Java has been the default choice for systems that need to keep running for a decade — core banking, policy administration, claims, treasury, payments, order management, regulatory reporting. It compiles to something predictable, the JVM tuning story is well understood, and the ecosystem around Spring, Hibernate, Kafka and the Jakarta EE stack is deep enough that new engineers can be productive without a long onboarding curve. Our Java practice is built around that longevity: we write services other teams will inherit, extend and audit years after we hand them over.
What we build in Java
Most of our Java work is transactional systems where correctness, throughput and traceability all matter at the same time. That includes core ledgers and posting engines for our core banking clients, policy and claims services in the insurance practice, settlement and reconciliation pipelines, KYC/AML orchestration, and integration layers that sit between third-party channels — mobile banking apps, agent networks, aggregators, regulators — and the systems of record behind them.
We also build the less glamorous but equally important pieces: batch jobs that close the day, reporting extracts for RBI, IRDAI, CBK, IRA, NAICOM and central bank returns, file-based interfaces with SWIFT, ACH, RTGS and card switches, and long-running workflow engines that survive restarts. When the requirement is "this must not lose a message and must reconcile at end of day," Java on a properly configured JVM is usually the right answer.
Beyond BFSI, we ship Java for logistics platforms, healthcare claim workflows, government e-services and enterprise back-office systems where the buyer expects a stack their internal team can maintain without importing a niche skill set.
The stack we standardise on
Spring Boot is the backbone for services — auto-configuration, actuator endpoints for health and metrics, profile-based configuration, and a testing story that scales from unit to full slice tests. On top of it we use Spring Data JPA with Hibernate for persistence, Spring Security with OAuth2/OIDC for authentication (integrated with Keycloak, Azure AD or the client's existing IdP), Spring Cloud Gateway or an API gateway of the customer's choice, and Spring Cloud Config or a vault-backed configuration store for secrets.
For asynchronous work and event-driven flows we use Kafka — with proper schema management via Confluent Schema Registry or Apicurio, idempotent producers, and consumers designed for at-least-once semantics with deduplication at the sink. Where the workload fits a classical queue we use RabbitMQ or IBM MQ, and for legacy shops we still integrate with JMS providers already in place. Batch is Spring Batch with chunked processing, restartable jobs and proper JobRepository configuration on the relational store.
Persistence is usually PostgreSQL, Oracle or MS SQL — chosen by the customer, not by us. For read-heavy or search workloads we add Elasticsearch or OpenSearch, and Redis for caching, distributed locks and rate limiting. Where the model is genuinely graph-shaped we bring in Neo4j; where it's document-shaped we use MongoDB. Every service ships with OpenAPI documentation generated from the code, not written by hand, and contract tests where the client-server relationship warrants them.
For newer greenfield work we increasingly use Java 21 with virtual threads, records and pattern matching. For long-lived customer estates we work with whatever LTS they are on — Java 8, 11, 17 — and we're comfortable running mixed-version estates while a migration is underway.
How we engineer
Production microservices with proper layering, DTO mapping, validation, OpenAPI documentation and integration testing — that is the baseline, not the pitch. Every service has a controller layer that only handles transport concerns, a service layer that owns the business rules, a repository or gateway layer for I/O, and explicit DTOs at the boundary so persistence models never leak into the API. Bean Validation annotations, MapStruct for mapping, and a single exception handler per service keep the code readable.
Testing is layered. JUnit 5 and Mockito for units, Spring Boot slice tests (@WebMvcTest, @DataJpaTest) for the layers we care about, Testcontainers for real Postgres/Kafka/Redis in integration tests, and WireMock for downstreams we don't control. For BFSI work we add scenario tests around double-entry invariants, idempotency keys and reconciliation windows — the tests that catch the bugs that end up in audit findings.
Observability is not optional. Every service exposes Micrometer metrics scraped by Prometheus, structured JSON logs shipped to ELK or Loki, and distributed traces via OpenTelemetry into Jaeger, Tempo or the customer's APM (Dynatrace, Datadog, AppDynamics — we work with what's already licensed). Health checks, readiness probes, graceful shutdown and proper connection pool sizing are configured before the first release, not after the first incident.
Delivery runs on containerised builds — Docker images built with Jib or buildpacks, deployed via Helm charts to Kubernetes (EKS, AKS, GKE or on-prem OpenShift/Rancher), with GitHub Actions, GitLab CI or Jenkins pipelines depending on the customer's tooling. SAST via SonarQube, dependency scanning via OWASP Dependency-Check or Snyk, and container scanning as gates in the pipeline.
Where Java sits in a wider engagement
Java rarely ships alone. On most engagements it's the durable core, with a React or Angular front end for internal users and customer portals, Node or Python services for lighter I/O-heavy work, and a CRM or ERP — usually Zoho, Odoo, Salesforce or MS Dynamics — for the customer-facing and operational workflows around it. Our Advanced Zoho Partner and Odoo Official Partner teams work alongside the Java engineers so the integration between systems is designed, not bolted on.
For clients who need to add Java capacity to an existing team rather than commission a full build, we offer named engineers through IT staff augmentation — Spring Boot developers, JVM performance engineers, architects — vetted against the same standards we apply on our own delivery.
Talk to us
If you're modernising a monolith, replacing a vendor core, or building something new where the buying committee has already decided it will be Java, we can help with the architecture, the build and the run. Share the shape of the system on /contact or look through /case-studies for representative work.
