Rocket^® EVA^™

Rocket EVA^™ is designed for core business systems, including mainframes and other mission-critical environments. It is optimized to work with complex, high-volume operational data where reliability and depth of insight matter most.

Rocket EVA^™ stands out by combining industry-leading data scanners with built-in mainframe expertise and AI-driven conversational intelligence. Unlike other MCP or AI solutions, it delivers the broadest and deepest visibility across core and hybrid systems, provides actionable recommendations through Analyzer and Recommender layers, and supports cost- and performance-optimization initiatives—all while fitting seamlessly into existing environments and modernization strategies.

No. Rocket EVA^™ is designed to abstract complexity and democratize access to system insights. Developers, operators, and support teams can interact with systems using plain language, regardless of prior mainframe experience.

Rocket EVA^™ complements your existing tools rather than replacing them. It acts as an intelligent conversational layer on top of your environment, helping teams quickly understand and act on information without switching contexts.

Rocket EVA^™ integrates directly with existing systems and workflows, preserving stability while introducing AI-driven intelligence. This allows organizations to modernize operations incrementally—without risky rip-and-replace initiatives.

Yes. Built on the open MCP standard and designed for extensibility, Rocket EVA^™ is well-positioned to support emerging AI agents, tools, and hybrid architectures as enterprise AI adoption continues to grow.

Model Context Protocol (MCP) is an open, standards-based framework that lets AI models securely access, interpret, and reason over enterprise systems and data. Rocket EVA^™ builds on MCP with the industry’s broadest and deepest system coverage, powered by Rocket’s advanced data scanners. This ensures AI doesn’t just connect to your environment—it understands it, enabling more accurate diagnostics, meaningful recommendations, and seamless integration with both mainframe and hybrid systems without disrupting existing workflows.