TAQNIK
AI-Powered Nutrition Planning

Nutrican

An AI-powered nutrition and diet planning application that creates personalized meal plans based on individual health goals, dietary preferences, and restrictions.

Nutrican AI-Powered Nutrition Planning App Screenshot

Project Overview

We developed an AI-powered nutrition and diet planning application that creates personalized meal plans tailored to individual health goals, dietary preferences, and restrictions. The platform uses advanced machine learning algorithms to analyze user data and generate optimal nutrition recommendations.

Our focus was on creating an intuitive, personalized system that makes healthy eating accessible and sustainable for users with diverse needs, from weight management and athletic performance to managing health conditions and dietary restrictions.

The application was built using a combination of mobile and cloud technologies, with particular attention to data privacy, algorithm accuracy, and user experience design.

Key Features

  • Personalized meal planning with AI recommendations
  • Comprehensive nutritional analysis and tracking
  • Support for multiple dietary restrictions and preferences
  • Progress tracking toward health goals
  • Recipe suggestions and customization
  • Grocery list generation and meal prep guidance
  • Integration with fitness trackers and health apps
  • Educational content on nutrition science
  • Community support and recipe sharing

Technical Implementation

Personalized Meal Plans

Developed a recommendation engine that generates customized meal plans based on dietary preferences, restrictions, health goals, and nutritional requirements.

Nutritional Analysis

Created a comprehensive database of food items with detailed nutritional information, allowing for accurate analysis of macro and micronutrient intake.

Dietary Restriction Support

Implemented advanced filtering algorithms to accommodate various dietary restrictions including allergies, intolerances, religious restrictions, and lifestyle choices.

Progress Tracking

Built a multi-faceted tracking system that monitors nutritional intake, weight changes, body measurements, and other health metrics with visual reporting.

AI-Powered Recommendations

Integrated machine learning models that adapt recommendations based on user feedback, adherence patterns, and progress toward health goals.

Sustainable Nutrition

Incorporated environmental impact data to help users make eco-friendly food choices, including carbon footprint information and seasonal recommendations.

Technical Specifications

Frontend

  • React Native
    Cross-platform mobile development
  • Redux
    State management
  • Styled Components
    Component styling
  • React Navigation
    Navigation system
  • Expo
    Development framework

Backend

  • Python
    Primary backend language
  • FastAPI
    API framework
  • PostgreSQL
    Relational database
  • Redis
    Caching and session management
  • Celery
    Task queue for background processing

AI & Machine Learning

  • TensorFlow
    Machine learning framework
  • Scikit-learn
    Data preprocessing and modeling
  • Pandas
    Data manipulation
  • NLTK
    Natural language processing
  • OpenAI API
    Advanced language model integration

DevOps & Infrastructure

  • AWS
    Cloud infrastructure
  • Docker
    Containerization
  • Kubernetes
    Container orchestration
  • GitHub Actions
    CI/CD pipeline
  • Prometheus
    Monitoring and alerting

Documentation

AI Recommendation System

The core of Nutrican is its sophisticated recommendation engine that generates personalized meal plans tailored to individual needs and preferences.

Key Components:

  • User Profiling: Collects and analyzes user data including demographics, health metrics, dietary preferences, and restrictions
  • Nutritional Requirements Calculator: Determines optimal macro and micronutrient targets based on age, weight, height, activity level, and health goals
  • Recipe Database: Curated collection of over 10,000 recipes with complete nutritional information
  • Matching Algorithm: Pairs user profiles with appropriate recipes using collaborative and content-based filtering
  • Feedback Loop: Continuously improves recommendations based on user feedback and behavior

Technical Implementation:

The recommendation engine uses a hybrid approach combining:

  • Matrix factorization for collaborative filtering
  • Deep neural networks for content-based recommendations
  • Reinforcement learning for optimization over time
  • Natural language processing for understanding food preferences

Project Results

Key metrics and achievements

  • 87% user retention after 3 months

    Compared to industry average of 35% for nutrition apps

  • 92% of users achieved their primary health goal

    Within their target timeframe or sooner

  • 4.8/5 average user satisfaction rating

    Based on in-app feedback and app store reviews

  • 78% reduction in meal planning time

    Reported by users compared to manual planning

Client Testimonial

Wellness Partners Inc.

"Nutrican has revolutionized how we approach nutrition planning for our clients. The AI-powered recommendations are remarkably accurate and personalized, while the intuitive interface makes it accessible even to those with limited technical skills. The ability to accommodate complex dietary restrictions while maintaining nutritional adequacy has been particularly valuable for our practice."
EL

Dr. Emily Liu

Chief Nutritionist, Wellness Partners Inc.

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Let us help you create a personalized nutrition solution that makes healthy eating simple, sustainable, and tailored to your unique needs.

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