Three things a Java platform usually needs, in the order it usually needs them. Spring AI integration, strangler-fig modernisation of legacy monoliths, and the event-driven, observable backbone that keeps both standing under real load.
Each of these names the actual pattern we'd apply, because you deserve to know what you're buying before the kick-off call — not after it.
Spring AI drops into your existing Spring Boot codebase. RAG grounds answers in your own data, with citations, so the model works from facts instead of inventing them — and swapping OpenAI for a self-hosted model stays a configuration change.
Strangler-fig decomposition, route by route, behind a facade. A transactional outbox keeps events and state consistent through the cutover, and every step has a rollback. Nothing goes dark on a Friday night.
Kafka topics with idempotent consumers and dead-letter handling. Circuit breakers, bulkheads and timeouts on every remote call. Traces, metrics and structured logs, so the failing hop is found in minutes, not guessed at.
Add RAG, semantic search, chat and MCP servers to your Java apps — explore our dedicated Spring AI services.
We do not claim to do everything. This is the list — Spring AI at the front, and the backend depth underneath it that makes an AI feature survive a real production load.
Retrieval-augmented generation, semantic search and MCP servers inside your existing Spring Boot app — grounded in your data, portable across OpenAI, Azure or self-hosted models.
/02Spring Boot services on Kafka with the transactional outbox, idempotent consumers, dead-letter handling and circuit breakers — so a failed stage never becomes a lost order.
/03Java 8/11 monoliths taken apart with the strangler-fig pattern and moved to Java 21 — route by route, with the old system still serving traffic and a rollback at every step.
/04Apache Spark pipelines that are idempotent and resumable, with row-level and aggregate reconciliation between source and target — a failed run picks up where it stopped instead of corrupting the target.
/05Terraform for infrastructure, Helm and ArgoCD for delivery, Kubernetes on AWS or GCP — every environment reproducible from a commit, not from someone's laptop.
/06Circuit breakers, bulkheads, retries with backoff and sane timeouts — plus traces, metrics and structured logs, so you find the failing hop in minutes rather than guessing.
/07Unit, integration and end-to-end suites, Testcontainers for real dependencies, k6 and JMeter load profiles, and SAST scanning wired into CI — reliability you can point at, not assert.
/08React and Next.js interfaces built against your own APIs — including the chat, search and admin surfaces that AI features need in order to be useful to a human.
Flexible engagement models that fit businesses of every size — from fast-moving startups to large enterprises.
A senior, embedded squad of architects and lead developers that works as an extension of your team — same faces, week after week.
Fixed-scope delivery for a defined platform, AI feature or migration, with clear milestones and transparent reporting.
Drop Spring Boot, Kafka or Spring AI specialists into your team to close a specific capability gap fast.
Tell us about your application and goals — we'll map the engineering path.