Connect Any Source.
Bring together live streams, historical records, files, control systems, databases, and custom pipelines.
Understand your system before it fails. Anomx detects abnormal behavior across live data streams, explains what changed, and helps teams act before small deviations become critical.
Analyze live operational streams as they change, not hours after the event.
Work across heterogeneous sources without forcing every team into one rigid data model.
Detect patterns that emerge across many channels, not only single-metric threshold breaks.
Move from "something is wrong" to "this changed, here is why it matters."
Organizations, users, integrations, jobs, files, notifications, audit logs, and system objects are part of the platform model.
Built around findings, feedback, system monitoring, and action, not just charts.
Replace fragile threshold rules with adaptive anomaly detection across live and historical signals.
See which sources, channels, jobs, or conditions contributed to a finding.
Trigger analysis, review, notification, and reporting workflows from the same finding.
Use compute workers for ML, AI, and algorithmic jobs without separating detection from operations.
Store findings, feedback, runs, artifacts, and context so every investigation makes the system smarter.
Connect to existing infrastructure through modular connectors and workers.
Connect live streams, databases, files, APIs, and control systems.
Run anomaly detection jobs across high-dimensional time-series.
Translate raw deviations into understandable findings.
Route findings into human review, AI workflows, and operational decisions.
Complex organizations generate more data than teams can interpret. Critical signals get buried in noise.
From control systems and data acquisition to compute jobs and model artifacts, Anomx is built around real infrastructure.
Early access is intended for enterprises and research facilities with high-value systems and serious anomaly detection needs.
Detect abnormal behavior across accelerators, beamlines, detectors, experiments, and supporting infrastructure.
Find deviations across production lines, equipment, energy usage, process signals, and quality data.
Monitor distributed assets, detect operational drift, and understand system-wide changes before failures cascade.
Move beyond static dashboards for live, heterogeneous data environments with complex dependencies.
Modern systems produce continuous operational streams across machines, software, sensors, and teams.
Static thresholds and dashboards struggle when normal behavior depends on context.
Teams need earlier signals, clearer explanations, and faster paths from detection to action.