Falkonry

Spot the unexpected. Mitigate the unplanned.

Anomalies reveal the first signs of trouble. Detecting them reliably means that your operations and design teams have real-time visibility and can speed up their actions. If you can do this without data science training or pre-judged correlations, then you can transform your enterprise.

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Time Series Patterns

Real-time visibility at scale

Uncover events from terabytes of real-time data without overwhelming your team

Improved time to mitigation

Identify where and when anomalies emerge, narrowing investigation scope.

Increase engineering output

Connect design with operations. Develop and test faster with data-driven insights.

Why use Time Series Intelligence to spot anomalies?

TSI creates a shared source of truth for dynamic systems by becoming a bridge between design and operations. Without programming effort and laborious context engineering, TSI cuts delays in operational decision making.

Difficulty to label normal

High noise levels in data

Concurrent anomaly causes

Tens of thousands of signals

Changing baseline operations

Limited compute resources

PatternIQ™ drives automated anomaly detection

Falkonry’s patented PatternIQ™ powers fast, scalable, and precise time series intelligence. Using data-clock-based task scheduling and efficient deep learning, it detects anomalies of shapes, waveforms and value distributions without supervision.

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Challenges of industrial-scale anomaly detection

Detecting anomalies at an industrial scale is complex. Data quality issues, frequent changes to production recipes, massive data volumes and the time demand on experts to provide supervision make scaled anomaly real-time detection difficult to achieve for most organizations.

  • Subtle visual and statistical behaviors are easily missed
  • Factor correlation during anomalies is mostly unknown
  • Rare anomalies makes learning very difficult
  • Data volume makes real-time detection too expensive
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Time Series Patterns

Anomaly detection typically produces too many alerts

Traditional anomaly detection generates a tsunami of alerts. Critical anomalies get lost in the alert fatigue that ensues. A good idea becomes an example of bad execution. Timely mitigation is never really achieved and data remains unused.

  • Noisy data increase false positives
  • Process shifts over time without much notice
  • Alert fatigue resulting from too many anomalies
  • Anomaly detection not used in daily operations
Learn how Falkonry avoids this curse ->
Time Series Anomalies

Helpful Resources

Technical Paper

Prioritized Anomaly Management in Steel Production Using Self-Supervised AI

Webinar

How SCAE improves physical maintenance despite limited factory resources using AI-powered guidance

Blog post

Quantifying the Impact of Anomaly Detection

AISTech

Automated, Scalable AI for Real-Time Monitoring of Steel Continuous Casting System

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