Use cases
MONITORING
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.
DETECTION
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.
DETECTION
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.
DETECTION
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.
TOOLS
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.
TOOLS
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.
Scenarios
Robotics
Our platform for Industry 4.0 analytics can be used to collect data from assembly line sensors and detect failures that affect quality, performance, or stability. We use models that account for hardware and sensor topology to reliably differentiate temporary local anomalies from failures that affect the manufacturing process.
Robotics
Our platform for Industry 4.0 analytics can be used to collect data from assembly line sensors and detect failures that affect quality, performance, or stability. We use models that account for hardware and sensor topology to reliably differentiate temporary local anomalies from failures that affect the manufacturing process.
Trаnsportation
Fleet management in the mining, shipping, and trucking industries, as well as in smart city applications, requires continuously collecting and analyzing signals from thousands of vehicles. We have experience building outlier detection and predictive maintenance solutions that help to reduce maintenance costs, trip times, and delays, and improve operations efficiency.
Trаnsportation
Fleet management in the mining, shipping, and trucking industries, as well as in smart city applications, requires continuously collecting and analyzing signals from thousands of vehicles. We have experience building outlier detection and predictive maintenance solutions that help to reduce maintenance costs, trip times, and delays, and improve operations efficiency.
Infrastructure
Failures in the transfer of chemicals and oil are associated with high risks, major losses, and liability costs. We help companies that operate such transfer processes and infrastructures to create anomaly detection solutions and troubleshooting tools that reduce reaction times, costs, and risks.
Infrastructure
Failures in the transfer of chemicals and oil are associated with high risks, major losses, and liability costs. We help companies that operate such transfer processes and infrastructures to create anomaly detection solutions and troubleshooting tools that reduce reaction times, costs, and risks.
How anomaly detection platform for IoT works
Our clients
MANUFACTURING
FINANCE & INSURANCE
HI-TECH
RETAIL
MANUFACTURING & CPG
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).
Workshops
We offer free half-day workshops with our top experts in data science and anomaly detection techniques to discuss your processes, analytics tools and technologies, and opportunities for improvement.
Proof of concept
If you have already identified a specific use case for anomaly detection, we can usually start with a 4‒8 week proof-of-concept project to deliver improvements and tangible results.
Discovery
If you are in the requirements analysis and strategy development stage, we can start with a 2‒3 week discovery phase to identify the right use cases for anomaly detection, design your solution or product using industry best practices, and build a roadmap.
Learn more
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.rnrnThe 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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