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Product Overview.

Synthetic data.
Real results.

Hazy is the synthetic data platform of choice for financial institutions that want to conduct sophisticated data analysis without compromising safety or speed.

User Reviews.

User Review

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About the Company.

Helping businesses unlock the potential in their data

Hazy is the most advanced and experienced synthetic data company. They accelerate innovation while increasing privacy at some of the world’s leading financial institutions. And they have fun and (sometimes) wear purple doing it.

They were founded in early 2017 by Harry Keen (CEO), James Arthur (CTO emeritus), and Dr. Luke Robinson (CSO). With one foot in big business and the other in academia, Hazy stands on the belief that privacy by design should not slow down innovation.

Originally a UCL AI spin-out, London-based Hazy was initially incubated by Post Urban Ventures and CyLon cybersecurity accelerator. Their startup began trying to fix the flaws of traditional data redaction and then data anonymization. They soon discovered anonymized data will always pose a risk to re-identification.

They looked at the opportunity in applying machine learning to synthetic data, which starts with the basis that with no real data there is no real risk. They pivoted their focus completely to AI-generated smart synthetic data and have worked to make it both differentially private and statistically equivalent. They quickly discovered that the organizations that would most benefit from smart synthetic data are highly regulated large enterprises with legacy architecture.

Other Products.

Hazy is one singular product. The product offers each of the following for your convenience:

Enterprise-class capability: A genuinely enterprise-class capability and track record of successfully enabling real-world enterprise data analytics use cases.

Time series data support: Advanced generative models that can actually preserve sequential and causal relationships in transactional and time-series data.

Formal privacy guarantees: Formal differential privacy guarantees that can be configured on a use-case-by-use-case basis to optimize the privacy vs utility trade-off.

Product Screenshots.

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