AI-Enhanced Engineering for Secure Research Systems

Challenge

The platform supported active health research workloads on top of inefficient, aging legacy code. Maintenance cost was rising, security and operational requirements had evolved past what the existing codebase could meet cleanly, and ordinary refactoring at the velocity available to the team would have run for many quarters.

The production timeline did not allow for a freeze. The system needed to keep running through the modernization, the team could not afford a multi-month feature pause, and the modernized components had to land without breaking dependencies or introducing regressions in a regulated environment.

What We Did

We identified the modernization bottleneck and introduced AI-augmented engineering workflows to dramatically accelerate the refactoring effort. Agentic development processes were brought into the engineering lifecycle for the work where they shined most, including boilerplate refactoring, test scaffolding, and mechanical migration patterns. Human judgment stayed on the architecture decisions, code review, and production cutovers.

Throughout the work, we maintained characterization tests so refactoring preserved behavior. CI gates blocked regressions before they reached merge. The cutover plan ran component by component with rollback safety at each step. The production system kept serving research workloads the whole time.

The pace exceeded what traditional refactoring alone could have achieved. The team modernized at a velocity that traditional methods could not match, and quality stayed high because the human-led oversight focused where it mattered most.

Results

• Modernized legacy system components at a pace traditional refactoring could not match, freeing the team to focus on architecture and security priorities instead of mechanical refactoring.
• Caught vulnerabilities and edge cases earlier in the development cycle, reducing the cost of fixing issues that would otherwise have surfaced in audit or production.
• Achieved zero production downtime during the modernization effort, allowing the client team to remain focused on core operations and research goals.
• Established a reusable workflow the team could apply to subsequent modernization rounds, with documented patterns and quality gates that survived our exit.

Approach Stack