SAARTHI
SAARTHI is an Ai-assisted disaster command center enabling smart risk assessment, resource allocation and coordinated emergency response
Built at AgentX Hackathon 2026
Team Peaky_Coders: Ayush Jaiswal, Madhura Chavan
Built with Python, Streamlit, HTML, CSS, SQLite, Pandas, Numpy, Pydeck, Plotly, NetworkX, Git, Github, streamlit community cloud
About the project
SAARTHI – AI-Assisted Disaster Response Command Centre
Problem:
During natural disasters such as floods and heatwaves, emergency response teams face challenges including scattered information, delayed decision-making, inefficient resource allocation, and poor coordination between agencies, hospitals, and volunteers. Traditional monitoring systems often provide alerts but lack integrated tools to prioritise emergencies and coordinate effective responses.
Solution:
SAARTHI is an AI-assisted disaster management platform designed to bridge the gap between disaster monitoring and coordinated action. It provides a unified command centre that helps authorities assess risks, identify vulnerable areas, prioritise incidents, allocate resources, and coordinate rescue operations. By combining disaster intelligence with operational workflows, SAARTHI supports faster, more informed decision-making.
How It Works:
SAARTHI integrates environmental data, disaster reports, geospatial visualisation, and analytical models into an interactive dashboard. Its key features include:
- Early Warning System: Monitors environmental conditions to highlight potential disaster risks.
- Severity and Vulnerability Assessment: Helps identify and prioritise critically affected regions.
- Resource Allocation: Supports the distribution of available emergency supplies and assistance.
- Coordinator Agent: Connects disaster information with response-planning workflows.
- Ripple Analysis: Examines potential cascading effects across roads, hospitals, and resources.
- What-If Simulator: Allows authorities to explore hypothetical disaster scenarios.
- Volunteer Coordination: Manages rescue assignments, dispatch, and progress tracking.
Built using Python, Streamlit, and SQLite, SAARTHI demonstrates an integrated approach to disaster-response coordination.
Impact:
SAARTHI aims to reduce response-planning delays, improve resource utilisation, strengthen coordination, and support vulnerable communities during emergencies. By transforming fragmented disaster information into actionable insights, it has the potential to improve emergency preparedness and response effectiveness.
SAARTHI: From Disaster Awareness to Coordinated Action.