The rise of digital services has led to an increased demand for efficient and scalable customer support solutions. An AI-powered chatbot can automate repetitive tasks, provide instant responses, and improve customer satisfaction. This work presents the design and implementation of a customer support chatbot that uses Natural Language Processing (NLP) and machine learning to handle queries, resolve common issues, and escalate complex cases to human agents when needed.
The chatbot architecture includes:
Intent Recognition: Uses NLP models to classify user queries into predefined categories (e.g., billing, technical support).
Entity Extraction: Identifies key information such as product IDs, dates, or account details.
Response Generation: Retrieves template-based or AI-generated responses.
Escalation Engine: Transfers unresolved queries to human agents with conversation context.
Analytics Module: Tracks response accuracy, resolution rates, and customer satisfaction.
Intent recognition accuracy: 91%
Average response time: < 1 second
Successful resolution without human intervention: 74% of queries
Improved customer satisfaction scores by 25% compared to a control group without AI support.