innovation on confidential data
Build machine learning models and data pipelines without compromising privacy
Pryml is a secure way to deploy applications
Banking and finance
Banks and financial institutions are highly regulated environments in which maintaining ownership of the data to be analyzed is essential to the business. Pryml allows machine learning on data that you will always own.
Private analytics for healthcare
In fields like healthcare and pharma, analytics is a more and more prominent activity where big data carries industrial secrets and sensitive information. With Pryml your industry moves, but your data doesn’t.
Data stays the way it was always meant to be: private.
Insights from private data
Organizations that outsource analytics for their business challenges run a high risk of disclosing data that is meant to stay private.
Encryption and obfuscation are not always practical solutions for computationally complex tasks like training machine learning models
Data that cannot be published usually hold essential signals for a particular business case. Providing a synthetic version of such signals represents the first step of the pryml approach to private information
Private data sources can be queried, searched and collected as long as regulations and protocols are not violated
Data scientists and engineers can securely deploy their pipelines as long as they do not compromise privacy and confidentiality