AI-assisted code migrations

Get off the legacy stackwithout the two-year rewrite.

Legacy migrations stall because reading the old system costs more than writing the new one. AI reads all of it — every branch, every stored procedure, every dead code path — in hours. I use that map to port your system deliberately, with tests that prove the new one behaves like the old one.

Scope your migration Read the method

6–10xFaster than a hand-written rewrite

70–85%Lower migration cost

4xBetter p95 latency after cutover

The economics

Where the time and money actually go

A migration is rarely slow because typing is slow. It is slow because understanding a decade-old system is slow. That is the phase AI collapses — and it is the phase that dominates the budget.

Phase

Traditional rewrite

AI-assisted

Codebase archaeology

Mapping what the system actually does — not what the docs say it does

3–5 months

1–2 weeks

Behavior spec & test harness

Characterization tests pinned to the legacy system’s real outputs

2–3 months

2 weeks

Translation & port

Module-by-module rewrite in the target language and framework

8–12 months

4–8 weeks

Parity chase

Hunting the edge cases the old system handled and nobody remembers

3–6 months

Continuous

Cutover & decommission

Shadow traffic, gradual rollout, retiring the old fleet

1–2 months

1–2 weeks

Typical total

17–28 months

8–14 weeks

The clock stops sooner

Every month a migration runs is a month you pay for two systems, freeze the roadmap and keep a team on hold. Compressing 18 months into 3 removes most of that carrying cost before a single line of code is priced.

A smaller team, not a bigger one

Rewrites usually get staffed with a dozen engineers to brute-force the reading. A senior engineer directing AI covers the same ground with far less coordination overhead — and no onboarding cliff.

Legacy licensing and support end

Application servers, commercial JDK support, per-core database licensing, EOL OS extended-support contracts. These line items disappear at cutover and they never come back.

Infrastructure right-sizes itself

Old systems are provisioned for a peak that happens twice a year. Containerised services on autoscaling infrastructure bill for what you use, which is typically a fraction of the standing fleet.

After cutover

What an updated stack buys you

The savings get you to the finish line. The modern runtime is what you live with afterward — and it is usually the part the team feels first.

840ms210ms

p95 API latency

~45s~4s

Cold start to serving

3 hrs9 min

Deploy, commit to production

11%82%

Line coverage

Latency drops because the fat comes out

A decade of accreted layers — an ORM wrapping a DAO wrapping a JDBC template, three serialization hops between services — gets replaced with a direct path. Most of the speedup is deleted work, not a faster language.

Elastic capacity instead of a standing fleet

Containers on Fargate scale on demand and scale back down. You stop paying peak-provisioned prices for a load that only shows up at month-end close.

Deploys measured in minutes

A build-and-push pipeline with rolling task replacement retires the WAR-file-and-restart ritual. Shipping a fix stops being an event that requires a calendar invite.

Async I/O for I/O-bound work

Most business systems spend their time waiting on databases and third-party APIs. Modern async runtimes hold thousands of in-flight requests per task instead of pinning a thread to each one.

Observability that was never bolted on

Structured logs, traces and metrics get designed in during the port rather than reverse-engineered later. You find out about regressions from a dashboard, not from a customer.

A dependency tree you can actually patch

Leaving an EOL runtime and unmaintained libraries behind turns “we cannot upgrade that” into a routine dependency bump — and takes a standing audit finding off the board.

Figures above are from a Java-to-Python engagement. Your numbers will depend on the shape of your workload — I measure the legacy baseline before starting so the comparison is honest.

The method

AI writes fast. The process is what makes it safe.

Handing a legacy codebase to a model and shipping what comes out is how migrations fail loudly. These are the rules that make the speed usable.

Map before you move

Nothing gets ported until I can show you what it does today. The inventory is the deliverable that makes everything after it predictable.

Tests come from production, not imagination

Characterization tests are generated by replaying real recorded traffic against the legacy system. Bug-for-bug parity is the default; every intentional deviation is written down and approved.

Delete before you translate

Access logs reliably show that a meaningful share of a legacy codebase is dead. Porting dead code is the most expensive way to keep it.

One migration at a time

Language, framework, database and cloud provider are four separate migrations. Bundling them multiplies the risk instead of adding it. The data layer usually stays put.

Shadow traffic before real traffic

The new service runs alongside the old one against live requests, returning nothing, while a harness diffs every response pair. Disagreements are found by machines, not by customers.

Keep the rollback warm

Traffic shifts by percentage with the legacy fleet still running. The old system stays up until the new one has a full billing cycle of clean production behaviour behind it.

Migrations I take on

Java → PythonJava → TypeScript.NET Framework → .NET / PythonRuby on Rails → Python / TypeScriptPHP → PythonPerl → PythonAngularJS → ReactOn-prem → AWSEC2 → Fargate / ECSMonolith → servicesOracle → PostgresStored procedures → application code

If your stack is not on this list, it is almost certainly still in scope. The method does not depend on the source language — it depends on being able to read the old system and replay its traffic.

Start with the map, not the rewrite.

Every migration starts with a two-week assessment of your existing system. You get the findings whether or not you hire me for the port.

A complete inventory of what your system does today
The dead code and unused endpoints you can delete instead of port
A phased plan with a real timeline and a real number

Schedule a call Get in touch