Legacy remediation
Your system works, but every change costs more than the last one.
We take it back under control: no big bang, no regressions, behavior proven equivalent at every step.
The third option
Faced with a legacy system, most companies see two options. A big-bang rewrite, which bets years of budget on a promise and fails more often than anyone admits. Or a freeze, which lets the inertia compound quietly. We practice a third one, Legacy Reclaiming: rebuild the system next to itself, piece by piece, with proof of equivalence at each step.
Never modify without a safety net
Before transforming anything, we capture what the system actually does (not what the documentation claims) into an executable behavioral reference: existing tests, characterization tests for the uncovered zones, and a detection power calibrated with mutation testing and fuzzing. We know, with numbers, what the net catches.
The old code stays the reference until the new one has proven equivalent: same inputs, same outputs, compared systematically. Every divergence is replayable and traced. Some turn out to be bugs of the existing system; those are documented and arbitrated with you, never silently fixed.
Module by module, each step reversible. At no point is production at risk.
The techniques come from Michael Feathers' legacy-code work and from the legacy displacement patterns. We cite our sources.
Digital twins, not mocks
A legacy system rarely lives alone: it depends on referentials, mainframes, partner APIs. To rebuild it outside production, we develop a digital twin of each dependency that matters: a simulator exposing the same contract as the real system (same routes, same message schemas, same error codes) and reproducing the slice of behavior your application actually relies on, fed with anonymized production data and synthetic data. The synthetic part comes from generators we build on the production distributions, to also reach the edge cases and atypical historical states where the hidden rules live. Your code cannot tell the twin from the real thing; only the environment configuration changes. A static mock fakes an answer. A twin reproduces a behavior.
Agentic AI under a verification harness
Agents execute the mechanical refactorings, with the accumulated knowledge of the system as their source of truth. Nothing lands without passing the harness and a review by our architects. Speed comes from the agents; confidence comes from the harness. How we work with them is public: read our AI Handbook.
What remains when we leave
your team can change again.
a permanent non-regression net, versioned with your code, running in your CI. Also your local development environment, independent of third-party availability.
the behavior of the domain, readable by the business and runnable as tests. Documentation that cannot drift, because it executes.
Where it starts
An audit is how a remediation starts: it gives the evidence, the maturity baseline and the ranked plan. The remediation turns that plan into a system that accepts change again.