Disaster News Application

In times of crisis, the app gather information from various sources and alerts journalists, public with authentic data and follow up instantly using AI.

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Inspiration

Disaster News application was born from the need to combat misinformation during fast-moving global events. In times of crisis, media outlets and journalists are flooded with unverified data, making it nearly impossible to distinguish fact from noise.

Our goal : empower journalists with AI-verified, real-time insights that ensure accuracy, speed, and trustworthiness in reporting.

The project is regarding automation on environment , sustainability and awareness for public and reporters at the time of urgency regarding any crisis

About the Project

Our project mainly focuses on three main categories of the theme:

  • Climate & sustainability → reducing waste, tracking carbon, community action.

  • Community engagement → volunteering, safety, local connection.

  • Online safety & misinformation

It was built to solve a growing issue: information overload and misinformation in crisis situations. Reporters and media outlets struggle to verify data during fast-moving events — our tool uses AI to aggregate, validate, and summarize reliable data in real time. Ultimately helps to fast spread of news and notify the people in the region respectively.

What it does

It also enables news agencies, media outlets, and independent reporters to receive AI-validated, real-time news intelligence.

Using a multi-agent architecture, the system:

  • Ingests and validates data from multiple verified APIs using a dedicated ingestion agent. Analyzes and summarizes insights with Bedrock-powered reasoning and contextual AI models.

  • Generates structured news stories ready for publication.

  • Creates derivative content such as social media posts and short videos, leveraging AWS AI Agent Core (Bedrock) for automated content generation and formatting.

The result — journalists get a verified, concise, and ready-to-publish story in seconds.

How we built it

Architecture:

Ingestion Layer: AWS Lambda-based API connectors and verification agents for data reliability scoring.

Core AI Layer: Built using AWS Bedrock Agents (multi-agent architecture) for summarization, validation, and content generation.

Frontend: React + GSAP for a modern newsroom dashboard experience.

Backend: FastAPI and DynamoDB,AWS Bedrock for LLM-powered data analysis, orchestrating AI calls and managing data pipelines.

CI/CD: GitHub Actions → AWS Amplify + ECR + Lambda deployments.

Deployment: Hosted on AWS with CI/CD through GitHub Actions. AI Layer: Utilizes Retrieval-Augmented Generation (RAG) to ensure contextually accurate responses.

What we learned

We learned how to integrate AI agents, data APIs, and news stream validation pipelines to filter and fact-check large volumes of information. We also deepened our understanding of prompt engineering, data reliability models, and UX for media professionals.

What's next for Disaster News Agent

🧠 Multi-Agent Expansion: Extend our AWS Bedrock agent architecture to include specialized roles — Fact-Checker Agent, _ Trend Analyzer Agent _, and Media Generator Agent — for deeper accuracy and automation.

🌍 Global Coverage: Integrate _ multi-language _ and region-specific news feeds to provide localized, verified insights for crisis events worldwide.

🎥 Media Automation: Evolve from text-based summaries to AI-generated news videos, complete with auto-voicing, captions, and visual data highlights using AWS media services.

📊 Real-Time Crisis Dashboard: Build a live interactive map displaying breaking events, verified sources, and trust-level scoring — powered by continuous AI ingestion.

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