Legacy is a question of entrenchment, not age.
A five-year-old platform can be legacy if it is deeply embedded, poorly understood, and uneconomic to change. The cost rarely shows up as a line item. It shows up as the migration nobody schedules, the report that takes three people, and the growth plan that quietly assumes the system stays as it is.
Conventional transformation offers two answers: keep patching, or replace everything. Both are expensive, and the second is usually slower and riskier than the business case admits. We work in the space between them. The question we ask is not “how do we replace this?” but “what value is trapped here, and what is the shortest credible path to releasing it?”
What we do
The work follows the same four moves regardless of the technology involved. What changes is how much of each is needed.
Using our EuclidAI investigation platform, we reconstruct behaviour, business rules, dependencies and edge cases from code, configuration, samples, logs and observed outputs—and prove each finding against the real system.
A migration that failed as a transformation problem often succeeds as an understanding problem. Once each element is understood well enough, conversion becomes forced rather than guessed, and can be run as deterministic tooling.
Where teams have built operations around a system’s limitations, we redesign the process first and only then decide what software has to change.
New access, automation, or insight layered around a stable core—so the business gets the outcome it needs without betting on a replacement programme.
What you get
- A verified map of how the system behaves, including the rules nobody wrote down
- A qualified route to value, with impact, feasibility, risk and operating cost compared side by side
- Running software—migration tooling, an access layer, or automation—not a recommendation deck
- A reusable knowledge base, so dependency on individuals falls instead of shifting to us
What we will tell you if it is true
Sometimes the honest answer is that the constraint is not worth attacking, or that a simpler approach beats AI. We say so early, before anyone has committed a budget to the wrong problem.
We are not here to tell IT what it did wrong. The systems we work on exist because they served the business well for a long time. Our job is to create new options where the economics or the technical possibilities have changed.
Which system has everyone learned to live with?
Bring us the one that is quietly shaping every other decision. A first conversation establishes whether the constraint is real and whether AI changes the economics.