Why We Invested in Datomize
For several years, TPY has had a strong conviction about the future of enterprise data. We believe that the volume and complexity of data will continue to increase exponentially. That this data can and should be used to expand business activity. And that currently, doing so could pose privacy and compliance challenges.
As a result, we’ve been systematically scanning solutions which could solve the following question:
How can you enable organizations to extract the most out of their data assets and stay in the clear?
To do that we’ve been exploring the AI workflow stack from data preparation, pipeline, modeling, monitoring, and analyzed the limitations that exist today. The quality of output of AI algorithms depends on the quality of the data they were fed with and trained on, so it is crucial that the data preparation sample is an accurate representation of real-world life. Highly regulated industries are restricted from using personal identifiable information (PII), and with the rise of privacy laws (such as GDPR, CCPA), AI capabilities are inhibited.
Alternative techniques have certain limitations compared with synthetic data generation
Our team examined several technologies attempting to solve the issues described above. These include homomorphic encryption, anonymization, and synthetic data. While synthetic data can’t and won’t solve for every single privacy related use-case, it is likely to emerge as the most accurate and secure solution to address AI modeling and QA testing for customers trying to navigate the issue of data privacy and security.
Datomize: a solution democratizing the use of private information and unlocking the potential for new business opportunities
Datomize generates high-quality synthetic data from existing datasets, while eliminating the risk of breaching sensitive personal information. One can think of it as a ‘digital twin’ of the data — on which you can work and run analyses, without exposing data you should not. Datomize provides its customers with an engine that takes their sensitive data and turns it into a synthetic replica, ready to be fed into AI/ML models.
Synthetic data is arguably the best solution today for use-cases where existing (real-world) data cannot be used or is not available to the required extent. Datomize’s platform synthesizes the existing dataset into a simulated, unidentifiable dataset for business applications that include development and training of AI models, QA testing, internal and external data collaboration.
The founding team balances a successful entrepreneurship track record with technological depth
Avi Weiss, the co-founder and CEO of Datomize, impressed us with his experience, transparency, and passion for the problem Datomize is solving.
As we dug further, we’d met with the broader founding team, including Dr. Sigal Shaked (CTO) and Roy Yogev (VP BD & Marketing). We liked the breadth of the perspectives they brought to the table. Dr. Sigal Shaked has an extensive background in the field, both in academia and in industry. Roy Yogev brings experience working with Fortune 500 companies as well as channels like Wipro and other IT service providers, which are important for Datomize’s go-to-market strategy.
We’re glad to lead this funding round, with the participation of Datomize’s existing investors F2 Venture Capital and are eager to see how this team revolutionizes how regulated industries collect, apply, and share data while protecting our privacy.
