Synthetic Data for Software Development.
Gretel for Software Development
- Quality Data at Scale
Backed by multiple generative AI models built specifically for synthetic data, you can scale your data needs as required without compromising on quality or privacy.
- Eliminate Privacy Risk
With tunable privacy filters and models built with differential privacy—complete with mathematical guarantees of privacy, you can rest assured data is safe and secure.
- Increase Speed & Quality of Releases
Synthetic data removes the data overhead spent on manual, low-value tasks, so teams can focus on building better products and increase product release cycles.
From test data management to data pipelines for CI/CD, access to safe, quality data is critical.
Low-quality mock data. Traditional privacy-preserving techniques, such as masking, often provide missing or incomplete datasets.
Sensitive data exposure. Access to production-grade customer data poses a high-risk of sensitive data leaks.
Inefficiencies & time wasted. Expensive internal resources spent in manual, low-value tasks, while overall time to value for a project skyrockets.