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Reduce cloud costs by 30% by moving your data to the desktop – no cloud, no GPU required.
Our Excel-driven tool generates 1 million rows of data in 30 seconds. This game-changing technology will supercharge your Excel users.
Provide large volumes of realistic data for training machine learning models, overcoming the limitations of scarce real-world data.
Generate anomalous samples to train and test anomaly detection when real data is difficult to obtain.
Create realistic fraudulent examples, balancing the dataset and improving model performance.
Simulate rare or extreme events that may not be well-represented in historical data, enabling more robust risk modeling by accounting for low-probability, high-impact scenarios.
Remove all direct identifiers like names, addresses, and ID numbers from original data, without use of any actual records or personal information.
Allow teams to exchange data that mimics real-world datasets without disclosing any sensitive or confidential information about individuals.
By eliminating the need to handle real the need to handle real data, while preserving its statistical properties, synthetic data offers a powerful solution for data privacy protection while enabling data-driven innovation across industries.
Accelerate your adoption of AI by providing high-quality synthetic datasets for training models related to fraud detection, credit risk assessment, customer segmentation, and more. This is your competitive edge.
By providing an unlimited supply of current, realistic test data, synthetic data generators enable automated generation to be integrated into CI/CD pipelines. This allows for faster feedback loops, automated regeneration, during the development cycle.
Synthetic data is a rapidly growing field that will be crucial for enabling the development of AI and machine learning applications, especially in industries where access to real-world data is limited for many reasons.
SDG can produce massive volumes of realistic test data on-demand. This allows development teams to thoroughly test their applications with test cases, edge cases, and scenarios that may be difficult or impossible to obtain from production data.
