Find & Seek

Inspiration

Over 600,000 people go missing each year in the U.S., and current search and rescue methods rely on slow, manual processes. Delayed information sharing, inefficient search strategies, and limited technology support hinder response times, making it harder to locate missing individuals quickly. First responders need faster, AI-driven tools to process information, detect individuals, and optimize search efforts in real time.

What it does

Find & Seek automates search and rescue operations using AI-powered detection, voice-to-text processing, and predictive mapping. By integrating Groq Whisper for speech-to-text transcription and Groq LLM for contextual search and scene understanding, our platform enhances emergency response by streamlining case processing, improving image recognition, and optimizing search zones dynamically. This approach reduces response time and increases the chances of successful recoveries.

How we built it

Find & Seek operates through an AI-driven pipeline that automates every stage of a search operation, transcribing emergency call audio into structured text using Groq Whisper and analyzing reports, images, and descriptions with Groq LLM to provide intelligent insights for refining search areas. The platform dynamically adjusts search zones, tracks movement patterns, and delivers real-time updates to responders. Built on a scalable, high-performance tech stack, the backend runs on Express.js for AI inference and data processing, while the React and Tailwind CSS-based front end offers an intuitive dashboard for 911 operators and SAR teams. Mapbox GL and Recharts enable interactive mapping, real-time heatmaps, and geofencing alerts to enhance search tracking.

Challenges we ran into

Integrating multiple AI models into a seamless pipeline required optimizing API calls, which we solved using efficient request handling in Express.js. Ensuring real-time data flow was another challenge, which we addressed by leveraging React Query for optimized state management and WebSockets for instant updates. Additionally, scaling the platform for large datasets while keeping it lightweight was achieved through optimized backend processing and caching mechanisms.

Accomplishments that we're proud of

Find & Seek effectively leverages AI, real-time data processing, and interactive mapping to enhance search operations. Groq Whisper transcribes emergency calls for instant case documentation, while Groq LLM enables intelligent search capabilities, extracting key insights from reports and imagery. With Express.js for a fast backend, React Query for real-time data updates, and Mapbox for live mapping, our platform provides a powerful, AI-driven solution for first responders.

What we learned

Through developing Find & Seek, we learned the immense potential of AI in streamlining search and rescue operations by automating transcription, analysis, and predictive mapping. Integrating Groq Whisper and Groq LLM significantly improved response efficiency, reducing processing time and enhancing search accuracy. Real-time data visualization using Mapbox GL and Recharts proved invaluable for tracking dynamic search zones, while our scalable tech stack ensured high performance under real-time demands. Challenges included optimizing AI inference speed and balancing automation with human decision-making. Moving forward, refining predictive models and expanding integration with SAR tools will further enhance operational effectiveness.

What's next for Find & Seek

Find & Seek is designed to revolutionize search and rescue operations by integrating AI-powered detection, NLP-based search, and predictive mapping. Our next steps include enhancing drone-based search integration, incorporating satellite imagery analysis, and improving AI-driven decision-making. By continuously evolving, Find & Seek aims to make search efforts faster, smarter, and more effective.