Fast under load. Standing after failure.
Reactive architectures, functional programming and modern language ecosystems: applications that stay responsive under load, resilient to failure and elastic by design.
Four properties, and you cannot pick three.
The Reactive Manifesto is not branding. Drop message-driven and elasticity goes with it.
Responsive
Systems that respond in a timely manner under all conditions: the basis of both usability and fault detection.
Resilient
Responsive in the face of failure, through replication, containment, isolation and delegation. No single failure cascades.
Elastic
Responsive under varying load, scaling resources with demand and avoiding contention points and central bottlenecks.
Message-driven
Asynchronous message passing for loose coupling and location transparency, which is what makes backpressure possible.
Six ways we engage.
From a two-week prototype to an embedded team. The engineering standard does not change with the engagement shape.
Functional programming & fast data
Immutable structures, pure functions and type-safe abstractions in Java, Scala and Rust: pipelines processing millions of events per second at predictable latency.
Rapid prototyping
Production-quality prototypes that validate an idea in weeks rather than months, so market feedback arrives early.
Application development
Full-lifecycle development from requirements to maintenance, with teams that integrate into your practices rather than around them.
Offshore & nearshore teams
Extend engineering capacity in overlapping time zones, with rigorous hiring standards and transparent process.
Legacy modernisation
Monoliths transformed incrementally: strangler fig, API-first decomposition and event-driven migration that keeps risk bounded.
Performance engineering
Systematic optimisation of throughput, latency and resource use: load testing, profiling, JVM tuning and architectural refactoring.
Chosen for the constraint, not the CV.
Each of these earns its place on a specific class of problem. We will tell you when the boring option is the right one.
JVM ecosystem
Java, Scala and Kotlin with Akka, Spring WebFlux and ZIO for concurrent distributed systems
Systems programming
Rust and Go where memory safety and predictable latency are non-negotiable
Stream processing
Kafka, Flink, Spark Streaming and Akka Streams for event sourcing and CQRS
Cloud-native
Kubernetes, Docker, Terraform and serverless for elastic auto-scaling deployment
What we bring to the codebase.
Specific practices rather than adjectives, each one is something you can ask us to demonstrate.
Build something remarkable.
Bring us the service that keeps falling over under peak load. We will profile it before we quote for it.