This content originally appeared on DEV Community and was authored by Honey Check
🚨 Building OperatorAILIVE: An AI-Powered 911 Triage System with Kiro
by Sathvik Vempati
Overview
In emergency dispatch, every second matters. During peak call volumes, 911 operators can become overwhelmed, leading to delayed responses. OperatorAILIVE was built to help bridge that gap — a conversational AI system designed to handle basic intake, assess urgency, and deliver structured, prioritized information to human dispatchers in real time.
This project was developed for the Kiroween Hackathon, where the goal was to demonstrate how Kiro can power intelligent, reliable, and safe AI-driven systems.
The Concept
OperatorAILIVE functions as an AI-assisted triage agent that supports human dispatchers when lines are full or call queues are high.
The system performs three main tasks:
- Emergency Intake: Allows callers to explain their situation in their own words.
- Automated Triage: Analyzes the conversation to determine the emergency type (Fire, Medical, or Crime) and assigns a priority level.
- Operator Handoff: Sends a concise, structured summary to the next available dispatcher for action.
Demo Line: (408) 617-9557 | OperatorAILIVE – AI Triage Assistant
Technology Stack
| Component | Function | Tools Used |
|---|---|---|
| Frontend | Live operator dashboard | React + TailwindCSS |
| Backend | Routing, webhook handling | FastAPI |
| AI Core | Natural language understanding, data extraction | Kiro AI SDK |
| Voice Services | Speech-to-Text & Text-to-Speech | Gemini Voice + Twilio |
| Hosting | Application deployment | AWS EC2 + Vercel |
Implementation Details
1. Structured AI Output with Spec-Driven Development
Emergency calls require precision — not just text generation.
To ensure reliable data transfer between the AI and backend systems, we defined a strict output schema in Kiro:
{
"Priority": "Enum(High, Medium, Low)",
"EmergencyType": "String",
"Location": "String",
"Summary": "String"
}
This schema forced Kiro to output consistent, machine-readable data for every call.
As a result, the operator dashboard received uniform and accurate information, eliminating the need for manual parsing or guesswork.
This approach reduced development time and improved reliability significantly.
2. Automating Workflows with Agent Hooks
After each intake session, Kiro automatically triggered a post-processing hook that formatted the extracted JSON and updated the dispatch queue.
This automation allowed new “Unresolved” tickets to appear instantly on the dashboard without additional API logic.
By integrating Kiro’s Agent Hooks into our FastAPI backend, we achieved a fully automated cycle — from conversation to structured data to visual output — demonstrating a complete and functional implementation pipeline.
3. Controlled Behavior with Steering Documents
Because emergency interactions can be sensitive, we used Steering Documents to guide Kiro’s tone and response style.
For instance, if a fire was detected in the input, Kiro responded with clear, calm instructions:
“Please leave the building immediately and move to a safe location. Can you confirm your address so we can send help?”
These steering rules ensured that responses were always empathetic, concise, and compliant with basic emergency protocol — maintaining a professional and reassuring tone throughout the exchange.
Integration Summary
| Function | Description | Kiro Feature |
|---|---|---|
| Data Structuring | Enforced schema for consistent outputs | Spec-Driven Design |
| Automation | Dashboard updates without manual logic | Agent Hooks |
| Tone Control | Context-based empathy and clarity | Steering Documents |
| System Integration | FastAPI + AWS deployment | Seamless orchestration |
Hackathon Fit
| Prize Category | Relevance |
|---|---|
| Frankenstein | Combines real-time AI orchestration, structured data handling, and live UI updates. |
| Implementation | Fully functional triage and automation workflow showcasing depth and integration. |
| Bonus Blog Prize | Comprehensive and clear technical documentation demonstrating process and results. |
Lessons Learned
- Structure before creativity: Defining a schema first saved hours of debugging later.
- Automation matters: Hooks eliminated repetitive backend code, making the system scalable.
- Empathy through AI: Steering documents made the model feel more natural and reliable under stress.
Overall, Kiro allowed us to focus on designing a safe, scalable workflow instead of reinventing AI infrastructure.
Next Steps
The next iteration of OperatorAILIVE will introduce multilingual support and voice sentiment analysis, enabling the system to recognize distress or panic through tone detection. This will further help dispatchers triage calls faster and more effectively.
Conclusion
OperatorAILIVE demonstrates how structured AI can meaningfully improve critical communication systems.
By combining Kiro’s architecture with robust design principles, we created an assistant that is not only technically sound but genuinely helpful in high-pressure environments.
This content originally appeared on DEV Community and was authored by Honey Check
Honey Check | Sciencx (2025-11-10T04:34:55+00:00) Building OperatorAILIVE: Architecting an AI-Powered 911 Triage System with Kiro. Retrieved from https://www.scien.cx/2025/11/10/building-operatorailive-architecting-an-ai-powered-911-triage-system-with-kiro/
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