Scrubbe is an AI-powered incident management platform that unifies signal ingestion, on-call scheduling, SLA tracking, and automated post-mortems to help engineering teams resolve production incidents faster. Its AI layer, Ezra, uses a 4-stage reasoning pipeline for root cause analysis and remediation, while integrations with Slack, GitHub, Prometheus, and Datadog streamline the full incident lifecycle. The platform also includes adaptive playbooks and service mapping to calculate blast radius during outages.
Funding
Funding not disclosed
Founders
Product
Problem
Engineering teams often detect incidents through fragmented monitoring tools but struggle to safely and quickly resolve them, as alerts and dashboards leave operators manually reconstructing cause across services. This manual effort increases mean time to resolution (MTTR), escalates execution risk, and makes it difficult to maintain governance and policy compliance during high-pressure production failures.
Solution
Scrubbe provides a governed control loop for understanding incidents, choosing the right action, and moving toward resolution safely. The platform ingests real-time signals from existing monitoring stacks, uses an AI intelligence layer called Ezra to reason over incidents and generate actionable analysis, and offers a full suite of operational tooling including on-call scheduling, SLA policies, playbooks, and service maps. Scrubbe covers the entire incident lifecycle—from signal ingestion through post-mortem—and executes actions only under policy, reducing execution risk while accelerating resolution. The platform integrates with Slack, GitHub, GitLab, Prometheus, Datadog, and PagerDuty, and provides a single REST API for complete programmatic control.
Target Audience
Primary customers are platform and infrastructure engineering teams who own production reliability, deployment safety, and cross-service coordination, as well as on-call engineers and SREs needing to manage incidents across many services and ownership boundaries.
Features
- AI-powered incident analysis with Ezra's 4-stage reasoning pipeline for root cause analysis, impact assessment, and remediation
- Signal ingestion from GitHub, GitLab, Kubernetes, Prometheus, Datadog, and PagerDuty
- Automated playbooks with step-by-step execution and adaptive learning from past incidents
- On-call scheduling with shifts, rotations, escalation policies, and silent hours
- SLA policies with configurable response and resolution time targets per priority level
- Service mapping to calculate blast radius and understand cross-service dependencies
- Post-mortem report generation with export capabilities
- REST API with interactive documentation and SSO authentication support