Anomaly detection
Identifying data points, events or patterns that differ significantly from what is normally expected. It is used to spot fraud, equipment faults, data errors, security incidents and unusual operational behaviour.
Why it matters
Many important events are rare and poorly labelled, which makes them hard to learn with supervised methods. Anomaly detection highlights the unusual so people can investigate, even when past examples are scarce.
Its value depends on the investigation process around it. Too many alerts cause fatigue; too few miss real problems. Thresholds should be tuned with the teams who act on the alerts.
In practice
For example, a UK food manufacturer might monitor temperature and vibration sensors on its production lines and flag readings that depart from each machine’s normal pattern, prompting a maintenance check before a fault stops the line.
Where Rodan fits
Rodan builds anomaly detection into operational monitoring and decision workflows in AI and Decision Systems work.