AI-powered observability that gives productivity teams cohort-level visibility into staging metrics. See what breaks before it reaches production.
Teams spend $50K+/year running Datadog or Grafana in staging, getting 90% noise and 10% signal. These tools were built for production uptime, not developer velocity. They don't understand cohorts, feature branches, or team-level patterns.
InnerScope ingests staging telemetry and organizes it by what matters: team, feature branch, sprint, or deploy cohort. AI surfaces the anomalies that predict production failures, not the noise that wastes your morning standup.
Three layers of intelligence on your staging environment.
Group staging telemetry by team, feature branch, or release train. See which cohort introduced latency, which team's changes degraded throughput.
Models trained on your staging patterns surface meaningful deviations, not false alarms. Learns what "normal" looks like for each cohort independently.
Predict which staging signals correlate with production incidents. InnerScope builds a feedback loop between what you saw in staging and what actually broke.
OpenTelemetry-native. Drop in alongside your existing instrumentation. No new SDKs, no agent installation, no vendor lock-in.
The best platform teams don't wait for production fires. They see them forming in staging, cohort by cohort, before a single user is affected.