Stop relying on manual reviews to find problems in your data. AI-powered anomaly detection continuously monitors your business metrics, automatically flagging unusual patterns that signal fraud, errors, or emerging issues.
Business anomalies—fraud, system errors, data quality issues, unusual customer behavior—often go unnoticed until significant damage is done:
Payment fraud, expense abuse, and revenue leakage can persist for months before manual audits catch them. By then, losses often exceed six figures.
Integration failures, calculation bugs, and data pipeline issues create bad data that contaminates analytics and drives poor decisions before anyone notices.
Process breakdowns—fulfillment delays, quality control issues, service disruptions—impact customers before internal monitoring catches the problem.
Sudden shifts in customer behavior, competitive threats, or market conditions get buried in daily metrics until quarterly reviews reveal the trend is irreversible.
Organizations rely on analysts to review dashboards and spot unusual patterns. But humans can't:
Machine learning models continuously monitor your business data, learning normal patterns and automatically flagging deviations that warrant investigation.
AI analyzes historical data to understand "normal" behavior for every metric, accounting for time patterns, seasonality, and contextual factors.
As new data arrives, AI compares it against learned baselines and flags statistically significant deviations immediately.
AI doesn't just look at individual metrics—it detects anomalies across combinations of dimensions that human analysts would never manually check.
AI doesn't just flag anomalies—it analyzes context and suggests potential root causes to accelerate investigation.
The system learns from feedback—when you mark anomalies as expected vs. concerning, AI refines its detection to reduce noise over time.
See how AI anomaly detection can catch fraud, errors, and emerging issues in your business data. Get a demo showing anomalies detected in your historical data.
Catch fraudulent transactions, expense abuse, and revenue leakage within hours instead of weeks or months, preventing significant financial losses.
Automatically detect integration failures, calculation errors, and data pipeline issues before they contaminate analytics and decision-making.
AI never sleeps—monitoring thousands of metrics across all dimensions continuously, catching anomalies that occur outside business hours.
AI provides context and root cause hints with each anomaly, allowing teams to investigate and resolve issues faster than manual detective work.
Detect payment fraud, expense abuse, invoice manipulation, and unauthorized transactions in real-time.
Catch integration failures, API errors, data pipeline issues, and calculation bugs before they impact business operations.
Identify unusual customer patterns that signal churn risk, account compromise, or changing market conditions.
Monitor fulfillment, production, support, and operational metrics to catch process breakdowns early.
Traditional threshold alerts require manual configuration and only catch extreme values. AI anomaly detection automatically learns normal patterns including seasonality, trends, and context—catching subtle anomalies that static thresholds would miss. It also adapts as your business evolves, while thresholds become outdated and generate false alarms.
Initial false positive rates are typically 20-30% as the system learns your business patterns. After 4-8 weeks of feedback and training, false positive rates drop to 5-10%. The system continues improving—mature implementations achieve 95%+ precision, meaning nearly every flagged anomaly warrants investigation.
You need at least 30-60 days of data for basic anomaly detection. More data (6-12 months) significantly improves accuracy, especially for detecting seasonal patterns. For metrics with limited history, we can start with simpler statistical methods and transition to ML-based detection as more data accumulates.
The system immediately alerts designated stakeholders via email, Slack, Teams, or other channels. The alert includes the anomaly details, severity score, confidence level, contextual factors, and suggested root causes. Teams can acknowledge, investigate, or mark as expected—all feedback improves future detection.
Basic anomaly detection can be operational in 2-4 weeks, including data integration and baseline establishment. The system becomes highly effective after 6-8 weeks of learning and feedback. Advanced implementations with multi-dimensional analysis and custom use cases may take 8-12 weeks.
Stop relying on manual reviews and quarterly audits. Let AI continuously monitor your business data and alert you to anomalies the moment they occur. See it in action with your data.