NutriGuard AI — Intelligent Food Label Analysis & AI-Powered Diet Planning
NutriGuard AI is an AI-driven nutrition platform designed to help users make smarter and healthier food choices.
Timeline
Work in progress
Role
Full Stack
Team
Solo
Status
In DevelopmentTechnology Stack
Key Challenges
- Orchestration
- Accuracy
- Conditional Edges
- Correct prompting for accuracy
- Memory Layer
Key Learnings
- Few-Shot Prompting
- Long-Term Memory
- Multi-Agentic Workflow
- Trivly
- Orchestration
Overview
NutriGuard AI is an AI-driven nutrition platform designed to help users make smarter and healthier food choices.
The platform leverages artificial intelligence to analyze food products, identify potentially harmful chemicals, recommend safe consumption levels, and suggest healthier alternatives. Alongside this, NutriGuard AI offers an AI-generated personalized diet plan feature, allowing users to create customized meal plans based on their preferences and health goals.
🟢 AI-Generated Personalized Diet Plans — Fully implemented
🟡 AI Food Label Scanning & Chemical Analysis — Work in progress
This project focuses on applying AI to real-world health and nutrition problems, emphasizing intelligent decision-making, personalization, and user awareness.
Key Features (In Development)
🧪 AI Label Scanner
Users can upload a food product’s ingredient list, which is then analyzed by AI to:
- Detect potentially harmful chemicals and additives
- Assess whether the product is safe for regular consumption
- Suggest healthier alternative products available in supermarkets or online
This feature aims to simplify complex ingredient labels and turn them into clear, actionable insights for everyday consumers.
🥗 AI-Customized Diet Plan
An AI-driven diet planning system built with a multi-step backend flow:
- The system first evaluates the user’s input prompt
- If the information is insufficient, the AI intelligently asks follow-up questions
- Once adequate context is collected, it generates a more accurate and personalized diet plan
This approach improves response quality by ensuring the AI operates on complete and relevant user data, rather than generic assumptions.
Development Progress
Current Status
- In Development: Core features being implemented
- Design Phase: UI/UX design iterations
- Security Focus: Privacy-first architecture planning
- Mobile-First: Responsive design approach
Completed
- AI-Customized Diet Plan: v1 for testing