Five practices that cover the lifecycle of an enterprise system — from the first architecture decision to the on-call rotation that keeps it running.
Most engagements combine two or three. A data platform needs cloud foundations; a custom system needs somewhere to run.
Systems built around how your business actually works — not around the constraints of an off-the-shelf product.
Explore this serviceNative and cross-platform applications built for the network conditions and devices your customers actually have.
Explore this serviceAWS and GCP landing zones, migration from on-premise, and the platform work that makes cloud spend predictable.
Explore this serviceThe engineering harness around the model — context, memory, agent orchestration and evaluation loops that make AI reliable in production, not just a demo.
Explore this serviceOngoing engineering ownership, architecture review and the senior judgement you need occasionally but cannot justify hiring full time.
Explore this serviceWe spend the first weeks on your domain and constraints. A proposal written before that is a guess with a price on it.
The first increment goes through the hardest integration, not the easiest screen. Unknowns get cheaper the sooner they surface.
Working software in your environment every sprint. Progress you can click on beats progress in a status report.
Documentation, runbooks and paired sessions so your team can take it — whether or not you keep us on support.