Sold by: Storm Reply GmbH
Legacy applications run critical parts of the business, but over the years they become hard to change, expensive to maintain, and slow to adapt. Every release carries risk, defects take long to fix, and the knowledge needed to evolve them is often undocumented or has left the organization. The AI-Powered Software Modernization Factory by Reply and vFunction turns these applications into cloud-native software on AWS. AI agents learn how your application actually behaves, split it into independent business domains, and rebuild each one as a modern application. Each domain can be released, scaled, and fixed on its own, so teams deliver new features faster, scale only what is needed, and resolve defects in a fraction of the time. Every application we deliver is fully tested against the behavior of the original system, so your business workflows keep working as before. A structured, repeatable approach cuts modernization timelines from years to months.
Overview
Organizations running legacy applications on Java, .NET, COBOL, SAP ABAP or other established technology stacks face growing constraints on scalability, agility and cloud adoption. Tightly coupled monoliths accumulate technical debt, slow down feature delivery, increase operational risk and depend on undocumented institutional knowledge. The AI-Powered Software Modernization Factory by Reply and vFunction addresses these challenges with a demonstrated, scalable and repeatable modernization offering on AWS.
The factory combines AWS-based AI agents, AI-assisted code transformation, runtime and static architecture analysis, graph-based dependency analysis and automated validation. Each phase produces verified, version-controlled artefacts that feed the next phase, and every agent action is logged for full auditability.
Pre-Phase – Connectivity & Discovery: Secure hybrid connectivity to the on-premises environment is established, and agentless discovery captures server inventory, utilization, application dependencies and communication patterns without changes to the source code. All credentials used by agents are kept in a managed secrets store.
Phase 1 – Discovery & Baseline: Dynamic analysis of the running application captures call graphs, class hierarchies and dependencies between components. Together with the source code, these insights are indexed in an AI knowledge base that makes the entire codebase searchable by meaning. AI agents generate smoke test and load test suites covering the application's workflows, while cloud monitoring and distributed tracing establish a functional, performance and trace baseline. For procedural or non-cloud-native stacks such as COBOL, RPG or PL/SQL, an initial AI-assisted transformation produces an intermediate Java or .NET codebase so that all downstream steps work consistently across technologies.
Phase 2 – Analysis & Domain Generation: Static and dynamic analysis identifies quality issues, vulnerabilities and dead code. Dependency data is loaded into a graph database, where graph analysis identifies candidate business domains, modernization waves and technical debt hotspots. An AI migration strategy agent produces the target domain map and modernization approach, which is reviewed and approved by your technical lead before transformation begins.
Phase 3 – Code Generation & Validation: A supervisor agent orchestrates specialist AI agents for code generation, unit test generation and documentation, processing multiple domains in parallel. Generated services and tests run through automated build and delivery pipelines and are validated against the Phase 1 baseline to ensure no functional or performance regression. Validated workloads are prepared for deployment on AWS container or serverless platforms, with a coexistence layer supporting incremental, phased cut-over.
**Typical use cases: **
Legacy Java/.NET to modern Java/.NET: Tightly coupled Java EE and .NET monoliths, including distributed monoliths with shared databases, are decomposed along business domains into independently deployable services, containerized and run on Amazon EKS, Amazon ECS with AWS Fargate or AWS Lambda.
COBOL to modern Java/.NET: Mainframe and procedural code such as COBOL, PL/I, Natural, RPG or PL/SQL is first transformed with AI assistance into an intermediate Java or .NET codebase, then re-architected into domain services through the same analysis, generation and validation pipeline.
SAP ABAP to modern Java/.NET: Custom ABAP logic is extracted from heavily customized SAP landscapes and re-implemented as Java or .NET services, integrated with the remaining SAP core through Amazon API Gateway during coexistence.
The solution is designed for enterprises of any scale and size that are committed to AWS but still operate large, complex on-premises application estates. Modernized workloads integrate with your existing identity, audit and policy infrastructure.
Outcomes: modernization timelines reduced from years to months, reduced technical debt and maintenance cost, full architectural visibility, consistent and traceable delivery, and cloud-native applications ready to adopt AI-driven capabilities on AWS.
Highlights
- Technology-stack agnostic, powered by AI agents: AWS-based AI agents analyze, transform, test, and document legacy applications built on Java, .NET, COBOL, SAP ABAP, and other platforms. A structured, repeatable three-phase methodology turns them into cloud-native applications on AWS, with incremental cut-over so existing systems keep running during the transition – reducing modernization timelines from years to months.
- Architecture built to scale in the cloud: Applications are re-architected into independent business domains on a contemporary technology stack. Each domain scales independently, so you pay only for the capacity you actually use. Failures stay contained within one domain instead of taking down the whole application, and built-in monitoring and tracing make issues fast to detect, locate and fix.
- Fully tested and proven: Before anything changes, AI agents generate a smoke test suite that covers every business workflow and captures the original application's functional and performance baseline. Every re-architected application is validated against this baseline, so all workflows keep working seamlessly even though the software has been rebuilt – and they run faster thanks to the new architecture.
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