Use cases

Connecting Generative AI with real-time mainframe data

Connecting Generative AI with real-time mainframe data - Use cases
Enterprise mainframes hold your most critical, current data—yet accessing it requires specialized skills, manual processes, and results in insights that arrive too late. Security teams spend days analyzing audit logs that are already hours to days old. Developers waste hours on debugging. Business analysts wait days for queries. The cost: delayed decisions, increased downtime, and missed opportunities.

Geniez AI connects large language models (LLMs) to any mainframe data source in real-time, transforming data sources such as complex SMF records, system logs, DB2, IMS DB, MQ and binary data into instant, actionable intelligence through natural language queries. No data extraction required. No mainframe expertise needed.
Connecting Generative AI with real-time mainframe data - Use cases
Operations use cases

Mainframe operations generate vast amounts of data, including logs and audit records such as job outputs, system logs, OPERLOG, and SMF records. While these are essential for diagnosing issues and understanding system behavior, their sheer volume and binary format make them challenging to analyze and comprehend.

Generative AI moves beyond simple data retrieval. It can synthesize, summarize, and even generate new, actionable knowledge from the raw input, turning overwhelming data quantities into a strategic asset for mainframe system operations. This accelerated analysis fundamentally changes how organizations derive value from their historical and real-time data streams.

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Connecting Generative AI with real-time mainframe data - Use cases
Security use cases

By integrating Generative AI with mainframe data and tools, organizations can bring intelligence and automation into their security operations. From proactively checking for vulnerabilities to analyzing audit records and verifying compliance, Geniez AI transforms traditional, manual security processes into fast, accurate, and explainable AI-driven workflows.

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Connecting Generative AI with real-time mainframe data - Use cases
Software development use cases

Using generative AI for software development goes further than just generating and explaining code. As developers write new code, they need to compile programs, run test jobs, check other data sets and look at source members. All these activities can be streamlined and optimized to become more efficient.

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Connecting Generative AI with real-time mainframe data - Use cases
Improving customer experience use cases

Using generative AI for software development goes further than just generating and explaining code. As developers write new code, they need to compile programs, run test jobs, check other data sets and look at source members. All these activities can be streamlined and optimized to become more efficient.

Customers who would like to give their customers better experience will connect generative AI directly to the mainframe real time data to enable them to have all of the mainframe data which is the most up to date and leverage natural language to query it live.

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Connecting Generative AI with real-time mainframe data - Use cases
DevOps use cases

Companies have a lot of data that has grown from its inception until today. Not all companies are able to know where the data is or how to find it? Connecting generative AI to mainframe data enables companies to find a needle in a haystack.

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Geniez AI

The enterprise framework for connecting LLMs and AI-agents to real-time mainframe data