Companies are increasingly using data analytics and AI to personalize interactions and provide tailored recommendations. Additionally, there is a growing focus on emotional intelligence and understanding customers’ needs beyond their transactional behavior. Some real-world examples are – Netflix uses AI to analyze viewing history and preferences, recommending personalized content, Spotify leverages data on listening habits to create tailored playlists and discover new music. The need for AI models in NPS and CX enhancement arises from the increasing complexity of customer interactions and the vast amount of data generated. AI can help in predicting consumer’s future behavior by analysing their demographic and purchase data and identifying patterns. This empowers businesses to create more tailored and meaningful customer experiences, resulting in greater satisfaction and loyalty. This project aims to develop a Generative AI-based Net Promoter Score (NPS) predictor to enhance Customer Experience (CX) in the retail industry. By leveraging advanced AI techniques like VAEs and GANs, the model will be able to analyze vast datasets of consumer behavior and demographics, providing more accurate and personalized NPS predictions.