Cleanlab Studio is an AI platform that can automatically diagnose issues with synthetic data, such as identifying which synthetic examples do not look realistic, poorly represented modes/tails of the real data distribution, and high-fidelity resembling real data. The platform allows users to effortlessly perform text classification on their uploaded dataset without writing any code, providing a label issue score for each example based on the confidence of the model's predictions. This helps identify poor-quality synthetic data and other shortcomings of the synthetic dataset. By using Cleanlab Studio, users can automatically detect outliers in both real and synthetic data, pinpoint areas that need refinement in the synthetic data generation process, and ensure the quality and accuracy of their synthetic data to avoid unintended consequences such as overfitting, propagating biases, and handling domain gaps. The platform is particularly useful for text-based applications, but its principles apply broadly across other types of synthetic data, making it a valuable tool for addressing challenges related to data scarcity, privacy, and more in the machine learning landscape.