Uzera's anomaly detection engine watches 17 behavioral and performance metrics every hour using Isolation Forest ML. When something deviates, you get the exact metric, the severity, the root cause, and the fix — before your users notice anything is wrong.
.png)
Here's the insight most engineering teams learn the hard way: static threshold alerts catch the obvious spikes. But slow behavioral drifts — rising rage clicks over 3 days, a 40% bounce rate increase over a week, session depth quietly declining — never trigger a single alert. And by the time users start complaining, the damage is done.
Uzera's Isolation Forest learns what "normal" looks like for your specific product and flags meaningful deviations across all 17 metrics — not just the ones you thought to set thresholds for.
Most segmentation tools give you one dimension to work with — usually individual user attributes. That works for basic use cases. But the moment your product serves multiple account types, plan tiers, or organizational structures, single-dimension segmentation breaks down.
Error spikes, slow page loads, JavaScript failures, session errors. The issues that break your product immediately and impact every active user until resolved.
Metrics monitored
Real Example :
Feb 17, 2025 — error_rate 90.2% vs. expected 5%. 1,057 JS errors in 1 hour. Detected at 02:17. Root cause identified: deployment at 02:10. Suggested fix: roll back. Team resolved by 03:05.
Rage clicks, dead clicks, high bounce rates, idle time spikes. Frustration signals that reveal UX failures and broken flows before they compound into churn.
Metrics monitored
Real Example :
A broken CTA button generated 340 rage click events over 3 days before it was noticed in a user interview. Uzera flagged it on Day 1 as a High behavioral anomaly.
Traffic drops, session count shifts, user volume changes. Anomalies that signal when your product's core engagement metrics are moving in the wrong direction.
Metrics monitored
Real Example :
A 38% traffic drop on a Tuesday afternoon flagged as a Business anomaly. Root cause: a partner integration had silently broken, cutting referral traffic. Detected 4 hours before the partner's team noticed.
Event volume changes, page view pattern shifts, click pattern deviations. Signals that reveal when users are engaging with your product differently — adoption drops, feature abandonment, onboarding stalls.
Metrics monitored
From Raw Data to Root Cause in Under 60 Seconds.
.svg){kind=small}
Uzera ingests behavioral and performance data continuously. Every hour, 17 metrics are extracted per domain — error rates, session behavior, UI interactions, traffic volume, and product event patterns. No manual instrumentation. No threshold configuration.
.svg){kind=small}
Each hourly snapshot is scored by Uzera's Isolation Forest model against your product's own baseline. The model learns what "normal" looks like for your specific traffic patterns, user behavior, and error rates — not industry averages or static rules.
Why Isolation Forest?
.svg){kind=small}
Anomalous hours are immediately classified by severity (Critical / High / Medium / Low) and category (Technical / Behavioral / Business / Product). The primary deviating metric is auto-identified along with the exact deviation magnitude.
Example Output:
.svg){kind=small}
Uzera's LLM layer synthesizes the anomaly data into a plain-English root cause analysis and 2–3 specific remediation steps. No interpretation required. Your team reads and acts.
The Feedback Loop:
.svg)
Can’t find the answer you're looking for?
Email us any time: help@uzera.com
17 behavioral and performance metrics per domain per hour, organized across 4 categories: Technical (error_rate, page_load_p95, session_errors, js_errors), Behavioral (rage_clicks, dead_clicks, bounce_rate, idle_time), Business (traffic_volume, session_count, user_volume), and Product (event_volume, page_views, click_patterns).
17 behavioral and performance metrics per domain per hour, organized across 4 categories: Technical (error_rate, page_load_p95, session_errors, js_errors), Behavioral (rage_clicks, dead_clicks, bounce_rate, idle_time), Business (traffic_volume, session_count, user_volume), and Product (event_volume, page_views, click_patterns).
17 behavioral and performance metrics per domain per hour, organized across 4 categories: Technical (error_rate, page_load_p95, session_errors, js_errors), Behavioral (rage_clicks, dead_clicks, bounce_rate, idle_time), Business (traffic_volume, session_count, user_volume), and Product (event_volume, page_views, click_patterns).
17 behavioral and performance metrics per domain per hour, organized across 4 categories: Technical (error_rate, page_load_p95, session_errors, js_errors), Behavioral (rage_clicks, dead_clicks, bounce_rate, idle_time), Business (traffic_volume, session_count, user_volume), and Product (event_volume, page_views, click_patterns).
Predictions start after your first 50 users are tracked. Uzera's model is pre-trained on 29,000+ labeled profiles — there's no months-long data ramp. Most teams see their first risk scores within 24–48 hours of integration. No data science team required.
17 behavioral and performance metrics per domain per hour, organized across 4 categories: Technical (error_rate, page_load_p95, session_errors, js_errors), Behavioral (rage_clicks, dead_clicks, bounce_rate, idle_time), Business (traffic_volume, session_count, user_volume), and Product (event_volume, page_views, click_patterns).
Uzera monitors 17 metrics every hour so your engineering and product teams don't have to. One alert per genuine anomaly. Root cause identified automatically. Plain-English fix suggestion included. Every time.
Connect Uzera and receive your first anomaly alert within 24 hours. No threshold configuration. No manual setup. No false positive flood.
.svg)
.svg)
