Anomaly detection

Minimize risks in your customer-facing, manufacturing, and IT management processes using machine learning.

Detect customer experience issues

We build solutions that collect thousands of application metrics and use machine learning models to detect anomalies in real time. Our algorithms can detect unexpected drops and spikes in traffic, conversion rates, session durations, and other mission-critical business metrics. Near real-time anomaly detection helps to quickly react to issues that affect user experience and minimize losses.

Detect stability issues

Our outlier detection solutions can monitor an extremely large number of system metrics in data centers and clouds, correlate them, and identify complex anomalous patterns and outliers that involve multiple metrics and cannot be detected through the analysis of individual metrics in isolation.

Detect equipment failures using IoT data

Our data scientists have analyzed numerous case studies and real-life projects to develop a comprehensive toolkit of computer vision algorithms for anomaly detection. We work with a wide range signals and data sources, including IoT sensor data, infrared and X-ray imagery, and video streams, to detect anomalies and prevent larger failures and outages.

Perform root cause analysis in seconds

We put a lot of emphasis on the operational aspect of anomaly detection, including speed and convenience of incident investigation. Our tools analyze the dependencies between metrics and automatically identify the segments potentially related to the current incident in the full volume of data. This helps operations teams to quickly perform root cause analysis and troubleshoot the issue.

Detect data quality issues

We integrate our models and algorithms into data processing pipelines and data lakes to continuously monitor data quality and automatically stop damaged or suspicious data from propagating into downstream data processing jobs and analytical reports. We use algorithms that are specifically designed for data quality applications and are capable of processing extremely large data volumes and integrating with big data platforms and public cloud services such as Spark, AWS EMR, and Google Dataproc.

Detect security breaches

Our solutions for fintech, ecommerce, and video games help to solve various security-related problems such as fraud detection, intrusion detection, and loss prevention. We combine supervised and unsupervised anomaly detection algorithms as well as short-term and long-term fraud detection models to achieve maximum efficiency.
How our anomaly detection solutions work
Autonomous anomaly detection using unsupervised algorithms

We extensively use unsupervised machine learning algorithms to detect outliers and anomalies even in settings where “normal” metric patterns and thresholds are unknown. This eliminates the need for manual threshold management, enables the detection of complex multi-metric patterns, and allows scale-out to dozens of use cases and thousands of metrics.

Ready for extreme volumes of data
Our solutions are designed to monitor a very large number of metrics in real time, detect unusual patterns that involve multiple metrics at the same time, and automate root cause analysis at scale.
Simplified root cause analysis
We integrate our solutions with incident management and alerting systems to automatically send notifications and create tickets for operations teams. Notifications include most relevant segments of metrics that are potentially related to the incident. This helps to quickly react to incidents, reduce the amount of time needed for investigation, and prevent failure propagation or security-related losses.
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We provide flexible engagement options to design and build an automated anomaly detection solution for your company. Contact us today to start with a workshop, discovery, or proof-of-concept (POC).

Workshop

We offer free half-day workshops with our top experts in anomaly detection to discuss your  challenges, potential areas of improvement, and industry best practices.

Proof-of-Concept

If you have already identified a specific use case that needs to be solved, we usually can start with a 4-8 weeks proof-of-concept project to deliver tangible results and business value.

Discovery

If you are in the stage of requirements analysis and strategy development, we can start with a 2-3 weeks discovery phase to identify right use cases for anomaly detection or predictive maintenance, design your solution using industry best practices, and build an implementation roadmap.