DESIGN AND IMPLEMENTATION OF AI-DRIVEN CHATBOT SYSTEMS FOR INTELLIGENT CUSTOMER SUPPORT

Abstract

Artificial Intelligence (AI)–driven chatbot systems have emerged as a transformative solution for intelligent customer support across multiple industries. These systems leverage Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning techniques to automate human-like conversations and improve service efficiency. The rapid growth of digital platforms has increased customer expectations for instant, personalized, and 24/7 support. AI-powered chatbots reduce operational costs while maintaining high service quality and scalability. This research presents the design and implementation of an AI-driven chatbot framework for intelligent customer interaction and automated query resolution. The system integrates intent recognition, entity extraction, context management, and response generation modules. A hybrid architecture combining rule-based and machine learning models is proposed to enhance adaptability and accuracy. Experimental evaluation demonstrates improvements in response accuracy, user satisfaction, and processing time. The proposed solution provides a scalable and efficient customer support model suitable for enterprise deployment.