Wired Assurance: Day 2 – Marvis Conversational Interface

Demo Drop Wired & Wireless AccessAI & ML
Screen capture from Wired Assurance demo video, showing the Monitor interface with percentages and line graphs of network metrics on the left. A pop-up window on the right shows Marvis responding to administrator requests in a chat-like format.
Demo

Marvis is a great listener.

With a conversational interface using natural language processing, Marvis is able to understand user intent and goals. Just describe your intent to the virtual assistant, and watch it get to work. See Marvis in action in this Wired Assurance Day 2 demo.

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You’ll learn

  • How natural language processing helps Marvis understand your intent and goals

  • How Marvis can help you troubleshoot and resolve client issues

  • How to avoid having to remember specific dashboards or CLI commands

Who is this for?

Network Professionals Security Professionals

Resources

Transcript

00:03 marvis is a new member of your it team

00:05 that you can actually work with

00:07 the latest enhancement is a

00:08 conversational interface where users can

00:11 see pending actions or inquire about

00:13 something else

00:14 users can click on suggested actions or

00:17 ask questions like what is happening

00:18 with this unhealthy access point

00:21 marvis is a purpose-built ai engine that

00:23 returns the relevant details about your

00:25 network based on log-mining

00:27 machine learning algorithms and an

00:29 overall rich data science toolbox

00:32 in this example marvis processes the

00:34 site's ap data

00:35 and concludes that an upgraded firmware

00:37 image can make the ap healthier

00:40 the user tells marvis yes and that's it

00:42 no need to search for the firmware or

00:44 upgrade button

00:46 marvis enables ai-driven support to

00:48 proactively resolve network issues

00:50 with live feedback and real-time data

00:53 here marvis verifies that the firmware

00:55 upgrade was properly executed

01:05 marvis's nlp has been enhanced with nlu

01:08 so it can understand what your intent is

01:10 even with misspellings

01:12 ask marvis to troubleshoot a specific

01:14 client by selecting it from the list

01:16 which yields the client's issues and

01:19 actions the user can take

01:21 this client appears to have some dhcp

01:23 issues

01:24 so asking about the failure timeline

01:26 yields a better understanding of the

01:28 history of the problem and when it

01:29 occurred

01:31 if you would like to see more marvis

01:33 pulls together supporting data for the

01:34 problem

01:35 and allows the user to dig in deeper

01:38 marvis contextualizes your intent to

01:40 show you what matters

01:41 like the exact client experiencing

01:43 issues and every time it failed to

01:45 connect

01:46 if the problem cannot be resolved marvis

01:48 can automatically

01:49 open support tickets in other systems to

01:52 get more eyes on the issue

01:54 the key takeaway of marvis is to focus

01:56 on making user experiences

01:58 better with streamlined operations to

02:00 quickly resolve problems

02:01 before users know they exist

02:20 you

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