Imagine asking ChatGPT a question like:

“Which parameter do I need to change to increase the maximum number of tasks in my CICS region, without impacting transaction performance?"

Now imagine it instantly gives you the right answer — not based only on public documentation, but based on your own mainframe configuration and real-time system performance data.

That’s the power of connecting Generative AI to mainframe data.

Why It Matters for Mainframe Teams

Mainframe environments are rich with decades of mission-critical data — logs, SMF records, configurations, performance metrics, and application insights. Yet, this data is often locked inside complex systems, accessible only through specialized tools and deep expertise.

By connecting Generative AI models like ChatGPT or Anthropic’s Claude directly to this data, we give mainframe teams a new superpower: the ability to interact with the mainframe in natural language — and get precise, contextual answers from live operational data.

Real Examples of What’s Possible

Here are just a few real-world examples of what becomes possible when GenAI meets mainframe data:

• Querying SMF data in plain English
  “Claude, is my high-availability configuration for CICS and DB2 still balanced?”
  The AI analyzes your SMF metrics and responds with a clear answer — including which system parameters differ, or when the last sync occurred.
  
• Detecting anomalies in real time
  “ChatGPT, this batch job usually takes 10 minutes — why has it been running for two hours today?”
  The AI can check historical run times, correlate system logs, and automatically raise a flag if it detects anomalies or dependencies causing delays.
  
• Operational insights on demand
  “What changed in the last 5 minutes that caused CPU utilization to spike?”
  Instead of searching through endless reports, the AI summarizes the relevant changes, such as a new workload, a dataset lock, or a parameter update.
  

Are LLMs able to read mainframe data?

Yes and no. No, they can’t read directly from the mainframe or understand mainframe data. So how can mainframe professionals today get answers like the examples above? Geniez AI framework was founded to exactly enable that - connect LLMs and AI agents to mainframe data but to bring the data to the LLM or AI agent in a language they understand. Geniez AI is a framework that includes MCP that runs on the mainframe that is able to read the SMF, parse it and provide it to the LLM in a language they understand and analyse.

Using Geniez AI - Yes, LLMs and read mainframe data 🙂

The Future of Mainframe Operations

Connecting LLMs to the mainframe doesn’t mean moving your data to the cloud. It means enabling secure, governed access through frameworks that respect enterprise security — such as running inference engines on-prem or through hybrid models.

Whether it’s ChatGPT, Claude, or any future LLM, the idea is the same:
Talking to your mainframe in natural language — with access to its data, context, and history.

Closing Thought

So, ask yourself:

What if ChatGPT really was connected to your mainframe?

Wouldn’t it be amazing if every question about your system — from performance to configuration — could be answered in seconds, in plain English?

Using Geniez AI - the future is here - NOW.

Contact us as contact@geniez.ai to get more information