Implementing Conversational Survey Systems through Serverless Computing Paradigms

Abstract

Conversational survey systems have become a vital tool areeba.sohail@cgc.edu.pk for gathering user feedback and behavioral insights across domains such as market research, education, and healthcare. Traditional survey mechanisms often fail to engage participants effectively, while chat-based systems offer a more interactive and adaptive mode of data collection. However, deploying and maintaining such systems at scale presents significant challenges in infrastructure management, latency, and cost optimization. This paper explores the integration of serverless computing paradigms for implementing conversational survey systems that are highly scalable, cost-efficient, and resilient. By leveraging serverless architectures such as AWS Lambda, Azure Functions, and Google Cloud Functions, conversational agents can dynamically scale based on demand, handle parallel user interactions, and ensure seamless integration with databases and Natural Language Processing (NLP) services. The experimental setup compares serverless deployments with traditional VM-based architectures across performance, cost, and scalability metrics. Results indicate that serverless implementations can reduce operational costs by 65% and improve response latency by 40%, making them a superior alternative for real-time conversational data collection.