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Big Banks Invest in Generative AI Startup Hazy

The National Building Society, Wells Fargo, and Intesa Sanpaolo became participants in the Series A financing round, during which investment funds were raised for the development of the British developer of synthetic data Hazy.

Big Banks Invest in Generative AI Startup Hazy

Source: Pixabay.com

This round was conducted by Conviction. UCL Technology Fund, M12 (Microsoft), ACT Venture Partners, Terra VC, Evenlode, Logo Ventures, and Sarus Ventures also participated in the funding round. $7,3 million was raised.

Hazy was founded in 2017. Initially, this startup existed as a subsidiary of University College London. The firm uses intelligent synthetic data generated by artificial intelligence. These arrays of information preserve the statistical quality of real data. At the same time, they do not contain real information, as a result of which there is no risk of privacy violations.

Synthetic data can be used as a temporary substitute for real information in the development of artificial intelligence/ML, software testing, and data commercialization.

Hazy has won the $1 million Microsoft Innovate AI prize for the best artificial intelligence startup in Europe. The company has also attracted large clients, including financial services giants Nationwide and Wells Fargo.

Harry Keen, CEO of Hazy, said that the new funding will allow the company to strengthen its status as a leading provider of synthetic data for enterprises that can benefit from inside information, and will allow exploring the greater potential of generative artificial intelligence to produce real business results without privacy restrictions.

The CEO of the startup believes that the development of generative artificial intelligence has the potential to become a catalyst for a revolutionary transformation of various fields of activity and to change the concepts of doing business. The company is currently working on an initiative that will expand the current offering by using some of the latest developments in large language models.

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