Organizations are investing significant resources in creating AI/ML models to address complex problems. However, the adoption and deployment of these models at scale are not proportionate to their creation rate due to challenges in monitoring model performance in production environments. Factors such as silent degradation and unreliable predictions can hinder the adoption of AI/ML models. TuringXai addresses this challenge by providing end-to-end visibility into model behaviors and predictions. By leveraging data drift detection techniques, it enables proactive optimization, thereby helping organizations adopt AI/ML models at scale. TuringXai facilitates efficient and reliable AI/ML model operations in production environments, resulting in improved business outcomes.