Rescufast.ai

Rescufast.ai is an AI-powered ambulance and emergency care app that delivers instant triage, real-time ambulance tracking, and smart hospital matching, making second count when lives are on the line

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Inspiration

Across Asia and many developing regions, people lose their lives not because ambulances don’t exist, but because they don’t arrive on time. Delayed responses, miscommunication, outdated infrastructure, and the lack of available hospital beds or difficulty finding the right hospital continue to plague emergency systems.

I was inspired by a simple but powerful idea: What if accessing emergency help was as fast and intelligent as ordering a ride? What if I could build a system that actually solves this problem?
Rescufast.ai was built to explore that future, where AI bridges the gap between distress and action.

What it does

Rescufast.ai is an AI-powered ambulance dispatch and emergency care assistant. It simulates a full emergency response flow from symptom reporting to hospital arrival, including:

  • AI Medical Triage: Users type symptoms or upload an injury photo, and receive simulated, intelligent feedback powered by large language models.
  • Ambulance Booking & Dispatch: A seamless booking interface with real-time animated ambulance tracking.
  • Smart Hospital Matching: Suggests nearby hospitals based on bed availability, cost, and distance.
  • Journey Tracking: Live route visualization from the user’s location to the hospital.
  • Operational Dashboard: Displays live system metrics including traffic, weather, and latency.
  • Emergency Communication: Simulated phone and video call capabilities with remote medical experts.
  • Responsive, Multilingual Design: Works across devices and supports multiple languages.

How I built it

I used the following technologies and tools to build Rescufast.ai:

  • Frontend: React, TypeScript, TailwindCSS for modern, responsive UI.
  • Backend & Auth: Supabase for user management and data simulation.
  • Maps: Leaflet with OpenStreetMap for live ambulance and route tracking.
  • AI Simulation: OpenRouter and DeepSeek to generate symptom analysis responses. Due to API limitations, I simulated the image part. Also using the free DeepSeek API, which is 20–30 seconds slow.
  • Logic Simulation: Custom time-based state management to control ambulance flow, tracking, and demo timing.

About 90% of the project was developed directly in Bolt. However, Supabase authentication and map integration were done outside Bolt using VS Code.

Challenges I ran into

  • Time-based flow synchronization: It was tricky to time the ambulance animations, popups, and dashboard events within strict demo constraints.
  • Simulating realistic behavior: Without access to real APIs or health data, I had to simulate hospital listings, ambulance dispatch, and traffic logic convincingly.
  • Balancing realism and performance: Ensuring visual smoothness while maintaining believable system behavior across devices was a significant UI/UX challenge.
  • Safe AI prompts: Fine-tuning prompts to return medically plausible outputs without generating unsafe advice requires careful testing.

Accomplishments I'm proud of

  • Created a fully synchronized, end-to-end emergency response flow that runs smoothly in under 3 minutes.
  • Designed a platform that is visually clean, functionally deep, and demo-ready — even without live data.
  • Successfully simulated multi-modal interaction (text, map, progress bars, calls, and popups) without backend complexity.
  • Built a solution that feels real and impactful, even in prototype form.

What I learned

  • How to simulate complex, real-time systems using simple frontend state logic and time-based animations.
  • The power of combining AI with logistics, not just diagnostics, from triage to transport decisions.
  • How to plan and execute a high-stakes demo experience, balancing technical constraints with storytelling and UX clarity.
  • Learned to design with empathy, especially when dealing with emergencies, accessibility, and language support.

What's next for Rescufast.ai

  • Real-world integration: Partnering with hospitals, ambulance providers, and health ministries to connect real data and dispatch systems.
  • Live AI + medical review: Implementing verified AI triage alongside licensed medical professionals for safety.
  • IoT and GPS integration: Connecting with actual ambulance tracking devices and patient vitals.
  • Expand to underserved regions: Deploying the app in cities where emergency response is still paper-based or uncoordinated.
  • Launch a pilot program: Begin testing Rescufast.ai in a controlled environment to measure real-time impact and efficiency gains.

I believe Rescufast.ai can one day become a core layer in national emergency systems — and this hackathon is just the first step.

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