Key Highlights
Motadata ObserveOps RUM captures all JavaScript errors, resource failures, and network issues in real sessions, correlates each to its backend cause, and alerts instantly before spikes affect users.
Capture each client-side failure that impacts user experience.
JavaScript exception capture with full stack trace, error message, and browser context.
Resource load failure tracking for images, scripts, stylesheets, and third-party assets.
Network request failure monitoring for API calls, XHR requests, and fetch operations.
Unhandled promise rejection capture for asynchronous front-end failure patterns.
Identify browser-specific defects, eliminating the need to manually filter through environments.
Error breakdown by browser type and version for compatibility-specific defect isolation.
OS-level segmentation identifying platform-specific rendering or JavaScript execution failures.
Device category segmentation distinguishing mobile, tablet, and desktop error environments.
Network condition segmentation for failures that correlate with connectivity constraints.
Connect client-side error symptoms with server-side and infrastructure causes.
Correlation of front-end API failures with backend distributed traces from the same request.
Log event correlation linking client-side error timing with server-side error log entries.
Database performance correlation connecting slow API responses with upstream query execution.
Infrastructure signal overlay showing server resource conditions during front-end error spikes.
Detect error pattern changes the moment they occur.
Threshold-based alerts triggering when error rates exceed configured frequency limits.
Regression detection identifying error types that appeared or increased after deployments.
Error spike velocity monitoring distinguishing gradual growth from sudden failure events.
Multi-channel alert delivery to operations, engineering, and on-call notification systems.
Prioritize investigation by the user experience impact of each error type.
Session impact scoring showing what share of sessions each error group affects.
Conversion impact analysis connecting error occurrence with downstream funnel abandonment.
User segment impact showing which geographic, device, or browser groups are most affected.
Revenue-weighted impact scoring for error groups in high-value transaction flows.
Understand how error patterns evolve and validate that fixes work.
Error frequency trend visualization over time ranges and deployment markers.
Pre-and-post deployment error comparison confirming regression introduction or resolution.
Recurring error pattern identification highlighting issues that reappear between releases.
Error resolution validation confirming fixes have reduced affected session counts as expected.
Intelligence
Front-end errors range from silent failures users never notice to blocking exceptions that end sessions. Lacking a way to distinguish their impact, each error gets equal attention regardless of whether it affects 1% of sessions on a non-critical flow or 30% of checkout sessions.
Error Analytics & Experience Failures makes the distinction explicit through impact scoring. Each error is scored by session reach and flow criticality, directing engineering effort toward the failures actually damaging outcomes.
How It Works
Capture JavaScript errors, resource failures, and network issues via the Motadata RUM agent.
Enrich error events with browser, device, OS, and session metadata at collection time
Group errors by type, stack signature, and affected user segment for consolidated investigation.
Correlate error events with backend traces and infrastructure metrics via Motastore.
Apply regression detection to identify error groups that intensified after deployment events.
Highlight impact-scored error groups through dashboards and spike-triggered alert notifications.
All front-end failures visible, prioritized, and connected to the backend cause.
Role-Based Value
Understand the business cost of front-end errors in affected sessions, conversion impact, and segment exposure, not raw error counts stripped of business context.
Understand the business cost of front-end errors in affected sessions, conversion impact, and segment exposure, not raw error counts stripped of business context.
Reduce the support ticket volume and escalations caused by undetected front-end failures.
Reduce the support ticket volume and escalations caused by undetected front-end failures.
Detect front-end error spikes at the moment of occurrence rather than through user reports.
Detect front-end error spikes at the moment of occurrence rather than through user reports.
Validate each deployment by checking whether new error groups appeared or existing ones intensified in the minutes after a release.
Validate each deployment by checking whether new error groups appeared or existing ones intensified in the minutes after a release.
From Visibility to Control
25% higher user satisfaction through elimination of front-end performance regressions and error failures.
Instant error spike detection replacing delayed discovery through user reports and support tickets.
Impact-scored prioritization directing engineering effort toward highest-consequence failures.
Backend correlation closing the investigation gap between client-side symptoms and server-side causes.
Deployment regression detection confirming error impact within minutes of each release.
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