Go Synthetic! for
Big Data Privacy
Unlock your big data assets, while keeping individuals' privacy 100% safe & secure.
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The Synthetic Data Revolution

Advances in machine learning enable simulating synthetic populations, that resemble the characteristics as well as diversity of actual people. For the first time, rich personal data assets become available at scale for many industries, that can be freely processed, analyzed and shared further.

Finally, big data & privacy protection are both possible!

The World's Most Advanced
Synthetic Data Engine

Mostly AI has developed a game-changing new technology for synthetic data generation, resulting in two uniquely powerful products.

Mostly GENERATE enables you to generate an unlimited number of highly realistic & representative synthetic customers, matching the patterns and behaviors of your actual customers at an unprecedented level. As synthetic data is anonymous and exempt from data protection regulations, this opens up a whole range of opportunities for your otherwise locked-up data, resulting in faster innovation, less risks and less costs.

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Mostly SIMULATE creates a single universal model for your customer's future behavior, giving you access to not only an individual predictive target (e.g. customer churn) but to the rich context of your customer's future. As an additional benefit, the model allows you to simulate hundreds of possible futures - per individual customer - resulting in  highly informative probability distributions instead of only point estimates. As you can query these simulations in exactly the same way as your historic customer data, predictions become easy to explore. Thereby, Mostly SIMULATE enables you to deeply understand your customers and to quickly identify valuable business opportunities.

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Companies That Trust Us

Use Cases

The accuracy of our Synthetic Data Engine goes far beyond what is known as mockup or dummy data. It enables you to share highly accurate, yet anonymous data at scale, with a broad range of internal as well as external partners, without putting your customers' privacy or your reputation at risk.

AI Training & Analytics

Advanced analytics, machine learning and artificial intelligence require broad data access, and new emerging tools & infra. Provide data at scale and with peace of mind in non-prod environments to your data scientists and AI engineers alike.

Predictive Analytics

Up to this point, every predictive target (e.g. customer churn) needed its own model. Now universal consumer behavior predictions are possible, where a multitude of predictive scenarios can be explored easily, which allows you to quickly identify new business opportunities.

Testing & Development

Accelerate the development, testing and integration of your next-generation, data-driven products and services by providing as-good-as-real data to your developers and testers.

Open Big Data & Innovation

Data is the new oil. So, fuel your innovation by broadly sharing granular level data with researchers, startups and innovators alike, increasing the chances for disrupting breakthroughs.

Customer Centricity

Restricting data access to a handful of engineers prohibits broader customer understanding. Establish a modern data-driven culture by openly sharing representative data at all levels to boost customer-centricity.

User Experience Design

Digital products only come to life with data. Thus, it is pivotal to provision highly realistic data, in all its diversity, to product owners and designers in order to deliver an intuitive, relevant user experience to your customers.

Vendor Validation

A range of SaaS providers offer compelling new solutions, but require data to be ingested for test-driving their services under realistic conditions. Provide synthetic in lieu of actual data to safely experience these new offerings in action.

Data Monetization

Share your data assets without putting privacy at risk, either directly or via data marketplaces. Synthetic data can be offered at its fullest detail, and thus delivers much higher value compared to existing anonymization techniques.

The Underestimated Risk of Masking

Masking or obfuscating of only a few data points, while leaving everything else intact, does not protect against de-anonymization. This risk is fully understood within the privacy community, but is commonly underestimated by decision makers.
The only way forward for risk-free innovation on top of sensitive data assets is synthetic data!
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