Open Models
- Models can be trained and used across distributed environments
- Sensitive data remains private throughout the process
- Results can be verified without revealing underlying inputs
This allows AI to operate in environments where trust and privacy are essential, leaving users in control.
Privacy-Preserving Systems
With techniques such as mixnets, homomorphic encryption, and more, Onai enables privacy across multiple domains:
- Communication
- Analytics
- Model training
- Inference
- Payments
Verifiable Computation
Onai advances zero-knowledge proofs so computation can be trusted without being exposed.
- Prove that a computation was performed correctly
- Verify results without accessing underlying data
- Enable private, verifiable payments and transactions
Verifiability and privacy can coexist.
Open Marketplace
By enabling an open network,
- Anyone can access and provide models and services
- Data providers can contribute without exposing sensitive information
- Participants can exchange value through integrated payments
This allows innovation and value creation to happen across a broad set of participants.
Large-Scale Distributed Computation
Onai enables computation at scale across distributed systems.
- Leverage idle and non-traditional compute resources across environments
- Run complex workloads without centralising infrastructure
- Make advanced computation accessible beyond large centralised providers
This opens the door to a more inclusive and scalable model of computing.