It is 3 a.m. and production is degrading. Walk me through detection, response, and what your MTTR actually is.?

The full executive answer

Detection is instrumented in layers: Sentry captures every unhandled error with full context in real time; OpenTelemetry distributed tracing follows each request across the stack so a degradation is localisable to a specific dependency rather than a mystery; circuit breakers around external dependencies — the AI providers above all — detect repeated failure and open automatically, converting a dependency outage into a controlled degradation instead of a cascade; and a scheduled SLO-alert job plus a per-region heartbeat watch the platform's own vitals. The AI layer additionally self-heals before any human wakes: provider failover to the second model vendor is automatic.

Response: immutable deployments make the most common remediation — roll back the last change — a minutes-fast, low-adrenaline action rather than a rebuild under pressure. The platform has an incident-management module and a status-page capability in the codebase, so structured incident tracking and customer communication run on rails. Managed-service failures at the hosting or database layer page their 24/7 teams, which is part of why a small company deliberately buys those layers.

Now the number you asked for, and the honest refusal: MTTR — mean time to restore — is a statistic computed over real production incidents, and we are pre-launch with no incident history. I will not invent one; our own reliability code refuses to present unmeasured targets as measurements and I hold to the same rule. What exists is the machinery that determines MTTR — detection latency, rollback speed, automated failover — and the commitment that from launch, every incident gets a blameless postmortem and the restore-time series is measured and reported. Per ITIL 4 and the SRE playbook, that is the order: instrument first, then measure, then promise.