Transaction monitoring
Flag unexpected amounts, frequencies, counterparties, locations, or combinations of behavior for review.
The alert is only the start
A useful system must account for normal cycles, changing behavior, sparse labels, and the cost of a false alarm. We define the response first, then design detection thresholds and explanations around the people who investigate each event.
Common applications
Industrial sensors
Identify shifts in vibration, temperature, pressure, or multivariate equipment behavior before a known limit is crossed.
Quality and defects
Detect unusual process measurements or product characteristics and connect them to batches, machines, or operating conditions.
Business operations
Surface unexpected movements in demand, inventory, margins, usage, or service performance with relevant context.
How we build the system
Define events and response
We agree on what needs attention, who receives it, available evidence, and the cost of missed or noisy alerts.
Profile normal behavior
Historical data reveals seasonality, segments, missing values, system changes, and candidate signals.
Test detection methods
Rules, statistical methods, and machine learning are compared against realistic replay data and investigation capacity.
Deploy and tune
Alerts include context and feedback capture, allowing thresholds and models to improve from reviewed cases.
Anomaly detection questions
Do we need labeled anomalies?
Not always. Unsupervised and semi-supervised methods can rank unusual cases, but reviewed examples help measure usefulness and tune the alert volume.
How do you control false positives?
We segment normal behavior, account for seasonality, combine signals where appropriate, and calibrate thresholds against the team's capacity to investigate.
Can detection run in real time?
Yes, if the source data and response process require it. Batch detection is often simpler and cheaper where a short delay does not change the outcome.
Will an alert explain why it fired?
We include contributing signals, comparisons, and relevant context whenever the selected method supports a reliable explanation.
Start with one costly exception
We will assess the data, expected response, and a practical way to test detection quality.
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