Lost Realm

UMT's AI-powered lost & found platform that instantly matches lost items with found ones. Save time,reduce stress,and get your belongings back faster through smart matching and real-time notifications

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Inspiration

Lost & Found systems at universities are often inefficient and frustrating. After witnessing countless students struggling to recover lost items at UMT, posting in WhatsApp groups and Facebook pages, we realized there had to be a better way. The idea for Lost Realm was born from the desire to modernize this process using AI and create a centralized, efficient platform for our campus community.

What it does

Lost Realm is an AI-powered lost and found platform that:

  • Automatically matches lost items with found ones using advanced description analysis
  • Provides real-time notifications when potential matches are found
  • Offers secure in-app chat for item claims and verification
  • Features a modern, intuitive interface for easy item reporting
  • Includes image-based matching capabilities
  • Maintains a transparent chain of custody for items
  • Sends instant notifications for potential matches

How we built it

We developed Lost Realm using a modern tech stack:

  • Next.js 14 with App Router for the frontend
  • Tailwind CSS with shadcn/ui for a polished UI
  • Supabase for real-time database and authentication
  • Clerk for secure user management
  • OpenAI's GPT-4 for intelligent item matching
  • TypeScript for type safety
  • Vercel for deployment
  • Real-time notifications using WebSockets
  • Custom matching algorithms for item comparison

Challenges we ran into

  1. Complex Matching Logic: Creating an accurate matching system that could handle various item descriptions and avoid false positives was challenging.
  2. User Authentication Flow: Integrating Clerk with Supabase while maintaining a seamless user experience required careful coordination.
  3. Real-time Updates: Implementing instant notifications and chat features while maintaining performance was tricky.
  4. Data Privacy: Ensuring user data and item information remained secure while still allowing efficient matching.
  5. Image Processing: Balancing image quality with storage constraints and implementing visual similarity matching.

Accomplishments that we're proud of

  • Built a fully functional, production-ready platform in a short timeframe
  • Created an intuitive UI that requires minimal user training
  • Implemented an AI matching system with high accuracy
  • Achieved real-time performance for notifications and chat
  • Developed a scalable architecture that can handle university-wide usage
  • Maintained high security standards for user data protection

What we learned

  • Advanced Next.js patterns and real-time data handling
  • AI integration best practices for practical applications
  • Complex state management in a real-time system
  • User authentication security considerations
  • The importance of user feedback in feature development
  • Performance optimization techniques for real-time applications

What's next for Lost Realm

  1. Mobile App Development: Native mobile apps for iOS and Android
  2. Enhanced AI Features:
    • Image recognition for better matching
    • Location-based recommendations
    • Predictive analytics for common lost items
  3. Campus Integration:
    • API for campus security integration
    • Multi-campus support
    • Integration with student ID systems
  4. Community Features:
    • Reputation system for reliable users
    • Community rewards program
    • Lost item prevention tips
  5. Analytics Dashboard:
    • Trends analysis
    • Hot spots for lost items
    • Success rate tracking

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