OpenMatter Network is announcing a significant expansion of its platform with new capabilities that make it easier for enterprises, developers, and researchers to build, deploy, and collaborate using sensitive data and AI while maintaining cryptographic control over how information is accessed, computed, and shared.
The new capabilities, available now as part of the commercially available OpenMatter Network platform, span secure application development, AI model management, privacy-preserving machine learning, and data collaboration, according to the company.
“When we launched OpenMatter in June, we weren't launching a finished destination,” said Renee Davis, CEO and co-founder. “We were establishing an architecture designed to grow with the needs of our customers and with the rapid changes taking place across AI and secure computing. These additions demonstrate how quickly we can extend the platform while preserving the cryptographic foundation everything is built upon.”
Among the most significant additions is MatterSDK, a new client layer that gives developers streamlined access to MatterChain capabilities while providing a foundation for building applications across the OpenMatter environment, the company said.
MatterSDK incorporates MatterVault, which uses threshold cryptography to protect API keys, credentials, and other secrets. Rather than storing a complete key in one location, MatterVault distributes key shares across multiple parties so no single machine can decrypt information on its own. MatterSDK gives developers streamlined access to this protection without requiring specialized cryptographic expertise.
For customers deploying AI across multiple providers, OpenMatter has added Model Router, providing a single gateway through which organizations can manage access to models from providers including OpenAI, Anthropic, Google, and self-hosted endpoints for on-premise deployments.
Organizations can establish routing rules, change models without redeploying applications, rotate provider credentials centrally, and see how different models are being used. Provider keys remain protected rather than being placed directly into individual AI agent environments, reducing exposure if an agent is compromised.
The company is also introducing MatterML V2, a major advance in OpenMatter's privacy-preserving computing capabilities, said the company. MatterML V2 enables multiple organizations to jointly train or run models across combined information without requiring any participant to expose its underlying data to the other organizations or to the computing infrastructure. Significant performance improvements enable secure multi-party computation through a graphical interface rather than specialized cryptographic programming, allowing analysts to execute complex privacy-preserving workflows without writing code.
Communities extend that model by enabling research groups, scientific organizations, and other member-led groups to organize around datasets, establish different levels of privacy, discuss and evaluate information, and govern what their communities endorse. The result is an environment designed to increase the usefulness of valuable information without requiring its owners to give up control of it, said the company.
“The future of computing is going to keep changing,” Davis said. “No one can tell an enterprise today exactly which AI models, computing environments or security challenges it will face three years from now. What we can give them is an architecture that is ready to evolve with that future.”
For more information about this news, visit www.openmatter.network.