E-commerce Platform: 40% Faster Incident Detection

Invisible failures during peak load

The client's engineering team had deployed an ELK stack 18 months prior, but it had grown organically into an unmaintainable monolith. All services wrote to a single index, retention was undefined, and dashboard performance was poor enough that engineers had stopped using Kibana during incidents.

During a Black Friday peak, a misconfigured payment service silently returned errors for 22 minutes before an engineer noticed the spike in customer support tickets — not through monitoring.

Full observability stack redesign in 8 weeks

We started with a two-day architecture workshop to map data flows and define SLOs. From there we rebuilt the ELK cluster with proper role separation, introduced data streams per service, and layered Elasticsearch APM on the three highest-criticality services (checkout, payment, inventory).

From blind spots to full visibility

Three months post-deployment, the team runs their peak-traffic periods with confidence. The payment incident that took 22 minutes to detect would now trigger a Elasticsearch alert within 90 seconds via anomaly detection on error rate.