OneEarth

Shop smarter with AI and conservation science—discover cruelty-free, wildlife-friendly alternatives.

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🌍 OneEarth

Inspiration

OneEarth was inspired by the urgent need to address wildlife conservation and promote cruelty-free consumer choices.
Many people want to make ethical decisions but lack accessible information about:

  • The impact of their shopping choices on wildlife and animals
  • How to find cruelty-free alternatives to harmful products
  • Conservation actions and their effectiveness
  • The current status of wildlife populations and threats they face

Our team wanted to bridge the gap between conservation science and everyday consumer decisions.

✨ What It Does

🌿 Wildlife Conservation Hub

  • Real-time access to IUCN Red List data for endangered species
  • Interactive exploration of conservation actions
  • Detailed species assessments with threat levels & conservation status
  • Search functionality for specific animals and conservation needs

🛍️ Cruelty-Free Shopping Assistant

  • AI-powered chatbot for vegan & cruelty-free alternatives
  • Database of 350+ luxury products with alternatives
  • Educational content about animal cruelty in fashion
  • Smart product filtering by category, price, and material

📊 Conservation Education

  • Visual representation of threats to wildlife
  • Focus areas highlighting critical conservation priorities
  • Interactive maps & data visualizations
  • Sustainable practice education

🛠️ How We Built It

Backend

  • FastAPI server providing RESTful APIs
  • IUCN Red List API integration with secure proxy
  • RAG system using Google Gemini API
  • FAISS vector database for semantic product search
  • Python with dependency management

Frontend

  • Next.js 14 with React
  • TypeScript for type safety
  • Tailwind CSS for modern UI
  • Component-based architecture

AI & Data Integration

  • Google Gemini 2.0 Flash for chatbot responses
  • Sentence Transformers for semantic understanding
  • FAISS indexing for fast similarity search
  • Real-time IUCN data

🚧 Challenges

  • API Integration: Rate limits, complex data, secure proxy handling
  • AI Model: Balancing speed vs. quality, API costs, RAG optimization
  • Data Management: Curating accurate cruelty-free alternatives
  • Performance: Optimizing vector search & caching
  • Cross-Platform: Responsive design & browser compatibility

🏆 Accomplishments

  • Comprehensive Solution: Bridged scientific data with consumer education
  • Advanced AI: RAG-based chatbot + semantic search
  • Real-Time Data: Live IUCN integration
  • Technical Excellence: Scalable, tested, and well-documented architecture
  • User Experience: Intuitive design with engaging visuals

📚 What We Learned

  • Technical: Balancing retrieval vs. generation in RAG, FAISS indexing
  • Conservation: Complexity of IUCN data, mapping conservation hierarchies
  • UX: People need actionable insights, not just raw data
  • Project Management: Separation of concerns, testing, documentation
  • AI: Prompt engineering + fallback mechanisms improve reliability

🚀 What’s Next

  • Enhanced AI: Context awareness, multilingual support, image recognition
  • Mobile App: iOS/Android apps with offline capabilities & push updates
  • Global Expansion: Local databases, regional product recommendations
  • Advanced Analytics: User behavior insights, conservation impact tracking
  • Community Features: Reviews, challenges, social sharing
  • Scientific Integration: Partnerships & citizen science integration
  • Education: Gamified courses, certifications on sustainable practices
  • Sustainability Focus: Carbon footprint tracking, lifecycle analysis

⚡ Tech Stack

  • Frontend: Next.js 14, React, TypeScript, Tailwind CSS
  • Backend: FastAPI, Python
  • AI: Google Gemini API, Sentence Transformers, FAISS
  • Data: IUCN Red List API, custom product database

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