AI Production Incident Investigator
Before engineers start manually digging through logs, AI reconstructs the incident timeline, isolates anomalies, and binds root-cause hypotheses to line-level evidence. Evolving from dialogue tools into verifiable, human-governed operational AI systems.
The Production Dilemma · Operational Chaos
2:35 AM: A P1 alert fires. What is your engineering team doing?
Alert Storm Noise
A single underlying database lock triggers cascading Envoy 504 timeouts, queue backlogs, and container probe failures across hundreds of alerts.
Tool Silos & Manual Stitching
SREs scramble across Grafana, Kibana, CloudWatch, and MySQL slow logs, losing 20 minutes just aligning millisecond timestamps.
The Dangers of Black-Box AI
Generic LLMs guess without verifiable evidence; naive autonomous scripts risk executing destructive commands under pressure.
Interactive Synthetic Demo · Try It Live
Hands-on: How AI Investigates a Real Production Failure
Select a scenario, trigger the correlation engine, inspect line-level evidence, review the generated RCA report, and experience the Human Approval Gate.
📡 Telemetry Ingestion (Raw Signals)
3 signal sources / 6 log entries🤖 AI Investigation Engine
Click "Run AI Investigation" above to see the AI engine ingest signals and synthesize an evidence-grounded hypothesis.
System Architecture · Engineering Depth
Not a Simple Prompt: End-to-End Investigation Architecture
The real enterprise challenge lies in read-only boundary isolation, PII sanitization, and strict execution governance.
- API Gateway Access Logs
- MySQL Slow Query Streams
- Kubernetes Pod Event Logs
- Prometheus Metrics Streams
- Strict Read-Only Access Scope
- Automated PII & Secret Redaction
- Non-intrusive Passive Ingestion
- Millisecond Timeline Alignment
- Cross-Source Causal Correlation
- Explainable Root Cause Inference
- Grounded Evidence Line-Binding
- Standardized Markdown RCA
- Full Decision Replay Lineage
- Tamper-Evident Audit Trails
- Engineer Mitigation Review
- Explicit ALLOW / BLOCK Gating
- Zero Autonomous Production Writes
The Differentiator · Architectural Comparison
Why Traditional APMs and General Chatbots Fall Short
| Dimension | Traditional APM / Dashboards | Generic LLM / Chatbots | AI Incident Investigator |
|---|---|---|---|
| Cross-System Correlation | Requires human engineers to stitch tabs | Lacks real-time infrastructure context | Automated millisecond timestamp alignment |
| Grounded Traceability | Scattered across disparate log stores | Prone to hallucination; no specific lines | Mandatory line-level log tethering |
| Auditability & Replay | Raw access logs without reasoning records | Unstructured chat transcripts | Standardized RCA & Decision Lineage |
| Safety & Governance | Prone to human error under high stress | Dangerous if granted unguided tool access | Built-in Human Approval Gate |
Next Steps · Production Feasibility
Interested in Validating This Architecture in Your Stack?
Whether in distributed microservices or hybrid database clusters, we deploy directly alongside your engineering team: understanding your existing telemetry and co-building a tailored, enterprise-grade PoC.