We help leading manufacturers, transportation, and energy companies to improve their physical operations using anomaly detection models and tools.

Use cases


Continuously analyze thousands of signals

IoT sensors generate a large number of signals, which can be challenging to monitor, analyze, and react to. Our platform for Industry 4.0 analytics can monitor thousands of signals continuously, learning normal patterns and detecting anomalous behavior.


Detect anomalies early, prevent propagation

High latency in anomaly detection can result in financial losses, major outages, and liabilities. At the same time, instant anomaly detection is challenging because of noises and outliers that lead to false positives. Our anomaly detection models are designed to optimize the trade-off between detection latency and accuracy using variable time windows and analysis of historical patterns.


Detect cross-metrics patterns

IoT metrics are often collected from complex environments that have multiple interrelated components. In such environments, the analysis of individual metrics can be inefficient because the presence or absence of anomalies in individual signals does not fully characterize the status of the entire environment. Our platform uses topology-aware deep learning models that account for dependencies among sensors and learn complex patterns that involve multiple metrics.


Detect anomalies in images

Anomaly detection in images and videos is one of the most efficient ways to monitor manufacturing and transportation processes, detect defects, and identify security issues. We have extensive experience in computer vision and labeling image data, which helps to develop reliable and efficient anomaly detection solutions.


Investigate issues using advanced tools

Anomaly detection is only part of a complex process that includes issue triaging, root cause analysis, troubleshooting, and feedback-based system tuning. Our anomaly detection models are engineered from the ground up to provide advanced insights that help to investigate issues: anomaly timeframes, severity scores, and correlated metrics. Our solutions also include advanced dashboards for visualizing these insights and performing root cause analysis.


Receive insightful and relevant alerts

Although alerting might appear to be a straightforward task, its practical implementation is associated with some challenges, such as creating insightful summaries that help to investigate the issue and fine-tuning the alerting thresholds and severity levels based on operations team feedback. Our solutions provide features that address these advanced aspects of alert management and tuning.


How anomaly detection platform for IoT works

Forecasting models
Most anomaly detection techniques are based on the ability to model the monitored process and forecast metric values. To make accurate predictions, we use state-of-the-art deep learning forecasting models that account for metric correlations and the topology of the sensor network.
The inference process continuously tracks the difference between the forecasted values and the ongoing sensor signals. The forecasting error is analyzed using multiple sliding windows and risk scoring models, the risk scores are thresholded, and alerts are generated.
Anomaly source analysis
In practice, it is not enough to just detect anomalous situations. Our platform for Industry 4.0 analytics includes models and tools that trace the issues down to individual metrics and facilitate root cause analysis.

Our clients

Finance & Insurance

How to get started

We provide flexible engagement options to help you build IoT anomaly detection solutions faster. Contact us today to start with a workshop, discovery, or proof of concept (POC).

Learn more

Add anomaly detection to your data with Grid Dynamics Starter Kit
This article describes our real-time cloud-based Anomaly Detection Solution. We cover its design and applicability to the most common use cases: monitoring, root cause analysis, data quality, and intelligent alerting. The solution is AI driven and implements a flexible approach based on extraction of normal state and behavior patterns, but it does not rely on purely statistical methods.
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Unsupervised real-time anomaly detection
Most modern application systems consist of multiple middleware components. This includes databases, queues, search engines, storage, caches, in-memory data grids, identity services, etc. They also include multiple stateful or stateless microservices and mobile application proxies—all connected by data and processing flows. This article presents a solution for real-time anomaly detection in metrics collected from such components.
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Read More on Anomaly Detection for Industry 4.0

This white paper describes the anomaly detection platform for Industry 4.0. This solution is developed to help manufacturers and energy and transportation companies reduce risks and improve the efficiency of their physical operations.

The white paper includes an overview of supported use cases, a summary of the solution features, high-level architecture, and a step-by-step guide that describes the deployment process.

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