ANOMX · SYSTEM INTELLIGENCE
The AI layer for an autonomous world.
Detect change. Understand why.
Give your systems the intelligence to act.
FROM REACTIVE TO AUTONOMOUS
A background agent that observes, reasons, and looks ahead. Connected to your data, models, and machines. Turning complex systems into intelligent ones.
BACKGROUND INTELLIGENCE
The background agent revisits connected data and system state on a schedule. It brings emerging deviations into context.
Cooling circuit · time-series analysis
Investigate data, jobs, and related assets together. Use specialist tools and model outputs to turn a deviation into a grounded explanation.
Temperature · flow rate · previous runs
Create a recommendation for review, or make the platform changes your team has explicitly enabled. Permissions and usage budgets define the scope.
Evidence attached · awaiting human review
Inspect earlier background runs, carry forward useful context, and avoid repeating the same findings. A continuous thread of operational intelligence.
Run history · evidence · follow-up
AUTONOMY WITH CONTEXT
Data streams, files, databases, and control systems brought into one operational context.
Detect anomalies and investigate emerging changes before they become bigger problems.
Turn evidence into recommendations and permitted platform changes. You define the boundaries.
SCIENCE AT THE CORE
Three complementary ways to detect the unexpected. One foundation for informed decisions.
Learn temporal behavior and compare new observations with a forecast. Residuals reveal where reality departs from expectation.
Rolling-window models · Darts integrationscore = | observed − predicted |Illustrative visualization · not measured data
Compress and reconstruct the system’s observations. Patterns that cannot be reconstructed well become candidates for investigation.
Principal component analysis · PyTorch autoencodersscore = ‖ observed − reconstructed ‖Illustrative visualization · not measured data
Compare observations in a feature space. Isolation and normality models surface unusual combinations that single-channel thresholds can miss.
Feature-space modeling · Isolation Forestscore = unusualness in feature spaceIllustrative visualization · not measured data
THE NEXT ERA
Build the future of autonomous systems with us.
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