AI-powered anomaly detection combines statistical analysis, semantic understanding, and custom rules to flag suspicious calls, fraud attempts, and unusual patterns automatically.
3
Detection Methods
Real-Time
Alerting
Ensemble
Approach
How advanced ai capabilities transform your call analytics
Isolation Forest algorithms identify calls that deviate from normal patterns — unusual duration, atypical sentiment, or unexpected issue types.
AI embeddings detect calls with unusual language patterns, suspicious requests, or conversations that don't match expected topics.
Define custom rules for your domain — flag calls mentioning specific keywords, exceeding duration thresholds, or matching known fraud patterns.
AI analyzes your historical calls to establish what 'normal' looks like for your business.
Every new call is compared against the baseline using all three detection methods simultaneously.
Anomalous calls are flagged with anomaly type, severity, and detection method for quick investigation.
Statistical outlier detection identifies calls with unusual numeric characteristics — duration, sentiment scores, issue counts.
Vector embeddings detect calls where the conversation content deviates significantly from expected patterns.
Define your own detection rules based on keywords, thresholds, patterns, or combinations of factors.
All three methods run simultaneously. Calls flagged by multiple methods get higher confidence scores.
Detected anomalies are classified by type — potential fraud, unusual length, high emotion, policy violation, etc.
Each anomaly gets a severity score to help prioritize investigation. Critical anomalies trigger immediate alerts.
Start analyzing 100% of your calls with AI. No manual QA sampling, no inconsistent scoring, no missed insights.