Geniez AI: The New Era of Generative AI on the Mainframe

Описание: In this interview, Gil Peleg, longtime mainframe innovator and founder of Geniez AI, explains how Generative AI on the Mainframe is transforming the way enterprises access and use their most critical data. After 27 years in the mainframe world—IBM storage development, client shops, and founding Model9 (later acquired by BMC)—Gil’s newest mission is enabling organizations to connect LLMs and AI agents to real-time mainframe data.

Geniez AI fills a major market gap: while generative AI tools like ChatGPT are everywhere, companies struggle to apply AI to Db2, IMS, VSAM, SMF, and other mainframe data sources. Running directly on the platform, Geniez leverages native security, scalability, Zip engines, Linux on Z, and modern accelerators, eliminating the need to move or replicate data.

A key innovation is their implementation of the MCP protocol, creating a standardized way to integrate LLMs, data pipelines, and AI agents. Customers are already using Geniez to analyze logs and SMF records, detect system issues, identify security vulnerabilities, respond to regulatory changes, and eliminate the need to write new mainframe code to support AI initiatives.

Gil shares that customers describe the experience as “magic”—asking natural language questions about their mainframe data and getting instant, actionable answers. He believes AI and the mainframe will inevitably converge, and Geniez AI will help companies adopt new AI capabilities immediately and securely.

Topics Covered:
Generative AI on the mainframe
Connecting LLMs to enterprise mainframe data
MCP protocol innovation
Mainframe security + hybrid AI architecture
Productivity and modernization
Customer use cases and ROI

Discover more security insights at planetmainframe.com

#GenerativeAI #MainframeAI #GeniezAI #GilPeleg #LLMintegration #MainframeModernization #MCPprotocol #Db2 #IMS #VSAM #SMF #HybridAI #AIAgents #EnterpriseAI #PlanetMainframe #GSUK

0:00 Introduction
0:41 What sparked Geniez?
1:16 What aspect of the market gap was inspirational?
2:20 What elements led to Geniez?
3:20 What is Geniez?
4:06 How are you bridging GenAI with mainframe systems?
5:09 How is your approach unique?
6:18 What is your data-first approach?
7:14 What sets Geniez apart in AI?
8:58 Does this make mainframe data more accessible?
9:55 What business value are clients seeing?
11:40 What technology is Geniez utilizing?
13:03 Why is MCP on the mainframe significant?
14:31 What feedback is Geniez receiving from users?
15:51 What's next for AI?
16:54 Advice for mainframe teams hesitant about AI?
17:51 Where can people learn more or see Geniez in action?
18:18 What's next for Geniez AI?