Engineering Infrastructure, DevOps, and Platform Services for Technology Companies β because the teams building products deserve internal tooling that actually works.
IT companies are the most demanding clients we work with β and deliberately so. When your client is a software team, they will read the pull request, they will notice the architectural shortcuts, and they will find the performance regression that your test suite missed. We welcome that scrutiny because it makes us better. The work we do for technology companies is typically internal platform engineering β the developer experience tooling, CI/CD infrastructure, test automation frameworks, and observability stacks that let product engineering teams move fast without breaking things. It is the work that is too important to deprioritise and too specialist to hire for in a single dedicated role, which is exactly why teams keep leaving it undone until the pain is no longer ignorable.
Internal developer portals, self-service infrastructure, golden path templates, and the scaffolding that lets a new engineer make their first production deployment on day two rather than week three. Built on Backstage, GitOps, and the toolchain your teams already use β not a greenfield replacement that requires a year of migration.
From slow, flaky Jenkins pipelines to fast, reliable GitHub Actions or GitLab CI workflows with parallelisation, caching, ephemeral environments, and SAST/DAST gates that catch problems before they reach production β not after a user files a ticket on a Friday afternoon.
Selenium, Playwright, Cypress, or k6 β designed around your application's architecture rather than copy-pasted from a tutorial. Realistic data generation, parallel execution, and reporting that tells engineers what broke and why, not just that something did.
Right-sizing, reserved instance planning, spot instance orchestration, and the architectural changes β not just configuration tweaks β that reduce your AWS or Azure bill by 30 to 50 percent without touching your SLA commitments or asking your engineering team to change how they work.
Distributed tracing, structured logging, and metrics collection built around your application's actual failure modes β not a generic dashboard that alerts on everything and explains nothing. OpenTelemetry, Prometheus, Grafana, and Phoenix for AI workloads.
Strangler fig migrations from monoliths to microservices, database refactoring, and API gateway layers that let you ship the new architecture incrementally rather than betting the company on a big-bang rewrite that takes three years and costs twice the original estimate.