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Conversational agents are seeing growing adoption across customer service and e-commerce platforms, serving as direct channels of communication between businesses and their clients. To support this demand, major technology providers, including Google, IBM, Microsoft, and Amazon, have established cloud-based platforms that apply artificial intelligence to process inputs and extract dialogue data. Selecting the right solution presents substantial difficulties, primarily due to the expenses linked with automated natural language processing and the technical expertise required in software engineering. To address these hurdles, an analysis of several development platforms was conducted. The work provides practical guidance to assist both technical developers and non-specialist users in identifying the most suitable tools for their specific chatbot development requirements.
Conversational agents are central to modern digital services, but implementing them can be prohibitively expensive and technically demanding. Comparing available platforms provides organisations with clear insights into which systems fit their operational needs without incurring unnecessary computational costs or requiring specialised artificial intelligence engineering teams.
This work directly informs businesses, developers, and non-technical staff seeking to deploy conversational agents in e-commerce and customer service. By evaluating existing market-ready platforms from major vendors, the findings are immediately applicable, aiding cost-effective selection and deployment decisions without requiring novel software development.
AI-generated from the published abstract. Always read the original work before citing.
Conversational agents are being increasingly adopted in various domains, such as e-commerce and customer services, and as a direct communication channel between companies and end-users. Several tools have been developed to facilitate their definition and deployment. They exploit existing cloud infrastructures and artificial intelligence (AI) techniques to efficiently process users’ input and extract conversational information. Major Information Technology (IT) companies, such as Google, IBM, Microsoft, and Amazon, have provided powerful tools to develop conversational agents. Still, choosing the most appropriate tool is not easy, as it may require high costs associated with automatic natural language processing (NLP) services and expertise in software engineering and AI. Therefore, this paper aims to analyze different tools to help developers and non-developers to choose the optimal tool for their specific scenario of creating a conversational agent.
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DOI: 10.3390/cmsf2023006005
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