Stop fighting fires. Start engineering reliability.
Your team is good at responding to incidents. The problem is that responding is all they have time to do. Every alert answered at 2am is engineering capacity your product roadmap will never get back.
CirOps engineers SRE practice into your platform: SLOs that reflect real business risk, incident frameworks that reduce mean time to resolution, and on-call structures that do not depend on individual heroics. Reliability becomes a system property, not a person's weekend.
SRE defines and improves the reliability practice. Ongoing operational coverage is agreed separately through Managed Cloud Operations.
SLO-led
reliability objectives
SLO-driven
Error budget management
Defined
Incident response practices
The Problem
Reliability improves when the operating strategy is explicit.
The incidents are not random. The same failure modes surface on the same code paths, in the same conditions, at the worst possible moments. The alerts fire but they're not calibrated to what actually matters. There is no error budget, so every decision about deployment risk is made by feel.
On-call is whoever happens to be available - not a structured rotation with documented runbooks and clear escalation paths. The result: engineers who are competent and well-intentioned but permanently reactive. Time spent on incident response is time not spent on the reliability improvements that would prevent the next incident. The cycle compounds.
SRE breaks this cycle - not by adding more monitoring dashboards, but by introducing an engineering discipline that treats reliability as a feature: measured, owned, budgeted, and improved systematically over time. This is what we build.
Reliability is a system property - not a person's weekend.
When reliability is engineered into the platform, your team stops being the last line of defence and starts being the people who build things that don't break.
What We Deliver
SRE delivered from SLO definition to reliability improvement.
SLO and SLA definition and measurement
We define Service Level Objectives for the critical services and user journeys in scope - availability, latency, error rate, or throughput - calibrated to user expectations and business risk. SLOs are instrumented against the available monitoring stack so the team can review service indicators and error-budget position.
Error budget management
Error budgets make SLOs actionable. We build the error-budget framework, integrate it into engineering decision-making, and document the rules that govern deployment pace when budgets are running low. Teams can use a healthy budget to inform delivery decisions and prioritize reliability work when the budget is under pressure.
Incident response framework setup
We design and implement the full incident response lifecycle: alert routing, severity classification, escalation paths, communication protocols, and post-incident review processes. Every incident type in scope ships with a runbook - specific to your architecture, validated against your actual infrastructure, machine-readable and easy to execute under pressure.
On-call rotation design
On-call is an engineering system, not a volunteer roster. We design rotations that distribute load fairly, set clear coverage expectations, define alert thresholds that minimise noise, and specify escalation logic so every on-call engineer knows exactly what to do when paged. We include on-call health metrics - alert volume, response times, escalation frequency - so the rotation can be improved continuously.
Reliability reviews and observability alignment
We run structured reliability reviews across your architecture: identifying single points of failure, dependency risks, and gaps in observability coverage. We align your monitoring and observability setup to the reliability goals in your SLO framework - so the signals you're measuring are the signals that actually matter to your error budgets.
Process
From reliability audit to operational resilience - four steps.
Reliability Audit
We begin by assessing your current reliability posture: incident history and frequency, existing monitoring coverage and signal quality, on-call structure and load, deployment processes, and architectural risk factors. The audit produces a clear picture of where your reliability gaps are, ranked by business impact. We surface the highest-risk areas first, so improvement work starts where it matters most.
SLO Definition
Working from the audit findings, we define SLOs for each critical service and user journey: what "good" looks like, how it is measured, and what the error budget is for each objective. SLOs are set at levels that are achievable, meaningful to business outcomes, and measurable from your existing instrumentation. We document the rationale for every SLO so your team understands the framework, not just the numbers.
Incident Framework Setup
We implement the full incident response framework: alert routing and severity classification, on-call rotation configuration, runbook creation for every defined incident type, escalation path documentation, and post-incident review templates. The framework is tested with a tabletop exercise before going live - so every engineer on-call has rehearsed the process before they need it for real.
Reliability reviews and improvement
We define an agreed cadence for SLO reviews, incident learning, runbook updates, and prioritized reliability improvements. Ongoing operational coverage is agreed separately through Managed Cloud Operations.
AI-Augmented
AI-assisted reliability engineering, with engineer review.
CirOps engineers use AI assistance within the agreed SRE scope. In reliability work, it can support signal correlation, diagnostic analysis, and draft documentation updates. Engineers validate the evidence and approve operational changes.
AI-assisted incident diagnosis. When an incident fires, AI assistance helps correlate signals across logs, metrics, and traces. Engineers validate the evidence, determine the cause, and decide the recovery action.
AI-assisted anomaly detection. AI-assisted monitoring can flag changes in latency distribution, error rates, or dependency health for engineer review. Detection depends on the available telemetry, thresholds, and workload behaviour.
AI-assisted runbook maintenance. AI assistance can draft runbook changes from reviewed incident evidence and infrastructure context. Engineers approve updates and test the procedures through the agreed review process.
Track Record
What the reliability practice establishes
SLOs
defined for critical services
Service-level objectives and indicators give teams a shared, measurable reliability model.
Runbooks
for incident response and recovery
Operational procedures are documented, exercised, and improved with the people who will use them.
Capacity
planned against demand and risk
Capacity planning turns expected demand into explicit technical and operational decisions.
Frequently asked questions
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Find out where your reliability gaps are - at no cost.
The No-cost Cloud Architecture Review covers your infrastructure, your incident history, your current observability coverage, and the gap between where you are and where your SLOs need you to be. Walk away with a clear picture of your reliability posture - no obligation, no pitch, just an honest assessment from engineers who have built SRE practice in production.