Uptivus reviews Fathom Analytics data such as page views and traffic shifts around downtime events so AI can evaluate possible user-impact context.
Spike and Drop Detection
We look for spikes, drops, and unusual behavior near outage windows to give AI more signal when classifying and explaining downtime events.
Context-Enriched Event Analysis
AI combines these analytics signals with monitor state changes to create a fuller picture of what happened before, during, and after the incident.
Analytics Correlation During AI Insight Runs
Autonomous and On-Demand Processing
During autonomous and on-demand AI insight generation, Uptivus evaluates Fathom Analytics data in the same incident time spans used for monitoring analysis.
Cross-Check with Monitoring Signals
Analytics data is checked against monitor metrics and anomalies that AI detects, helping surface potential relationships that pure uptime checks might miss.
Connection Discovery
AI flags candidate links between traffic behavior and monitored performance changes so teams can investigate likely causes faster.
AI Reports with Analytics + Monitoring Evidence
Unified Incident Narratives
Reports combine monitor evidence with analytics evidence to explain what changed operationally and what changed in traffic behavior.
Evidence-Linked Findings
AI separates strong correlations from weak ones and highlights the specific signals used in each finding to keep reports auditable.
Operational Decision Support
Teams get clear summaries for post-incident review, triage prioritization, and deciding what systems or releases to inspect next.
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