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Posted on Nov 10

Building OperatorAILIVE: Architecting an AI-Powered 911 Triage System with Kiro

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🚨 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.

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