Stop guessing. AI finds the root cause
TigerOps automatically correlates metrics, distributed traces, logs, and deployment events to identify the exact root cause of every production issue — with a confidence score and a path to the offending code.
Root cause analysis is broken
The average production incident requires an engineer to open 4–6 tools, manually correlate timelines, search through gigabytes of logs, and hope the right person is in the Slack thread. This is not engineering — it's archaeology.
Every signal. One correlation engine.
TigerOps ingests and correlates every observability signal type automatically — no manual configuration required.
Metrics
CPU, memory, throughput, error rates, latency histograms from every service and infrastructure component.
Distributed Traces
End-to-end request traces across every microservice hop, with code-level spans for databases, caches, and external APIs.
Logs
Structured and unstructured logs from every service, automatically parsed, correlated to traces, and ranked by relevance.
Deployments & Changes
Every code deploy, config change, and infrastructure event as a first-class signal overlaid on your metrics and traces.
How AI root cause analysis works
From anomaly to confirmed root cause — step by step.
Anomaly Detected
METRICMulti-signal detection identifies an anomaly across metrics, error rates, or latency — with baseline deviation scoring to distinguish real issues from noise.
Trace Correlation
TRACEAI maps the anomaly to affected distributed traces, identifying the exact service call chain where the failure propagated — not just where it surfaced.
Log Analysis
LOGRelevant log lines from all affected services are automatically surfaced and ranked by relevance. No more manually grepping across dozens of log streams.
Change Correlation
CHANGERecent deployments, config changes, and infrastructure events are automatically overlaid on the timeline to identify change-induced regressions.
Root Cause Confirmed
ROOT CAUSEAI synthesizes all signals into a root cause determination with a confidence score, impact scope, and a direct link to the offending code change or configuration.
Before vs. after TigerOps RCA
Know exactly what broke and why
Stop the multi-tool archaeology. Let AI correlate every signal and surface the answer.