Enabling models to learn from data across organizations,
San Francisco, California, United States
Ntropy is building a data layer that will enable models to access multi-organizational data at scale in the same way as training on a single dataset. This has only been made possible in the last few years, through advances in manifold learning algorithms and privacy-preserving computing.

Enabling machine learning models to train on data across organisations with minimal privacy risk and engineering overhead would unlock enormous benefits for all parties involved. Such a model can be significantly more accurate and robust to outliers than one which only has access to a single dataset. It would allow companies to build better products and open up new markets, doctors to make better patient diagnoses, governments to make better policy decisions in unprecedented times, drug companies to find better cures for diseases, businesses to have better access to financial resources they need and so much more.
Why Work With Us
Over the last few decades, progress in technological innovation has relied on democratising access to some of its key ingredients: knowledge (open publishing platforms), algorithms (code repositories) and computing (cloud providers). A new component is becoming increasingly critical for both large and small players. Data. The vast majority of valuable data today sits in silos, trapped behind barriers of regulation, privacy, schema standards and competitive risk.
Founding Team

Ilia Zintchenko

Co-founder & CT0