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Java EE / J2EE modernization

Out of the app server,
into modern services.

The Java EE monolith — EJBs, an aging Spring core, a heavyweight application server — was the enterprise standard for a reason, and it still carries critical work. We move it to a modern JVM stack and clean services one slice at a time, parity proven before any cutover.

Book a discovery call How the slice loop works →
The stack

A monolith built to the standard of its time.

A legacy Java EE or J2EE application is usually not failing so much as ossifying. Heavy EJBs, container-managed transactions, and a tangle of XML deployment descriptors bind the code to a specific application server and a release cadence measured in quarters. An old Spring version, long past active support, sits underneath. Everything ships together as one deployable, so a change to one corner means a full regression of the whole, and scaling means standing up another copy of the entire monolith. The JVM itself is as strong as ever — the constraint is the framework generation and the architecture, not the language.

What modern looks like

Jakarta EE and Spring Boot, built for ten more years.

The target is concrete: a modern LTS JVM running Jakarta EE or Spring Boot, with the app-server monolith decomposed into domain services along real business boundaries. We keep the runtime upgrade separate from the decomposition — you can clear the javax-to-jakarta move and get onto a supported JVM first, then carve out services as the seams emerge, in any cloud, including regulated and air-gapped environments.

Monolith to domain services
The single deployable is decomposed into domain microservices along real business boundaries, so teams can change and ship one service without regressing the whole.
EJB to a modern JVM stack
Heavyweight EJBs and the application server give way to a current, lightweight JVM framework — no container-managed ceremony, no app-server lock-in.
A modern SPA frontend
Servlet-and-JSP screens become a modern single-page frontend over a clean API layer, decoupled from the server-side render cycle.
Cloud-native and observable
The result is containerized, cloud-portable, observable by default, and able to scale a hot service independently instead of cloning the monolith.
How we do it

The slice loop, applied to Java EE.

A slice is a single behavior — one service, one bounded context. Each is analyzed, rebuilt on a modern JVM stack, validated in shadow against the monolith, and promoted only on parity. A strangler facade shifts traffic gradually; the legacy app retires last.

01
Analyze
AI deep-reads the application — EJBs, the servlet and JSP layer, the old Spring wiring, and the deployment descriptors that bind it to the app server — captured as a living spec. The pass runs 10× faster than manual review: typically 12 weeks down to 2.
02
Transform
One behavior at a time — a single service, a single bounded context — is rebuilt on a modern JVM stack. Scope stays small enough to reason about end to end.
03
Validate
The new slice runs against the Java EE app under shadow traffic until behavior, data integrity, and the edge cases match. The parity gate stands between every slice and production.
04
Cut over
A facade in front of the monolith shifts traffic gradually. Diverge, and it rolls back. The legacy module is decommissioned only once the modern slice is proven.
Where the advice stops
Slice-by-slice depends on being able to carve out a bounded context and observe it in production. Some Java EE patterns resist that cleanly: container-managed transactions and EJB timers that span what should be separate services, JNDI and JMS wiring bound to one application server's implementation, or a javax-to-jakarta namespace break that has to be cleared before a slice can run alongside the old code. We name those hard cases in Discovery rather than after kickoff — and where the groundwork to find real domain seams comes before any slicing, we will tell you. Read the full approach →