AI Assisted Modernization Case Study

A global investment firm needed to modernize business-critical trading systems without disrupting live workflows.

The platform was large and complex: more than 700,000 lines of code across nearly 30 repositories, spanning Java, C#, C++, SQL/PL-SQL, Python, JavaScript, and COBOL. It also included 30+ database schemas, 370+ batch jobs, and 15+ external integrations.

The client asked Findev to migrate the first wave of production services from a retiring on-premises Kubernetes platform to its strategic AWS EKS environment, while preserving existing behavior and producing auditable evidence that the migrated services still worked as expected.

Problem

The estate combined monolithic legacy components on outdated frameworks (Java 1.8, .Net Framework 4.5, Python 2.6) with newer distributed services. 

Its polyglot and fragmented architecture made dependencies difficult to trace, while critical business logic was scattered across code, logs, and institutional knowledge.

Automated regression coverage varied significantly between components, leaving key migration paths hard to verify. Most test cases covered only happy paths, and end-to-end integration testing was fragmented.Logging and monitoring were inconsistent across the estate, making the system harder to observe and more expensive to maintain. 

Solution

Findev developed and applied an AI-driven, phased modernization method to the live application estate:

• App Assessment — Complexity Meter:  we defined a set of complexity metrics and used them to analyze the codebase, infrastructure, and dependencies with AI. The output was a complexity and readiness scorecard, together with more than a dozen evidence-backed migration risks.
• Knowledge Discovery — Discovery Agent: we mined the source code and around 10 GB of production logs to reconstruct undocumented business workflows, edge cases, and rules, turning them into behavior and use-case documentation. 
• Test Generation — Test Forge: built and auto-debugged a comprehensive suite of 190+ cases and 490+ assertions, covering critical business workflows end to end, including regression, performance, and resilience scenarios
• Modernization: rebuilt the legacy monolith into microservices and moved the platform to AWS EKS. We also eliminated redundant technologies by rebuilding most of the backend in Java, reworked the CI/CD pipeline, and enabled centralized application observability. 
• Deployment: deployed all migrated services to the target AWS EKS environment and confirmed their quality and functionality by running the full validation suite. 

Quality was enforced at every step: each phase's analysis, tests, and results were re-checked by an independent AI reviewer and approved by an engineer before work continued. AI did the high-volume analysis, documentation, test generation, and implementation; engineers set direction and kept the architecture and release calls — roughly a 90% AI / 10% human delivery model.