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.

翻译：应急响应系统会生成来自多个机构和系统的数据。在实践中，以符合下一代9-1-1数据标准的方式跨数据源关联并更新这些信息仍具有挑战性。理想情况下，此类数据应被视为持续更新的操作流，其中新的事实被即时整合，从而为不断演变的事件提供及时且统一的视图。本文提出SentinelAI，一个数据集成与标准化框架，旨在将应急通信转化为标准化的、机器可读的数据集，以支持集成、复合事件构建以及跨源推理。SentinelAI实现了一个由专门智能体组成的可扩展处理流水线。其中，EIDO智能体摄取原始通信并生成符合NENA标准的紧急事件数据对象JSON。