Synthetic Data
Artificially generated data that mirrors real-world data, produced using statistical techniques and AI methods through deep learning and generative models. It preserves the key statistical patterns and relationships of the original data. Because of this, it is used to supplement real datasets when it is scarce or privacy is a huge concern.
Privacy and Regulatory Compliance
Synthethic data removes personal and sensitive information while preserving statistical structure. This enables model development, testing, and sharing without exposing PII or breaching GDPR regulations.
Edge Cases
When real data is dominated by normal cases and critical failures are underrepresented, synthetic data allows intentional creation of rare, extreme and high risk scenarios to train and stress test models.
Speed, Scale and Cost Efficiency
Collecting and labeling real data is slow and expensive. Synthetic data can be generated on demand, fully labeled and scaled instantly to accelerate model iteration.
Bias Control
Real data reflects historical bias and uncontrolled distributions. Synthetic data enables deliberate balancing, controlled feature relationships, and fairness testing across populations.
