)
Application Modernisation — Accelerated by AI
Open Reply's AI-first methodology compresses legacy modernisation. AI agents analyse code, generate backlogs, and build in parallel under expert oversight.
The Problem
Legacy systems are expensive to maintain, difficult to scale, and increasingly risky to operate.
Ageing technology stacks such as COBOL mainframes, legacy .NET applications, outdated databases or applications written in outdated / niche languages, accumulate technical debt that slows innovation, drives up operational cost and increases the risk of disruption.
Meanwhile, the talent pool for maintaining these systems shrinks every year; only one person knows that application…
Some core systems need to be replaced by new Commercial off the Shelf (COTS) applications, but all too often they provide 50% more functionality with a significant increase in cost, when only 10% of the new functionality is actually required.
Sometimes we can’t get away from a large, costly and, potentially, multi-year programme to replace our legacy with a new COTS, but this should not be the default by any means, and for many applications todays it’s far more efficient to replace with a modern technology stack.
But traditional modernisation approaches can be slow, costly, and high-risk, they have tainted our outlook. Manual reverse engineering of legacy codebases takes months. Rewriting business logic from scratch introduces defects. Large teams burn through budgets before delivering value.
)
The result: Organisations remain trapped on platforms that can't support modern business needs
The Opportunity
Modernised applications unlock immediate and compounding value:
Cloud-native platforms scale elastically, eliminating over-provisioned infrastructure.
Modern architectures enable rapid feature delivery and continuous deployment
Modern technology stacks attract and retain engineering talent.
Current frameworks and platforms receive active security updates and meet modern compliance standards,
Modernised platforms provide the foundation for AI-powered capabilities and data-driven decision making
Our Approach
Open Reply's AI-first methodology compresses modernisation timelines while maintaining rigorous quality standards.
AI agents ingest and analyse the entire legacy codebase to extract business logic, data models, dependencies, and integration points. What traditionally takes months of reverse engineering is compressed to days. The output is a comprehensive understanding of what the system does and why, not just how.
We work with your team to select the right target technologies based on your constraints: cloud provider preference, existing team skills, regulatory requirements, and strategic direction. Where you have preferences, we align. Where you don't, we recommend. The result is a target architecture with clear bounded domains, data models, and a structured migration path.
AI transforms the legacy analysis and target architecture into a prioritised backlog of self-contained user stories. Each story is designed to be independently implementable and testable — purpose-built for parallel execution by AI agent teams.
Our AI agent teams iterate over the backlog in parallel. For each story, agents implement the solution, generate tests, and validate against acceptance criteria. A separate agent then performs adversarial review — checking for defects, security vulnerabilities, and architectural compliance. Only code that passes all automated and human quality gates is merged.
Senior engineers oversee every stage. All AI-generated code is human-reviewed before merge. Architecture decisions are owned by experienced solution architects. Quality is never traded for speed. Our experienced UX/UI designers can transform the usability of an old application by simply applying design best practice, but if the look and feel can change the usability can be completely transformed.
Why This Approach Works
Traditional Modernisation
Months of manual reverse engineering
Sequential development, one feature at a time
Manual story writing and test creation
Large teams required for large scope
Documentation as a final afterthought
High risk of business logic loss during rewrite
Open Reply AI-Accelerated
Days of AI-powered codebase analysis
Parallel execution across multiple workstreams
AI-generated stories, tests, and documentation
Lean, senior teams amplified by AI agents
Living documentation maintained throughout
AI extracts and preserves business rules systematically
Modernisation in Practice
)
Financial Services — COBOL Mainframe to Cloud-Native Go on AWS
A retail bank's COBOL core processes millions of daily transactions but costs seven figures yearly to maintain, with a shrinking developer pool. AI agents extract business rules, enabling phased migration to Go microservices on AWS with DynamoDB
)
Healthcare — Legacy C# and SQL Server 2008 to Cloud-Native Platform on Azure
A healthcare provider's .NET 4.5 patient system can't meet compliance or scale to telehealth, and SQL Server 2008 is near end-of-life. AI maps the data model and stored procedures, enabling rebuild on .NET 8, Azure SQL, and Azure Functions.
)
Manufacturing — Monolithic Java ERP to Event-Driven Microservices on GCP
A global manufacturer's fifteen-year-old Java ERP has become the bottleneck for supply chain digitisation, with one defect able to take down the platform. AI decomposes it into bounded contexts, migrating to Kotlin on GKE with Pub/Sub via strangler fig.
)
Insurance — Legacy VB6 Claims System to Modern React and Node.js Platform
An insurer's VB6 claims system has no tests and logic scattered across form handlers. AI reverse-engineers the full claims lifecycle from triage to settlement, enabling rebuild on React and Node.js on on-prem Kubernetes for data residency.