Journal of Artificial Intelligence and Capsule Networks
IRO Journals
Volume 8 — Issue 1 — March 2026

An Intelligent Shopping Cart Chatbot Using Amazon Lex

https://doi.org/10.36548/jaicn.2026.1.002
Pages: 19-41
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Thilagavathi Narayanamoorthi
Department of Computer Science and Engineering, SA Engineering College, Chennai, India
Sridevi Sridhar
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
Devakunchari Ramalingam
Goldsmiths University of London, London, UK
Leema Nelson
CURIN Advanced Research, Chitkara University, Punjab, India
March 2026
Volume 8 — Issue 1
Journal of Artificial Intelligence and Capsule Networks
PDF

How to Cite

Narayanamoorthi, Thilagavathi, Sridevi Sridhar, Devakunchari Ramalingam, and Leema Nelson. 2026. “An Intelligent Shopping Cart Chatbot Using Amazon Lex”. Journal of Artificial Intelligence and Capsule Networks 8 (1): 19-41. https://doi.org/10.36548/jaicn.2026.1.002.
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Keywords

— Chatbot
— Machine Learning
— Amazon Web Services (AWS)
— Amazon Lex
— AI
— Shopping Cart Chatbot
Published: 18-03-2026

Abstract

The advanced Artificial Intelligence (AI) technology has smart agents to improve the customer satisfaction and efficient operation for e-commerce applications. The chatbots facilitate the communication between the customers that saves the human intervention, increase the speed of response and quality of services automatically. This proposed work explains the development and implementation of an AI-based customer service chatbot with the help of Amazon Lex for online shopping. This work also discusses the improved quality of response. When the data has been received, the system will detect the customer objective and guide them using a series of questions based on products, delivery information, payment methods and general shopping support. The chatbot will evaluate the user input, receive key parameters and provide customized responses with the help of Amazon Lex's Natural Language Understanding (NLU) and the slot-mapping functionalities. This system recognizes the purpose of the response and generates effectively for customers can receive the relevant data about the details of products and their services rapidly. This paper also shows the AI-based conversational interfaces improve the customer experience, automatic communication and support customer service in online shopping. When compared to the rule-based and proposed chatbot model improves the classification accuracy as 92% with accurate response metrics.

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