An AI Brain For Your Business?

Channel: Alex Kantrowitz

Published at: 2025-07-01

YouTube video id: v7uOFE7KvO4

Source: https://www.youtube.com/watch?v=v7uOFE7KvO4

We often hear about AI innovation, but
rarely what's possible to build with the
technology in a business setting. So
today, we're going to talk with the CEO
and founder of a company called Snowfire
AI that's built what he calls a brain
for your business. And I'm joined today
in studio by that man, Greg Anugoo is
here. Greg, great to see you. Welcome to
the channel, Alex. Thank you for having
me today. Thank you for being here. And
I should note that this video is being
sponsored by Snowfire AI. So appreciate
you being here, Greg. And let's talk a
little bit about what brought you to
generative AI in the beginning because
you spent 20 years in cyber security
before you decided to make your move
into the AI field. For me, uh it was a
career in in data, career in cyber
security and uh a real passion for
utilizing data in business to make
incredibly smart decisions to to run the
business. What opportunity did you see
like why now? What's the opportunity? I
was working at Rackspace, 300,000
customers, and
just the amount of information about
each customer that needed to be
harnessed into a pattern, a set of
understandable behaviors, anomalies that
stood out. There's that's a lot of
customer data. And so early on, I think
the programming um when I was at
Rackspace was to look at how can we
harness that data in a meaningful way,
but it it's the scale that really was
the challenge there. And then I went to
deepwatch and scale was still a
challenge, but it wasn't customer data.
This time it was information flow from
all of the systems in cyber security.
And my patent that we built um I'm a
co-patent co-authored on is and we
really beat deep, you know, built
deepatch on this patent. It was really
interesting how looking at all of the
cyber security data that's out there and
scoring that to determine enterprise
maturity. So looking at data sets and
that's just the layer of cyber. And so
combined with rackspace and customer
information and then deep watch and
systems information, I started to see a
real need for a system that could do
in total the analysis of that kind of
scale. Scale on the information and data
side and scale on the information and
customer side. That really is a genesis
piece for me. So, I've got a chance to
take a look at your software and we're
going to talk about what it does for a
business and how it allows them to make
deeper decisions about their operations
and basically their their broader
business completely. Uh, but you said
that when you worked in cyber, you saw
CEOs using data uh to make decisions.
So, what type of decisions would you see
them make? Some of the best executives
on the planet are highly intuitive and
their their gut and their feeling, the
intuition, the instinct is a part of
their general experience that they bring
to the table to make really smart
decisions. And some of the best
executives on the planet are highly
intuitive and their their gut and their
feeling, the intuition, the instinct is
a part of their general experience that
they bring to the table to make really
smart decisions. And now with what we
have built, you're able to pair that
intuition with an incredible logic
analysis and processing engine that of
AI. And that intuition when paired with
AI now for the logic and processing at
scale at speed of of business. Now you
have these superhuman executives and
that's what we're really setting out to
try and try and do. And we're going to
talk a little bit about how the platform
works, but again I just want to zero in
on this. What sort what sort of
decisions do you look at like when you
because all good software solves a
problem? Yeah. So what sort of decisions
are executives making that you think uh
your platform or generative AI in
particular can help them with? Yeah.
It's the complex ones. So when you look
at the kind of data that we ingest, we
ingest data from inside your business.
So when you look at all of the metrics
that an executive mainly a CEO, a CFO, a
CRO,
those executives have to be looking at
metrics a lot. So we surface surface
those in real time and we allow that
executive to harness that data and to
use AI to contextualize that data in a
meaningful way that they can use to make
a decision on the business. Are we going
left or are we going right? What do we
have to fix? And we call that heat
mapping. And so the AI does that
automatically. So you load data, it
performs all the calculations in less
than 24 hours, gives you all the
metrics, and then we score that for
heat, and then we basically give you
what's called a signal. And that signal
is a contextualized set of data to take
an action from. So this could be a
financial decision, a personnel
decision, it could be a business
decision, a strategy decision. All of
these things can be done inside of our
system. But here's the kicker. We also
are pulling in data from the outside
world. things like your competitors,
your suppliers, your partners, your
customers, and the technology that
you're using. And so those things can
also influence the business in a massive
way. So when we combine this, it's
internal signals, external signals, all
personalized around that executive.
Okay. So we talk about the decisions,
financial decisions, strategy decisions,
personnel decisions, and we're going to
talk about specifically how the platform
will help these CEOs handle this. But I
guess I want to ask you the generative
AI question here which is uh this seems
like you know we had the data in all
these systems have existed uh for a long
time and so I want to know what in
particular about generative AI has
allowed you to build a system that that
enables CEOs effectively to gain
visibility into everything within their
organization. Yeah. And and take action
on it. Yeah. What we have done is is
basically enabled every single piece of
software inside of your business to be
sent into Snowfire's isolated data
store. What makes us unique is that that
isolated data store reduces
hallucination and increases accuracy
because the models and the way in which
we're analyzing the data is local to
that company. And so we call that our
supervisor agent. Then when you look at
the decisions that have to be made
across every executive layer, we have an
agent that's automatically built around
every one of those executives. And so
you don't have to do any engineering at
all. You don't need a data warehouse or
a data stack. All you need is the
software you have in your business today
to be sent to Snowfire and in less than
24 hours we engage with those different
sets of data and then we crossorrelate
those to produce meaningful metrics
across the business. The problem that
you have discussed in your question is
that the promise of the 80s when we
started doing centralized uh data stores
is that we never really crossorrelated
them and now we have meaningful data
engineering and data warehousing but
it's super complex and it takes too
long. So with snowfire what we have
solved is the ability to just send the
data and in 24 hours get to a decision
and that's really the big difference
here. The AI is contextualizing
everything in between. Okay. What do you
mean by that contextualizing everything
in between? Yeah. So the AI understands
your business because it's scraped your
web domain. It's looked at all the
different things that it can find on
public web. It's put that into the
isolated data store. Then it also goes
out and looks at what's connected to
your website, what types of suppliers
you have, your competitive landscape.
All of these things are dynamically put
into our data store. So the supervisor
agent really knows your business. So
it's going to search the web inside and
out and then sort of make assessments
and and come to some conclusions inside
of Snowfire.
That's that's the first part of it. The
the second part of it because that's
this that's the company data. Okay. So
we have to understand how to
contextualize business decisions for the
company and then we extrapolate that to
every layer where every executive is a
different piece. And so a CEO has very
specific things that they have to be
deciding on in the business. Things that
are more meaningful than say a CRO or a
CFO or CMO or CTO. These things are
different. But each of these agents now
interacts with the supervisor agent that
runs the company analysis paired with
the individual personalized analysis for
that executive and then it pulls in the
outside world. So you have inside the
company executive running inside the
company executive pulling data from
outside the world all the external
intelligence that's available. So what
does this look like inside the system?
Yeah, you basically just log in, you
invite users and when you are tailoring
your onboarding, you tell us what you
are, who you are, where you are, and
then you give us a little bit of
information about maybe the way in which
you decide and make decisions in your
life. All of that is then built into the
emulation agent. Okay. And then so what
is a CEO looking at inside the platform
when they're making choices? Yeah,
there's three ways to consume
information in our platform. The first
one is in what we call the research AI
and that's where you typically see most
people comfortable. It's an
interrogation center, a prompting
center. You can ask any question, but it
also has an entire library of all the
metrics available. So, as you send a new
data source, every 24 hours, the AI
calculates that and adds all of that
metric library into the entire
experience. We call that the large
metric model. Okay, we haven't heard
anybody say that yet. That one's ours.
So, this large metric model is where you
start from. And then you start to
favorite and star and tune and then
provide feedback loops where the AI
picks up on these preferences and starts
to build around that. Then it moves up
into heat maps and you have business
units. So let's say you have uh finance,
sales, marketing, ops, customer success,
all of these different business units
are automatically heat scored as well.
And then the final layer is the very top
which is that there are so many things
to action on. How do we prioritize
those? And the AI starts to sort and
filter and give you what's most
important to decide today. Alex, I think
our goal here is that you get your
coffee in the morning and you get your
snowfire and you program your day. Okay.
So, take me through like what a typical
day would look like if someone is using
this platform. Yeah. Like give us like
the real user journey here. Yeah. So,
you just log in. Um, we do have
multifactor authentication for the
enterprise. So, you're going to have to
have it send you a text message and then
you'll authenticate in. um that'll give
you the ability to then land in your
dashboard. Your dashboard has all of the
signals for today the signals from the
business that are good because we have
to preserve those the signals from the
business that are early warning systems
or concerning and then things that are
bad and things that we do have to really
dedicate some time and maybe partner
with other parts of the business on.
Okay. So, just make up a fake user. What
could they be looking at that's deemed
good, bad, and okay? Yeah. So, let me
tell you what I got. I got one this
morning for Snowfire. We run Snowfire
using Snowfire. So, our web traffic has
gone through the roof since we've
launched. And one of the things that
we're looking at now is daily average
users and returning users or session
duration or particular parts of the
system that people are clicking on. So,
I was looking at some product metrics
this morning and it's signaling to me
that our daily average use is going way
through the roof, but that the sessions
are going longer. So, now the next
question I have is how do we keep that
going? How do we keep people in the
system getting them more and more data?
So me as the CEO of Snowfire, I got a
product analytic because we're a product
company and it tells me data is looking
good. Here's how you should preserve it.
I could assign some of that to our chief
product officer and make sure that
they're taking that particular decision
and keeping it healthy, keeping that
metric of daily average users healthy.
So the idea is that this data would show
up in disparate systems and with
Snowfire you're just going to bring them
all together and then give a natural
language uh summary through generative
AI or that's pretty close. But also it's
not just NLP or a natural language
summary. It's it's actually about the
metrics too. So we're looking at trend
lines. We're looking at standard
deviations outside of the mean. We're
automatically doing all of this scoring
to tell you the health of something the
minute that it happens. Okay. So this
isn't like afterthought
decision intelligence. This is
day-to-day real- time decision
intelligence. And so then talk a little
bit about how somebody would react if
there was bad data. Yeah. Uh bad data or
bad insight. Data that says something is
bad is going on. Gotcha. Okay. Cuz
there's two conversations here. That's
true. Let's do that one as well. When
something is wrong, let's say that there
is maybe a financial signal from the
business. Maybe we're behind on invoices
or um cuz that's one of the examples,
one of the things that our customers use
right now today. CFOs are looking at you
know days outstanding for invoices and
or CRO are looking at maybe the month
over month is down. Maybe the calendar
year over you know month overmonth
calendar year is down. Some of these
metrics will appear immediately which
show you things that you really need to
be looking at. So if I'm the CFO
invoices are outstanding. I got to pull
that money in. We've got to figure out a
way to do that. and it's telling me that
we've got an issue here. And you can go
through and interrogate the entire
business layer, find out which ones are
late, and figure out how to make a
recommendation to fix that. You can
assign that to officers inside the
business or other folks underneath you
and your your team. Okay, so let's say
it's CRO because this is where it gets
really interesting. Let's say you have
forecast drift and there's uh CRO is
saying here's the forecast for this
quarter, but Snowfire is saying
something a little different. What's the
delta? How do you figure out what you're
going to be able to pull in if you lose
maybe your top deal for the quarter?
What other things are available for you
to pull in? What can you focus on? Can
you take the win rate from an existing
rep and figure out how to pull that
forward? Um, all of these types of
scenarios, these simulations are pretty
hard to do. We do them like that. Okay.
And it's interesting because you're
talking about the way that Snowfire
operates. And I'm hearing, okay, it's
going to bring in data from Google's
analytics, maybe data from your finance
system, data from your CRM, and then
give some actionable insights for
whether it's the CEO or or um also
officers underneath them, right? Yes.
So, is there something about bringing
the whole operation in that gives you an
advantage? Because I'm thinking about
like, all right, let's say you're the
CRO. Well, why am I now in snowfire and
not in Salesforce? Yeah. Okay. So, first
and foremost, um, every single customer
that has come to us so far has given us
their sales data and 90% of them are
Salesforce and they're very frustrated
with the intelligence that they get out
of that system. It does happen to be the
system of record. it does happen to be a
very meaningful input device in the
business but to be able to surface
information from that siloed data source
has been hard for them. They would like
to forego that alto together and to
combine it with Google Analytics, all
your social network data, all of your
overall website traffic analytic data,
maybe G4, uh, combined with like
HubSpot, combined with your intent data.
That's the intelligence that the CRO's
that are partnering with us want to see
that the CEOs are investing in that
entire stack of growth. They want to see
that harnessed. Those are the minds that
are coming to us. Interesting. So could
the you're using some generative AI in
there, right? Some geni models. So can
the models then make observations like
for instance across groups. Could it
tell the CEO for example something like
um we see your web traffic is down and
also pipeline is down. Yes. And then or
we see your your web traffic is way up
in the Midwest and oh by the way your um
your seller in the Midwest is now your
top seller. So whatever you were doing
to increase maybe that ad campaign you
did to increase traffic in the Midwest
maybe spread that across the whole
country. There are ripple effects to
every piece of data in the business and
Snowfire is the first system I've ever
seen that tries to create the correlary
between a particular metric and another
metric that will be affected by it. They
may be coming from different systems. So
if we are making an investment in some
kind of this is this is one that just
came up to me because I'm trying to
solve this myself today. uh we do a lot
of like keyword search now we're trying
to figure out does the world want to
talk about you know generative AI
generative BI business intelligence for
the modern age like all of these
keywords right so if we were to make a
change in our keyword structures that
would mean that the overall leads that
are coming from that keyword from that
paid search are going to change
downstream and therefore HubSpot's going
to be seeing less leads and then
therefore Salesforce is going to be
seeing less opportunity for our
salespeople and then it's down for the
quarter. These are streams of conscious
flow through the business that we are
harnessing. Now, you know, I used to
work in sales and marketing, so I'm very
familiar with the sales blames marketing
and marketing blames sales dynamic. But
I am a CRO. I want to know why the
quarter is down so that I can have a
really good reason for explaining to the
board when I have to sit in front of
them why we didn't hit the numbers this
quarter, right? And so with a system
that like sort of encompasses all data,
you can actually get answers from that
versus kind of sitting on Marquetto and
sitting on Salesforce and saying, "Ah,
this is the absolute truth. You're
saying answers.
We're giving signals, right? It's not
just answers, right? We're signaling
this. It's early in the quarter. You
made a change. You're going to see a
downstream effect later in the quarter.
Just so you know, this is a signal. This
is an early warning signal. You can
interrogate the data and always get good
answers. LM are great at that. What
we've built as a system, it harnesses
the ability to take all that data and
surface the meaningful signals that you
can decide on now. So that's when it's
going to end the platform. It will have
like a beaming red light that's like
this needs attention now, which is
something that might have just gone on
for a while and not been detected. It
could be turning from green to yellow,
from yellow to red, and then you could
get an email in your inbox every morning
that says, "Hey, you might want to pay
attention to this." And you talked a
little bit about assigning people to
certain tasks. So talk a little bit
about that because I guess like an
executive would be sitting in your
platform seeing what's going on in the
business. Totally realizing that you
know okay let's run with our example of
advertising leading to growing pipeline
uh maybe just with right with can they
write within the system then assign that
to the CMO and say hey can we start
turning on that campaign everywhere
else? Can I tell you a story that
happened to me today? Yes please. So our
our head of product uh and I we talk a
lot and he's studies AI as much as I do.
We're very passionate about it. And so
in our system this morning we got a
signal and it was the new release of
Claude 4, right? Anthropic has done a
great job with their new model. They've
released it. We're one of the early
adopters of this particular LLM and we
really like Claude because of its
mathematics capability. It's wonderful
for math. So it's really wonderful for
Snowfire in terms of our partnership.
We're kind of a math platform that
signals, you know, early warning systems
and then we turn it into language. So, I
sent Chris this article this morning. I
said, and I gave him a couple of to-dos
in this, which was like, hey, Cloud4 is
available. Let's look at the new spec
sheet on it. Let's look at the overall
analysis capabilities that's going to
advance for our product roadmap. And I
gave him these entire list of things
that would be very meaningful for him as
a head of product to interrogate our
systems. What kind of things can we do
with this that we couldn't do yesterday?
And here's what's beautiful. I sent that
to him. All he has to do is click the
send it to the Snowfire AI button and it
takes the entire news article, takes all
of the information from that web that
particular web page. It takes the
reasoning from the model. It takes the
suggestion and any of the things that
I've written out for him. All of that
gets sent to the AI. He doesn't have to
do anything other than interrogate and
study. Now, he could pivot from there,
but pretty much most of the work has
been done. I found the signal. I sent
him the signal. I gave him a couple of
things that I think would be interesting
for the business. Sent it to him.
Interesting. Wait, so did the signal pop
up within for it was in my newsfeed. Oh,
that's interesting. Um, when the models
get better, you mentioned uh Claud's for
uh Cloud Open 4, which is their latest
model. When the models get better, have
you found your system capable of doing
more things? Yes. Talk about that. Yeah,
the road is Oh, this is a wonderful
question. Um, kind of gets me excited
because we see a very beautiful future
where the models are able to do so much
that the products don't have to. Um, and
this is this is kind of scary for
startups in a way, but when you look at
the competency of what you're building,
you choose the right model for its
particular functions, right? So, we
really like things like perplexity for
scraping and web search. Uh, we like
claude for mathematics. Um, we're
looking at something from Google called
Notebook LM right now to pull together
massive language structures and and to
deliver an entire entire audio file of a
boardroom in advance, right? These types
of things that are being built by the
LLMs
make really meaningful product
advancement capable in companies like
ours. Um, and our competency becomes
what we focus on. Our competency is
providing decision intelligence as early
as possible to executives. That's not a
competency of an LLM, but their
advancement helps us do that better.
Right? So, it's the AI combined with um
being able to plug into all these
different systems and then I guess get
the web data as well that really is the
where you start to see things happen. So
as to this question of like whether I
was going to ask you are you worried
that uh LLMs will one day do what you
do. I think your perspective is because
of the integrations and the specific use
case not likely. Well I don't really
worry very much because Hollywood's
already made all the movies that tell us
how how this is going to go. So right
now we're wait what's your perspective
on that? Right now we're in the age of
discovery. We're making life easier. Uh
and then come the Terminator movies and
then we're all living in the center of
the earth in the Matrix. So apparently
that's that's how the okay the the
Hollywood version of this. But I
fundamentally believe that we're meant
to be getting our time back. I really
love this idea that that as an exe and
staying in our lane as an executive I
get the ability to have a system that
processes and analyzes all of my data in
real time pulls out the signals that
mean something to me contextualizes that
for me and allows me to action on it as
fast as possible. That's where we're at
as a company.
If I play this all the way out, my
internal mechanism is to give people
back time, time to be more creative,
time to be more strategic, maybe time
for the family, but in general, time, I
think, is really important. And so, um,
you know, the the future for us is all
about having executives that had have
their coffee and have their snowfire
every morning. Okay. We just had some AI
critics who said on the show who said
basically
you could give people more time but
they'll end up just getting uh more
work. So what do you think about that? I
think that's a there's probably a lot of
truth to that. Okay. I think human
beings are are wrought with uh
distraction. Okay. And so there's
probably some truth to that. I think the
executives that align really well with
us though Mhm. are finding a way to
supercharge their creative centers and
to supercharge their emotional quotient
centers. Um the the overall ability to
process data is very hard for the human
mind especially at massive scale. So why
not offload that and allow us to do what
we're really meant to do which is to um
create lead and the only constant that
I'm aware of in in physics that applies
to humanity is change. So keep changing,
right? And and to see inventions move
forward, to see um I mean for those that
that have children, you know, do we want
our kids sitting in front of keyboards
processing when we can have an AI that
can do that? I don't know that I would
want that for my children. I think I
would want them to have something that
processes, but to allow them to be the
creators. Hm. So, what do you think kids
I mean this is a question we have all
the time on the show about what kids
should be studying and what the next
generation of jobs will look like. So,
you have some thoughts on that front. I
do. What would your perspective be? I
do. Um
I have young kids. Okay. So, mine my
horizon has to be quite a quite a ways
out, you know, 20 plus years. I I think
that
Can can we split this up? Would you mind
if we chop this in half? Like kids today
and then kids tomorrow. Yeah, let's do
that. Okay. I think I think it's easier
cuz I do think that there's like this
thing that happens where um there is a
there's a shift, a fundamental shift.
Okay. So, kids today should be thinking
about how to
go into the workplace
and if you're giving your employer 40
hours, how do you maximize the 40 hours
so that you love what you're doing? M
because then it doesn't feel like a job
as much, right? We've always known that.
But an AI can help you accelerate
through things that are monotonous,
right? And I think that's that that
minutia. Uh the muck and the meer, you
know, from te that's Texas talk. Now I'm
bringing some Texas up here in New York.
Um these things are are are
they're not fun. And so if we can create
fun from our work, that's where we
should be as human beings. That's where
things are that's where life is lived.
Now we all want to work well like as
human beings we used to till the field
like we you know hunters gathers
whatever the historical uh framework you
want to talk from we all have that but
here's the thing as this advanced more
and more and more what I think is going
to be really tough for the young
generation is going to be a question of
like why would I ever do that okay right
they're going to look at jobs that we're
doing today and even jobs that the next
generation is doing and then 20 years
from now they're going to look at that
and go I'm not going to do that h I'm
not even going to think about that. So I
think the conscious shift in in the
prioritization of time happens in our
children in a massive way. Fascinating.
So it's sort of and wait then the the um
the longer time horizon. Yeah. That is
the longer time horizon. I think that
the kids in the longer time horizon just
say nope. They don't even give their
mind to it. So then what do they do? I
think they do what's fun. Okay. I think
they're all about the fun.
I I hope so. I I hope so, too. Um, but
this is also pre-Terminator movies.
That's true. So, you think we're going
to get to Terminator style stuff? Oh,
man. Um,
yeah. You do? I do. Okay. I I hope not.
Warfare is permanent. Okay. And um, you
know, humanity is is a war torn species.
I think that we invent incredibly
dangerous things and I do think that
those incredibly dangerous things will
keep it interesting for quite a while.
Yeah. Well, anyway, I I used to not be
afraid of these AIs and now more and
more I'm like there's some fear here.
So, by the way, what do you think about
a CEO's job? Because it does change the
CEO's job as well. Yes. I think they're
the biggest benefactors in this new age.
Yeah. Being being a CEO is uh
it's exhausting. Mhm. You wake up every
morning and your best part of you is
trying to solve a problem. The best CEOs
that I know, they don't go to work and
they're like, "Oh, I'm going to build
this today or I'm going to uh I'm going
to dream this up today." No, the best
CEOs that I know that that are running
the best companies. They just fix
problems. And so, what I think is going
to be cool about what we're doing is,
and maybe this is a little selfishness
talking here, is I want to have
something tell me what problems I should
be focusing on every morning. Okay? But
it's is it just set pro fix problems
like don't the best CEOs also set vision
that's a part of it but if you know
where the problems live the vision
becomes a lot more clear that's true
okay so and so basically what you think
in the future with generative AI is that
CEOs will effectively I guess a lot of
time they they spend now um sort of
mining for issues and in meetings and
through presentations and maybe AI can
just kind of cut that oh it's way worse
than that. So tell me more about it.
It's way worse than that. Uh I have to
prepare for a board meeting every
quarter, guaranteed every quarter and I
have uh what was promised last quarter
plus what I've learned throughout the
entire quarter of the business that the
board does not yet know about that needs
to be narrated. So I'm going to go ahead
and I'm going to send an email off to 10
different people to activate on 10
different things that will take about
two weeks and I'll be waiting in the w,
you know, I'll be waiting to get the
answers from them from different data
sets. Every one of those 10 people that
I asked something from, they're all
working from different data. They're all
working from a different perspective in
the business, maybe a different business
unit in the business. So now that data
comes back to me and I've got maybe two
or three days before the board meeting
to prepare all of this and I've got to
unify this symphony of of sound that
doesn't match. That's a that's a tough
job. That's that's just one part of the
job. What about the fact that the data
let's go back all the way down to the
data layer. All of those people are
pulling from different pieces of data.
Someone mined out of Salesforce, someone
mined out of HubSpot, someone mined out
of Netswuite, someone into Service Now.
All of these silos that we have in the
business have different answers. So I
have to then go to my board and shift
the narrative a bit so that these things
match up when they don't really need to.
Why would you do that anymore if you
knew that you could send it all to one
system that har harmonizes and unifies
this data into story into compelling
action and into matching narrative?
Yeah, I the old way seems antiquated in
that sense. It's lost. Yeah. And it's
painful. You think this reflects a lot
of changes we're going to see just
across the economy? I do. I think I
think we have to. I think it's too bit
beautiful of a vision and too available
in this time to close our eyes and be
Mhm. you know to wave it goodbye. I
think we we're all like, you know, like
the light. We're all going toward it. Um
and and I think this one is going to
make our lives easier, especially of
executives. I mean, think about what I
usually see in boardrooms. You've got a
CEO who's trying to convey a narrative
to the board, and you've got a CFO and a
CRO. CFO is always being asked for more
and more money, and the CRO also is
being asked for more and more money.
Yeah. Right. So, these these three
personas I think are the ones that are
going to be most alleviated of pain with
systems like ours. Okay. I want to ask
you a couple more questions before we
wrap. Um what is So, you've coined the
term adaptive AI. Yeah. What is adaptive
AI? Oh, yes. Okay. Fas fascinating. So
when we decided to build the system, one
of the things that was readily apparent
to us was that there was nothing that
was shifting as an AI around the
executive. This data set from the
company, what is the company? What does
it do? What are the nuances of of what
the company does? Does the AI know that?
Part one. Part two, every executive is
different. Their role is different. The
demands are different. And the metrics
are different. So that has to be unique.
But also each of these executives makes
decisions differently. Some have logic
centers of intelligence and we think
this is the only kind of intelligence.
It's not. Some have emotional centers of
intelligence. Some have instinctual
centers of intelligence. So this
adaptive AI takes that intelligence
analysis of the centers. It takes the
the command of the job which is really
the the one that I love the most. It's
so fun to look at the nuances of these
jobs. And then it takes the actual
company. And when you combine that
entire stack of company, executive, and
decision style, when that is put
together, it is adaptive. Okay. And you
have a patent on that. It's pending.
Okay. Okay. What What is the patent
process like? Oh, uh, you write it and
you rewrite it and then you rewrite it
again and hopefully they keep sending it
back until hopefully you've got great
lawyers like we do at Pillsbury and, uh,
and in general, they can take a lot of
the pain away, too. Okay. Um, but what
we saw that's really unique about what
we've built is that there's nothing that
combines these things. Everybody's
trying to build an agent and or a
singular agent. I think I already think
that's dead. Really? I do. I really do.
So, the discourse is kind of like off.
Why build an agent when you can have a
platform full of agents that already
work on your behalf in 24 hours, right?
Well, why would you do that? I don't
know. Yeah. Well, some company the
answer there's going to be some
specialized use cases. There is there's
very unique things that exist inside
that business and and we want it to work
a certain way. So before we give you a
moment to shout out uh Snowfire and tell
people where to uh where to find it and
how to sign up uh is there anything else
that you think is worth uh knowing
anything else that I should know or our
viewers should know about the moment the
opportunity the software?
I think our viewers are
reticent right now to trust AI and I
think that that is really fair. One of
the things that's important that we
really want everyone to see is a future
where you're not sending your data to an
LLM.
You're not going to load your data into
chat GPT. You're not going to you're not
going to do that. Okay? You're going to
want that in an isolated data store.
You're going to want that personalized
around you and you're going to want that
to be tailored to the needs that you
have to run your business. And if that
is a if those three things resonate with
you, then we think that there is a
beautiful partnership with regards to
your daily activity and the ability to
command it for decision intelligence. We
we see that as the future. You know,
there's um there's a couple of things
that I usually like to to leave folks
with like questions. I I find that the
human mind does not like being told
things as much as it likes being asked
things. So here's three questions that I
would like to leave the resonation with.
So this is the resonant frequency when I
leave the room to me or the viewers.
Yes. Okay.
Hit it.
How will you get your time back?
Question number one. How will you get it
back? And when you get it back, what
will you do with it?
what will you do with it? And then I
think the biggest of all of the
questions that I like to ask folks is,
do you see a world where you have gotten
your time back and you now know that
you're going to do more with it, but do
you see a world where you're actually
changing the way you live in meaningful
ways that extends the time that you have
to do the things that you want? And if
all of those things that are highly
personal, those are personal questions.
If all of those that get painted on your
shirt and you figure that out and then
you apply that to business, this is the
executive that we we think is the
future. This is the executive that's
highly conscious, highly self-aware, and
highly tuned in. Okay. So, tell them
where to find Snowfire. Yeah. Snowfire
dubdubdub.fire.ai.
Okay. We have uh we have a really cool
phone number 844 snf i e snowfire and uh
I'm at greg snowfire.ai
and in general um you can find us uh
these days flying around the country and
serving our customers but um you may not
need to find us as much as you might
need to find our free trial. Okay. Uh
it's a 14-day free trial. Come in and
get start stood up and uh flow your data
systems in. we can give you up nearly a
thousand data sources and let you try
this crossorrelation for yourself. All
right, there you go folks. You got the
CEO's email. Greg, thank you so much for
joining us here today. Great to see you.
Alex, thank you so much for having me.
Pleasure to be with you. Definitely
great having you here. Thank you
everybody for watching and we'll see you
next time here on the channel.
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