Tech that powers what you are actually running.
We work across a broad stack so we can adapt to your environment, rather than asking you to adapt to ours. Our engineers use Copilot and Cursor as part of how they work, with every output reviewed before it ships.
AI and intelligence
Data engineering
Cloud and platform
Application development
Automation and quality
Security and observability
Where our teams
run deepest.
Specialisms where we can move fastest on a requirement, because the depth of experience behind them is broad rather than a single person resume.
AI and applied ML
Engineers building LLM-powered products, RAG systems and AI agents. That means LangChain, LangGraph, fine-tuned models and the MLOps layer that keeps them running in production, not just the initial demo.
Cloud and platform engineering
Multi-cloud architecture on AWS, Azure and GCP, Kubernetes, Terraform and platform engineering for teams that want a self-serve developer platform they can actually hand to a second team without rewriting the runbooks.
Data engineering
Pipelines, lakehouses, real-time streaming and the semantic layer that makes data actually usable. Databricks, Snowflake, dbt, Kafka and the orchestration tooling that keeps it all running.
SDET and quality automation
Engineers who sit at the intersection of development and testing. Playwright, Cypress and Selenium for UI automation, k6 and JMeter for performance, Pact for contract testing, all wired into the CI pipeline rather than run separately afterward.
Python
AI and ML work, FastAPI backends, data pipelines and automation. Python is where most of our AI engineering happens, where a lot of the data work lives, and where scripting connects the systems that do not otherwise talk to each other.
.NET
ASP.NET Core, C# and Azure-native application development. Web, desktop and enterprise backend work for organisations already running Microsoft infrastructure, including migrations from Framework to Core where the business cannot afford a full rebuild.
Salesforce
Sales Cloud, Service Cloud, Experience Cloud and Einstein AI integrations. Custom development, automation and the work of connecting Salesforce to the rest of the stack rather than leaving it as a standalone CRM.
ServiceNow
ITSM, change management, CSM and HR Service Delivery on the Now Platform. Incident, problem and discovery workflows, integrations via IntegrationHub and custom scoped applications where configuration alone does not reach. Now Assist where AI-driven triage actually reduces manual work rather than adding a layer of review.
Java
Spring Boot, microservices and enterprise application development. Java still runs a significant share of enterprise back-end software and we have the engineers who know both the language and the architectural patterns the systems around it usually depend on.
How we decide what
goes into a build.
The stack serves the team that has to maintain it
We pick tools based on what your team can own after we hand over. If a newer framework fits the problem and your engineers know it, great. If the team is Java through and through, we build in Java and we make it good.
AI tooling is part of how we work now
Our engineers use Copilot and Cursor for code generation and test authoring, with every output reviewed before it ships. It is not an experiment. It is how we keep velocity high without trading away code quality.
We evaluate before we commit
New tools earn their place through internal proof-of-concept work, not through enthusiasm after a conference. When something is production-ready, we use it. When it is not, we wait.
Tell us what you are building. We will show you how we would deliver it.
One conversation is usually enough to tell whether we are the right partner. If we are, we will come back with a clear plan. If we are not, we will tell you that too.
Talk to us →