The most expensive modernization is the rewrite that never finishes. Mature systems contain years of business rules that are documented nowhere except in the code. We modernize incrementally: each step is small enough to test, release and roll back, and the business keeps running throughout.
Java and Spring upgrades without surprises
Moving from Java 8 or 11 to 17, 21 or 25, and from Spring Boot 2 to 3 and 4, touches far more than the version number: the javax to jakarta namespace change, Spring Security configuration, removed APIs, changed defaults, and third-party libraries that may not have followed. Open-source support for Spring Boot 3.5 ended in mid-2026, so many teams now face two major steps at once. We map every blocker first, plan the order of upgrades, and automate the mechanical part with OpenRewrite recipes where they are reliable. The judgement calls stay with engineers.
From Java EE to Jakarta EE, Spring or Quarkus
Many applications still run on older Java EE application servers. Sometimes the right move is a modern Jakarta EE runtime, sometimes Spring Boot, sometimes Quarkus. We look at what the application actually uses (transactions, messaging, security, remote interfaces) and choose the target with the lowest risk and long-term cost, not the one that is fashionable.
Hibernate and persistence upgrades
Hibernate 6 and 7 changed the query engine, the type system and several defaults. Queries that worked for years can return different results or generate very different SQL. We compare the SQL before and after, cover critical queries with tests against a real database, and fix mappings deliberately rather than suppressing warnings.
Reducing technical debt where it costs most
Not all technical debt deserves attention. We look at where change frequency and complexity meet: the modules your team touches every week and fears every time. Removing unused dependencies and dead code, clarifying module boundaries and adding tests in those places pays back quickly. Debt in stable code that nobody changes can often wait.
Incremental decomposition
When a monolith has become a bottleneck, the answer is rarely to split it into dozens of services at once. We usually start by making it a well-structured modular monolith, then extract services only where independent deployment or scaling justifies it, using the strangler fig pattern and anti-corruption layers so old and new can run side by side.
Managing regression risk
Every modernization step is protected: characterization tests that capture current behaviour, parallel runs that compare old and new results, feature flags, canary releases and a rollback plan agreed before each release. Production stability is the constraint every decision is measured against.