Insider Brief
- OpenMatter Network has expanded its platform with new capabilities for secure AI model management, application development, privacy-preserving machine learning and data collaboration.
- The additions include MatterSDK, MatterVault, Model Router and MatterML V2, giving developers and enterprises new tools for managing credentials, AI models and sensitive data.
- MatterML V2 supports multi-party machine learning without exposing underlying data, while Model Router provides centralized management of AI models from multiple providers.
PRESS RELEASE — Less than three months after its commercial launch, OpenMatter Network today announced a significant expansion of the 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. More importantly, they demonstrate one of the founding principles of the company: the platform is not a fixed solution for today’s computing environment, but an extensible Verification Architecture — a cryptographic foundation that validates what happened without controlling how it happened — capable of incorporating new technologies and capabilities as enterprise computing continues to evolve.
“When we launched OpenMatter in June, we weren’t launching a finished destination,” said Renee Davis, CEO & 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. 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 also introduced MatterML V2, a major advance in OpenMatter’s privacy-preserving computing capabilities. 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. This release comes with a 1000x increase in efficiency according to early benchmarks performed by the OpenMatter cryptography team.
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.
“These aren’t isolated features being bolted onto a platform,” Davis said. “They are examples of what an open Verification Architecture makes possible. Customers should be able to take advantage of new models, new cryptographic techniques and new ways of collaborating without having to replace the foundation underneath them every time computing changes.”
That ability is particularly important as AI develops at a pace that makes traditional technology refresh cycles increasingly impractical. New models, providers, security challenges and computing approaches are emerging continuously, making adaptability itself an important enterprise requirement.
OpenMatter was designed around that reality. Its underlying platform separates verification from the individual applications, AI models and infrastructure operating above it, allowing new capabilities to be introduced while maintaining a consistent cryptographic foundation for protecting data and verifying computation.
“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, visit https://www.openmatter.network/.
