This paper investigates the application of artificial intelligence (AI) in online stores and e-commerce platforms, emphasizing the design and implementation of intelligent filtering systems to enhance product discovery and user experience. Traditional filtering methods, which rely on predefined rules and static parameters, often fail to account for complex user behaviors and contextual preferences. In contrast, AI-driven filters dynamically adapt to user intent, providing more accurate and personalized product recommendations. This study details the complete development process, including the methodology for locally hosting the AI model to ensure high performance, data privacy, and system scalability. Furthermore, the technical environment supporting the solution is described, covering the infrastructure architecture, virtual machines (VMs), hosting platforms, and integration pipelines used to deploy the system efficiently.