HyperReal: The ultimate synthetic data generator

Leverage synthetic data to enter the data age!

Generate high quality synthetic data sets with a few mouse clicks

With HyperReal you can analyze production data and extract condensed statistical signals from it, including relationships between different entities. A powerful toolbox of methods lets you define the desired properties of your synthetic data sets. With the easy and intuitive GUI you can create arbitrarily big data sets for your development and test systems.

Your synthetic data management life cycle
in three simple steps:


Let HyperReal analyze your production data sets and automatically create data profiles:

  • capture the essential signals in your data
  • condense these signals with mathematical precision
  • preserve authenticity and key properties from your source data


Define exactly how much you want to change your source data. Tweak and mould your data profile in any way you want.  Correct for biases, introduce drift and model new future scenarios.

  • introduce drift, trends and noise to make your models more robust
  • create anomalies so that your models can learn from rare events
  • de-bias your data to ensure fairness in your AI predictions


Generate data sets of any size in any kind of environment in no time. Share specific data sets with select partners and stakeholders.

  • generate data sets of arbitrary size: scale-down or scale-up as needed
  • connect to all types of data stores and existing workflows and tools
  • use custom generators to make generation super easy

Design any synthetic data set you want

Random data sets contain no useful information anymore. They are virtually worthless. In order to extract the most value from your production data, you need to design your desired data set properties in a targeted manner.

Synthetic data is not random!

HyperReal lets you freely change you data sets as much as you want: shift, stretch and skew all attributes and preserve all signals that you need. Tweak and adjust your generated data sets to cover all scenarios.

HyperReal lets you shift, tweak and nudge your synthetic data set any way you need it. That way you can decide what key signals you want to keep and what you want to change to either protect privacy or to reflect potential future business scenarios so that your Machine Learning and AI models will still work and give you high-quality predictions.


Powerful Methods

  • Sample data from many types of random distributions
  • Generate data from predictive models based on production data
  • Use fictitious names, places and product
  • Keep relationship structures intact (referential integrity, foreign keys)
  • Scale data sets up and down as needed (up-/down-sampling, subsetting)
  • Apply conditional probabilities to realistically model data properties
  • Generate time series and other sequential data structures

Enterprise IT Ready

  • Read from all types of databases and data stores
  • Write synthetic data to blob storage in your data lake or SQL databases
  • Authenticate users with enterprise directories via LDAP and OAuth
  • Deploy HyperReal on-prem and in the cloud
  • Container packaging simplifies software lifecycle management
  • Integrate with existing workflows and tools via RESTful APIs
  • Keep a complete history of all generated data sets

No-code User Interface

  • No code environment eliminates need for scripting effort
  • Enable data sharing throughout your organization and with third parties
  • Intuitive interface enables a broad user group to use as self-service
  • Generate data with custom interface tailored to your business reality
  • Browse and compare statistical properties of synthetic data sets
  • Manage data generation and run history
  • Create many different variants of synthetic data set to mirror business scenarios

Achieve more with your data!

Alternative digital synthetic data that are indistinguishable from reality


Get started now!

"*" indicates required fields

This field is for validation purposes and should be left unchanged.