Transition from reactive troubleshooting to high-availability engineering. We build comprehensive observability stacks, automated incident response protocols, and actionable SLO/error-budget discipline — so reliability work is driven by data, not by whoever is paged at 2am.
Who this is for
Teams who are paged reactively more often than they’d like, who don’t have an agreed definition of “reliable enough” for their most important services, or who are scaling past the point where tribal knowledge and manual runbooks hold up.
Expected outcomes
- A small set of SLIs/SLOs tied to what users and the business actually care about, with error budgets that inform — rather than block — the pace of shipping.
- Alerts that page on budget burn and user-facing symptoms, not on every infrastructure metric crossing an arbitrary threshold.
- Documented, testable runbooks and a blameless post-mortem process that feeds learnings back into the system instead of just a document nobody reopens.
Our approach
We typically start with a short discovery phase to baseline current incident load, existing telemetry, and on-call health, then propose a phased plan: instrument the critical path with OpenTelemetry, stand up or consolidate dashboards and alerting, define initial SLOs with your team (not for them), and run a tabletop or chaos exercise to validate the response path before handing it over. Indicative timelines depend on the number of services in scope and your current observability maturity — we’ll size this during discovery rather than quote a generic number up front.
Client responsibilities & exclusions
You’ll need to make time for domain experts to help define what “reliable” means for each service in scope — SLOs set without the team that owns the service tend not to stick. This engagement establishes the practice and tooling; it is not, by default, an ongoing on-call coverage contract or a service-level agreement. Ongoing operational coverage can be scoped separately if you need it.
Related: Kubernetes & CNCF Ecosystem