Abstract
Traditional Interactive Voice Response (IVR) systems, characterized by rigid "press 1 for service" menus, have long been a source of customer friction and operational bottleneck. This paper explores the fundamental paradigm shift toward LLM-powered conversational agents that utilize advanced Natural Language Processing (NLP) and real-time Speech-to-Text (STT) to provide human-like phone interactions. By leveraging Large Language Models, these agents can understand complex intent, maintain context, and resolve queries without the frustrating loops of legacy technology. The study examines the technological architecture required for a low-latency "neural loop" and analyses its impact on key business metrics such as Average Handle Time (AHT) and First Call Resolution (FCR). Furthermore, the research highlights successful implementations within the Indian context, specifically focusing on multilingual support for the MSME sector. The study concludes that the integration of conversational Artificial Intelligence (AI) is essential for businesses seeking to provide 24/7, high-quality support without the linear costs associated with traditional human-centric call centers.
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