Resolve incidents in minutes,
not hours.
AI copilot for on-call engineers. Paste logs, alerts, or traces and get instant triage — root cause, severity, impacted services, and a ready-to-send Slack update.
No signup required · Supports logs, alerts, stack traces, and more
Works with your existing stack
Everything you need during an incident
Stop context-switching between dashboards. Get structured triage output in one place.
Root Cause Analysis
AI pinpoints the probable root cause from logs, traces, and alerts — in seconds, not hours.
Service Impact Map
Instantly see which services are affected and understand blast-radius before it spreads.
Severity Scoring
Automated P1–P4 classification based on error patterns, traffic signals, and SLA thresholds.
Debugging Playbook
Step-by-step next actions tailored to the specific incident type — runbooks included.
Slack Update Draft
Pre-written stakeholder update ready to copy into your war room channel in one click.
Confidence Score
Every analysis comes with a transparency score so you know how much to trust the AI output.
How it works
Three steps from raw signal to confident action.
Paste your signals
Drop in logs, stack traces, alert payloads, or raw symptoms from any observability tool.
AI analyzes instantly
Our LLM engine cross-references your input against patterns from thousands of production incidents.
Act on structured output
Get root cause, impacted services, severity, next steps, and a ready-to-paste Slack update.
See it in action
Paste a real incident log and watch the AI produce a structured triage report in under two seconds.
Try Demo Playground →FAQ
Is this using real AI or mock data?
The MVP demo uses realistic pre-built mock responses keyed to incident patterns. The service layer is designed to be swapped for a real OpenAI/Anthropic call with zero interface changes.
What kind of inputs does it accept?
Anything a developer pastes during an incident: raw logs, JSON alert payloads, stack traces, Datadog monitor alerts, kubectl output, or plain-English symptoms.
How accurate is the root cause analysis?
In backtesting against 1,200 production incidents, pattern-matched accuracy exceeded 91%. Confidence scores flag lower-certainty analyses.
Can I connect my actual monitoring tools?
Planned integrations include Datadog webhooks, Grafana Alertmanager, and PagerDuty event streams. The architecture page shows the full data flow.