Israeli artificial intelligence startup Datomize raises $6 million for its real-time codeless machine learning platform

There is a massive data shortage caused by data privacy laws that are limiting the effectiveness of the development and training of AI/ML models. According to the Gartner Hype Cycle for Privacy released July 2020, authored by Bernard Woo and Bart Willemsen, “One of the major problems with AI development today is the burden in obtaining real-world data and labeling it so that AI may be trained effectively. Synthetic data addresses the problem of volume and variety for sparse, nonexistent or difficult to get data.”

Datomize is a Tel Aviv, Israel-based startup that produces synthetic data so that financial organizations can use users’ data without compromising their privacy. Datomize liberates the use of sensitive data in order to reap its value, without compromising privacy.

The startup solves this problem by synthesizing new data that preserves the behavioral features of the original data without violating personal privacy regulations. Designed based on real customer data and insights from global banks, Datomize is uniquely able to process highly complex data structures with multiple dependencies and is fully scalable to process thousands of tables with millions of records. Datomize improves the efficiency and speed of developing and training AI/ML models and applications for hundreds of use cases, as companies become more data-driven in the new online economy.

Today, Datomize announced it has closed a $6 million seed funding round for the commercialization of its synthetic data solution that accelerates time to market for artificial intelligence (AI)/machine learning (ML) models and new products that drive business growth. This funding round was led by TPY Capital, as part of its thematic focus on groundbreaking data and analytics startups, with participation from its first investor F2 Venture Capital, which backs visionary big data and AI companies.

Founded a year ago by Avi Weiss, Sigal Shaked, Roy Yogev, Datomize’s synthetic data solution accelerates and streamlines the development, training, and testing of AI/ML models and applications that drive business growth. Avi Weiss, the CEO, was previously the co-founder of ObserveIT before it was acquired by Proofpoint.

Datomize preserves the behavioral features of the original production data without violating personal privacy regulations, to speed up time to market for new products and services and rapidly generate insights that drive digital transformation. Providing the speed and scale required by global financial institutions and highly regulated industries, Datomize processes highly complex data structures with multiple dependencies and is fully scalable to process thousands of tables and millions of records

“Datomize’s synthetic data revolutionizes the AI/ML and IT lifecycle by removing the major bottleneck that prevents the successful deployment of AI/ML models and continuous delivery of evolving applications,” said Avi Weiss, Founder and Chief Executive Officer (CEO). “Datomize makes the generation and management of synthetic data simpler, more effective, and efficient, so that highly trained and hard to find data scientists can focus on analysis and strategy.”

“Datomize has the potential to enable enterprises to unlock the value of one of the most prized assets – their data. It eliminates one of the biggest concerns of executives and regulators, jeopardized privacy, by producing synthetic data which can be used for multiple business applications,” commented Dekel Persi from TPY. “We are particularly happy to back a founding team which combines a successful entrepreneurship track record with technological depth.”

Jonathan Saacks from F2 commented, “We are backing Datomize because they have the right solution at the right time. They are supplying exactly what organizations need to accelerate their digital transformation initiatives. Designed using real-world data from international banks, Datomize has the ability to unlock the value organizations can receive from their data with the scale and speed global organizations need so that their AI/ML models are more powerful, accurate, and can become fully operational faster.”