AI-Generated Product Authentication System

The growth of e-commerce platforms has significantly changed how people purchase products. Today, customers usually check product ratings and reviews before buying any item online. These ratings help them understand the quality and reliability of a product. However, many online shopping platforms still rely on simple rating systems that mainly consider star ratings and customer reviews. Because of this limitation, some products may continue to show high ratings even when many customers return them due to quality issues or delivery problems. This can mislead new customers and reduce their trust in the platform. To overcome this issue, this research proposes a dynamic product rating system for an e-commerce platform. Unlike traditional rating systems, the proposed system calculates product ratings by combining several factors such as customer reviews, delivery feedback, and product return information. If a product is returned frequently, the system automatically reduces its rating so that the displayed rating better reflects the actual product performance. The system is developed using the MERN stack and also includes an AI chatbot that assists administrators in searching, monitoring, and managing products more efficiently. The main objective of this system is to improve transparency in product ratings and help customers make better purchasing decisions.