Safe Intelligence is a platform for validating, robustifying, and monitoring machine learning models. The product provides capabilities to analyze model performance and robustness, identify fragilities and counter examples in trained models, and formally verify regions against classes of perturbations. The platform supports analysis of deep neural networks, decision trees, and random forests. It enables validation by examining whole regions of input space rather than individual data points, and checks performance under domain shift conditions. The robustification functionality aims to remove fragilities, improve fairness, and reduce variance by addressing unexpected behavior in trained models. The monitoring component provides continuous tracking of standard model metrics over time and alerts for new fragilities or emerging issues. The platform includes formal verification capabilities for neural networks, including verification against convolutional perturbations via parameterized kernels and geometric robustness validation. Research contributions include work on bound propagation-based neural network verification and Hölder optimization techniques. Safe Intelligence targets organizations deploying AI systems that require validation and robustness assurance for their machine learning models across various applications.