Infrastructure Observability Baseline for Growth-Stage Support Teams

Growth-stage teams often have monitoring tools, but still learn about incidents from customers first.

That is not a tooling problem alone. It is usually a signal-design problem.

Observability Baseline: What to Instrument First

Start with the signals that impact customers directly:

• request success rate by key service path,
• p95/p99 latency for customer-facing actions,
• dependency health (identity, payments, messaging, data stores),
• queue lag for async workflows,
• support-ticket surge by incident keyword.

This is where operations and customer support should work from a shared dashboard.

Alerting Rules That Reduce Noise

Too many teams alert on everything and trust nothing.

A practical model is:

1. Page alerts for confirmed customer impact.
2. Action alerts for early risk indicators.
3. Digest alerts for trends and technical debt tracking.

If every alert feels critical, none of them are.

Real Delivery Example

A fintech support environment we supported had over 300 active alerts, but incident detection still lagged.

Red Shore worked with the client to redesign alert tiers and align support routing with telemetry.

Results after eight weeks:

• 41% reduction in non-actionable alerts
• Median incident detection improved from 18 minutes to 6 minutes
• Support received pre-written impact notes for top incident classes

That last point reduced escalation confusion and improved customer confidence during live events.

Make It Useful for Frontline Teams

Observability should not live only in engineering dashboards. Support leaders need a “what this means for customers” view:

• affected features,
• expected response language,
• known workaround status,
• next update timestamp.

This is where reliability and customer experience meet.

If You Do One Thing This Month

Audit the top 20 alerts that fired last month. Mark each one as actionable or noise. Then delete or downgrade at least 30% of noise alerts.