Legacy modernization is usually slow because so much of the work is reading and re-deriving intent from code nobody fully remembers writing. We use AI-assisted tooling to speed up that comprehension work: mapping dependencies, drafting test coverage, and flagging risky code paths, while our engineers make the judgment calls a model shouldn't be making alone. This is a newer part of our practice: we don't yet have a dedicated flagship case study for an end-to-end legacy migration, so we're not going to invent one. This page describes our approach honestly, and we're happy to walk through it in more depth on a call.
AI-Assisted Code Comprehension: Faster mapping of dependencies, data flows, and undocumented behavior in code that's been running for years without documentation.
Test Coverage Before Migration: AI-assisted test generation for the specific paths being modernized, so changes are verified rather than assumed safe.
Incremental Migration: Bounded, shippable increments instead of a big-bang rewrite that leaves the system unreleasable for months.
Engineers in the Loop: AI tooling accelerates comprehension and test generation; our engineers make the architectural and risk calls.
This is an active, growing part of our practice rather than a service we've run dozens of times. We're not going to cite a case study we don't have, so if you want specifics on how this would apply to your system, the most useful next step is a conversation, not a slide.
