Proportion Fairness
We developed a sample-based approach to proportion fairness which utilises our pre-trained synthetic data generator. The platform takes in a target number of samples, along with target proportions for multiple variables before creating a more robust data set that can be used for insights or to retrain models. Build on a foundation of balanced data.
Proportion Fairness
We developed a sample-based approach to proportion fairness which utilises our pre-trained synthetic data generator. The platform takes in a target number of samples, along with target proportions for multiple variables before creating a more robust data set that can be used for insights or to retrain models.
Greater insights with smaller samples
With this approach our Proportion Fairness technology is capable of scaling research data to allow for more robust insights into public opinion as well as product and market research.
Polling Agencies
Create larger robust populations allowing you to deliver more accurate predictions at a lower cost to clients.
Market Research
Scale and augment your responses to ensure decisions are driven by fully representative data.