Anomaly DetectionSolutions
Automatic detection of numbers and events that look wrong, so a human learns about the problem hours after it starts, not at month-end.
Anomaly Detection Solutions: what the work involves
Problems often hide in routine data: a sudden dip in checkout completions, a supplier invoice far above its usual range, a server that quietly slows, a branch whose cash deposits look odd. Dashboards reveal them only if someone happens to look at the right chart on the right day, and fixed thresholds trigger too many alerts to be trusted.
We define what normal looks like for each metric, including daily and weekly rhythm, holidays and growth. For a single series we may use seasonal decomposition or robust statistics; for many related signals, methods such as isolation forests or autoencoders. Each detector is tested by replaying history in which you already know the incidents, to count what it would have caught and how many false alarms it would have produced. Alerts arrive in Slack, email or WhatsApp with a chart, the expected range and likely contributing segments, such as one city or product. Recipients mark alerts useful or noise, and that feedback adjusts sensitivity. Detection runs on a schedule you choose.
Core features
Learned normal behaviour
Daily, weekly and seasonal patterns are modelled for each metric, so normal peaks are not flagged.
Multiple detection methods
Statistical, tree-based and neural approaches are matched to the shape of your data, not applied blindly.
Historical replay testing
Detectors run over past data with known incidents, showing what they would have caught and what noise they add.
Contextual alerts
Messages show the chart, expected range and the segment, product or location driving the change.
Feedback tuning
Marking an alert useful or noise adjusts thresholds and suppresses repeat false alarms.
Alert routing
Different metrics notify different owners by Slack, email or WhatsApp, with quiet hours respected.
What we get right before launch
Alert fatigue
Too many false alarms train people to ignore the channel. We set sensitivity from replay results, group related alerts, and review noise levels after launch.
Anomalous is not the same as bad
A viral post or a successful promotion also looks anomalous. Alerts describe what changed and let a person decide whether it is a problem.
Data quality gaps
Broken tracking or a late data feed looks like a collapse. We add freshness and completeness checks, so pipeline faults are reported separately from business anomalies.
Tools and technology
- Python
- scikit-learn
- PyTorch
- statsmodels
- FastAPI
- PostgreSQL
- Grafana
- Slack API
Common questions, answered
Do we need labelled examples of anomalies?
Not to start. Many methods learn normal behaviour without labels. Known past incidents are still valuable for testing, and feedback on alerts gradually builds a labelled set for improvement.
How many false alarms should we expect?
It depends on sensitivity, which is a trade-off with missed events. We test on your history, show the balance, and let you pick. Expect some tuning in the first weeks.
Can it work on real-time data?
Yes, for streams with a suitable pipeline. Many use cases are fine with checks every few minutes or hourly, which is simpler and cheaper than strict real-time processing.
Is this the same as fraud detection?
They overlap. Fraud detection scores individual events using labelled fraud where available. Anomaly detection looks for unusual patterns generally, and is often a complement or a starting point.
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Ready to start your Anomaly Detection Solutions project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.