NUTRICOACH

Type of dish and allergies
Users budget
Menu Plan
Shopping List
landing page

Inspiration

NutriCoach was inspired by a real-world problem many students and households face daily: figuring out what to eat while staying within a limited budget. Meal planning is often stressful, time consuming, and disconnected from local food realities, especially in African communities where affordable and practical meal options matter. We wanted to build an intelligent system that combines AI, budgeting, and cooking guidance into one simple experience. Instead of just creating another chatbot, we focused on building a tool that could actually help users make smarter food decisions and reduce the stress of planning meals every day.

What it does

NutriCoach is an AI-powered meal planning and cooking assistant that generates affordable weekly meal plans based on: daily budget dietary preferences allergies cooking goals

The platform also provides: step-by-step cooking instructions ingredient lists shopping lists meal cost estimation embedded cooking video guidance

The goal is to make healthy and affordable eating more accessible and practical.

How we built it

We designed NutriCoach as an AI-assisted web application using: React for the frontend FastAPI for backend APIs MeDo AI for intelligent meal planning and cooking guidance

The AI system was structured carefully using prompt engineering to generate: realistic meal plans cooking instructions budget-aware food suggestions shopping list outputs in structured JSON

We also focused heavily on UI/UX to ensure the experience felt simple, modern, and beginner-friendly.

Challenges we ran into

One of the biggest challenges was controlling AI output consistency. Since meal plans involve budgets, dietary restrictions, and structured cooking data, we had to design prompts carefully so the AI could generate reliable and usable responses.

Another challenge was balancing simplicity with functionality. We initially had many feature ideas, but we learned that focusing on a strong MVP was more important than overbuilding.

We also had to think deeply about accessibility, affordability, and how to make the system useful for real-world users instead of building a generic AI demo.

Accomplishments that we're proud of

Built an AI-powered meal planning system that generates affordable weekly meal plans based on user budgets and dietary preferences
Successfully integrated AI-driven cooking guidance with structured meal generation
Designed a clean and modern user experience focused on accessibility and simplicity
Created a smart shopping list system that consolidates ingredients automatically
Developed support for African cuisine and multiple African currencies, making the platform more locally relevant
Structured AI outputs into usable JSON for reliable frontend rendering
Rapidly transformed an idea into a functional MVP using vibe-coding workflows and AI-assisted development
Learned how to balance product scope, usability, and technical execution under hackathon time constraints

What we learned

This project taught us: how to design AI-driven systems around real user problems the importance of structured prompt engineering how to build fast using vibe-coding workflows how to simplify product scope for rapid MVP development how AI can be used beyond chatbots to create practical decision-support systems

Most importantly, we learned that great products are not just about technology they are about solving meaningful problems in ways people can actually use.

What's next for NUTRICOACH

We plan to expand NutriCoach with: nutrition tracking grocery integrations personalized health recommendations multi-language support broader regional food support

Our long-term vision is to build a global AI-powered nutrition platform that makes affordable and healthy eating accessible to everyone.