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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64780
Volume:
10 |
6 |
Jun 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of CSE
Sardar Patel Institute of Technolofy
Department of CSE
Sardar Patel Institute of Technolofy
Department of CSE
Sardar Patel Institute of Technolofy
Department of CSE
Sardar Patel Institute of Technolofy
Emergency response systems in India continue to face operational challenges including delayed call processing, communication barriers, fragmented inter-agency coordination, and limited real-time situational awareness. Existing emer-gency workflows rely heavily on manual interven-tion, which may delay critical decision-making dur-ing high-pressure situations. This paper presents the Critical AI Response Engine (C.A.R.E.), an AI-assisted emergency response framework designed to support automated distress analysis, multilingual communication handling, geolocation-aware respon-der coordination, and real-time monitoring. The proposed system integrates large language model (LLM)-based conversational analysis with speech transcription services and geolocation (PostGIS) to assist emergency operators in triaging incoming in-cidents more efficiently. A modular architecture consisting of input processing, AI-assisted analy-sis, dispatch coordination, and monitoring layers is proposed and implemented using modern web and cloud technologies. Functional observations from prototype deployment demonstrate an average end-to-end turn latency of 2.14 seconds, with respon-sive real-time interactions across speech recognition, NLP classification, and response generation stages. The study highlights the architectural design, imple-mentation methodology, operational workflow, and deployment considerations of AI-assisted emergency management systems in the Indian context.
KEYWORDS
The Critical AI Response Engine (C.A.R.E.) successfully demonstrates that artificial intelligence, natural language pro-cessing, and real-time geolocation technologies can be inte-grated into a unified platform to modernize emergency re-sponse infrastructure. Through automated distress call clas-sification, intelligent responder dispatch, and synchronized multi-agency communication, C.A.R.E. addresses key inef-ficiencies in traditional emergency frameworks. Functional testing confirmed the system’s ability to pro-cess emergency calls with an average end-to-end turn latency of 2.14 seconds and consistent sub-500 ms latency across in-dividual pipeline stages, including speech recognition, NLP classification, and response generation. The multilingual voice recognition feature enhances accessibility across In-dia’s diverse linguistic population, while the automated coor-dination framework minimizes human error and operational redundancy. The proposed framework demonstrates the feasibility of in-tegrating conversational AI services, multilingual communi-cation support, geolocation-aware dispatch systems, and cen-tralized monitoring within a unified emergency response ar-chitecture. The modular system design enables scalability, maintainability, and adaptability for future deployment sce-narios while supporting real-time coordination between emer-gency response agencies.
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Publication Date
6/11/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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