What if you could chat with your wireless network? With an increase in demand for conversational commerce, AI voice assistants have evolved as the next tech to transform business. While voice assistants like Siri and Alexa continue to gain a broader acceptance in homes, voice chatbots are becoming the pivotal point in customer service, board rooms and Human Resources. Meet Marvis- the first AI-driven virtual network assistant.
Now, you can ask questions and get intuitive answers on demand with a wireless expert. With simple natural language processing (NLP) and Information Theory to analyze large amounts of data and conclude subtle inferences, Marvis has the power to extract insights from designated systems or troubleshoot an issue for you.
Marvis uses machine learning to execute unique troubleshooting and helpdesk functions like anomaly detection, confidence ratings, and event correlation. When applying APIs, this can trigger automated workflows for rapid problem resolution or avoiding wired and/or wireless device issues.
Marvis Anomaly Detection
To keep things simple and avoid having to jump through multiple systems, Marvis adds its anomaly detection to the Mist SLE dashboard so that administrators can proactively identify service impacting events. This assures rapid drive and resolution of the root cause of issues. By leveraging data science tools for automated service, Marvis determines baselines and trigger notifications upon service-impacting deviation from its baseline. It doesn’t end there; with the API driven interface, detected anomalies have the power to trigger external tasks like creating a help desk ticket, without any manual intervention.
Root Cause Analysis
A part of Mist’s data science toolbox, Bayesian Interference, is implemented to identify causes of issues by calculating the highest probability percentage to the issue occurring on the network. This technology delivers a most accurate root cause analysis to a speedy identification and resolution.
Marvis corresponds information across a massive knowledge base to determine the range and significance of a problem. This assists you in prioritizing issues and accurately assigning resources.
For example:
- Did an upgrade cause a wireless issue?
- Is it specific to client, group of clients, entire site, or many locations?
- Is it happening rarely, occasionally, or frequently?
- Is there a coverage issue or is a DHCP server responding slowly?
- What is the scope and impact of the problem?
Opposed to other solutions, Mist adopts data science and cumulative SLE performance to master and increase the wireless experience. Wireless is about experience, not just connectivity. Maximize your user experience with faster troubleshooting, which mean less downtime, while proactively fixing problems before users know they exist.
