Stack
Our Technology Landscape
We use the technologies shown below most frequently across our projects, with Java and the JVM ecosystem forming a significant part of our application engineering work. Our engineering is not limited to this stack; technology choices depend on the system, its requirements, and the problem being solved.
Application Engineering
Java and Spring Boot form a significant part of our application engineering work, complemented by Kotlin, Android, and Swift where the platform and system require them. We build and evolve backend services, APIs, concurrent workloads, distributed application components, and mobile applications, with architectural decisions grounded in runtime behaviour, resource usage, and operational requirements.
Data, AI & Computational Systems
Python is used across data analysis, modelling, and specialized computational workloads, including energy and smart-grid systems. Our work also includes neural computing, machine learning, physics-informed approaches, and other computational methods where software needs to reflect complex domain behaviour. C++ and other specialized technologies are used where workload characteristics or performance requirements justify them.
Data & Persistence
PostgreSQL is often our primary relational choice, while Oracle and MySQL remain relevant where existing systems or specific requirements call for them. We also work with MongoDB, Cassandra, Neo4j, and Elasticsearch when document, distributed, graph, or search-oriented workloads justify a different persistence model.
Messaging & Distributed Systems
Kafka, RabbitMQ, and Redis are used according to the role they need to play in the system — from event streaming, CDC, and asynchronous processing to work queues, coordination, caching, and low-latency distributed state. We design messaging and concurrency around delivery semantics, ordering, backpressure, failure behaviour, and operational visibility.
Infrastructure & Runtime
Kubernetes, containerd, Terraform, and Linux form a significant part of our infrastructure work, from declarative environments and containerized workloads to deployment and runtime operations. We treat infrastructure as part of system engineering, with attention to resource behaviour, configuration drift, failure containment, observability, and repeatable delivery.
Technology Is Part of the Architecture
Technology choices should follow the system, not the other way around. If you are evaluating a new stack, evolving an existing platform, or dealing with technical constraints that require deeper engineering, we can discuss the system and the decisions behind it.