SentinelAI: A Multi-Agent Framework for Structuring and Linking NG9-1-1 Emergency Incident Data
This addresses data integration problems for emergency response systems, but it appears incremental as it builds on existing standards and methods.
The paper tackles the challenge of correlating and updating emergency incident data across multiple sources to align with Next Generation 9-1-1 standards, presenting SentinelAI as a framework that transforms emergency communications into standardized, machine-readable datasets for integration and reasoning.
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.