Modernize without stopping. Ship without breaking.

The strangler-fig pattern. New capability is built alongside the legacy system behind an API façade, traffic moves route by route, and the old system shrinks each sprint while still running production. Every increment is independently reversible, and an automated regression harness built from live behaviour proves nothing broke. One UK lending platform moved off a 22-year-old monolith across 14 increments with no freeze weeks.

You do — in every engagement model. Source code, CI/CD pipelines, infrastructure-as-code, documentation and design systems are yours, held in your repositories from day one rather than handed over at the end. We don’t build dependency on us as a commercial strategy.

That’s the normal starting point. We use automated code and dependency discovery to rebuild the map, capture actual runtime behaviour into an automated regression suite, and treat that suite as the specification — so the question becomes “does it still behave the same?” rather than “was the documentation right?”

Three lines, and they should be modelled separately in the business case: infrastructure and licence run cost (usually the fastest payback, often funded by retiring redundant applications), engineering throughput (release lead time and change-failure rate), and risk avoidance (unsupported runtimes, single-person dependencies, audit exposure). We put numbers against each in the assessment rather than quoting industry averages.

Yes — that’s most AMS transitions. Knowledge acquisition runs alongside the incumbent where possible, runbooks are created as a transition deliverable rather than an intention, then operations move to SLO-based support with a standing technical-debt burn-down backlog. We transitioned 46 applications for one telecommunications group with no coverage gap at handover.