Typically based on data warehouse or data lake implementations, current solutions only hold a subset of mainframe data, hours and even days old. To refresh the data, companies rely on expensive and complex ETL and CDC processes that must be maintained using deep mainframe expertise to support continuous data requirements of the enterprise, while cloud applications use pre-defined API to access data in a fixed pattern that often requires updates to mainframe application code in order to be enhanced.
The Geniez GenAI Framework overcomes the inherent connectivity challenges between mainframe systems and modern AI applications, providing a pathway for businesses to improve customer experiences, enhance operational efficiency and drive new revenue streams without migrating data or incurring significant infrastructure modernization costs.
LLMs, AI agents and other AI applications, running on-premises or in the cloud, access the framework using standard GenAI protocols such as MCP and A2A or using the provided python SDK. The Geniez GenAI Mainframe Databot runs on the zIIP engines and leverages the Telum II processor and Spyre accelerator to efficiently retrieve real-time data from popular data sources such as DB2, IMS, MQ, RACF, VSAM and COBOL-mapped data sets.
With a security-first patent-pending design, AI applications authenticate to the framework using securely generated API tokens, mapped to real mainframe user IDs. All access to mainframe data sources is performed using a verified user ID and controlled via the mainframe’s native access control system.
A web-based administration interface provides extensive security controls, data observability and application debugging capabilities to simplify management and accelerate development of AI applications without compromising on the mainframe core strength of reliability, availability and serviceability.
