DisasterIQ

This project was inspired by the gaps we observed in emergency response systems, where people in distress often struggle to communicate their needs clearly and responders face difficulty in prioritizing cases efficiently. We realized that delays, poor coordination, and lack of intelligent classification can significantly affect both safety and resource management. Through building this system, we learned the importance of designing scalable and modular architectures, integrating AI for smart risk assessment, and ensuring real-time responsiveness across platforms. We developed our solution by combining a chatbot interface for collecting user reports, a backend system for automated risk classification, and a dashboard for monitoring and coordinated action. Throughout the process, we faced challenges such as fine-tuning the classification logic, ensuring seamless integration between components, and maintaining system reliability under different scenarios. Overcoming these challenges strengthened our understanding of how technology can be used responsibly to create faster, smarter, and more human-centered crisis response systems.