BlueGen.ai Reviews & Product Details

BlueGen.ai is a synthetic data platform for organisations that cannot freely use or share their real data, such as hospitals, energy companies, statistics offices, universities and banks. It generates privacy-safe synthetic data from tabular, time-series, relational and longitudinal datasets so teams bound by GDPR can share data for research, build and test software, and train machine learning models without exposing the individuals behind the data. BlueGen is a TU Delft spin-off based in the Netherlands. The generation model learns from complete individuals. Every variable linked to a person, household or transaction goes into the model together, so it picks up how those variables relate. It then generates new individuals instead of masked or shuffled copies of real records. An analysis or model built on BlueGen synthetic data is designed to reach the same conclusions as one built on the real data, and the evaluation report shows whether it does. That report compares the synthetic data with its source on three fronts. Resemblance covers distributions, relationships between columns and missing-value patterns. For utility, the report runs the actual analysis or model on both datasets and compares the outcomes. The privacy section measures the risk that someone could recognise an individual from the source data or infer their attributes. For a survival analysis, for instance, the report shows the Kaplan-Meier curves and hazard ratio table for real and synthetic data. BlueGen handles the data structures common in healthcare, energy and the public sector: flat tables, time series such as smart meter and sensor readings, panel and longitudinal data with irregular measurements, and relational datasets with multiple linked tables, including combinations of these. The pipeline is built to preserve referential integrity across tables, survey skip patterns, the order of dates, hard constraints between variables, nested category hierarchies and columns with thousands of distinct categories. Typical use cases range from research and analytics to software testing and AI training. Research teams share synthetic data internally or with external analysts and can apply their findings to the real data afterwards. Development teams get representative test data that includes realistic outliers and invalid records, rule-based fields such as names and ID numbers, conditioned scenarios, and millions of rows for performance testing. An API supports automated test pipelines. Data science teams rebalance classes, generate more varied examples of rare cases and fill missing values representatively, which helps meet the accuracy and fairness requirements the EU AI Act sets for high-risk models. At Schneider Electric, adding BlueGen synthetic data to the training set raised prediction accuracy by more than 10%. BlueGen can run on-premise as a Docker container on your own infrastructure, with no internet access to the container, operated by your own team or by BlueGen through secured remote access. A secured environment hosted by BlueGen is also available. Organisations working with BlueGen include LROI, TU Eindhoven, the University of Amsterdam, IQVIA, Digital Dubai, CBS, EDF, Alliander and Schneider Electric.