Amber AI Alert

Inspiration

Every year, nearly 460,000 children are reported missing in the U.S. alone, and many cases take too long to resolve. Families live in constant fear, and first responders face fatigue and slow information flow. Traditional Amber Alert systems save lives but are limited by manual processes and delayed notifications. We wanted to create a system that acts faster, coordinates better, and gives every child the best chance of being found safely.

What it does

Amber AI Alert is an AI-powered platform that monitors reports, analyzes data, and automatically distributes alerts in real-time. By using multiple AI agents working together, the system ensures that authorities, media, and communities receive accurate notifications instantly, reducing the critical hours a child is missing.

How we built it

• Languages: Python, JavaScript
• Frameworks & Platforms: Solace Agent Mesh, FastAPI, React
• AI & Cloud Services: Cerebras LLM for natural language alert generation
• Databases & Storage: In-memory store, optional S3/PostgreSQL for persistence
• Other Tools: WebSockets, Docker

The system uses a network of AI agents to process incoming reports, prioritize alerts, and coordinate actions across multiple platforms in real-time.

Agents

• Alert Receiver - Receives and validates AMBER Alert reports
• AI Analyzer - Assesses alert urgency and priority using AI reasoning
• Broadcast Agent - Coordinates alert broadcasting across multiple channels
• Camera Agent - Manages automated camera scanning in geofence zones
• Tip Processor - Receives, processes, and verifies tips from the public
• Geo Intelligence - Creates geofence zones for camera scanning

Challenges we ran into

We faced several challenges during development. Ensuring the system remains stable while multiple agents run concurrently was critical, as any downtime could delay alerts. Managing real-time data flow without delays was another hurdle, requiring careful orchestration of message queues and microservices. Additionally, integrating the Cerebras LLM for dynamic natural language alert generation pushed us to refine our AI workflows and ensure reliable output under heavy load.

Accomplishments that we're proud of

We successfully built a fully functional, real-time AI-powered alert system that can generate and distribute Amber Alerts instantly across multiple endpoints. The integration of multiple AI agents using Solace Agent Mesh allowed us to create a scalable architecture capable of handling thousands of alerts simultaneously. This demonstrates both the technical robustness and practical impact of our system.

What we learned

Through this project, we gained hands-on experience orchestrating multiple AI agents in real-time. We learned the importance of robust data handling for critical alert systems and how to deploy and manage AI-powered microservices reliably. Additionally, we explored techniques for optimizing AI response times under heavy load, ensuring that the system remains fast and effective when it matters most.

What's next

Looking forward, we plan to integrate additional AI models to provide better predictive insights and enhance the alert generation process. We also aim to improve the user interface and notification systems to enable faster action by first responders. Finally, we hope to partner with authorities and NGOs to make the system part of official emergency workflows, expanding its real-world impact.

Built With

• cerebrasllm
• docker
• fastapi
• javascript
• python
• react
• solace
• websockets