By Gaurab Chhetri - Hackathon Project
ResQMe - AI-Powered Emergency Response System
ResQMe is an AI-driven emergency response platform built at RiverHacks 2025, winning SerpApi’s Community Engagement Track ($3,000 prize). It combines SOS triggers via iMessage, AI-based triage, real-time responder dashboards, and live context fetching to deliver rapid, context-aware emergency aid.
ResQMe is an AI-powered emergency response system that we built during RiverHacks 2025 in Austin. Over an intense 30-hour sprint with little to no sleep, our team designed and developed the platform to tackle public safety and accessibility challenges identified by the City of Austin.
The project went on to win SerpApi’s 1 Community Engagement Track, bringing home a $3,000 prize.
What It Does
ResQMe brings together real-time data and AI to connect people in distress with nearby responders, ensuring faster, more informed assistance.
Core features include:
- 📱 SOS triggers via iMessage (LoopMessage) 2 – simple, message-based distress calls
- 🤖 AI triage engine – severity prediction and next-step guidance using WebAI’s Navigator 3
- 📊 Responder dashboard – real-time monitoring of incidents with severity, user profile, and location
- 🌎 Contextual data fetching – nearby hospitals, police stations, fire stations, and live weather via SerpApi 1
Architecture
- Frontend: Next.js, Tailwind CSS, TypeScript
- Backend: Node.js (Express), Prisma ORM
- Database: PostgreSQL
- AI/Triage: WebAI Navigator (local LLM API)
- Messaging: LoopMessage (SMS/iMessage integration for SOS triggers)
- Deployment: Vercel / Render
- External APIs: SerpApi for geolocation context
The system was split into modular apps:
- User Profile App for emergency info & contacts
- SOS System Backend for incoming triggers & routing
- Responder Dashboard for responders to track incidents
- NearbyMe for fetching live hospitals, police/fire stations, and weather data
Hackathon Highlights
- 🏆 Winner – SerpApi’s Community Engagement Track
- 💰 Prize – $3,000
- ⏱️ Built in under 30 hours with a 5-member team
- 🌐 Tackled public safety challenges in collaboration with the City of Austin
- 🤝 Strong support and mentorship from WebAI, SerpApi, and LoopMessage
Team
ResQMe was built by a collaborative team: Amul Poudel 4, Dipesh Pandit 5, Diwas Pandit 6, Samar Ranjit 7 and me.
Why It Matters
In emergencies, seconds matter. ResQMe streamlines the process of calling for help, triaging severity, and guiding responders with the right context, all with minimal effort from the user. By combining LLM-driven reasoning with location-aware data, the system showcases how AI can play a role in making public safety more accessible, responsive, and community-driven.