Off end-of-life systems and platforms, incrementally, without the big-bang rewrite.
We move enterprises off aging, end-of-life, and unsupported systems — one slice at a time, with behavior preserved and a tested way back at every step.
The system stays live while it moves. Fixed scope, one accountable lead, payment tied to the outcome, full IP assigned to you — landing in production in tranches over weeks, not a years-long freeze.
Because the cost is invisible until it isn’t. An end-of-life platform doesn’t bill you for a rewrite — it bills you in security patches that no longer ship, a budget that gets eaten keeping the old thing alive, and a shrinking pool of people who can still work on it.
Then a dependency hits end of support, a compliance auditor flags the unsupported stack, or the one engineer who understood it leaves, and the migration you deferred becomes the emergency you can’t schedule.
The scale of that drain is documented in public record: most of the money goes to standing still. It is the clearest illustration of the legacy tax. A legacy migration done right is how that diverted budget comes back — and getting it done without breaking the business is the entire job.
“Legacy migration” isn’t one motion. It’s a set of specific moves off specific dead ends, each chosen for a system that’s holding the business back. For each, what it does, the benefit, and how it plays out:
Moves code off an unsupported language or framework version — an end-of-life PHP, .NET Framework, Java, or AngularJS — onto a current, supported one, preserving behavior as it goes. Benefit — security patches return, hiring gets easier, and the platform can take new features again.
Example: an app stranded on a framework that stopped receiving security updates is migrated version by version, so the team can patch a vulnerability the same week it’s disclosed instead of being told the fix doesn’t exist for their version.
Moves a workload off a platform the vendor has sunset — an unsupported OS, a discontinued application server, an end-of-support runtime — onto a current one, running old and new side by side rather than flipping a switch. Benefit — you exit the compliance and outage risk of unsupported software, without a high-stakes single cutover.
Example: a service running on an OS past its support date — a finding every auditor flags — is migrated onto a supported platform slice by slice, clearing the audit item before it becomes a failed control.
Moves data off an end-of-life or expensive database engine to a current, supported one — with schema mapping, record-for-record reconciliation, and a rehearsed switchover that keeps the system available. Benefit — the data moves without loss and without taking the business offline.
Example: a database nearing end of support is replicated and reconciled ahead of time, then cut over in a planned window with a tested rollback — so customers never notice the engine changed.
Stands up the new system behind a routing layer that shifts live traffic from old to new piece by piece, reversibly, instead of a single all-or-nothing switch. Benefit — every step is small, verified, and reversible, so risk is bounded to one slice at a time.
Example: a checkout flow is migrated route by route while the rest of the monolith keeps serving — if a re-routed slice misbehaves, traffic flips back to the old path in seconds, and the migration continues rather than rolling all the way back.
Captures what the legacy system actually does — including the undocumented quirks the business now depends on — into automated tests that guard every cutover. Benefit — the new system does what the old one did, including the edge cases nobody wrote down.
Example: a billing rule that only ever lived in twenty-year-old code is pinned by a regression test before the migration, so the new system reproduces it exactly instead of quietly changing a customer’s invoice.
Moves a system off end-of-life hardware or an expensive licensing model onto a modern, supported footprint — often, but not always, the cloud. Benefit — the recurring cost and the looming hardware deadline both go away.
Example: an app pinned to aging on-prem servers facing a refresh is re-platformed onto a supported environment, trading a large hardware refresh and a renewing license for a footprint the team can actually maintain.
The scope below is the difference between a migration that lands in production and a rewrite that stalls. This is the move off legacy specifically — for the broader re-engineering program around it, see our application modernization work; when the destination is specifically the cloud, see cloud migration services.
We inventory the legacy estate, map what depends on what, and surface the undocumented behavior and the systems that should be retired rather than moved — run as our readiness and discovery assessment, ending in a ranked, costed, slice-by-slice plan before any code is touched.
We design the strangler-fig path: the routing layer, the seams where old and new can run side by side, and the order of slices — so the migration is a sequence of small, reversible steps rather than one switch.
We execute the per-slice move — off an unsupported language or framework version, off a deprecated platform — preserving behavior, with each slice verified against the legacy system before the next begins.
We map the schema, replicate and reconcile the data record-for-record, and run a dress-rehearsed cutover in a planned window with a tested rollback — so the data move is a non-event, not an outage.
We capture the legacy system’s real behavior — quirks included — into an automated test suite that guards every cutover, so the new system matches the old one where it must and the edge cases nobody documented don’t get lost.
Every migration runs under an NDA and a security review; for regulated estates (fintech, healthcare) controls and audit trails move with the workload. After cutover we harden the new stack, document it, and train your team to own and extend it.
What you get when you hire us — all assigned to you under full work-for-hire
The same delivery model behind all our modernization work, tuned for migrations — one accountable lead, fixed scope, payment tied to the outcome, no handoffs.
Inventory the estate, map dependencies, surface undocumented behavior, and decide what migrates versus what retires.
Output: a slice-by-slice plan, a costed case & success metrics
Design the strangler-fig routing and the order of cutovers, smallest-risk first, with a rollback path defined for each slice.
Output: a sequenced cutover plan, rollback per slice
Build each slice on the modern stack and run it in shadow against the legacy system, comparing behavior before any traffic moves.
Output: a verified slice & a passing behavior-parity test set
Shift live traffic to the new slice in a planned window, rollback ready — the legacy equivalent goes dark, scheduled for retirement.
Output: a slice in production, legacy equivalent retiring
Migrate the next slice, then retire the legacy footprint and train your team to own the fully migrated system.
Output: a fully migrated system you own, old stack decommissioned
Each slice reaches production on its own; most engagements reach steady state in 4–8 weeks per tranche.
The hardest part of legacy migration isn’t the first move off an old platform — it’s doing it again and again, as each new stack ages in turn, without ever taking the live system down. That repeated, downtime-free discipline is the one we’ve held longest, woven through the work above rather than parked in a testimonial.
We’ve run Bridge Athletic since its 2012 startup build, and over 12+ years we’ve carried that product through multiple rounds of legacy migration, re-platforming, and code re-engineering — moving off each aging stack as it reached its limits, paying down technical debt every pass, while the product never went offline.
It grew into a platform now used by USC, the LA Rams, and MLB and MLS teams. That is what a migration done right looks like over time: not a one-off lift, but a system that keeps moving off its dead ends and keeps running.
That incremental approach isn’t a preference — it’s what the evidence supports. The Standish Group’s CHAOS research found large, all-at-once projects succeed less than 10% of the time against roughly 90% for small ones (CHAOS Report, 2020), which is exactly why we migrate in small, reversible slices.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, who has delivered multimillion-dollar systems for one of the world’s largest automobile manufacturers and is personally accountable for every engagement. We’ll tell you plainly when a system should be retired or left alone rather than migrated — which a vendor billing by the rewrite won’t.
We’ve done the decade-long version. A product carried through 12+ years of legacy migration and re-platforming without ever going offline (Bridge Athletic) is a different proof than a one-time lift — and it’s the standard we bring to your cutover.
Incremental and reversible by default. We migrate in small slices behind a routing layer with a tested rollback on each, because the data on big-bang rewrites is unforgiving. Reversibility isn’t a premium tier; it’s how we work.
Behavior preserved, not approximated. The migration is guarded by tests built from your system’s real behavior — including the quirks the business depends on — so the new stack does what the old one did.
Destination-agnostic. We migrate to whatever fits — a modern on-prem footprint or the cloud — chosen on your constraints, not a partnership.
Built to transfer. The migrated system, the test suite, the cutover runbooks, and the documented retirement of the old stack are assigned to you, and your team is trained to run it when we step back.
Migrating transactional and decisioning systems off end-of-life platforms where data integrity and an audit trail must survive every cutover intact. Fintech software →
Moving patient and clinical systems off unsupported stacks into HIPAA-compliant architectures, with controls and logging that travel with the workload. Healthcare software →
Migrating the aging monoliths behind checkout and catalog off the versions that buckle at peak, slice by slice, outside the selling season.
What teams want to know before they move off an end-of-life stack.
We migrate in slices behind a routing layer — the strangler-fig pattern — instead of a single switch. Each slice is built on the modern stack, run in shadow against the legacy system to confirm it behaves identically, then cut over in a planned window with a tested rollback ready. If a re-routed slice looks wrong, traffic flips back in seconds and the migration continues. We’ve carried a production platform (Bridge Athletic) through 12+ years of migration and re-platforming without it ever going offline; that downtime-free discipline is the default, not the premium tier.
Rarely, and only when an incremental path genuinely isn’t possible. The evidence is against it: the Standish Group’s CHAOS research found large projects succeed less than 10% of the time versus ~90% for small ones. A big-bang rewrite concentrates all the risk into one launch that rarely lands; an incremental migration spends that risk down a slice at a time. We’ll tell you honestly if a clean-slate rebuild is the right call — but it’s the exception, not our default.
We pin the legacy behavior into tests before we move it. That includes the undocumented quirks the business has come to depend on — the billing rule that only ever lived in old code, the edge case nobody wrote down. We capture them as automated regression tests, run the new slice in shadow against the legacy system to compare outputs, and only cut over once the behavior matches. The point is that the migration is invisible to your users and your data, not a behavior change in disguise.
Legacy migration is the move off end-of-life systems and platforms specifically — old language and framework versions, deprecated platforms, databases nearing end of support. When the destination is specifically the cloud, that’s cloud migration services; when the work is the broader re-engineering program around a system, that’s application modernization. The three overlap, and one engagement often touches all three — we scope which one you actually need rather than selling the biggest.
We map the schema, replicate the data, and reconcile it record-for-record against the source before anything cuts over. The switchover is rehearsed in a planned window with a tested rollback path in place, so if reconciliation surfaces a mismatch we revert without loss. For fintech and healthcare estates, audit trails and compliance controls are part of the cutover plan — they move with the data, not after it.
Each slice reaches production on its own, and most engagements reach steady state in 4–8 weeks per tranche, under a fixed-scope engagement with one accountable lead and payment tied to the outcome. Total cost depends on the size of the estate and how many slices need real re-engineering versus a straightforward version migration — our development cost guide gives real ranges, and the assessment turns it into a fixed number before the migration starts. There’s no open-ended “rewrite the whole thing” line item.
You do — completely. The migrated system, the behavior-parity test suite, the cutover and rollback runbooks, and the documentation of the retired legacy stack transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate and extend it. Keep us on a reduced retainer or take the keys; the engagement is built around the handover.
Thirty minutes · No pitch deck
Bring the system you’re stuck on — an end-of-life platform, an unsupported framework, a database past its support date — and we’ll map the dependencies, name the trade-offs, and lay out a costed, incremental path off it that keeps the business running the whole way.