Ai At The Frontline: Transforming Emergency Dispatch With Automated Decision Making and Real-Time Call Analysis

Rapid reaction times are critical for effective emergency response; however, conventional dispatch systems rely heavily on human operators, which can introduce delays and inconsistencies in time-sensitive situations. To address this limitation, this paper presents an AI-based virtual assistant designed to support automated decision making in emergency dispatch centers. The proposed framework processes incoming emergency calls in real time by converting speech to text, extracting relevant information, and classifying incidents using a supervised support vector machine (SVM) model. The system supports both multi-class incident categorization for operational understanding and binary emergency detection to facilitate prompt dispatch decisions. Experimental evaluation on large-scale dispatch data demonstrates that the proposed approach improves response efficiency and reduces manual processing overhead compared to traditional workflows. These results indicate that AI-driven dispatch assistance can enhance the speed, reliability, and consistency of emergency response operations, with particular relevance to police-related incidents and potential applicability to other emergency services.