Overview
A financial services company processing thousands of daily transactions was constrained by slow, high-risk deployments and reactive incident management. Despite a capable engineering team, the absence of automation meant that deployment risk was limiting product velocity.
Starting State
- Deployments: 2–4 hour manual process, coordinated over Slack
- Testing: Manual QA cycle required before every release
- Monitoring: 4 separate tools (CloudWatch, Datadog, ELK, custom dashboards) with no unified view
- Incidents: Detected via customer complaints or manual dashboard checks
- Deployment frequency: Every 3–4 weeks
Transformation Programme
CI Pipeline (GitHub Actions)
- Build, unit test, integration test, and container image publish on every PR
- SAST scanning (Semgrep) and container image scanning (Trivy) as required gates
- PR preview environments deployed automatically for QA review
CD with GitOps (ArgoCD)
- All production deployments via ArgoCD — no SSH access to production clusters
- Canary deployments with automated rollback on error-rate increase
- ApplicationSets for consistent multi-environment deployment configuration
Observability Unification (LGTM Stack)
- Prometheus + Grafana + Loki + Tempo replaced all 4 existing tools
- SLO dashboards covering availability, latency, and error rate per service
- PagerDuty integration with on-call routing and escalation policies
- 12 runbooks authored for the most common incident types
Results
Deployment confidence increased immediately — teams began shipping fortnightly rather than monthly within 8 weeks of the new pipeline going live. The 40% downtime reduction came primarily from faster detection (automated alerting) and faster resolution (runbooks + distributed tracing).