We assess before we build, name the tradeoffs before you commit, and hand back something your team can run without us. The shape of the engagement follows the problem rather than a standard package.
We lead with the outcome and then explain the mechanism. If a recommendation depends on an assumption we cannot verify, we say which assumption and what would change if it turns out to be wrong.
Findings are written about systems, not people. If something is badly broken, you’ll hear the worst of it first rather than at the end of a deck.
Access to the environment early, an internal owner who can make decisions, and a willingness to hear that the first answer might be “this problem is not the one you asked us to solve.”
Capabilities
Designing AWS environments that survive growth, audit, and staff turnover.
Account and network structure, identity boundaries, data flow, failure modes. Delivered as infrastructure as code so the design and the running system cannot drift apart.
A written account of what you have, what it costs, and where the risk sits.
We inventory the environment, compare it against the workloads it actually carries, and rank findings by consequence rather than by scanner severity. You get the worst finding first.
Making releases routine and repeatable, so problems surface before your customers see them.
Terraform, CI/CD, Kubernetes platform design, and monitoring that alerts on user-visible symptoms rather than on every metric that moved. Behind it, experience running Linux fleets in the thousands. The goal is fewer people needed in the room for a routine release.
Making the AWS bill explainable before trying to make it smaller.
Tagging and allocation first, so spend maps to teams and products. Reductions come second, and we tell you which ones trade cost against resilience.
Standing in for a cloud leader you haven't hired, or working alongside the one you have.
Strategy, governance, vendor decisions, and the work of getting a plan agreed across engineering and finance. Usually fractional — sometimes covering a vacancy, more often giving an existing leader senior backup and a second opinion to test decisions against.
Building for HIPAA, SOC 2, and PCI DSS rather than papering over them.
Identity, encryption, network boundaries, logging, and incident response, with evidence collection designed in from the start — so an audit reads what the system already records rather than reconstructing it after the fact.
Infrastructure sized against real workloads rather than a vendor sizing guide.
GPU-accelerated infrastructure for model training and inference, multi-node cluster design for medical image analysis, and multi-petabyte storage strategy. Specified against benchmarks and production metrics, because at this scale a sizing mistake is expensive to unwind.
Engagement
A conversation, then a written scope with our estimate of what the work will take, billed at an hourly rate. If we think the work is smaller than you asked for, we say so here.
We look at the real environment, not the diagram of it. This stage produces a written findings document ranked by consequence, and it is useful on its own if you stop here.
We name the tradeoffs — what gets faster, what gets more expensive, what we are choosing not to fix yet — and you decide. Nothing gets built before this.
Work lands in your repositories and your accounts, as code, reviewed by your team. There is no black box we hand over at the end.
Documentation, a walkthrough with the people who will operate it, and a defined end. A consultancy that cannot be fired without breaking your platform has failed.
What good looks like
Most of what we do on an architecture engagement comes back to where the boundaries sit, and this is one shape we reach for often. The boundary that matters is the account: a team can move quickly inside its own account, and a mistake there cannot reach another team's account or its audit trail. What varies between clients is how many accounts, and where the network boundary sits.
Our first conversation is about your problem, not a proposal.