Technologies
AI workloads are spiky, expensive and stateful in awkward ways. The infrastructure underneath has to make cost visible, scale without drama, and let you answer what changed when something breaks at four in the morning.
From a first deployment pipeline to running the whole platform behind a product.
Environments defined in a repository rather than assembled by hand in a console.
Shipping that is boring, repeatable and fast enough to do several times a day.
Running services reliably without over-engineering the platform.
The infrastructure specifics of running models and agents in production.
Knowing what is happening before a customer tells you.
Making the bill explainable, then making it smaller.
Least privilege as the default rather than a later hardening exercise.
Moving what you have without stopping the business to do it.
Cloud problems rarely announce themselves. They arrive as a bill, an outage, or an audit question nobody can answer.
Inference spend does not scale with users in any intuitive way. Without attribution, the first signal that a feature is uneconomic is a monthly invoice several times larger than expected.
The environment was built by hand over two years by people who have since left. It works, but nobody can recreate it, and nobody is willing to change it.
A channel full of CPU warnings trains a team to ignore alerts entirely. When something genuinely breaks, the signal is buried in noise.
Chosen for what a project needs, not for novelty. Everything here is something we run in production and can support.
Six stages, each ending in something you can review. No long silences between kickoff and delivery.
01
We inventory what is running, what it costs and what is undocumented, then agree targets for availability, recovery time and spend.
02
Accounts, networking, identity and environment boundaries are designed up front, because these are the hardest things to change later.
03
One service taken end to end through the new pipeline into a real environment, proving the approach before everything follows it.
04
Infrastructure codified and workloads moved incrementally, with both paths live during cutover and a rehearsed way back.
05
Permission review, failure injection, restore rehearsal and load testing — so recovery is something we have done rather than something we assume.
06
Dashboards, alerting and cost reporting handed over with runbooks, so your team can operate it without us on call.
We are a software engineering team that works on AI, not an AI team learning to write software.
01
The same team builds the applications running on it, so the platform is shaped around real workloads rather than a reference diagram.
02
Infrastructure is code, reviewed like code. If it only exists because someone clicked something once, it is not finished.
03
Spend is attributed per feature and alerted on, so an uneconomic AI feature is caught in week one rather than at the quarter.
04
Backups get restored and failovers get exercised. An untested recovery plan is a hope, not a plan.
05
We have no reseller margin on any cloud. The recommendation is based on your workload and your team's experience.
06
Your accounts, your repositories, your runbooks. Nothing here requires us to stay.
Tell us what you are running or what is costing more than it should. We will come back with an honest view of the effort involved and where we would start.
