FPT’s Enterprise Modernization Technology (EMT) Solution, powered by Azure OpenAI, helps enterprises modernize legacy mainframe systems such as COBOL, JCL, PL/I, RPG, and Assembler into modern, cloud-native applications written in Java, Shell, and Python.

By integrating Generative AI and Microsoft Azure capabilities, EMT enables organizations to analyze, document, refactor, and migrate legacy applications with up to 30% faster transformation speed, 40% lower cost, and 50% reduced migration risk compared to traditional approaches.

With automated code discovery, AI-guided dependency analysis, data migration, and testing automation, EMT provides a faster, smarter, and safer modernization journey from mainframe to the cloud.

Type of User that Benefits

CIOs and IT Leaders – Seeking to reduce technical debt, cost, and risk in modernization programs. CTOs and Enterprise Architects – Looking for a scalable AI-powered modernization and re-architecture framework. Development and Migration Teams – Requiring automation for analysis, documentation, and testing. Cloud Transformation Specialists – Targeting seamless migration to Azure IaaS, PaaS, or SaaS with automation and intelligence.

Customer Pain Points Addressed

High maintenance costs and dependency on legacy skillsets – outdated systems require rare expertise and high support effort. Incomplete documentation and knowledge loss – decades-old systems lack updated references, increasing modernization risk. Complex interdependencies between programs, data, and batch jobs – manual analysis slows down modernization and increases error rates. High risk and uncertainty in large-scale migration – traditional methods lack automation, visibility, and consistency for cloud readiness.

How FPT’s EMT Solution Addresses These Challenges

EMT Brain – The central AI coordination hub that orchestrates data, agents, and automation across the entire EMT suite via APIs, ensuring consistent analysis, validation, and modernization flow. EMT Radar – Discovers, classifies, and visualizes assets and dependencies across programs, jobs, and data. Provides insight into modernization readiness and potential risks. EMT X-ray – Performs deep dependency and duplication analysis, reconstructs process flow, and generates detailed modernization blueprints. EMT Mirror – Handles reverse engineering by rebuilding design-level documentation, business rules, and data lineage from existing source code. EMT Bridge – Acts as the connector between legacy and modern environments. Automates batch, online, and report job migration with AI-assisted syntax parsing and report redesign. EMT Stream – Automates data migration from SAM/VSAM, IMS, and DB2 to PostgreSQL and modern relational databases, ensuring accurate mapping and validation. EMT Forge – Enables forward engineering, modernizing and re-architecting legacy applications into modular or microservice-based designs. EMT vAIid – Automates unit, pattern, and integration testing to ensure functional equivalence between legacy and modernized systems. EMT Runtime – Provides runtime support and testing utilities for modernized applications, including AI agents for code suggestion, refactoring, and test case generation. EMT Assistant – Delivers AI-driven recommendations, contextual help, and transformation summaries for developers and migration teams. Flezi NEXUS – Provides an integrated environment combining frontend and backend frameworks, API management, DevOps toolchains, and cloud-native readiness. It serves as the DevOps and Operations platform enabling continuous integration, deployment, and monitoring of modernized systems. Flezi CUDERX – Implements the Agentic Software Engineering framework using the BMAD-METHOD™ and MCP (Model Context Protocol) integration, built on a knowledge-based system for mainframe and midrange modernization with 10+ specialized software engineering AI agents

Outcome

30% faster modernization and refactoring through AI-driven automation. 40% lower overall cost in assessment, migration, and validation phases. 50% shorter testing and verification time via automated validation. 90% documentation accuracy achieved through AI-assisted knowledge reconstruction. Increased reliability and compliance with continuous governance from EMT Brain.

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