CauseFlow turns a raw monitoring alert into a root cause analysis and remediation proposal — without requiring manual investigation. Here is what happens at each stage.
1. Alert ingestion
CauseFlow receives alerts via webhooks from your connected monitoring tools, or through incidents created manually from the dashboard.
Each incoming alert is validated, deduplicated, and stored as an incident record ready for analysis.
See Integrations for setup instructions.
2. AI analysis
CauseFlow’s AI analyzes the alert and classifies its severity. Based on the incident type and your connected integrations, it gathers evidence from the relevant data sources — logs, metrics, infrastructure state, recent code changes, and database health.
The analysis runs automatically and typically completes in 60 to 90 seconds.
3. Root cause report
Once the analysis is complete, CauseFlow produces a unified root cause report that includes:
- Root cause explanation — a plain-language description of why the incident occurred
- Confidence score — a measure of how well the evidence supports the conclusion
- Supporting evidence — citations from the analysis findings
CauseFlow proposes one or more specific actions to resolve the incident. Examples:
- Restart a service or container
- Rollback a deployment to a previous version
- Scale a resource (increase instance count, adjust memory limits)
- Create a pull request with a targeted code fix
Remediations never execute automatically unless you explicitly enable auto-remediation in your settings. By default, every action requires human approval before it runs.
5. Learning
After an incident is resolved, CauseFlow extracts a pattern from the investigation. Patterns capture the relationship between alert signals and root causes.
Patterns are matched against future incidents to accelerate analysis and surface known solutions. They improve in accuracy over time as your team resolves more incidents and provides feedback. 
