initial implementation)
- Planned integration with ElevenLabs for enhanced Scottish voice realism
Deployment
- Hosted on AWS for scalability and reliability
Secure user registration and login system for personalized experiences.
Users select moods such as happy, sad, stressed, tired, or fatigued to receive customized wellness tips.
AI-generated voice responses provide health advice using speech synthesis.
Stores and displays the last five moods with timestamps.
Generates exercise, nutrition, and relaxation advice tailored to user mood.
Dark-themed interface with Scottish-inspired visuals.
AWS hosting ensures high availability and accessibility.
- User Authentication Module
- Mood-Based Recommendation Engine
- Scottish AI Voice Module
- Mood Tracking System
- Personalized Health Suggestions
- UI/UX Design Module
- Cloud Deployment Module
The Outcome
What changed afterwards.
- Improved user engagement through interactive voice-based responses
- Delivered personalized wellness advice based on emotional states
- Enhanced user retention with mood tracking features
- Provided scalable cloud-based accessibility
- Demonstrated practical implementation of AI-driven health guidance systems
Before
Health advice platforms relied on static recommendations with minimal personalization and limited user interaction.
After
Users receive personalized health guidance, voice-based recommendations, and mood-driven suggestions in a visually engaging environment.
Stack
What we used.
Screenshots
The interface.
Click any screenshot to enlarge.
FAQ
What the project record answers.
What problem did AI Health Coach solve?
Traditional health advice platforms often provide generic recommendations without personalization or engaging user experiences, leading to low user motivation and reduced effectiveness in maintaining consistent wellness routines.
How was AI Health Coach built?
The solution involved designing a mood-based AI health coaching system that combines personalized logic, voice synthesis, and data tracking to deliver customized wellness advice. Frontend. Next.js. Responsive UI with Scottish tartan-inspired theme. Dark-mode visual design. Backend. Flask (Python). AI processing logic.
What were the results of AI Health Coach?
Improved user engagement through interactive voice-based responses. Delivered personalized wellness advice based on emotional states. Enhanced user retention with mood tracking features. Provided scalable cloud-based accessibility. Demonstrated practical implementation of AI-driven health guidance systems.
What technology stack was used for AI Health Coach?
AI Health Coach was built with Next.js, Flask, Python, SQLite, REST, AWS and JWT. Coding The Brains selects the stack per project and ships the codebase to the client, who owns it outright.
What would a project like AI Health Coach cost?
Individual project fees are not published. Coding The Brains sells fixed-price packages: a 24-hour Rapid Launch from $5,000, a 5-day Production Launch from $12,000, and a 14-day Enterprise Build from $25,000. Scope, number of integrations, and whether auth, permissions, and audit logging are required determine which package fits.
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