AI Accelerated Engineering for Legacy Modernization

Overview

Modernizing legacy systems is rarely about rewriting code faster. It is about preserving behavior, controlling risk, and enabling future evolution without disrupting critical operations.

In this case study, First Line Software (FLS) applied AI Accelerated Engineering to modernize a mission-critical internal Peer Review System used by portfolio managers, team leads, and operations staff in the financial services domain. The legacy system functioned as a technical core for day-to-day portfolio reviews, compliance-driven workflows, and operational oversight.

Any modernization effort had to preserve the system’s exact business behavior while transitioning it to a modern, maintainable architecture. Strict regulatory requirements meant zero tolerance for downtime or and no room for behavioral changes. 

The legacy application had accumulated extreme technical debt. UI, business logic, and data access were tightly coupled in a single monolithic form, making even small changes slow, risky, and prone to regression. Documentation was minimal, and critical system knowledge existed primarily in the minds of current or former employees.

These constraints required a behavior-first, controlled approach to modernization, where AI could accelerate delivery without replacing engineering judgment, validation, or accountability.

Challenge

The client needed to modernize a mission-critical legacy desktop application without changing its business behavior. Strict regulatory oversight meant zero tolerance for downtime, making any disruption to existing workflows unacceptable.

Built decades ago on VB6 and ADO, the system was keeping the client from making any technological innovations. Incremental changes to UI, business logic, and data access could introduce compliance and regression risk.  Any maintenance or feature developments that did occur were slow, high-risk, and undocumented. As a result, modernization required an approach that could preserve existing behavior exactly while reducing legacy-related risks and enabling future evolution.

These constraints shaped an AI Accelerated Engineering approach focused on preserving behavior while reducing modernization risk.

Our Role

FLS was responsible for architecture design, modernization strategy, legacy system analysis, and full-cycle delivery, including validation and implementation.

The scope and timeline included:

• MVP delivered in one month
• Production-ready version delivered in two months
• Additional enhancements intentionally deferred beyond the initial scope

The solution was delivered by one full-stack engineer, supported by AI-assisted software development practices and continuous collaboration with client-side domain experts to validate system behavior.

Approach

Given the need to preserve exact system behavior under strict regulatory constraints, FLS adopted a behavior-first, AI-accelerated engineering approach focused on safety, validation, and controlled change.

Key elements of the approach included:

• Behavior-first modeling
  Business behavior was formalized using Gherkin scenarios, defining what the system does rather than translating legacy code line by line. These scenarios acted as behavioral contracts throughout the SDLC.
• Separation of migration from enhancement
  Achieving 1:1 parity was treated as a standalone phase, with new features intentionally deferred to reduce risk.
• Component-level decomposition
  The monolithic legacy system was decomposed into isolated components, allowing changes to be contained and validated independently.
• AI-assisted implementation
  AI was used to accelerate implementation tasks, including code and test generation, based on clearly defined scenarios and architectural decisions.
• Continuous validation and human oversight
  System behavior was continuously validated against the legacy application, with human-in-the-loop QA enforcing correctness at every step.

In practice, AI accelerated delivery within well-defined boundaries, while responsibility for design, validation, and accountability remained fully with the engineering team.

Solution

FLS delivered a modern, web-based Peer Review application that fully preserved legacy behavior while transitioning the system to a modular, maintainable architecture.

The solution enabled a seamless transition for users without retraining or workflow disruption, while establishing a foundation for future development.

Core capabilities included:

• Account-centric peer review workflows
• Team- and manager-level workload views
• Portfolio summaries and detailed reporting
• Year-to-date trade history views
• A foundation for future ESG metrics and role-based access control

Technology

The technology stack was selected to support predictable delivery, maintainability, and engineering productivity, while ensuring secure operation in a regulated environment.

Core stack

• Next.js (React) with TypeScript
• Prisma ORM
• Microsoft SQL Server (Azure SQL Database)
• Azure App Service with containerized deployment
• LDAP / Active Directory integration via NextAuth

Reliability and visibility

• GitHub Actions for CI/CD automation
• Structured application logging using Pino

Accelerators and patterns

• Reusable UI components built with shadcn/ui and Tailwind
• Standardized architecture patterns (Feature-Sliced frontend with a BFF layer)
• Infrastructure-as-code templates for consistent deployments

This case demonstrates how AI Accelerated Engineering can improve engineering productivity and delivery speed without sacrificing correctness, by applying AI within a behavior-first, human-controlled SDLC—particularly in complex, regulated legacy modernization scenarios.

January 2026