Platform & Cloud · FOUNDATION + SKYWAY
CI/CD pipelines that gate on the right things, IaC that owns production, observability stacks the on-call rotation can actually use, and named SLOs in the SOW. Senior platform engineers who run what they ship.
The problem
The shape: a CI pipeline that an engineer wrote three quarters ago and has been duct-taping ever since; observability that's a Datadog dashboard nobody opens because the signal is buried; on-call rotations that exist on paper but route to the most senior engineer regardless of fairness; runbooks that are a wiki page from 2023; and SLOs that get cited in design docs but never enforced. Reliability decays quietly between incidents.
We engineer the deployment, observability, and operating cadence as a continuous discipline. CI/CD with policy-as-code gates that prevent regressions rather than detect them. Observability designed around the questions on-call actually asks at 2 AM. Named SLOs with error budgets that drive engineering priority. Runbooks that ship with the application code. The result is a platform that compounds reliability over quarters, not one that decays between launches.
Where it ships
Specific applications we’ve built and operated. Not speculative: every example below is grounded in a real shipped engagement.
8x
deploy frequency
GitHub Actions, GitLab CI, Argo CD, or Jenkins: picked by fit, not by hype. Build pipelines, deployment promotion, policy-as-code gates, secrets handling, and progressive delivery (canary, blue-green, feature-flag-gated).
OpenTelemetry instrumentation, structured logging, metrics, distributed tracing, and SLO dashboards. The questions on-call asks at 2 AM are designed for, not retrofitted.
Named SLOs per service, error budgets, runbooks per signal class, on-call rotation, blameless post-mortems, and a quarterly SLO review cadence. Reliability as an engineering practice, not a milestone.
Documented IR plan rehearsed quarterly, post-mortem templates, and the data pipeline that surfaces the leading indicators of regression. We help engineering organizations move from reactive to anticipatory.
Argo CD or Flux as the production source of truth, with environment promotion through pull requests rather than ad-hoc kubectl. Policy-as-code prevents drift; signed commits enforce attribution.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion. Not slogans, operating rules.
Discovery starts with the question 'what does reliable mean for this workload, and what does the customer pay for it'. SLOs and error budgets calibrated to user impact, not to engineering convenience. The targets land in writing before tooling decisions.
CI/CD pipelines with policy-as-code gates (OPA, Conftest, Checkov), secrets handled through cloud-native secret managers, and progressive-delivery support (feature flags, canary, blue-green). Every resource lives in IaC; no untracked drift.
OpenTelemetry instrumentation in application code, structured logging with consistent schema, metrics that map to SLOs, and runbooks that ship as code alongside the application. The on-call rotation has actual support, not a wiki link.
Pager rotation with explicit pager-volume targets, weekly on-call retrospective, monthly SLO review, quarterly architectural review against incident patterns. We measure pager fatigue and design against it.
Capabilities
Stack
Selected work
Common questions
Augment is the most common pattern. We embed senior platform engineers alongside your team, share the same backlog, and ship through the same CI. Where we do greenfield platform work end-to-end, we hand off with documentation and a 90-day shadowing period.
One senior platform engineer for one quarter. Below that, the engagement model and department backing don't pencil out. Single-task work (e.g., 'set up our GitHub Actions') is better served by a freelancer or your existing team.
Yes: through Managed Services. Named SLOs in the SOW, our engineers in the pager rotation, and a published response-time matrix. Most engagements run as shared rotation with your engineering team rather than as a black-box outsource.
All three. We pick by fit. Most workloads are better served by managed services (ECS, App Runner, Cloud Run, container-based serverless) than by Kubernetes. We deploy Kubernetes when the workload genuinely needs it, and we will tell you when it doesn't.
Calibrated to user impact, not engineering convenience. Discovery includes a workshop with product, engineering, and customer-facing teams to identify the user journeys that matter and the failure modes that hurt. SLOs land in writing in the SOW; they're enforced through error-budget-driven roadmap conversations.
Pipeline + IaC build for one workload: 2–4 months, $150K–$500K. SRE program with SLOs, observability, on-call: 4–8 months, $400K–$1.2M. Embedded platform squad: $30K–$80K per month per engineer. Managed Services: $30K–$100K monthly retainer. Brackets published honestly so visitors self-qualify before the first call.
Within Platform & Cloud
Talk to us
A senior engineer plus the FOUNDATION + SKYWAY department lead joins the first call. No discovery gauntlet, no junior reps.