CrisisGuard

CrisisGuard uses AI to predict and detect emergencies, sending real-time alerts and safety guidance to help people stay ahead of disasters.

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Inspiration

During disasters, people often don’t get information in time. By the time alerts reach them, it’s sometimes already too late. I started thinking: What if there was a way to warn people before things got worse? That idea led me to build CrisisGuard — a system focused on giving people early warnings and helping them stay safe during emergencies.

What it does

CrisisGuard is an AI-powered platform that: Tracks real-time data from news and public sources Uses AI to detect possible disasters or risky situations Sends instant alerts based on the user’s location Helps with basic evacuation guidance Provides live updates about ongoing crises The goal is simple: Help people act early instead of reacting late.

How I built it

I built CrisisGuard as a simple but scalable web application: Frontend: React + Vite Backend: Node.js / serverless functions AI: Used NLP to analyze news and identify threats APIs: News APIs for real-time updates Maps API for location and routing Cloud: DigitalOcean for hosting

  • Workflow: Fetch real-time data Analyze it using AI Identify potential threats Send alerts to users

Challenges I ran into

Figuring out how to filter useful information from noisy data Handling real-time updates efficiently Working with multiple APIs together Managing everything within a hackathon time limit

Accomplishments that I am proud of

Built a real-time AI-powered crisis alert system from scratch within a limited hackathon timeframe Successfully integrated multiple APIs (news + maps) into a single working platform Designed a system that focuses on real-world impact and user safety, not just features Implemented location-based alerts, making the solution more practical and personalized Managed to turn an idea into a functional prototype under time pressure Learned and applied AI/NLP concepts to detect potential threats from live data

What I learned

How to build an AI-powered real-time system Integrating APIs and handling live data Improving problem-solving under time pressure Focusing on building something with real-world impact

What's next for CrisisGuard

Turning this into a mobile app Adding more reliable data sources Improving AI predictions Making alerts faster and more accurate

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