A revolutionary approach to leveraging large language models and generative AI privately, securely and with complete autonomy.

Build better and more accurate models with secure, compliant access to sensitive data. Secret Computing® eliminates the tradeoff between data privacy and usability.




Which are you?

We speak your language

More Data Helps Build Better Models.

Yet, much of it is often locked away from ML initiatives due to privacy and security risks. Secret Computing® empowers data teams to leverage sensitive data securely and build accurate models.

Train models with more features or more samples by leveraging sensitive data across multiple privacy zones – from within or across organizations – without guaranteeing localization and privacy-preservation.

Access data from your notebook and popular ML frameworks. Our Python library and REST API enable advanced cryptographic functions with minimal code changes.

Secure your models from various privacy attacks such as inference and reconstruction. Unlock multiple ML use cases like secure federated learning and encrypted model serving for compliant data processing.


  • “Working with Inpher to leverage their technology to access non-public data sources aligns with our objective to enhance our informational edge and generate value for the CPP Fund over the long run.”

    Daniel Wroblewski
    Daniel WroblewskiManaging Director, Alpha Gen Lab, CPP Investments
  • “We are excited to be partnering on a proposition that will deliver a secure, end-to-end consented data sharing solution leveraging Inpher’s patented technology, enterprise-ready platform and expert team for the next wave of innovation.”

    Danny Tyrrell
    Danny TyrrellCofounder, DataCo Technologies
  • “The ‘bad guys’ have all the same technologies we do. But the one thing they cannot obtain is the scale of training data we could through collaborative sharing, such as through the potential Inpher offers.”

    Hays W. “Skip” McCormick
    Hays W. “Skip” McCormickData Science Lead BNY Mellon


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