AI Based Contact Extraction for CRM

Traditional Customer Relationship Management (CRM) systems depend heavily on manual lead processing, fragmented customer records, and static workflows, which reduce operational efficiency and affect opportunity management. This paper presents an AI-based intelligent CRM framework that integrates automated contact extraction, lead management, and machine-learning-driven opportunity prediction within a unified system architecture. The proposed framework utilizes Natural Language Processing (NLP), intelligent lead analysis, and MERN-stack technologies to automate CRM workflows and enhance business decision-making. A Lead Conversion Prediction Model is incorporated to estimate the probability of converting leads into business opportunities based on customer interaction history, lead source, communication behavior, and engagement metrics. The backend is implemented using Node.js, Express.js, MongoDB, and JWT-based authentication, while React.js provides a responsive frontend interface. The system supports contact management, lead creation, dashboard analytics, ticket handling, and AI-assisted automation. Experimental evaluation demonstrates improved operational efficiency, reduced manual workload, scalable performance, and enhanced prioritization of high-potential leads. The proposed framework provides a scalable foundation for future AI-powered CRM systems integrating predictive analytics and real-time customer intelligence.