Our technology empowers AI and data teams across industries to unlock valuable insights from sensitive data using the power of privacy-preserving machine learning

Use cases

Collaborative, secure 
healthcare AI

  • Multiple hospitals and institutions have patient data which belong to different cohorts. 

  • AI teams struggle to access this distributed data and deploy algorithms in clinical environments. The primary cause for this lack of access is - patient privacy.

  • With Fluid’s federated learning platform, multiple hospitals and institutions can collaborate to create an AI solution which can learn from these silos of patient data in each of these institutions - at the same time preserving patient privacy

More robust fraud


  • Multiple banks and financial service providers experience different flavors of fraud. These banks are trying to fight fraud with AI systems.

  • AI systems at each of the banks learns from the limited fraud data they have access to. To be able to identify all types of fraud, AI system needs to learn from data aggregated from different banks, but this data is very sensitive and private in nature therefore cannot be shared.

  • With Fluid, multiple banks can aggregate the fraud detection intelligence without exposing the actual data and their IP. Each bank now has a better performing fraud detecting AI system.

Enhanced Drug Discovery

  • AI for drug discovery requires large and diverse datasets for training. However, drug related data is locked in multiple institutions due to their sensitive nature.

  • This can be a hindrance for developing effective AI models, as each silo only represents a fraction of the diversity of molecular data. The AI model cannot generalize well when trained only on one of the silos.

  • With Fluid, AI teams can collaborate with institutions to access different silos to create more effective AI models - which can learn from diverse molecular data without ever exposing the sensitive data itself.