EcoptiAI: E-Commerce Process Optimization and Operational Cost Minimization Through Task Automation Using Agentic AI

The rapid expansion of e-commerce has increased the complexity of operational processes, making it challenging to manage tasks such as product cataloging, customer responses, delivery updates, and feedback collection efficiently. These challenges often result in elevated operational costs and decreased customer satisfaction. This paper introduces EcoptiAI, an agentic AI-powered framework leveraging a transformer-based model as an intelligent agent to automate essential e-commerce processes. EcoptiAI minimizes manual effort, streamlines workflows, and optimizes procedural costs by handling tasks such as product description generation, catalog updates, personalized customer communication, and empathetic delivery status updates. The system employs an empathetic tone for delay notifications, distinguishing itself from standard cold responses, and automates customer feedback analysis, ensuring an enhanced customer experience. The transformer model underlying EcoptiAI has been trained using a uniquely structured dataset created from diverse e-commerce-related sources, including product catalogs, customer reviews, and operational logs. The experimental analysis demonstrates that EcoptiAI reduces procedural costs by 52.7% on average and achieves high-performance metrics, with an accuracy of 92.42%, precision of 92.44%, recall of 92.40%, and an F1-score of 92.41%. The findings indicate the transformative potential of agentic AI in driving cost-effective, automated e-commerce operations while enhancing customer satisfaction. This paper provides a comprehensive evaluation of EcoptiAI’s design, implementation, and impact, paving the way for scalable and intelligent e-commerce automation solutions.