SentinelAI: A Multi-Agent Framework for Structuring and Linking NG9-1-1 Emergency Incident Data

TL;DR

SentinelAI is a scalable multi-agent framework that standardizes and links emergency incident data from multiple sources to improve real-time situational awareness in NG9-1-1 systems.

Contribution

It introduces a novel multi-agent processing pipeline that transforms raw emergency communications into standardized, machine-readable data compliant with NG9-1-1 standards.

Findings

Effective data standardization across agencies

Supports real-time incident updates

Enhances cross-source incident reasoning

Abstract

Emergency response systems generate data from many agencies and systems. In practice, correlating and updating this information across sources in a way that aligns with Next Generation 9-1-1 data standards remains challenging. Ideally, this data should be treated as a continuous stream of operational updates, where new facts are integrated immediately to provide a timely and unified view of an evolving incident. This paper presents SentinelAI, a data integration and standardization framework for transforming emergency communications into standardized, machine-readable datasets that support integration, composite incident construction, and cross-source reasoning. SentinelAI implements a scalable processing pipeline composed of specialized agents. The EIDO Agent ingests raw communications and produces NENA-compliant Emergency Incident Data Object JSON.