Strangler-pattern replacement
A routing facade in front of the legacy system, with capabilities migrated behind it one at a time and traffic shifted gradually.
Replacing systems that still run the business, module by module, while they keep running the business.
Legacy systems rarely need replacing because they stopped working. They need replacing because change has become expensive, the people who understood them have left, and the platform they run on is approaching end of support.
We avoid the rewrite that never lands. Using the strangler pattern, new services take over one capability at a time behind a routing layer, with the old system authoritative until the new one demonstrably matches it. There is no weekend where everything changes at once.
Capability by capability behind a facade, with the ability to route traffic back at any point.
Characterisation tests capture what the system does today — including the bugs downstream processes now depend on.
Each phase carries its own cost, risk and payback so funding is renewed on evidence rather than faith.
The right route depends on how much of the system's value is in its behaviour versus its code.
A routing facade in front of the legacy system, with capabilities migrated behind it one at a time and traffic shifted gradually.
Moving off end-of-life hardware and data centres to AWS, Azure or GCP, with infrastructure as code and costs modelled before the move.
Extracting bounded contexts into services where it demonstrably pays — not shattering a working system into distributed complexity for its own sake.
Schema modernisation, dual-write phases and reconciliation reporting until old and new agree on every record.
A documented, secured API layer over a system of record you are not ready to replace, unblocking every other project waiting on it.
Characterisation and regression suites built around the current behaviour before any refactoring begins.
Map capabilities, dependencies and change frequency, then order the slices by business value and technical risk.
Put a facade in front of the legacy system, add characterisation tests and start measuring real traffic and usage.
Build the replacement, shadow-run it against production traffic, compare outputs, then shift traffic progressively behind a flag.
Retire the legacy component only once traffic is fully migrated and reconciliation has been clean for a full business cycle.
Work that took a quarter starts taking a sprint, because the safety net makes changes provable rather than nerve-wracking.
The facade, the tests and the monitoring deliver value before a single component has been replaced.
Slice-by-slice payback means the programme survives a change of sponsor or a tighter budget year.
By never running one. Each slice is scoped to a few months, delivers measurable value on its own, and leaves the system in a shippable state. If funding stops after phase two, you keep the benefits of phases one and two — you are not left stranded halfway through a rewrite with nothing in production.
That is the common case. We reconstruct behaviour from the running system: characterisation tests derived from real traffic, database analysis, log mining and structured interviews with the people who use it daily. The tests become the specification, and they are usually more accurate than the documentation would have been.
No. Cloud is a means, not the goal, and for some workloads — hardware-attached systems, strict data-residency cases, predictable heavy compute — staying on-premise or hybrid is the cheaper answer. We model the three-year cost both ways and let the numbers decide.
Book a free consultation call. You will speak to a senior architect, not a salesperson, and leave with a technical direction and a realistic budget band — before you commit to anything.