Production SRE & Observability
A fast-growing B2B SaaS company ran production workloads on Kubernetes and AWS but lacked a formal reliability practice. Engineering teams faced constant alert fatigue, no agreed SLOs, fragmented logs and metrics, and long mean time to recovery when customer-impacting incidents occurred.
Business impact at a glance.
Mean time to recovery for Sev-1 incidents decreased by approximately 40%.
Alert noise to on-call engineers reduced by roughly 60% after SLO-based routing.
SLO compliance and error budget status visible to engineering and leadership weekly.
Release-related customer-impacting incidents declined over two consecutive quarters.
Postmortem action items tracked to completion with measurable follow-through.
Business Challenge
- On-call engineers received hundreds of noisy alerts with no clear severity or ownership.
- No SLIs, SLOs, or error budgets to guide release decisions or capacity planning.
- Metrics lived in CloudWatch, logs in multiple tools, and traces were rarely used.
- Incident response relied on ad-hoc Slack threads with inconsistent postmortems.
- Recurring production outages followed major releases without reliability gates.
Our Approach
We led a structured SRE engagement: defined critical user journeys and SLIs, implemented a unified observability stack, redesigned the incident lifecycle with runbooks and blameless postmortems, and introduced release reliability checks aligned to error budgets.
Phase 01
Discovery & alignment
Workshops, process and systems review, success metrics.
Phase 02
Design & planning
Architecture, experience and workflow design, delivery plan.
Phase 03
Build & validation
Implementation, integration, testing, demos, refinements.
Phase 04
Go-live & enablement
Controlled rollout, training, handover, post-launch tuning.
What We Delivered
- Defined SLIs and SLOs for availability, latency, and error rate on core API and background job paths.
- Deployed Prometheus and Grafana with standardized dashboards and SLO burn-rate alerts.
- Consolidated log correlation and trace sampling for faster incident triage.
- Introduced PagerDuty routing, on-call runbooks, and severity-based escalation paths.
- Added CI/CD reliability gates and pre-release checks tied to error budget policy.
- Ran game-day exercises to validate incident playbooks and on-call readiness.
Technology Stack
We finally have production reliability we can measure and improve. Incidents are calmer, releases are safer, and leadership trusts our uptime numbers.
Director of Platform Engineering
B2B SaaS
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