Root Cause Analysis
Deductive AI dramatically accelerates incident resolution by automatically investigating incidents, testing multiple hypotheses in parallel, and presenting evidence-backed root cause analysis.The Challenge
Traditional incident investigation is slow and manual:- Sequential Investigation - Teams check one system at a time
- Limited Context - Hard to correlate events across systems
- Knowledge Gaps - Requires deep expertise in multiple tools
- Time Pressure - Every minute counts during an incident
How Deductive Helps
Deductive AI joins your on-call rotation and starts investigating incidents before you even begin. It:- Builds Multiple Theories - Tests several hypotheses in parallel instead of one at a time
- Collects Evidence - Queries code, metrics, logs, and infrastructure simultaneously
- Builds Causal Timelines - Links code changes, infrastructure events, and telemetry
- Presents Findings - Shows root cause with supporting evidence
Example: Production Incident
The Incident
At 3:47 PM, your error rate spiked from 0.1% to 15%. Users reported payment failures.Traditional Investigation
- Check monitoring dashboard (2 minutes)
- Search logs for errors (5 minutes)
- Review recent deployments (3 minutes)
- Check infrastructure changes (4 minutes)
- Correlate findings manually (10 minutes)
- Total: ~24 minutes
With Deductive AI
You ask: “What caused the payment service errors at 3:47 PM?” Deductive immediately:- Queries metrics - Confirms error spike at 3:47 PM
- Searches logs - Finds “database connection pool exhausted” errors
- Reviews code - Identifies recent PR #1234 that changed connection pool settings
- Checks infrastructure - Finds auto-scaling event at 3:45 PM
- Builds timeline - Links all events together
The Root Cause
Deductive presents:Root Cause: PR #1234 reduced the database connection pool size from 50 to 10, which became insufficient when traffic increased due to the auto-scaling event at 3:45 PM. Evidence:Recommended Fix: Revert connection pool change or increase pool size to handle peak traffic.
- Code change:
src/db/pool.ts(PR #1234, merged 2 hours ago)- Error pattern: “connection pool exhausted” starting at 3:47 PM
- Infrastructure: Auto-scaling increased traffic by 3x at 3:45 PM
- Timeline: All events align within 2-minute window
Key Features
Parallel Hypothesis Testing
Instead of checking systems sequentially, Deductive tests multiple theories simultaneously:Causal Timeline Building
Deductive automatically builds timelines that show how events relate:Evidence-Backed Conclusions
Every root cause analysis includes:- Primary Conclusion - The most likely cause with confidence score
- Supporting Evidence - Specific logs, metrics, code changes
- Alternative Theories - Other possibilities considered
- Recommended Actions - Next steps to resolve
Best Practices
Connect Comprehensive Integrations
The more data sources Deductive has access to, the better its analysis:- Code: GitHub, GitLab for code change tracking
- Metrics: Prometheus, Datadog for system health
- Logs: Elasticsearch, OpenSearch for error details
- Infrastructure: AWS, Kubernetes for resource changes
- Alerts: PagerDuty, Incident.io for incident context
Provide Context
Help Deductive understand your systems:- Document service dependencies
- Share runbooks and troubleshooting guides
- Explain normal vs. abnormal patterns
- Provide team-specific context
Review and Refine
Deductive learns from feedback:- Confirm correct root causes
- Correct incorrect analyses
- Provide additional context
- Share resolution steps
Results
Teams using Deductive AI for root cause analysis report:- 5x faster MTTR - Average resolution time drops from hours to minutes
- Higher accuracy - Evidence-backed conclusions reduce false positives
- Better learning - Patterns captured for future incidents
- Reduced stress - Automated investigation reduces on-call burden
Next Steps
- Connect Your Integrations - Set up data sources
- Get Started - Try your first investigation
- Cost Optimization - Discover other use cases