Analyze & Correlate
Patterns, anomalies, and correlations across your telemetry.
AI Insights runs over the whole lake rather than a sampled recent window. It collapses repetitive log lines into the handful of patterns actually behind them, flags where behaviour departs from its own history, and connects movements across logs, metrics, and traces that would otherwise be three separate investigations.
Pattern compression
Millions of log lines, a handful of patterns
Production emits the same lines endlessly. AI Insights groups them by structure — variable parts abstracted, counts preserved — so a night of noise becomes a short list a person or an agent can actually read. This is CtrlB Decompose, running continuously over the lake.
Anomaly detection
Measured against everything, not a recent window
Most tools compare today to the last two weeks, because that is all they keep hot. AI Insights holds full history as open Parquet, so “unusual” is judged against a service’s real baseline — seasonality, deploy cycles, quiet weekends and all — instead of a fortnight that happened to look calm.
Recent-window view
Inside the last 15 days the spike looks like ordinary variance.
Full-history view
Against a year of baseline it is three standard deviations out.
Cross-signal correlation
One movement, seen across logs, metrics, and traces
Because all three signals land in the same lake, a latency step in metrics, a burst of a log pattern, and a slow span in traces are one finding at one timestamp — not three tools open in three tabs, reconciled by hand.
One finding, three signals. The log-pattern burst, the latency step, and the slow spans share a timestamp and a deploy — so AI Insights reports them as a single correlated event, not three unrelated alerts.
Transparent findings
Every finding is a query, not a black box
A finding opens into the exact query that produced it, the sample it ran on, and the window it covers. Adjust the query, widen the window, or drop it into the Data Lake — none of the reasoning hides behind a score you cannot reproduce.
Analytics processor queue backing up
SELECT pattern_id, count(*) AS n FROM logs WHERE service = 'analytics-processor' AND ts > now() - 6h GROUP BY pattern_id HAVING n > baseline(pattern_id) * 3
What you get
- Repetitive logs compress into patterns a human or an agent can reason over.
- Anomalies are measured against full history, not the last fifteen days.
- Correlations span signals, because all three live in one data layer.
- Findings are queries you can open, inspect, and adjust — not opaque scores.
Other core components
All four run over the same lake of open Parquet in object storage you own.
Collect & Control
Configure, update, and govern every collector and agent from one place.
Search & Investigate
Full-text search, SQL, metrics, and traces on object storage.
Agent Access
Give any agent fast access to complete operational history.
See what your own lake is already telling you.
Point AI Insights at live data and read the patterns, anomalies, and correlations back as queries you can open — not scores you have to trust.