HyperReal: Synthetic data for the enterprise

AI-generated synthetic data for Software Development and Data Science, legally compliant and respecting privacy

Real production data and synthetically generated hyperreal data

Can you tell the difference?


Quickly and easily generate data that looks and feels like your actual business.

Generate high quality data in an instant, with a few mouse clicks. Drive your IT initiatives with speed and at low costs, while being fully compliant with legal requirements and building trust with your users and community.

Create Business Value
with AI-generated Synthetic Data

Why synthetic data will become the default for enterprises in the future




Production data only reflects past historical realities. To make sure your predictive models are future-ready they need to train on augmented data that include rare events, shifts in behaviour and without bias.

Speed & Costs

Using production data for software development and AI training is a complex process that incurs costs and takes time. Synthetic data is cheap to produce and requires no legal compliance or supervision from Data Governance. It gives you full agility for your IT initiatives.


Data privacy is not optional. Legal requirements weigh heavily and compliance builds trust. Doing this with production data is hard. Working with synthetic data makes this massively easier. Share data with third parties without any headaches.


Democratize data access. Easy data sharing allows you to overcome silos and break down barriers. Extract value and gain fresh insights from complex and holistic data sets.

Who needs synthetic data?

Synthetic data is key for both software development and data science: they need data that looks and behaves like actual production data – on-demand and without delays.



Software Development

Software developers and testers need data that is as close to production data as possible, except that they must not use production data!

  • Synthetic data respects privacy and is not subject to data protection regulation. It ensures safe operations without legal headaches.
  • Synthetic data can easily be augmented to reflect various business scenarios that need to be considered – from rare events to large-scale data sets. This allows to cover all possible test cases and hardens your software quality.
  • When you introduce new components into your IT landscape, you need to make sure they are fit for purpose. Using synthetic data for your Data Proof-of-Concepts is the safest and fastest way to accomplish that.
  • When moving to the cloud, synthetic data will give you additional safety and add extra layer of protection for your data. Bad actors will never get access to your production data.

Data Science

Data scientists and machine learning engineers build models that learn from data.

  • Work freely with training data: synthetic data is statistically identical to production data and simultaneously fully privacy-respecting. No need to worry about exposing sensitive data.
  • Scientists can tweak and augment synthetic data in multiple ways. This allows them to augment training data, eliminate bias and better detect anomalies. This helps to ensure that predictive models keep giving predictions with optimal accuracy.
  • Creating synthetic data with particular target scenarios in mind allows data scientists to stress-test their models and identify breaking points. This allows to monitor AI in production and select optimal triggers for model retraining.
  • Share and reuse data with different groups and build a common repository of training data.

By 2024, 60% of the data
used for the de­vel­op­ment of AI
and an­a­lyt­ics projects
will be syn­thet­i­cally gen­er­ated.

Can you tell which one is (hyper)real?

Realistic data for all industries.

Companies across all industries are faced with challenges regarding their data sets. Ensuring compliance with data privacy regulation and augmenting data sets for AI training are tasks for every business.

But synthetic data is not random. It must reflect real business scenarios with high fidelity to provide real value. HyperReal lets you generate life-like data for many different settings.


  • Create artificial people, places and products
  • Generate transactions that reflect business interactions
  • Use time series to reflect sequential and temporal relationships
  • Model behaviour to synthesize realistic events

Alternative digital synthetic data that are indistinguishable from reality

Move to synthetic data now!

Get in touch if you want to know how you can leverage HyperReal and start using synthetic data now.

"*" indicates required fields

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