SafeMate: A Modular RAG-Based Agent for Context-Aware Emergency Guidance
This addresses the gap between institutional emergency knowledge and public accessibility, potentially improving emergency preparedness and response for non-experts, but it appears incremental as it builds on existing RAG and tooling methods.
The paper tackles the problem of public inaccessibility to emergency information during crises by introducing SafeMate, a retrieval-augmented AI assistant that provides context-aware guidance to general users, though no concrete performance numbers are reported.
Despite the abundance of public safety documents and emergency protocols, most individuals remain ill-equipped to interpret and act on such information during crises. Traditional emergency decision support systems (EDSS) are designed for professionals and rely heavily on static documents like PDFs or SOPs, which are difficult for non-experts to navigate under stress. This gap between institutional knowledge and public accessibility poses a critical barrier to effective emergency preparedness and response. We introduce SafeMate, a retrieval-augmented AI assistant that delivers accurate, context-aware guidance to general users in both preparedness and active emergency scenarios. Built on the Model Context Protocol (MCP), SafeMate dynamically routes user queries to tools for document retrieval, checklist generation, and structured summarization. It uses FAISS with cosine similarity to identify relevant content from trusted sources.