Real-Time Employee Attrition Monitoring and Recommendation System

Employee attrition is an important challenge that impacts organizational productivity, staff morale, and financial stability. Increased attrition rates result in higher costs of recruitment, workflow disturbances, and reduced efficiency overall. The paper presents a Real-Time Employee Attrition Monitoring and Recommendation System that combines rule-based scoring and AIanalysis through GPT-4 for forecasting and avoiding attrition risks. The system captures employee survey responses, assigns quantifiable scores along predetermined metrics such as job satisfaction, work-life balance, and career development, and processes structured scores through GPT-4 to create tailored recommendations for both employees and HR teams. A Firebase backend provides secure data storage, real-time updating, authentication, and visualization in the form of interactive dashboards, allowing HR teams to study workforce trends and undertake proactive retention measures. The results show that this hybrid model enhances early attrition detection by 30% over conventional approaches, enabling timely $H R$ interventions and enhanced workforce retention. The performance of the system is measured on the basis of attrition prediction accuracy, response time, and intervention success rate.