Geniez

Geniez AI develops the Geniez GenAI Framework, software that connects large language models, AI agents and enterprise AI applications to real-time mainframe data sources using natural language. The framework is positioned as an alternative to ETL, change data capture and data-warehouse/data-lake approaches, which the company describes as producing lagging, partial copies of mainframe data at high CPU, complexity and security cost. It is designed to install without prerequisites in a matter of hours and to work with any LLM or AI agent running on premises or in the public cloud.

The product is organized around four use cases: chat assistants for mainframe professionals; extensions that add mainframe capabilities to AI code assistants; AI-driven mainframe security audits and source-code vulnerability scanning; and enrichment of enterprise AI applications with mainframe data. On top of the framework, Geniez offers a set of curated agents branded "Genies" — an Operations Genie (system troubleshooting, operational insights, natural-language SMF queries), a Security Genie (audit and compliance, permission-change detection, vulnerability checks), an Application Modernization Genie (natural-language database queries, mapping of active and inactive code, streaming mainframe data to cloud) and a Capacity Planning Genie (MIPS consumption by job, CP versus zIIP utilization, job execution comparison). For developers, a software development Genie supports JCL job submission and spool search, dataset access including VSAM and COBOL-copybook-mapped datasets, JCL and program code generation and explanation, ABEND and JCL error remediation, DB2 query and update, and MQ message get/put from within the IDE.

The company targets mainframe operations staff and DBAs, mainframe developers, AI and data professionals, and security professionals, and cites customer sectors including finance, insurance, retail, government and healthcare as areas of GenAI investment addressed by the product.

Founding story

The founding team previously built Model9, which provided mainframes with access to cloud storage. They started Geniez AI around the question of whether mainframes could speak the same language as modern AI applications, having observed that mainframes remained on the sidelines of the generative AI wave because enterprises relied on ETL and CDC pipelines that produced lagging data, consumed CPU and network resources, took months or years to implement and introduced security risk by moving sensitive data off-platform. The stated mission is to securely and efficiently connect LLMs and AI agents to real-time mainframe data without requiring deep mainframe skills, enabling modernization without replacing the mainframe. [2][3]

Business model

Enterprise B2B software licensed to large mainframe-operating organizations, delivered as an agent that installs on the customer's mainframe together with a web-based administration interface; the company also publishes whitepapers, demo videos and self-service trials ("Try it yourself") alongside demo requests. [0][1][4]

Traction

Company-reported customer-measured statistics include more than 200 daily users at Fortune 50 companies, average annual savings of over $8M per customer, more than 10,000 hours gained through automation, more than 50 agentic workflows in production, more than 700M tokens used year-to-date by customers, savings of over $3M per incident, roughly one hour gained per developer per day, agentic workflow creation in under five minutes and time to value of under two hours. Public testimonials come from a principal architect (Muraleedharan T V) and Mark Wilson, a GSE UK board member. [0][1]

Latest developments

The $6M seed round was announced in September 2025, with proceeds directed at engineering, go-to-market, enterprise partnerships and deeper GenAI platform integrations. Current site materials feature a demonstration of extending Amazon Q with mainframe capabilities and a blog post on AI-native mainframe defense. [0][1][6][7]