Mainframe
Mainframe systems continue to power business-critical operations across industries such as banking, insurance, manufacturing, and logistics, by ensuring reliability, security, and high-performance processing. At the same time, evolving business demands, integration or modernization needs require organizations to reimagine their mainframe strategy for the new digital age.
IBA Group combines deep mainframe expertise with AI-driven tooling to enhance development, support, modernization and migration of mainframe applications.
We also integrate GenAI into mainframe development workflows and industry-standard IDEs like IntelliJ IDEA and VS Code, enabling faster delivery cycles and higher developer efficiency across the entire software lifecycle.
Mainframe Application Support and Modernization
Ensure stability, reduce costs and improve transparency of legacy systems.
Mainframe Support (AI-Enhanced)
AI-enhanced Mainframe Support (L2/L3 levels) provides an AI generation of Application Knowledge Model (Digital Twin), e2e process of incident analysis and resolution powered by AI Batch Incident Management Assistant, code change, testing and deployment.
COBOL Code Recovery Service (AI-Assisted)
COBOL Code Recovery Service involves a multi-step process of recovering missing or mismatched COBOL programs from IBM MVS executable modules, generating source code that is functionally equivalent to the original program. The service includes an enhanced AI recovery option to add business context to the restored code.
DevOps for Mainframe
DevOps for Mainframe builds automated CI/CD pipelines to considerably improve the delivery process for mainframe applications. It includes a version control system, integration of analysis and monitoring tools, as well as automation of build, test and deployment processes.
Mainframe System Decommissioning
Mainframe System Decommissioning provides a full decommissioning cycle, including the discovery phase, communication with upstream and downstream applications, deactivation activities, archiving of data, and deletion of application assets.
Cloud Migration for Mainframe Applications
Transform mainframe workloads and move them to scalable cloud environments.
Mainframe Fast-Track Migration with AI
Mainframe Fast-Track Migration with AI service uses an AI-powered approach to create a clean inventory of active components, uncover integration points, and extract business rules, create full specification of the actual business process, form migration roadmap, substantiated by a PoC and accelerated with AI-assisted code transformation.
Mainframe Application Replatforming to Cloud Native
Mainframe Application Replatforming to Cloud Native provides complete migration of batch and online mainframe workloads, data, and security rules to any cloud, including the replication of major z/OS® subsystems using partner solutions.
Extending Mainframe Capabilities
Integrate mainframe systems with modern applications and digital platforms.
Extension of Mainframe Applications to Web Users
Extension of Mainframe Applications to Web Users provides E2E development of web-based services and modern user interfaces while keeping existing business logic on the mainframe.
Mainframe API for Modern Enterprise Solutions provides a solution to extend business-critical mainframe applications through REST APIs for consumption by new services on-premises or in the cloud using a microservices approach.
Zowe Integration
Zowe Integration enables a seamless connection of mainframe systems with modern DevOps toolchains, providing API-driven access to mainframe resources and enhancing user interfaces while maintaining legacy business logic on the mainframe.
Challenges we solve
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Mainframe Services: Frequently Asked Questions
Mainframe modernization involves analyzing system dependencies, improving code maintainability, and enabling integration with modern technologies. It may include API enablement, workload optimization, and gradual transformation of legacy components while preserving critical business logic.
Yes, mainframe systems can be migrated to the cloud using approaches such as replatforming, redesign/re-engineering, or hybrid architectures. The right approach depends on system size, complexity, target architecture and business requirements.
Mainframe costs can be reduced through workload optimization, automation of support processes, partial migration to cloud environments, and decommissioning of unused components. A structured approach helps balance cost reduction with system stability.
Key risks include incomplete dependency mapping, potential loss of business logic, and disruption of critical operations. These risks can be mitigated through detailed system analysis, phased migration strategies, and thorough testing.
Organizations address the mainframe talent gap by partnering with specialized providers, adopting automation and AI-assisted tools, and improving knowledge transfer and experience management practices.
Modernization is typically chosen when core business logic should be preserved and gradually improved. Migration is considered when infrastructure constraints, costs, or scalability requirements make a full or partial move to cloud environments more effective.
Replatforming moves applications to a new environment (cloud, on-prem, etc.) with minimal code changes to business logic while adapting application to new infrastructure, databases and middleware, while refactoring involves modifying or rewriting parts of the application code on the same platform to improve flexibility, performance, and integration capabilities.
The cost of mainframe application modernization or migration varies depending on size, complexity, and chosen approach. Projects can range from targeted optimizations to large-scale transformation programs, and are typically evaluated based on long-term cost efficiency and business impact.
Migration timelines depend on system complexity, integration landscape, and migration strategy. Projects can range from several months for specific workloads to multi-year programs for large enterprise environments.