Data is fragmented
Healthcare information is split across institutions, which makes broad analysis and collaboration difficult by default.
Healthcare collaboration without compromising data privacy
There is enormous value in healthcare data, but it is fragmented across institutions and is too sensitive to freely share. Onai enables data collaboration without data exposure.
The problem
Healthcare data is one of the most powerful resources for improving outcomes, discovering treatments, and advancing research
But it is also one of the most constrained.
Data is siloed across hospitals, research institutions, and organizations. Using it together typically requires centralization, moving sensitive data into shared environments, navigating legal overhead, and accepting significant privacy and security risks.
As a result, many important questions are never answered, and many potential insights remain out of reach.
Centralization creates overhead
Pooling data typically means legal complexity, operational friction, security challenges, and long delays before useful work can even begin.
Privacy creates false tradeoffs
Organizations should not have to choose between keeping data private and unlocking its value.
Our approach
Use all healthcare data without exposing it
Onai enables computation across distributed datasets while keeping the underlying data private and under the control of its owner.
Instead of moving data into a central location, data remains within each institution; queries, models, and computations are executed securely across sites; and results are aggregated without revealing raw data.
This allows organizations to collaborate as if their data were unified without ever centralizing it.
Data remains local
Each institution keeps custody and control of its underlying healthcare data.
Computation runs across sites
Queries, models, and analysis workflows execute securely across parties.
Ensures security through cryptography
Sensitive data is never exposed to any participant or any third party.
What this enables
Unleash AI without sacrificing privacy
Multisite data analytics
Run analyses across multiple institutions as if working on a single dataset without moving or exposing underlying data.
Multisite model training
Train machine learning models on distributed datasets, improving performance and generalization while preserving privacy.
Secure inference with external models
Use models from third parties without exposing sensitive data, or deploy models across institutions without revealing the model itself.
Case study
Cross-institution analytics without data sharing
A group of institutions wants to understand patient outcomes across populations, but cannot pool their data due to privacy, regulatory, and operational constraints.
Using Onai's approach, each institution keeps its data locally while participating in a shared computation. Queries are executed across parties, and only aggregated results are returned.
The outcome is equivalent to centralized analysis without any institution relinquishing control of its data.
Who this is for
Built for organizations that need healthcare data to work together
01
Researchers
Collaborate across institutions, access larger effective datasets, and run analyses that would otherwise be infeasible without navigating complex data-sharing agreements.
02
Health systems
Participate in a broader ecosystem where data can be utilized securely across organizations, enabling new forms of value, insight generation, and data-driven marketplaces.
Bring all healthcare data into play securely
Onai is building the infrastructure that allows healthcare data to be used collectively, without compromising privacy or control.
Contact us at [email protected] if you're interested in collaborating.