>> Narrator: Live from San Diego, California
it's the CUBE.
Covering CISCO Live US 2019,
brought to you by CISCO, and its ecosystem partners.
>> Welcome back to San Diego everybody
you're watching the CUBE, the leader in live tech coverage.
My name is Dave Volonte
and I'm with my co-host, Stu McMannen.
This is day two for CISCO Live 2019.
We're in the Dev-Net zone.
Stu, I was walking around earlier in the last interview
and I think I saw Ron Burgundy out there.
(laughter)
>> Stay classy San Diego.
>> Right, right.
Lingping Gao is here.
He's the founder and CEO of NetBrain technologies
just outside of Austin.
Lingping, thanks very much for coming on the CUBE.
>> Thank you, Dave.
>> You're very welcome, so I want to ask you,
I always ask founders, your passion for starting companies,
why did you start NetBrain?
>> Well, maybe tired of doing things manual,
that's one side.
The other side of it is I used to took an exam,
called CCIE, a lot of folks do it here.
I failed my first try, it was a big blow to my ego,
so I decided we got to create a software
to help them pass.
>> (laughs)
>> This is actually the genesis of NetBrain,
help people to be better at doing network management work.
>> That's a great story, so tell us more about NetBrain.
What do you guys are all about?
>> We're the industry's first just-in-time
network automation software.
Our mission is to democratize network automation.
Every engineer, every task,
they should start off with automation
before human beings touch the task.
>> Yeah you know, if we go back, let's say 10 years ago,
people were afraid of automation, you know.
They thought it was going to take away their jobs.
>> They still are.
>> And they still are, we'll talk about that.
And I want to ask you about the blockers.
And they were fearful, they wanted to touch things,
but the reality is, people talk about digital transformation
and it's really all about
how you use data, how you leverage data,
and you can't be spending your time doing all this stuff
that doesn't add value in your business.
You have to automate that
and move up to more valuable tasks.
But, so, people are still afraid of automation, why?
What's the blocker there?
>> They have the right reason to be afraid
because so many automation was created once,
used exactly once, right?
Then you have the cost of traditional automation.
You have the complexity to create a network automation.
You guys realize that network automation,
you cannot have network automation
only work on a portion of your network.
You have to work on majority, if not all of your network.
So that's become very complex,
just like you want a self-driving car.
You can go buy a Tesla, a new car.
You can drive on its own.
But if you wanted your 10-year-old Toyota
driving on its own, retrofit it,
that's very complex.
Well that's today in network automation has to deal with.
You have to deal with multi-vendor technology,
multi-years of technology.
So people spend a lot of money.
The returns are very small,
so they have the right to be afraid of it.
But the challenge there is, what's the alternative?
>> Wait, before you go there,
if I understand it, just playing back,
they're solving a very narrow problem.
They do it once, maybe twice.
Maybe a rudimentary example would be a script.
>> Yeah.
>> Right, and then it breaks, or it doesn't,
or if something else in the network changes,
and it really doesn't affect that, right?
>> Yeah, I think that too many network engineers,
it's like, well, I'm sitting there,
I've got all my geek knobs and I get everything done,
and they say, no don't breathe on it
'cause it's just the way I want it.
>> That's right.
>> And that can't be.
That doesn't scale, it doesn't respond to the business.
I need to be able to respond fast to what is needed
and things are changing in our environment,
so it's something that I couldn't,
as a person or a team, keep up with myself,
and therefore I need to have more standardized components.
I need to have intelligence that can help me.
>> Let's say, let's say.
>> So we've laid out the generalized.
We've laid out the problem.
What's the better approach?
>> Well, if you're looking at the challenge today,
you have to have developers, which a lot out here,
development engineers know how to script,
and the network engineers who know how networks operate,
to work together.
So there's a data part of it,
there's a knowledge part of it.
These two have to meet to create network automation,
and that network automation has to be a scale.
So the challenge of traditional network automation,
why does it fall short,
lies on if you're going down technical level,
is one is data.
Too many data unstructured.
And the other one is on knowledge.
The knowledge cannot be codified.
So you have the knowledge sitting in people's head.
Like a programmer had to work
with a network engineer together,
you make the cost higher, you make it very unscalable.
So those are the challenges.
So how fast forward we have to do?
So NetBrain, for the last 15 years,
should decide to look at it differently.
We created something called
operating system of total network,
and actually use this to manage
over a thousand vendor model's technology.
And through the platform,
you can continually add in new things into this platform.
So the benefit of it is, the network engineer, anybody,
can create automation.
They don't have to know how to write a code.
And the developer who knows the code
can also use this platform.
Or the people familiar with technology, like Ansible,
they can integrate that to NetBrain.
>> Okay, so you have all this data,
which like you said is unstructured,
so those don't have any meaning.
Data's plentiful, insights aren't, right?
And then you have this what I call tribal knowledge.
Joe knows how to do it but nobody else knows how to do it.
So you're marrying those two.
How are you doing that?
Are you using machine intelligence,
and iterating, building models?
Can you add some more colors to how you go about that?
What's the secret sauce?
>> We took a hybrid approach.
First of all, you have to model the entire network.
With this what we call operating system of total network,
we have about 10, 12 thousand of variables
modeling a device.
And that 12 thousand variables adds to across your,
let's say, a thousand node network,
there will be 12 million variables describing your network.
That's first.
Then, on top of the 12 million variables,
when we continually monitor the software
and the machine learning,
gives something called a baseline data.
But on top of it, the user,
the human being will have the knowledge
on what is considered normal, what is considered abnormal.
They can add their intelligence through something
called the executable runbook on top of this system,
and that system now can be run at any time,
which talking about, when somebody attacking you,
when network is on the fort.
Or through a human being or a task,
now the automation can be run just in time.
>> So the expert, the subject matter expert,
the domain expert, the person with the knowledge,
he or she can inject that knowledge into your system,
and then it iterates and it improves over time.
>> That's right, and it iterates and improves,
and then other people can open the hood,
can continue improving intelligence.
So the whole automation in the past,
it was, why is it only used once?
Because it's a closed loop.
It's a script.
It's a UI input and output, just text.
So it wasn't designed with a comprehensive model behind it.
So you do it, you're building your model,
you're writing your logic within the same piece of code.
We decided, we think that you cannot scale that way.
>> Okay, so obviously you can stop Dave
from inputting his lack of knowledge into the system
with security control and access control,
but there must be a bell curve
in terms of the quality of the knowledge
that goes into the system.
You know, Joe might be superstar,
and you know, Stu maybe doesn't know as much about it.
No offence, Stu.
So how do you, sort of, balance that out?
Do you try to reach an equilibrium,
or can you weight Joe's knowledge more than Stu's knowledge?
How does that work?
>> So the idea that this automation platform
has something called the executable runbook.
The executable runbook can share
and collaterally improve by three sources.
One is the engineer themselves.
The other one is by underlying engine.
So we talked about AI and machine learning, we have that.
We also have a look engine.
We basically adjust that by ourself.
Certainly, it's through collaborative partners,
for example, CISCO, who run many years of hardware.
They have a lot of knowhow.
They have tech.
That knowledge can be pushed to the user.
We actually have, in our system,
a partnership with CISCO tech.
Thousands of scripts can be run through that brand
without user involvement,
and getting the benefit of without talking with tech,
getting the answer.
>> So I think you actually partially answered
the question I have is,
how do you make sure you don't automate a bad process?
>> Yeah.
>> And maybe talk a little bit about kind of
the training process to your original why of the company.
Is it to make things easier,
or what's the ramp up period for someone that gets in?
Give us a little bit of how many engineers
you guys have worked with.
>> Well a moment ago,
I mentioned our mission statement of NetBrain
is to democratize network automation.
You know, used to be network automation,
only the guru's guru do it, developers and et cetera,
and young generation.
My generation who use command line,
this is not us, right?
This is the saying, you know.
But we believe, nowadays, with the complexity of network,
with the cloud computing, with the cyber security demand,
the alternative to network automation
is just no longer viable.
So we really put a lot of thought into it and say,
how we can put network automation into everyone's hand?
So the things we did has three angles of it.
One is, automation can be created by anyone.
The second, meaning developer, network,
anyone who have knowledge of network can create automation.
The second piece of it, automation can launch at any time.
Somebody attacking you middle of the night,
they don't tell you, automation can launch to protect you.
A network is out, you don't have people
who are dying on the job,
automation can launch the diagnosis,
so it's got to launch at any time.
The third one, is can adapt to any work flow.
You have trouble shooting, you have network changes,
you have compliance, you have documentation work flow.
The automation should be able to adapt
to any of this work flow.
The top integration, for example, we have,
is with ServiceNow.
So there's a ticket, human being shouldn't touch the ticket
before automation has done its share.
Then human should have come in,
and then you continue to use automation.
>> So you're talking about democratizing automation,
network automation.
So anybody who sees a manual process that's wasting time
can sort of solve that problem,
is essentially what you're doing.
>> That's exactly what we're doing.
>> So is there a pattern emerging in terms of best practice,
in terms of how customers are adopting your technology?
>> Yes, now we see more and more customers
creating these things almost like a club.
The power user and, we often call, the normal user,
they have the knowledge in their head.
The pattern emerging that we saw is
they now work proactively to see
how can I put that knowledge into a set of executable format
so that I don't get asked on it all the time.
So that I can do the things more meaningful to me,
than I'll be repeating the same things 10 times a month.
And I should, one of my, we call the shift to the left.
Level one doing level two,
the machine doing the level one's task.
Level two, level three are doing more meaningful things.
>> How different is what you're doing at NetBrain
from what others are doing in the marketplace?
What's the differentiation?
How do you compete?
>> Network automation, so far, has been piecemeal.
I think a fragment.
It's a thing that has done
typically in three big categories.
One is wholesale on hardware approach.
You replace the hardware with SDN, SD-WAN, SDNet.
There's automation capacity built into it.
I call the Tesla approach.
Buy a Tesla, and you can drive, self-driving.
The second approach is software approach.
It is where we are leading.
Build a model of your entire network,
apply machine learning and statistical analysis behind,
but also, more importantly, an open architecture,
allow human being to put their intelligence into this.
That's second approach.
And then the third approach is actually service.
Let an outsourcer take you, help you,
or moving your workload into cloud,
because there's better automation there.
So we are focusing on the middle portion of it,
and the landscape is really
where we have over two thousand enterprise customers,
and they're automating.
This is not just once or twice a week,
but one thousand times a day.
We're really excited that the automation in that scale
is transforming how network is being managed.
It enables things like collaboration.
It used to be people from here, people from offshore,
couldn't work together because data and knowledge
is hard to communicate.
With automation, we see collaborations happening,
more collaborations happening.
>> We've been talking about automation in the network
for my entire career.
It feels like the promise has been there for decades.
>> That's right.
>> Feels like the last couple of years,
we've really seen automation, not just in networking,
but we've been covering a lot,
like the robotic process automation,
all the different pieces of IT,
or seeing automation bring in.
Give us a little bit of look forward.
What do you predict is going to happen with automation and IT
over the next couple of years?
>> In the future, that's great.
You know, we have cloud computing, we have cyber security,
we have the scale of network driving network automation
to the front and center of the solution.
And my prediction in the next five years
probably surrounding,
one, I think that automation going to be ubiquitous,
going to be everywhere.
No human being should touch a ticket
without automation doing it first, the task first.
Second, we believe things called
the collaborative network automation will be happening.
In other words, network automation is following the packet
from one network entity to the other entity.
The example would be
your manager service provider and the enterprise,
they are a collaborative manager network,
a common network.
But when they something wrong,
we don't know which part has issues,
so automation defined by one entity
could be wrong across multiple.
Service provider like cloud provider also have automation
can be initiated by the enterprise client.
We also see the hardware vendor like CISCO
and their customer has collaborative automation happening.
So the next five years will be very interesting.
Many ways to manage an operating network
will be finally go away.
>> Lingping, last question, give us a business update.
You mentioned two thousand customers,
you've hundreds of employees,
any other business metrics you can share with us?
Where do you want to take this company?
>> We really want to be behind every enterprise.
Our mission is to democratize the network automation.
We're looking at, in the next five years,
our business should grow ten times.
>> Well, good luck.
>> Thank you.
>> Thanks very much for coming on the CUBE.
Great story.
>> Thank you.
Thank you for the interview.
>> Congratulations for all your success.
All right, keep it right there everybody,
Stu and I will be back,
Lisa Martin as well is here with our next guest
live from CISCO Live 2019 in San Diego.
You're watching the CUBE, we'll be right back.
(lively music)