Expand Your Data. Reach Your Goals Without Limits.

Why Synthetic Data?

Synthetic data generators learn from real-world samples—even if sparse, partial, or incomplete—to produce new, realistic datasets that preserve multivariable statistical relationships without exposing sensitive or identifiable information. This enables safe, large-scale data creation for AI training, simulation, and exploratory analysis, amplifying patterns of interest.

• Controlled Data Generation: Create datasets that emphasize specific conditions such as rare disease combinations, financial behavior, fraudulent patterns, or edge cases that are hard to find in real data.
• Synthetic Data Replication for Safe Distribution and Use: Train Boltz AI™ on private, proprietary, or licensed data to generate datasets that replicate the joint distribution of the original—without exposing the source.
• Data Labeling: Generate realistic labeled datasets for training and evaluation when real-world labeled data is limited.
• Data Augmentation: Expand small or imbalanced datasets by introducing statistically valid variations to improve model generalization.
• Privacy Preservation: Replace sensitive records with statistically similar synthetic data to comply with regulations like HIPAA or GDPR.
• Filling Gaps & Imputation: Complete missing records or generate entirely new datasets when data is incomplete.
• Prototype and Development: Generate realistic synthetic data from limited or external sources to prototype products, dashboards, or models—even before real production data is available.

Whatever the use case, Boltz AI™ helps you achieve your goals with realistic synthetic data.