The project titled “Data Fabric for Generic Cloud-AI Product” focuses on building a data fabric architecture that integrates cloud and artificial intelligence (AI) technologies to streamline data management and enable automated insights generation. The goal is to create a robust infrastructure where data from 10+ sources can be efficiently processed and transformed with a 60% increase in data handling capabilities and 50% greater processing efficiency. This approach enables 40% higher data capacity while reducing production costs by 30%. Instead of relying on traditional recommendation systems, this project emphasizes the use of generative techniques for data synthesis, anomaly detection, and predictive modelling across diverse domains such as banking, healthcare, and utilities. The application of generative AI improves data variety and addresses challenges like data sparsity and bias. Performance evaluation is critical, and parameters like precision, recall, F1-score, mean average precision and diversity will be used to assess the effectiveness of the synthetic data generation methods.