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Home » MAGNE.AI and MarsCat Build End-to-End Web3 Privacy

MAGNE.AI and MarsCat Build End-to-End Web3 Privacy

Partnership Targets Decentralized Privacy Infrastructure

Kelly Cromley by Kelly Cromley
May 8, 2026
in Market News, News
Reading Time: 3 mins read
0
magne ai

MAGNE.AI and MarsCat have announced a strategic collaboration focused on creating a fully decentralized privacy infrastructure for Web3 ecosystems. Revealed on May 7, 2026, the partnership aims to combine secure hardware technologies with decentralized networking architecture to deliver what the companies describe as an end-to-end privacy layer for blockchain applications and digital communications.

The initiative reflects growing concerns within the Web3 industry regarding continued dependence on centralized servers, cloud systems, and third-party infrastructure. While decentralized applications have long promoted privacy and user ownership, many platforms still rely partly on centralized backend systems that may expose users to data vulnerabilities and security risks.

Under the collaboration, MarsCat will reportedly manage the decentralized networking infrastructure while MAGNE.AI focuses on building hardware devices designed specifically for secure Web3 engagement. Together, the companies intend to establish a privacy-focused ecosystem that minimizes centralized points of failure and strengthens user data protection.

MAGNE.AI and MarsCat are collaborating to create an end-to-end decentralized privacy infrastructure combining secure hardware with serverless peer-to-peer networking technology.

MarsCat Expands Serverless Web3 Communication

MarsCat operates as a decentralized application engine and distributed connection network designed to support privacy-first communication without relying on centralized servers. Its infrastructure allows decentralized applications to function through peer-to-peer networking, eliminating the need for central data relays or notification systems.

The platform’s architecture enables users and applications to communicate directly with one another, which may reduce risks associated with centralized infrastructure breaches. MarsCat also utilizes encryption technologies and temporary data storage through its RelayX system to improve communication reliability and strengthen privacy protections.

RelayX has reportedly secured approximately $3 million in funding from investors, including Animoca Brands, Cogitent Ventures, and Cryptogram Venture. Industry observers suggested that the funding support may strengthen MarsCat’s long-term expansion efforts while enhancing institutional confidence in decentralized privacy infrastructure.

The partnership also builds upon MarsCat’s broader strategy of developing strategic alliances across the decentralized cloud computing sector. One such collaboration includes an integration with Origins Network, which combines infrastructure capabilities to support decentralized cloud services.

MAGNE.AI Focuses on Hardware-Level Security

MAGNE.AI has approached Web3 privacy challenges from the hardware side, emphasizing secure devices, on-device artificial intelligence processing, and blockchain-based identity systems. The United States-based technology company develops mobile-centric infrastructure focused on digital identity protection, asset security, and decentralized engagement.

According to the company’s design model, the hardware device itself functions as a core component of the overall security framework. Private keys are reportedly generated and stored inside certified secure hardware environments, while sensitive operations take place within Trusted Execution Environments designed to isolate critical processes from external threats.

MAGNE.AI’s infrastructure integrates hardware-level encryption, Trusted Execution Environments, and on-device AI processing to strengthen mobile Web3 security.

🤝 We're excited to partner with @MarsCat_Global

MarsCat is a P2P decentralized connection network and Web3 application engine powered by its proprietary RelayX protocol, delivering privacy-first, serverless, and scalable communication infrastructure for decentralized… pic.twitter.com/KS4rrgb5Rv

— MAGNE.AI (@Magne_Ai) May 7, 2026


The company also utilizes its MHash Layer-2 network, built on the OP Stack blockchain infrastructure, to support low-latency and mobile-focused decentralized applications. Analysts indicated that combining secure hardware with decentralized networking may help address persistent concerns regarding endpoint vulnerabilities and centralized data transmission systems.

Combined Infrastructure Aims to Eliminate Centralized Weaknesses

The collaboration between the two companies is centered on eliminating weak points that often exist between decentralized applications, devices, and communication layers. MAGNE.AI’s secure hardware infrastructure complements MarsCat’s serverless peer-to-peer network, creating what both firms describe as a unified privacy stack.

MarsCat’s networking system reportedly allows data to leave devices without passing through centralized routing points, while MAGNE.AI’s secure hardware ensures that sensitive information remains protected locally on the device itself. Together, the companies are working toward a decentralized framework covering devices, transport infrastructure, and application-level computing.

MAGNE.AI has additionally expanded its ecosystem through partnerships with ManusPay for AI-enabled payment solutions and Manadia for verifiable data settlements. The company believes its infrastructure addresses a rapidly expanding market involving billions of smartphone users, increasing demand for AI chips, and the growing adoption of Web3 wallets.

The partnership seeks to establish a fully decentralized privacy stack that secures devices, communications, and application infrastructure without relying on centralized systems.

Industry observers believe the collaboration highlights an emerging trend in Web3 development where companies focus on strengthening interoperability and cross-layer security instead of competing across isolated infrastructure segments.

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