Synthetic data generation

What is synthetic data ?

Synthetic data is information generated artificially by algorithms or computer processes, rather than collected from real-world sources. These data are designed to mimic the characteristics of real data, and are used in a variety of contexts, including artificial intelligence (AI) and machine learning.

Synthetic data uses machine learning to artificially generate new data, rather than altering or modifying real-world data.

What are the advantages of using synthetic data ?

  • Data quality
  • Scalability
  • Ease of use
  • Bias
  • Confidentiality
  • testing and validation
Data quality

Synthetic data offers superior quality by accurately simulating the characteristics and behaviors of real data. By controlling the variables and scenarios generated, researchers and developers ensure that their models are exposed to representative, high-fidelity data, significantly improving the performance and reliability of final applications.

Ease of use

Our synthetic data generation platform has been designed to be accessible and easy to use, enabling even non-specialist users to create customized data sets.


By using data that mimics the behavior of real data without exposing sensitive information, organizations can embark on ambitious AI projects while complying with strict data protection regulations and preserving user trust.


Whether for testing specific scenarios or training models on large, complex variations, synthetic data can be adjusted on demand to meet changing project requirements.


By carefully controlling the generation parameters, it is possible to create balanced, diverse data that fairly represents different populations and scenarios.

Testing and Validation

They enable systems and algorithms to be tested under a wide range of conditions and scenarios, including extreme or rare cases, which is essential for validating the reliability and performance of AI systems.

The different types of data




Time series


Quality assessment