Service • Anomaly Detection • XenarAI

Real-Time Anomaly Detection
Catch problems before they become critical.

Our ML-powered anomaly detection systems monitor your data streams 24/7, identifying unusual patterns, fraud attempts, system failures, and data quality issues in real-time. Built for industries where downtime and errors cost money.

Get Started How It Works

What We Detect

Use Cases

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Fraud Detection

Financial transactions, payment fraud, identity theft, unusual account activity

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System Failures

Server crashes, network issues, performance degradation, resource exhaustion

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Data Quality Monitoring

Missing data, schema drift, outliers, data pipeline failures

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Industrial IoT

Equipment failures, predictive maintenance, sensor anomalies, quality control

Features

How It Works

Advanced machine learning models that learn normal behavior patterns and flag deviations in real-time with minimal false positives.

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Real-Time Processing

Process millions of events per second with sub-second latency. Get alerts immediately when anomalies are detected.

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Low False Positives

Advanced ML models learn what's normal for your data, reducing false alarms and alert fatigue.

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Adaptive Learning

Models continuously learn and adapt to changing patterns, seasonal trends, and business cycles.

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Easy Integration

REST APIs, webhooks, Kafka streams, or direct database connections. Integrate with your existing infrastructure.

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Custom Dashboards

Real-time visualization of detected anomalies, trends, and system health metrics tailored to your needs.

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Smart Alerting

Multi-channel alerts (email, SMS, Slack, PagerDuty) with severity levels and customizable thresholds.

How a project looks

XenarAI delivers custom anomaly detection systems hands-on, with direct contact and a transparent roadmap from discovery to production.

Typical phases

1. Discovery (1-2 weeks) – understanding your data sources, anomaly types, and detection requirements.
2. Prototype (2-4 weeks) – first working model trained on your data, tested with real scenarios.
3. Integration (2-6 weeks) – connecting to data streams, setting up alerts, building dashboards.
4. Long-term run – continuous model refinement, threshold tuning, and performance optimization.

Interested in anomaly detection for your business?

Whether you're in finance, manufacturing, SaaS, or IoT, we can design a detection system around your real workflows – not generic templates.

Let's talk

Reach out via the contact section on the main page and describe your current monitoring setup and challenges. You'll get a short proposal describing what anomaly detection could automate first.

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Real-Time Anomaly Detection Catch problems before they become critical.