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Improve Operations with
Plexteq Anomaly Detection Solution
Every day, businesses generate massive volumes of data. If leveraged correctly, that data can help businesses make better decisions, faster. One way is through anomaly detection.
Detecting anomalies can stop a minor issue from becoming a widespread, time-consuming problem. By using the latest machine learning methods, you can track trends, identify opportunities and threats, and gain a competitive advantage with anomaly detection.
We Will Help You
Detect customer experience issues
We build solutions that collect multiples 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 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, imagery, and video streams, to detect anomalies and prevent larger failures and outages.
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.
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.
Run anomaly detection using discovery by a team of data or business analysts visually monitoring dashboards to find unexpected behavior.
Time Series Techniques
Detect anomalies through time series analytics by building models that capture trends, seasonality, and levels in time series data. When new data diverges too much from the model, either an anomaly or a model failure is indicated.
Have knowledgeable people label a set of data points as normal or abnormal. An analyst then uses this labeled data to build machine learning models that can predict anomalies on unlabeled new data.
Autoencoders & Machine Learning
Use the latest machine learning techniques and autoencoders to detect and respond to anomalies in real time through a neural network.
Use unlabeled data to build unsupervised machine learning models that predict new data. Because the model is tailored to fit normal data, anomalous data points stand out.
Have an analyst attempt to classify each data point into one of many pre-defined clusters, then create a separate cluster for all data points that do not look similar to normal data, a.k.a. the anomalies.
How It Works
Data pipelines setup
Understand the operational processes and how equipment works
Collect and process data from industrial systems and equipment
Apply the existing ML approaches or build a customized one
Continuously train the end-users/engineers how to use a solution properly
Transform your business via actual integration into operational processes
Have a question?
How Our Anomaly Detection Solutions Work
Large retailers commonly operate complex ecosystems of transactional applications, marketing automation software, and customer data management systems. Our solutions continuously monitor streams of metrics from applications, detect issues in real time, and automatically send alerts with comprehensive reports to operations teams.
Technology companies often monitor streams of online transactions to detect technical issues, DoS attacks, fraud, and other types of abnormal behavior.
We help them to build solutions that automate these processes.
Manufacturers collect large amounts of data from sensors and IoT that need to be monitored and analyzed. We help them to automate these processes and identify anomalies using data science.
We assist energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly, aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior.
Telcos operate in one of the most competitive landscapes across industries. The margin for error is miniscule, and the time to react is minimal. Our solutions keep tabs on billions of daily events across all network types and layers to identify service and network degradations in real-time.
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