Exhibitor Products

Multiple organizations, departments, or sovereign partners can collaboratively train, refine, evaluate, and deploy models while keeping their underlying datasets within their respective environments.

The capability supports AI use cases where broader or more diverse data can improve model performance but privacy, security, sovereignty, or regulatory requirements prevent conventional centralized training.

Applications include collaborative defense AI, fraud and financial crime models, healthcare research, life sciences, enterprise AI, and other environments where sensitive data is distributed across multiple owners.

Duality combines technologies such as federated learning, confidential computing, homomorphic encryption, and policy-driven controls to protect data and model interactions throughout collaborative AI workflows.

Organizations gain the benefit of broader collective intelligence while maintaining control over sensitive data, models, and infrastructure.