Paper Title
Building An E-Commerce Chatbot: An Introductory Framework With Rasa and Bert

Abstract
In today's era, the growth of online shopping has emphasized the importance of chatbots that can effectively manage customer inquiries. Traditional e-commerce platforms require users to manually search for specific products, which can be time-consuming and cumbersome. A chatbot powered by advanced NLP technologies like BERT (Bidirectional Encoder Representations from Transformers) enables users to interact naturally, asking questions such as "Is this product available?" or "What are the reviews for this product?" This conversational approach significantly enhances the shopping experience. By leveraging BERT's contextual understanding capabilities, the chatbot processes complex and multi-turn queries, delivering accurate and relevant responses. The procedure involves data preprocessing, model training, and deploying the chatbot within the Rasa framework. Results show substantial improvements in query handling and response accuracy, making it an indispensable tool for e-commerce applications. This paper outlines the steps to create an efficient e-commerce chatbot using Rasa and BERT, offering insights into the development process and key outcomes. Keywords - Rasa, Chatbot, NLP, BERT and AI