SafeMate: A Modular RAG-Based Agent for Context-Aware Emergency Guidance

TL;DR

SafeMate is a modular AI assistant that provides real-time, context-aware emergency guidance to the public by integrating retrieval and summarization tools, improving accessibility of safety information during crises.

Contribution

It introduces SafeMate, a novel retrieval-augmented agent utilizing the Model Context Protocol for dynamic, accurate emergency guidance tailored for non-expert users.

Findings

Effective retrieval of relevant safety content using FAISS.

Dynamic routing of user queries to appropriate tools.

Enhanced accessibility of emergency information for the public.

Abstract

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.