AI/Big Data Analytics

AI and Machine Learning (ML) to Improve Client Experience and Ensure IoT Device Performance & Security.

Voyance performs both real-time and batch analytics for a detailed end-to-end analysis of client experience with the ability to predict outcomes. The platform employs Artificial Intelligence and Machine Learning (AI/ML) techniques, such as clustering and regression, on all of the input data sources to automatically determine baseline performance, detect hidden trends, and identify anomalous behavior. Voyance is the first AI-based solution to deliver unmatched IoT operational assurance with the integration of IoT security and device performance analytics in a single platform.

Service Baselining

Automatically determine ‘normal’ performance for user experience and IoT device behavior. Detect deviations in real time with alerts and see historical changes.

Recommendation Engine

AI/ML is used to make predictive recommendations and quantify the impact of suggested changes. Root causes for incidents are automatically surfaced.

Incident Alerts

Automatic identification and characterization of network incidents. Baseline deviations are analyzed, and when appropriate alerts are generated and assigned a priority level. Sophisticated analytics detect hard to find problems and minimize false alarms.

IoT Device Classification

Devices are automatically identified and classified by network data analysis and behavior. Massive ML data set with globally cloudsourced data is constantly being refined.

IoT Security Threat Detection

‘Outlier’ devices, deviating from normal behavior, are automatically identified. Triggers are based on changes from historical behavior, comparisons within device groups, and detection of know threats.

Multi-Dimensional Analysis

Insights into extremely complex questions, such as “Which Windows laptop users on the corporate SSID are experiencing poor Skype for Business user experience. Correlation, indexing and visualization of every client incident across the relevant dimensions such as root cause, location, SSID, VLAN, OS, etc.

Cross Stack Analysis

Detailed data correlation of incidents with all of the factors across stack, (e.g. when client experienced poor Web experience, at the same time was Wi-Fi, the WAN or any other network service experiencing problems?) Eliminate potential root causes and point IT in the right direction for solving problems and improving user experience.

Minute-by-Minute Correlation

Correlation at near real-time yield root causes about what happened at the same time that could have caused the problem. For WAN flow analysis, per minute correlations yield the number of high link utilization minutes plus specific users or apps that contributed to help answer the question question “what caused the Internet to be inaccessible at a certain time on a given day?