OpenMatter Network Expands with New Capabilities for Secure AI, Computing and Data Collaboration

Melbourne, Florida - OpenMatter Network has recently announced significant enhancements to its platform, enabling enterprises, developers, and researchers to construct, deploy, and collaborate on sensitive data and AI while preserving cryptographic control over data access, computation, and sharing. These new capabilities, now available as part of the commercially available OpenMatter Network platform, encompass secure application development, AI model management, privacy-preserving machine learning, and data collaboration.

The company's CEO, Renee Davis, emphasized that the platform is not a static solution for today's computing environment but an extensible Verification Architecture that can incorporate new technologies and features as enterprise computing continues to evolve. This extensibility was demonstrated by the introduction of MatterSDK, a client layer that provides developers with convenient access to MatterChain capabilities and a foundation for building applications within the OpenMatter ecosystem.

A key component of MatterSDK is MatterVault, a solution that employs threshold cryptography to safeguard API keys, credentials, and other sensitive information. Instead of storing complete keys in a single location, MatterVault distributes key shares among multiple parties, ensuring that no single machine can decrypt the information on its own.

In response to the growing trend of deploying AI across multiple providers, OpenMatter has introduced Model Router, a unified gateway that allows organizations to manage access to models from providers such as OpenAI, Anthropic, Google, and self-hosted endpoints for on-premise deployments. Developers can establish routing rules, swap models without redeploying applications, centrally rotate provider credentials, and monitor the usage of different models. By keeping provider keys protected, the risk of exposure is minimized if an agent is compromised.

OpenMatter has also launched MatterML V2, a major improvement to the platform's privacy-preserving computing capabilities. MatterML V2 allows multiple organizations to jointly train or run models using combined data without any participant exposing their underlying data to the other parties or the computing infrastructure. Early benchmarks indicate a 1000x performance improvement for secure multi-party computation through a user-friendly graphical interface, eliminating the need for specialized cryptographic programming.

This release is accompanied by a 1000x efficiency boost, according to the OpenMatter cryptography team.

The company's new capabilities also extend to community-driven models, where research groups, scientific organizations, and other member-led groups can collaborate around datasets, establish different levels of privacy, discuss and evaluate information, and govern what their communities endorse. This environment aims to enhance the value of valuable information while preserving control for its owners.

Renee Davis, OpenMatter's CEO and Co-Founder, highlighted that these features are not isolated additions to the platform but rather examples of what an open Verification Architecture can achieve. She stressed that customers should be able to leverage new models, cryptographic techniques, and collaboration methods without having to replace the underlying foundation with each change in computing technology.

As AI technology rapidly evolves, making traditional refresh cycles impractical, OpenMatter's architecture has been designed to accommodate this reality. Its underlying platform separates verification from individual applications, AI models, and infrastructure, allowing new capabilities to be introduced while maintaining a consistent cryptographic foundation for data protection and computation verification.

OpenMatter is positioning itself as a future-proof solution for enterprises navigating the ever-changing landscape of AI, secure computing, and collaborative data sharing.