Google’s AI Comeback, Enterprise Agents, The Real Path to AI ROI — W/ Promevo CEO Karthik Kripapuri

Channel: Alex Kantrowitz

Published at: 2026-02-11

YouTube video id: hgpvEvuAugE

Source: https://www.youtube.com/watch?v=hgpvEvuAugE

Power companies finding value today from
cutting-edge AI models. Let's talk about
it with Promevo CEO Karthik Keyur Purri,
who joins us today in a conversation
brought to you by Promevo. Karthik,
great to see you. Welcome to the show.
Thank you, Alex. Thanks for having me.
So, a little bit about Promevo, you work
extremely closely with Google's AI
technology, which has been like a pretty
good place, I would say, over the past
few months to be. So, I'd love to hear
just from your perspective because no
one watches this as close as you do.
Your Your company works extremely close
with them. Um what have you seen over
the past few years from Google? And what
do you Do you think that they're
building a lead over the compet- the
competition? Well, I hope so for the
last part of it because we are
we are all we do is Google, right? I
mean, that's all we're focused on and
there's a good reason for it. Um kind of
probably should take a step back, you
know, for me as we look at it and we
just came off of our annual kickoff with
our company and way we look at this is
where the technology has been in the
market and what Google has done. I mean,
what like 2023, you know, late part of
2023, Chat GPT took the whole by storm
and it's what's big novelty act. I mean,
by the way, it's real, right? I mean,
not just novelty, but it's a novel for
for the end user, right? From a com-
commercial user um
adopting this technology. And yeah,
Google probably got a little caught
flat-footed at the beginning, possibly,
but the pivot that has happened in the
last couple of years, I couldn't be
happier as a as a partner of Google and
the support that we have gotten from
them in uh driving this with the clients
as well. And the technology stack
itself, right? I mean, so uh you know,
the the approach of being open
uh in you know, you know, the technology
itself is open, it's secure, and the
hard pivots into model like, you know,
um you know, you know, Gemini Pro 3.0
that where we are in right now and we're
just giving multimodal opportunities,
it's been it's been very um
rewarding both for us, me as an operator
of a company, as well as our clients as
well, right? So, I think it's you know,
the overall the biggest thing AI for our
clients as well has been a big thing for
us in terms of providing security in our
full stack. So.
Yes, so just to explain what this means.
Basically, Google will come up with
these new models. They're not going to
be able to help the whole world
implement them. So, what you guys do is
you have built expertise within the
Google models and then you will help the
implementation on the business side.
>> That that is exactly correct, right?
Like the last year alone
about 18 months ago, right? We started
work partnering with Google to provide a
lot of adoption services, especially
around Gemini for Google Workspace. We
have done about 250 to 300 workshops at
this point, which which is pretty
incredible from the investment
standpoint for Google investing in
clients and us providing the service for
them. And we see the adoption
like all things in all things in the
world at different scales with our
clients, but there is a really good
reason for that, right? I mean, but we
are seeing the adoption hitting
mainstream and, you know, the demand for
it increasing day by day. Yeah.
So, yes, we know that Google was a
little flat-footed at the beginning.
Yes, everyone knows, I think,
theoretically that things have gotten a
lot better. But from your perspective,
just talk to us about the tangibility of
this stuff and
when we hear, "Okay, it's gotten
better." What have you seen on the
ground? The models have gotten better at
handling complex queries, they're
hallucinating less. Talk a little bit
about the progression.
I think the progression the big
progression, right? It I think is going
from a novelty, like you know, "Hey,
give me answer some questions, do some
basic stuff." To where we are right now,
I feel like with with the advent of
agents at this point, where you can do
agent assembly lines. The models have
gotten very good, you know, you know,
very close to being good at discern
discerning. There's still some work to
be done in terms of hallucination. And
but Google's done a very good job of
grounding, you know, the models,
providing visibility into it. And that's
the reason why we are super excited to
partner with them. And look, there's
still some work to be done here, right?
I mean, you know, like and and this is
not just a Google only race, right? I
mean, today the models are at a point
where next, you know, you're going to
get announcement with OpenAI. There's
going to be, you know, there's going to
be leaps that are happening back and
forth, which overall I think like like
one of the things we like to say
internally is this is as worse as these
technologies is going to be. It's only
going to get better as things go. And a
lot of this still we feel like is
grounded in the fact that organizations
still struggle with data integrity and
that is fundamentally what is the
primary blocker, right? In terms of
like, you know, really AI mo- I mean,
models and agents working and reaching
the full potential of this point. That's
what how we feel about it and that's
where we come in with our consulting
services and provide that help for our
clients.
Yeah, so what does that mean that the
data integrity is not there and that's
the main thing holding them back? I
mean, the big thing, right? I mean, I
mean, I'm a I'm probably aging myself.
This is the same thing that happened in
the 1990s and, you know, the the the
digital transformation. The biggest
blocker that we see with digital
transformation is where data is very
siloed within an organization. It is not
really really available. And then who
has the proper definition of the single
source of truth uh for that data that
exists in organization. So, once you're
able to align as an organization on what
that is, models can get better about
grounding themselves and providing that
level of
uh insight and analysis that I think
agents can then act upon and that
provides better outcomes for our
clients. And that's what what we see,
you know, uh when in even internally
like as a as a
as a COO at Heartwell, I find that to be
the biggest thing that because we want
to be able to act on it in the most uh
in an efficient manner possible. So.
Okay, and then one more question about
this. You said they've gotten better at
discernment. Yes.
>> What does that mean? That they're making
better decisions? Well, they're getting
better decisions based based on the
information that's available, right? And
you know, and I would say that is you
know, especially with Google uh you
know, where with you know, with with the
models that gotten
very efficient, very you know, and then
there's a lot of transparency that's in
there which allows you know, have a
visual into how the thinking process
that happens in that. We are able to see
it in you know, inside what is
happening, what the decision is and
you're able to tune how much of the
hallucinations, you know, you can
tolerate, what you don't want to
tolerate. I'm I'm obviously, you know,
speaking in very basic terms here,
right? There's a lot of you know, you
can data sovereignty is a big thing so
you don't want it to go outside, you
know, like things that you don't you
know, want to base decisions on and that
level of you know, um
you know, control that it's given to the
user has gotten them at least the Google
models gotten be very, very effective.
At least internally that's what we see
and this is what our clients are saying.
So. Okay. Great. And there was, you
know, it's interesting. A couple months
ago there was this discussion is AI a
bubble and is there any enterprise value
uh in artificial intelligence and now
it's like the entire conversation has
shifted to what software companies can
actually, you know, survive this moment.
And I think that that to me shows that
there's there's been this moment where
there's
companies like yours have taken the AI
models, used the improvements and
translated that into real business
outcomes. That's happening now. So I
just want to hear your perspective. When
the model gets better, how does that
translate into your ability to do more
with a with a customer?
Well, um
I our CTO John Pederson and I we each
spend a lot of time thinking about this
and talking about this and one of the
things we believe, right, the idea of
the AI platform, right, is getting
closer and closer. I mean, I know you
know, I'm going to quote Satya, right,
who talked about, you know, the idea
like a few years ago he talked about the
idea of, you know,
these
the
platforms like CRM platforms that sit on
top of data that's existing in the
organization and that's what we talk
about AI platforms that are on top of it
and how we work is going to
fundamentally change. You know, in the I
really I think we're and I'm not trying
to like you know, scare anybody to think
that PowerPoint will go away, but you
are seeing PowerPoint the people whose
reliance on PowerPoint is less than it
used to be, right? And you know, I we
feel like the like as a
as an operator of companies, most people
we don't want a plethora of AI
tools out there, neither do we want to
have a IT sprawl with a whole bunch of,
you know,
platforms that exist. What we want is
the ability to action and do our work in
a you know, with the data that's already
existing in our organization and how do
we interact with it and how do we make
decisions on it? And I feel like that's
where we are right now and you know, I
don't know what the time frame is, but I
feel like it'll be a day when I can just
ask what I need to do and I don't have
to go check my email, I don't have to go
check my CRM, I don't have to check my
financial systems. I'll be able to
interact with the data that exists in
our organization.
Right. And so then as the AI models have
gotten better, that's allowed you to do
more of that or what exactly have the
model improvements translated to? Like
one of the things I will say here,
right, an example of that would be your
you know,
we were talking about before about how
we have a you know, internally to our
organization, we are asking our people
to be
30% more effective, right? I don't I
never use the word efficient because
efficiency means like all kinds of weird
things and people are like, "Oh, what's
about my job?" It's not about it, right?
I mean, like the idea is, okay, if you
got 30% of your time back, you know,
let's take our finance team for in our
organization. We know they cannot take
PTO the first week of the month because
they're closing. Right? It's impossible
for our CFO and our financial
controllers and our FP&A team. So, what
we have challenged them is how can we
work in a way in which a lot of the
tedious mundane task that's working in
our own bespoke billing system that we
have in there, how can that be automated
so you can actually take PTO? Right? So,
that to me is what, you know, the agents
that we are building and you know,
it's sitting on top of Looker or
BigQuery, you know, that can action the
mundane and gives time back. Whether you
be use it for PTO or taking time off
that much deserved or do something else
interesting in your career is what we
talk about when when we talk about the
gains that we want to make in people's
growth and in our company's growth.
That's what That's what internally we
have challenged our organization to be
good at. Right. And that's working
today. Well, we we are we are in the
journey. That we starting we have
started on this one that, you know,
we we have we have kicked off a couple
of projects at this point and more
mostly focused on, you know, people
ideas coming from our organization not
top-down. So, that is one example I gave
you about the close process. How do we
get that tedious done? One of the things
that the other thing is we one of our
co-workers she brought up the idea of
how do we automate a lot of RMAs. We do
sell a lot of Chromebooks for education
and you know, we are a full stack Google
partner. And one of the things that
happens is Chromebooks sometimes don't
work which you know, people don't want
to hear, but you know, it happens,
right? Technology doesn't work. So, we
she came up with an idea of how she got
herself first of all GenAI certified,
right? And then she wrote the prompts to
kind of figure out how do I automate
this and then working with one of our
practice leaders, she wrote an agent to
figure out how to automate the process
and we're about to launch it. So, the
idea is can we get eight hours back for
our plan in a on a weekly basis? That's
the whole idea and that it's working.
Yeah. So.
Okay. All right. And I definitely have
more questions about that, but um I want
to continue along our our line here
talking a little bit about um what
Google enables you to do because you
said another thing that sort of
uh made me perk up, which you said that
the the model or Google models, you're
able to tune them in a way uh that you
might not be able to do elsewhere. That
if you you can have like a dial
basically that says, all right, less
hallucinations, but maybe a more rigid
answer, uh more you know, potential for
more hallucination, but more creative
answers. That's what I'm I'm guessing.
And uh one thing I've heard is that
maybe that that Google is not a a black
box, but maybe a gray box. So, talk a
little bit about that and how that
enables you to implement the technology
better. Yeah, I think, you know, when
you're when you're interacting Vertex,
you know, Vertex AI, which is at the
core of what how Google, you know,
exposes the you know, the thinking, you
know, to us, you know, like or to any of
the users in there, you're able to dial
back and, you know, how much, you know,
broad that you want the model to be or
how narrow you want it to be focused in
the grounding that happens. All of that
to me, you know, you want to the whole
idea of using these models, right? Is
one based on developing trust. And what
is the model using to make the decision
it's it's uh you know, it's going to
give answers uh based on. And what we're
trying to figure out is the more
transparent it is, the better it is for
the user and for the model to even
understand the context of who you are
and how you work and all of the stuff
that happens in there, right? So, I
think that is what I mean by that. I
mean, like the idea of like with Google
has been has done a very good job of it
in the last year or so. What we are
saying is,
you know, first of all, one is making
sure that the data that you are, you
know, inputting into the model, it is
not used to train models globally,
right? Which is the most important
thing. As, you know, as we develop we
are on we are we develop our own SaaS
platform. The last thing we want to have
is, you know, any code checks that we do
is opened up, as you've seen in the news
that's happened for a couple of big
enterprises. Like the IP indemnity that
Google's providing, it's I mean it's
impressive. And, you know, that's that's
Those are the things that we are When we
interact with CTOs, they are talking
about how much they appreciate and the
confidence level is growing with what
Google's providing in terms of how to
interact with the models better in a
more safe and a secure way.
Can I ask you, like, why Google versus
open source?
Well, first of all, like
to me, uh it kind of goes back to, you
know, the idea of like thinking about
managed services, right? Like Like one
of the most 99% of the companies, I
would say, what they need is probably
need to consume this as a managed
service in my mind. And the full stack
of Google's investment, right? If you If
you look at the full scale of it from
infrastructure, models, and open I They
have ability to have interact with other
models, the investments that they've
made in, let's just say, in developing
agent A2K, which is, you know, open to
everybody, right? And agent-to-agent uh
integration, all of that, you get almost
the best of both worlds. Like, why
recreate the wheel with the
infrastructure that and spend that you
don't need to do? And the work has been
done, so consuming this and you get the
latest, you know, learning uh the LLMs
that you are already getting, it's from
a managed service provider like Google
that's already invested in that, you
know, and then and you can calibrate it
the way you want to consume that. To me,
yes, there are some bespoke cases where
you, you know, where there's
You know, we have a couple of clients
who are building their own models and we
are totally uh it makes sense for them.
But 99% of our other client with the
clients that we have are probably just
okay with using just, you know, what I'm
not quite off-the-shelf, but, you know,
what is generally available to, you
know, for consumption.
Yeah, [snorts] we had uh Arthur Mensch
from Mistral in recently, and he was
also talking about how AI is effectively
a managed service, that it is a
an intense technology to implement, and
you could be a technology company and
sort of, you know, take a model and
customize it yourself. But, for most
businesses, the way to consume it will
be working with some other company as an
implementer and an optimizer, and having
them build the AI for your use case, and
that's where you get ROI. I I would
agree with that. I mean, and I think,
personally, I feel like this is one of
those
buy versus build decisions that face us
all operators in the world. Yeah, I
mean, like I We know, when we talk
internally about investment decisions,
what we talk about is, is this something
we want to be good at, right? Yes. And
if you want to be good at, there's two
ways to think about this, whether do we
have practitioners building it for us,
or do we want to have an experiment and
try to build this ourselves? And the
time to market and time to
effectiveness, all of that stuff, you
know, comes into play here. And that is
That is a tough call to make, right?
But, most cases, I think it's made
tougher by people who want to say we are
probably the We are We don't need to be
We have to learn, continue invest in it,
continue nurture it. That takes a whole
lot of thing that takes maybe away from
the core value of what mission of the
company feels like to me, right? Yeah.
So, then, tell me, where do companies
get ROI, or when do companies get ROI
from this versus when do they not? Like,
what are the setups where, you know, a
company goes full steam ahead and says,
"All right, we're going to implement
AI." And they actually end up realizing
a return on their investment versus when
do they not? Because there's definitely
been, I think, the people who aren't
getting value out of it are louder.
Yeah.
Maybe it's a competitive disadvantage
to, you know, shout to the world that
this is working for you. So, talk a
little bit about the the divergence here
and and where people get the most value
from it, or when they're able to exploit
that. I I think, look, I think, um,
I start with We said we've done like 250
workshops, you know, like
what adoption
we always talk about, right? Whether it
be take, you know, we find the best use
cases, right? Where the best success
clients have is when they start small.
When they have a clearly defined KPI in
mind on what is it they want to
accomplish, right? The two examples I
gave you are tangible. And we have
actually have said, "Okay, we're going
to start small like an RMA automation."
Or you know, a finance close operation.
But there's a whole bunch of things in
there that you can talk about how you
want to automate. So, when and then
there's also buy-in from the top down
about the success of what, you know,
what, you know, something needs to
happen. The biggest thing I will also
say here that we see where ROI works and
not work is, you know, the opposite is
also true where we don't see ROI is when
you try to boil the whole ocean. We're
like, "Oh, we're going to be an AI-first
company." I don't know what that means,
right? Uh when when somebody says that,
it sounds great in a board meeting or in
a, you know, press clipping maybe in a
release, but I don't know what AI-first
means. I mean, unless you're a software
developer, it's fine. I mean, you want
to be grounded in how you develop your
your software.
But the idea is to start slow, be very
clear, and then, you know, ensure that
you know, you understand what outcomes
you want to drive in there, and then
expand to the next use case, and then,
you know, and then be methodical about
it, right? Like all uh you know,
transformation work, this is also at at
the core of it, right? I mean And the
last thing I'll also say is it's not,
you know, while I think we are there as
consultants to come and help, the SMEs
who run the business are key to, you
know, making sure that the use cases are
clearly defined, and we can help
facilitate the technology portion of it,
how the technology works for the
business operations, but it's important
that the key stakeholders are absolutely
engaged in, you know, driving the, you
know, the results that we need. So,
yeah. That's That's That's how we follow
it. And uh we try to punt when we we
don't see the buy-in and we we try to
recommend it gently, but with the
clients who do the right way, you see
tremendous amount of uh returns. So.
Have you seen the
the percentage of implementations where
clients are getting ROI go up or have
you seen even
uh cases where it used to be pilots
don't go into production, more of those
pilots are going into production? Yeah,
I mean so I can well, I can tell you a
few examples of where this has happened,
right? Like Bold Bond, I think we have
some personally we have a few personal
with them and they're a great example of
a company that has, you know, started
out, you know, in Google Workspace
Gemini for Google Workspace and they saw
70% adoption, right? Which is the first
key metric that culturally there's a
massive of adoption across the
organization. Then they went to the next
use case about how do you visualize, you
know, provide visualization of products
on the website, you know, so that takes
freeze up more time to sell and then
then they moved then we're helping them
automate a lot of their, you know, um
order processing and everything like
that. And they are the great
transformational organization that
really understood how to start it and
then how do you expand it because and
that's why I would say is probably what
more of 2026 has in store for us like
that. I mean, because that that is if
somebody can replicate that, right? Over
and over again, you're going to see a
lot more outcomes in there. And the
other thing we also is a good barometer
in my in our thing is, right? We, you
know, like you know, I I can one more
thought on this one about outcomes that
we need to drive here. But when we see
massive engagement after we do a pilot,
right? when when it's not limited to
like the managers or an executive
organization, when you see the
attendance that's happening and then the
usage build up even using Gemini for
Workspace, right? You know culturally
that is getting adopted because people
do not want to do the boring tedious
stuff over and over again, right? And
then when that catches, you know, fire,
it it's going to, you know, really
adoption is get driven. Innovation
starts at the very edge, and then it's
nurtured and fostered there, right? So,
that is that is how we think about this.
This If you want to use cases I can
give, but that's kind of, you know,
that's a good poster child of how we
want to see this roll out. So. Yeah,
very interesting, especially the range
of use cases. Just in that gold bond
example, AI for workspace, order
optimization,
um you know, trying to visualize
products.
Do you have a sense Are the models today
sort of equally good at all different
things, or do you have a sense as to
like what the models are best at, and
where they could improve? Look, I think
the models today I I will I'll zoom out
and give a general answer of what I
think the models do really well today,
right? Maybe that's probably specific,
so I don't upset any product managers
anywhere. But
>> [laughter]
>> But what I would say the, you know, the
models are very good at reasoning,
understanding of given context, and it's
it's it's they're very good at that.
Where I think they can be a little bit
better is and, you know, it's obviously
we want always limit hallucinations,
and, you know, it's the idea of becoming
a little bit more uh
It's a just like people, I would say, is
becoming more self-aware, right? As in,
you don't need to give an answer. If you
don't know something, say you don't
know. It's something we tell our kids
[clears throat] and, you know, people
meetings, right? So, don't feel like you
The idea of like you have to provide an
answer needs to probably be minimized a
little bit, and that's probably where I
think the models in general all probably
need to get better at in my mind. And do
you ever see them say, "I don't know"?
Or is that still They're still kind of
>> I when when the context is
uh off, they're better at it. But more
often than not, it tries to connect dots
that don't need to be connected in my
mind sometimes, yeah.
>> Yeah. It's like the the friend who's
read everything, and and does seem like
they always have a response. Maybe they
don't, though.
>> Exactly. Um And I I I think that
>> I know how you feel." It's another
favorite example of that.
>> [laughter]
>> Yeah. Oh, yes, I know how Yeah, sure you
do, AI model. Okay.
Uh I said I said we were going to get
back to this. I want to talk a little
bit more about what's going on inside
your company. So, you actually have an
initiative where you've tasked a good
chunk of the company to create their own
agents. And we're kind of coming at this
moment where um
where we have this surge of people
building their own, some on uh you know,
Mac minis and setting them loose in the
world. Uh and so, I'd love to hear an
update from you about how that's going.
Well, um what we did actually is we we
have not tasked people to do build
agents, okay? What we have told them is
be we are going to be grounded in AI
because if you're going to be consulting
with our company with our clients, we
want to be lead by example and show use
cases that we actually want to show them
because what we want to show is have our
clients see a first meet with our CFO
and say, "How do they, you know, close
books and how do we integrate?" Same
thing with our finance team, our client
success teams, all of that the
visualization, right? So, what we have
really It's one of the ways that I can
think about how do you accelerate the
learning curve because most people,
right? This is new to a lot of people
including our own co-workers internally.
I mean, you know, I you know, so
the way we are trying to do this, right?
Like a couple of years ago, you know,
one of the things that we do our
employee feedback with our companies
annually and um you know, one of the
things people are like, "Oh, you want us
to get all certifications and
everything, but give us time." So, on
Friday afternoons, we don't have any uh
meetings. So, we have block learning
blocks. And [snorts] I will say we're
I'm very proud of the fact that people
actually have taken that and then they
most of them are GenAI certified in our
company at this point, right? Google's
uh courses and uh certifications that it
provides.
And what we have seen, right? The I So,
we asked people to like, you know, you
don't have to build this yourself. What
we want is you to think about the things
that the use cases that you go through
that you think this technology now can
be applied. So, we actually did in I you
know we had our company um outing in
Austin and most of our company ended up
doing a hackathon along with our
developers and our practice leaders and
they worked through a few of the use
cases that we have and then we are going
to use the next few months to go and
implement this just like we tell our
clients how to implement it, right?
Look, like all things it's a question of
prioritization, right? And we went
through three of the use cases in Austin
and there's about 20 more that's behind
it, right? And so but the coolest thing
about this is you now realize there's so
much opportunity to be unlocked at this
point and and the people are thinking
about it the right way and you know
about how to solve things that people
usually don't like to do on over and
over on a on a day-to-day basis, right?
I mean, you know, like I don't want to
create a power you know Google Slide
every time and I want to be able to have
a starting point that just it's more
conversant with us. So that is one thing
that we have done internally. The other
thing I'll also say here is it is you
know this probably
you know not that much of surprise, but
we are a software provider as well,
right? We we have G Panel which
provides, you know, workplace automation
and management software for all of our
workspace clients and we use
you know
generative AI to actually accelerate our
road map you know our code development
in our software development life cycle
process and which has led to like
acceleration and it it started last Q4
and we're seeing the productivity happen
a lot. I mean, so you know, those are
the kind of things that we see a lot of
that's happening operationally as well
as our own software development you know
on both of them it's been very very
useful and productive for us. Yeah.
Okay, fascinating. All right, let me ask
you one last question here because
we've talked a little bit about it, but
I think we should go a little bit
deeper. Sure. You're talking about how
you're working to implement AI in
workspace and all these different these
different knowledge work tasks. How do
you think AI changes knowledge work
economy?
Hmm.
That's a that's a very broad interesting
question. I mean
Can you elaborate a little bit more when
you talk about when you said the impact,
what do you mean by that? Like
>> Yeah, no thanks thanks for clarifying. I
think that like I wonder how our work
day changes, how the workplace changes
as we include more and more AI into it.
We won't hold you to it but I you know
we can dream a little bit here.
>> I think I I kind of I've been touching
on this throughout this conversation,
right?
>> Yeah. Um
Look, I think the
the doing more with less is not going to
end anytime soon, okay? I mean, you
know, even the weird ground you know,
we're hiring but you're also seeing some
uh I don't think it's all AI related,
it's people related personally, but
that's a different conversation all
together. But within the workspace,
right? Within the work day, how it
changes is this for me. So I'll can give
you a tangible example of this. We had
an offsite in Q4 to start our planning
for 2026. Right? We knew what our
strategic objectives were and that you
know, you've been enough board meetings
and you can say, "Oh, now we're going to
convert that into some OKRs and it's
going to the wordsmithing we actually
used Gemini to actually feed this into
it. It we actually fed this into our
plans for 2026 and
the OKRs by department aligned with our
overall strategic objectives for a
3-year time zone and the 2026 plan, we
did this in 1 hour.
Amazing. So it is something that
>> is that is incredible, right? That's
tangible and we actually showed the
board our how we did that that process.
That is pretty cool stuff, yeah. It is
pretty neat, so. Yeah. You know, it's
interesting cuz there are some
organizations that are out there that
are like, "Do we do this? Do we not?"
And I think being someone who's leading
a company who is so far forward in this
you You really have to wait for
permission and that must be freeing.
Yeah, I It's one of the reasons I work
in a smaller organization, right?
There's not we we can do what we are The
one advantage that we have as a 100%
company, right, is we can move fast and
we can adopt technology faster, and uh
it's just what That is advantage we
have, and we want to be we always never
want to lose that. And this is such a
transcendent uh you know uh thing that
is happening in our in our in our world.
I mean so awesome to be part of it,
right? You know, for all of us. I know
it's scary, but it's also pretty
exciting that, you know, you can um
take advantage of it and also use the
experience we have as an organization
from the the the tribal knowledge we
have and use it to accelerate this fast.
That's how we look at it, so it's pretty
exciting, you know, I think I'm also a
I'm a nut for change, so.
>> [laughter]
>> Well, you have to be in this industry.
All right, so
>> we are a growth equity backed company,
and that's all that's what they pay us
to do, so this is great, so yeah.
Totally. If people want to learn more
about Promevo, where do they go?
Well, there are a couple of things,
right? You can go to our website,
promvo.com. We and then we're on follow
us on LinkedIn. We we you know, we
provide a lot of blogs, and then you
know, and I love to do a plug. We have
one of the best ISV um
uh you know, platforms that we have for
the Google Workspace. G panel.io is its
own website, where we have great tools,
ROI tools, and everything as well. And
yeah, and our goal is to be integrating
Gemini into you know, our G panel, so
you can have conversion Workspace
administration. You know, right uh with
with our which is a Nobody wants to be
writing scripts anymore, right? We want
to be able to interact with it directly.
The G panel already does it today, but
you know, we also do you know, and then
talk to look for our services blogs and
our you know, we are we are pretty Our
team provides a lot of it from our
experience with the clients, so yeah,
please do.
Okay, good stuff, Karthik Ruppapuri.
Thank you for coming on the show. Great
having you.
>> Alex. Appreciate it. Yeah.
All right, everybody. Thank you for
listening and watching, and we'll see
you next time here [music] on Big
Technology.