Data cannot be fully utilized
Privacy, regulatory, and organizational constraints prevent many valuable datasets from being shared or centralized.
AI that runs across systems
Modern AI is powerful, but it depends on centralizing data or trusting a single provider. Onai enables models to be trained and used across distributed systems without exposing data or relinquishing control.
The problem
Today's AI infrastructure is fundamentally centralized
To train models, data must be aggregated into a single environment. To use models, users must send sensitive data to model providers.
As a result, much of the world's data cannot be used effectively with AI, and many applications are blocked by confidentiality concerns.
Inference requires trust
Sensitive inputs are exposed to external systems, which blocks adoption in settings where confidentiality actually matters.
Our approach
Train anywhere. Run anywhere. Expose nothing
Onai enables AI to operate across distributed systems while preserving privacy for both data and models.
This creates a system where data, models, and computation can interact without requiring trust in any single party.
Distributed training
Models can be trained across multiple datasets without centralizing data.
Private inference
Inference can be performed without revealing query data to the model owner.
Protected model deployment
Model providers can deploy models without exposing the model itself.
What this enables
New AI workflows without centralization
Training
Privacy-preserving distributed training
Train models across datasets that cannot be shared or centralized, improving performance while maintaining strict data boundaries.
Inference
Confidential inference
Run queries against models without exposing sensitive input data to the model provider.
Models
Model privacy and protection
Allow model owners to deploy and utilize models without revealing model weights or intellectual property.
Network
AI as a network
Application developers can build on a distributed network of models, accessing capabilities across providers rather than relying on a single centralized API.
System in practice
A network for private AI computation
Onai's infrastructure connects data holders, model providers, and application developers into a shared system.
Over time, this forms a new kind of ecosystem where models, data, and compute can be accessed and utilized across participants, without requiring centralization.
Data
Data remains with its owner
Data holders keep local control while still participating in training and inference workflows.
Models
Models are deployed across a distributed network
Providers can make capabilities available across counterparties without collapsing everything into a single host.
Queries
Queries execute securely
Requests run without exposing sensitive inputs or forcing users to trust a centralized model provider with raw data.
Results
Privacy guarantees stay intact
Outputs return with the confidentiality properties of the workflow preserved for both data owners and model providers.
Who this is for
Built for the parties that need AI across boundaries
Application developers
Build applications using powerful models without requiring users to send sensitive data to centralized services.
Model providers
Deploy models in a way that preserves intellectual property while expanding access and usage across a broader network.
Data holders
Participate in AI workflows, training and inference, without exposing underlying data.
A new foundation for AI
Onai is building infrastructure that allows AI to operate across boundaries, unlocking data, protecting privacy, and enabling a more open and collaborative ecosystem.
For more information, meet us.