August 18, 2026 • 30 mins
Pat founded six successful search tech companies over 30 years, with partnerships in telecom, defense, and advertising. As Seekr Technologies CEO, he pioneers AI solutions, redefining content evaluation and empowering users with bias free applications.
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Speaker 1 (00:00):
Chelmsford, Massachusetts, small town right outside of Boston, about twenty
miles east of that west Sorry, And I went to
school in Chelmsford and then went on to college at
Suffolk University in Boston. Lived in the Boston area for
a while, New Hampshire for a couple of years, and
then married in nineteen eighty four, and then moved to Washington,
(00:23):
d c.
Speaker 2 (00:23):
In nineteen ninety five.
Speaker 3 (00:25):
And you still have a little bit of that fantastic
Boston accent because I've lived up there and it's one
of my favorites. And listen, I'm curious. You know, everybody's
got all these incredible paths in this series. When we
talked to CEOs about what they want to do coming
out of school, what they thought they were going to do.
As you were coming out of school, what did you
want to do?
Speaker 2 (00:42):
You know, it's a great question. You know.
Speaker 1 (00:44):
I graduated with a degree in finance and math, and
it was generally worthless at the time I graduated, but
I ended up getting hired at Northrop Grumming and they
put me on a project that involved building a guidance
system for kind of the next generation missile program that
(01:07):
was taking place, and I just became fascinated with this
whole idea of a navigation system, and so, you know,
since that point in time, I've always been in something
related to navigation, whether it's pure navigation or search technology,
and kind of where I am with Seeker is sort
of an extension of both, which is really interesting. So,
(01:30):
but I had never set out to do that. I
hadn't even thought about it.
Speaker 3 (01:33):
Well, I got to tell you, you know, it's always fascinating
the journey that we get on here, and I'm always
in awe of people that start their own companies. It
doesn't matter if it's for profit nonprofit. I've talked to
so many different leaders out there, but one of the
most fascinating things is people like you have got a
great idea. They see a hole in the industry, and
it's not necessarily that they want to be their own boss,
but they have something special that they think they can
(01:55):
add to the world out there, which you've done for
five and a half years now. But I would love
to hear the origin story of Seeker. What was the
impetus behind it, the idea, the origin about why you
wanted to start this company.
Speaker 2 (02:06):
Yeah, so I had in the mid nineties.
Speaker 1 (02:09):
You know, I had just completed working at a large company,
Digital Equipment Corporation, and I was approached by a New
York banking firm, Allen and Company in New York, and
they asked me if I would be interested in taking
a look at a small company that they had, and
it had it involved search technology, and I said, sure,
I'll go ahead and do that. And since that point
(02:32):
in time, so now, geez, over thirty something years, I've
always built and had my own company because I'm a
very curious person and I read a lot and I
kind of look ahead at the future and I say,
is this something there that I could build or that
I could be part of that would really make a difference.
And so my career has really always been built around that.
(02:54):
So the first company that I started was really in
the national security and fence area, where the analysts at
NSA were trying to figure out how to aggregate as
much information in the world as they.
Speaker 2 (03:09):
Could for national security.
Speaker 1 (03:10):
They didn't have a good way to search through all
the different volumes of data, and so we invented that,
created a company that did it, and then took it
public and I sold it to Intel for one point
two billion in nineteen ninety nine, and then post nine
to eleven, they needed a company that could help target
and track down terrorists and all of the different threats
(03:33):
that we had post nine to eleven. So I built
a second company that did that and took that public
as well, and ultimately ended up selling that. And then
for the last fifteen years, I've recognized a couple of
different areas where the unique search technologies can play a
role in solving certain kinds of problems, whether it would
(03:54):
be an advertising or telecommunications business, and so forming Seeker,
it was sort of a kind of a natural, kind
of confluence of all the things I had learned over
the last thirty years and in search and Seeker was
really put together because in somewhere around the six fifteen
(04:15):
and sixteen you began to hear the words things like
polarized nation and people that were so disagreed on every
every major issue. And I kept saying to myself, why
is this the case? Like what is happening? And so
we built a team to analyze news, social media blogging,
(04:37):
and what we discovered was that there was a huge
amount of nation state influence that was taking place in
our news in our social media there were the confluence
of things like poor journalism to generate you know, sensationalism
and advertising, coupled with cognitive biases that were built into
(05:00):
the stories or angles that wanted to be built in,
all sort of amplified by large nation states, and we said,
this is really going to drive us further and further
apart as more and more people look to the news
or look to social media for information, and this idea
of these quick sound bites and these quick headlines, that's
(05:22):
all people were looking at. And as a result, every
major issue that the United States face was fought out
in this division, in this divisive way. So we started
a company Seeker that took a look at what were
the reasons, the causes, what's the underlying root of this,
and ultimately it grew into the platform that we have today,
(05:45):
which allows anybody in the enterprise business or government business
to trust the inputs that.
Speaker 2 (05:51):
Are going in, follow how the data.
Speaker 1 (05:54):
Is transformed, and then transparently deliver a result, an AI
result that can be immediately traced back to the origins
of the data, so that everyone can essentially trust the
input and defend the output standing well.
Speaker 3 (06:10):
Listen before we get into some of your products and
into the weeds and all the incredible things that you
do with your team. Pat, I hope you can indulge
me in something because I get this fortuitous opportunity to
talk to people like you that have had multiple companies.
And I'd love to use an analogy from my standpoint
of being in sports radio for almost thirty years. Every
time I've left a radio station where I left it
(06:33):
in great shape for the next person takeover, I've always
been worried about what they're going to do with it,
even though I've got nothing to do with it anymore.
As you sell these companies, and whether you stayed on
or not, you always worry about this incredible product that
you've come up with and then you hand it over
to somebody. Yeah, you're getting a nice payoff, but it
was your baby. Can you give any advice? And what
(06:53):
was it like to have these companies, develop them from
a start, incubate them, leave them in a really good spot,
then sell them off to somebody. What's that like us?
Speaker 1 (07:03):
The first thing is you have an instinct that says
that you want to qualify the buyer, and all buyers
are not equal, so you want to look at somebody
that's buying you for a particular reason that is important
to you. So many of the companies I've had and
the products I've done have been really important to the nation,
(07:23):
and I wanted to make sure that they were in
the right hands. And so some of the blue chip
companies like a Microsoft or a lockheater or an Intel
that have purchased companies from me, that was my first
priority was to make sure that that that they were
the right kind of companies, and then second that they
take care of the people, because the people are really
(07:45):
why the company is successful, and you want everybody to
feel good about something like this and to be rewarded
for it.
Speaker 2 (07:53):
In many ways. It's not just financial, but it.
Speaker 1 (07:56):
Could be the quality of work, it could be the location,
it could be a whole host of things. So you
look for I think intrinsically, you look for those kind
of values to be able to you know, before you
hand it off, and then I think you can hand
you can do it in good conscious.
Speaker 3 (08:12):
All right, well, listen, it's not going to surprise you.
But every leader and CEO we talked to and founder
team is everything the people. Sometimes the staff can be
smarter than you, or they're in a lane that you're
not good at. And it is always a reoccurring theme.
So I'm glad that you mentioned your team Pat. We'll
talk more about them a little bit later, but I
didn't want to and you were kind of leading into this.
But I'd love for the people that are being introduced
(08:34):
to Seeker Technologies for the first time ever. If you
were to give a thirty thousand foot view about what
exactly you do, what would you tell everybody?
Speaker 2 (08:42):
So we are we.
Speaker 1 (08:43):
Are a generative AI platform that allows you to build
a model or launch a model within thirty minutes or
less that can completely change the way that you were
doing a particular operation within your company.
Speaker 2 (09:04):
Everything is everything.
Speaker 1 (09:07):
About it ties together this concept of explainability. So from
start to finish, you know where the data came from,
you know how it was transformed, and you know how
you got the result. And should you come into any
scenario where there's a question on the answer, we can
provide the full kind of package around how the answer
(09:29):
was derived and you could defend that answer in any scenario,
whether it's legal, financial, national security, Should any of the
question the result of your answer, and that is completely
unique in the world of AI today.
Speaker 3 (09:43):
Tell me a little bit about your mission and vision
as well.
Speaker 1 (09:46):
So our mission really is to challenge some of the
more difficult ideas out there about AI, where you've got hallucinations, bias, inaccuracies,
cyber attacks, personal informa. All of these things are question
marks that people have about deploying large scale AI. We
(10:07):
address those in the Seeker products so that all of
that is covered upfront.
Speaker 2 (10:13):
And you can be assured that those points are covered.
Speaker 3 (10:16):
Tell me a little bit about the products. After a
Seeker Flow, You've got the Seeker Guard, the Seeker Intel,
and the Seeker GEO. Tell me about the products.
Speaker 1 (10:26):
So built on top of the Seeker Flow platform are
those three.
Speaker 2 (10:31):
Products that we kind of own and operate.
Speaker 1 (10:34):
Seeker Guard allows you to evaluate models, So if you're
looking for a model that has particular characteristics, we can
evaluate that model for you. Break down the guardrails, understand
whether there are any concepts in there, or biases or
potential foreign influences that you do not want to be
(10:55):
deployed within, say your data center environment.
Speaker 2 (10:58):
The Seeker Intel is all about discovering.
Speaker 1 (11:02):
New information, so whether it could be financial fraud, it
could be targeting and tracking for particular assets, say within
national security, and it utilizes AI to be able to
look at all the different possible possibilities that exist for
something to be present that that was non obvious, let's say.
(11:27):
And then in the geo environment, it's all about extending
the model to go beyond text, so visual identification of objects, images, videos, audio,
anything that is in text that gets extended out in
the visual models.
Speaker 3 (11:43):
Pat, I know you're in a super secret business, so
you don't have to mention any of your clients. But
as you started this company and where you are five
and a half years later, did you want you spread
your net just domestically with government organizations or even private
sectors that worked on high level security stuff or is
this more of a worldwide thing you had.
Speaker 1 (12:00):
Planned to Yeah, we started commercially actually to build out
improve the model, and so we began to, you know,
build it for banks. We have telecom companies, we had
supply chain, and it took about two years for us
to get all of the relevant clearances, the security requirements,
everything that we needed to really pursue classified business or
(12:25):
even unclassified in certain areas. And so now we have
a pretty balanced company, which is about fifty percent commercial
fifty percent government, and we like to keep it that
way because it allows us it's the same common product,
but the learnings from each of these different you know, verticals,
strengthen the product because they're they're common. So the same
(12:49):
reason the government needs explainability is the same reason a
health insurance company would so that they don't deny a
drug or a loan denial system.
Speaker 2 (12:58):
You know, within a bank. You know, you can explain
how that happened.
Speaker 1 (13:01):
All of these things, So they're common requirements, they're just
different implementations.
Speaker 3 (13:05):
Pat without any assumptions on my end that there's any
other company in the world that does exactly what you
and your team do. With that said, if there are
similar companies out there that are competing for the same
type of business, how do you differentiate yourself from them?
Speaker 1 (13:19):
So the primary differentiator is the fact that we have
built this thread through our entire platform called context attribution,
data attribution, and model attribution.
Speaker 2 (13:32):
Those three concepts.
Speaker 1 (13:34):
Are very unique. We have thirty patents built around them.
They separate us from everyone. Someone can do one or
two of those, but all three as a package are
unique in the world. And it's those three that are
increasingly becoming necessary to compete. In government business, many of
(13:55):
the executive orders that have just been written by the
President all address governance and guardrails and explainability, so we
are completely.
Speaker 2 (14:04):
Compliant and fully down the road there.
Speaker 1 (14:07):
And then in international business they have something called Sovereign
AI which gets at protection of privacy of information of
confidential systems, and it also gets at the explainability, So
we are fully down the road and further ahead than
anybody in both of those dimensions which are increasingly becoming
(14:30):
you know, part of the fabric of implanting AI in
corporations or governments.
Speaker 3 (14:35):
Well, Pat, this is a good segue. I'm going to
make an educated guess at about ninety eight percent of us,
which I'm included in that, really don't know too much
about AI except either chat, GPT or claude as I phone,
and we use it for simple things. It's a part
of you and your team's life daily. I'm curious about
when AI was introduced in your life, where it was
then to where it is now and how helpful it is.
(14:57):
And also, you know, what's the future of A because
I know it's so fluid and it's moving so quickly
out there.
Speaker 1 (15:03):
Companies like mine, people like myself, you know, we've seen
AI very early on in our careers. I mean I
started out where you know, robotics and machine vision were
all driven by AI. So AI was not an uncommon
thing and has always been in search technology, in a
variety of other guidance systems things of that nature. But
(15:26):
what really I think exploded AI was the fact that
for the first time, when something like chat GBT arrived,
the average person could communicate with the with the system
and get it to do get a computer to do
things that it could never have done before. And it
took out all the need to be a software programmer
(15:48):
to be able to use the power of you know,
that kind of compute in capacity.
Speaker 2 (15:54):
And when that occurred, it opened up I think a.
Speaker 1 (15:58):
Lot of windows into AI and directionally where it could go.
And my first thought was that, boy, the quality of
that information that CHATGBT and others are trained on is
extremely poor. That reasoning model is going to be very deficit.
But the average person may accept that because of the
(16:21):
novelty of it and the use of it and the
consequences of it for an individual, but a corporation or
a government could never accept those hallucinations, those inaccuracies, those
potential biases that are built in. So I immediately knew
that the direction that we had to head in and
(16:41):
what that looked like given the acceptance rate that we
saw on the consumer side, Well.
Speaker 3 (16:47):
That makes a lot of sense. I did want to
ask you. We do joke in this series that it's
not always unicorns and rainbows all the time for everybody
that runs companies. There are some challenges out there. What
kind of challenges present you in your team today?
Speaker 1 (16:58):
Well, I think the biggest challenge of all is the
right level of technical people.
Speaker 2 (17:05):
That you can find to you know, for your company.
Speaker 1 (17:10):
That challenge, you know, is well documented on the West Coast.
I mean you can see it, you know, with some
of the proclamations that are made around the cost to
hire an engineer, the team swapping that's going on, where
you know, the lead for Google goes to the lead
for Meta, who goes to the lead to Apple. I
(17:30):
mean you can see all this these dynamics taking place,
and I think that you know, we're a we as
a small company, have been successful at bringing people on
because of you know, the promise, the work that's done,
the location, the culture.
Speaker 2 (17:45):
But as you get bigger, that becomes more challenging.
Speaker 1 (17:47):
And I think that's going to be the biggest challenge
for any company is there are only so many people
in the world that know this, that are experts at it, that.
Speaker 2 (17:56):
Can lead these things.
Speaker 1 (17:57):
You know, over time it'll change, but right now it's
a rare commodity, and that's the biggest challenge.
Speaker 3 (18:02):
You know, this is a great segue because I wanted
to talk to you about leadership, and I could also
ask you ten million questions about leadership and your company,
your team, but specifically to what you're talking about. I'd
love to really get inside your brain and what you
think about this. Loyalty is not what it used to be.
I think you can I agree just in general terms,
because you talked about switching companies, and I think for
(18:23):
young people that are talented, sometimes it's can I work
at home all the time? Can I get the biggest salary.
There's a lot of different reasons why people move around,
as opposed to thirty years ago when somebody a man
or woman would stay at a company for twenty or
thirty years and then retire. It's just a different world.
Right now, you and I can agree on that. With
that said, can you speak a little bit more about
(18:45):
not only your experiences in your company, but loyalty, people
working off site and all the things that come without
the salary, about how you manage that and continue to
retain people and why we're living in the world keep
on going in that direction.
Speaker 1 (19:01):
Yeah, So it is a challenge to shape let's say,
historical beliefs with current, you know, actualities. I don't think
anybody saw COVID coming and what that really meant. And
that's a true anomaly that you know, has never probably
occurred in the last fifty sixty years. There hasn't probably
(19:22):
been any event like that. Maybe the Vietnam War was
something as disruptive as that in terms of how people
thought about work and their loyalty. But what we tried
to do here is, at first, you know, we accepted
that reality of work at home and that kind of thing,
and but what we tried to do here is drive
(19:43):
the level of capability and the level of curiosity on
building new and interesting things and how to solve difficult
problems which naturally people gravitate to. That that are of
the mindset that you want to ultimately we build your
company is they're always interested in solving something really difficult,
(20:05):
or building kind of a next generation type of a product,
or being a leader in a certain space, because that's
what gets the engineering technical people excited.
Speaker 2 (20:16):
They don't want to be you know, they want to
be inventive.
Speaker 1 (20:20):
They want to they want to be important to the
product they build. So we try to build a culture
around that, and as a result, we've had more and
more people wanting to come into work, wanting to be
around that kind of dynamic environment, want to get involved
in really interesting new technologies. And so we've had to
(20:44):
drive it that way as opposed to mandates like you'll
come in one day a week.
Speaker 2 (20:48):
Or two days or three days, because those don't I
don't think work.
Speaker 1 (20:52):
I mean maybe in some verticals they do, but in
ours they don't. It's got to be driven by the
leadership that says we are pushing towards new, exciting, never
been ford doone kind of technologies.
Speaker 2 (21:05):
You should come along with us.
Speaker 1 (21:07):
That's how Elon Musk got all his people on SpaceX, right.
These people stayed there because they had a belief that
they were doing something special.
Speaker 2 (21:15):
Something unique, something earth shattering.
Speaker 1 (21:18):
Right, And I think that is the kind of leadership
that I look for that I try is we that's
what gets people up in the morning and want to
come to work and want to sit with their boss
or their teammate and say, yeah, I want to this
is incredible. We're solving some of the greatest problems for
national security or something, you know, those kind of things,
(21:40):
and that's how I think, you know, that's how I'm
leading the company, and I look for people that can
do that. I've been very lucky to have several people
that have been with me for a long time, so
kind of like again SpaceX, he had people that started
eighteen years ago and kind of believed in him, and
I have been lucky enough to have that. But that
kind of enthusiasm and loyalty sort of spreads down the chain,
(22:04):
and that's why that's how we run the company.
Speaker 3 (22:07):
Well, I appreciate you sharing all that and what I'm
gathering from you and you and I found out that
we're big sports fans offline when we were chatting a
little bit, and I learned culture through sports and you
can apply that to the business world. You and I
know that and I think what you're also saying too,
with the culture that you have there at the company
at Secret Technologies, simple acknowledgment with your people and also
(22:31):
listening to them too. I don't think enough leaders do that.
Would you agree now?
Speaker 1 (22:34):
I think listening is a real is a skill that
people don't think about, but it's one of the most
important skills because you learn a lot when you listen.
Speaker 2 (22:45):
You also.
Speaker 1 (22:47):
Giving that other person this signal that you know you're
interested in, that you're you know, a polite, thoughtful person,
and that usually gets you.
Speaker 2 (22:58):
Further than the kind of behavior.
Speaker 1 (23:02):
And so being a good listener to your team and
people are really important. That's That's how I've learned the
most is to listen to a wide diversity of people
from different areas in the company telling me something.
Speaker 2 (23:18):
Yep. My job is to synthesize it and make a decision.
But I can't do it.
Speaker 1 (23:22):
With poor data. And the only way I'm going to
get that data is to listen.
Speaker 3 (23:26):
All right, thank you for all of that.
Speaker 2 (23:27):
Listen.
Speaker 3 (23:28):
I want to put a pin in work just for
a second, if we could and talk about philanthropic and
charity work. I know you're super busy with secret technology.
But when you do have time to be a part
of anything, what do you like to spend it with.
Speaker 1 (23:38):
Yeah, so my wife and I really sort of thought
about a couple of things.
Speaker 2 (23:43):
One is, we certainly appreciate everything.
Speaker 1 (23:47):
To do with veterans, and you know, my father was
in the military. Her she has the same kind of
heritage in her family and so we wanted to return
back to them. So we really appreciate some of these
veterans associations, Veritae funds, tunnels to towers, anything that sort
of returns back to people that serve the country in
(24:09):
some way and you know, are disadvantaged at this point
in their life.
Speaker 2 (24:14):
Second area is.
Speaker 1 (24:15):
That illnesses that we have had in our family that
we would love to you know, donate to, So things
like the Als Charity and the Hard Fund and things
like that we do. And then we're also part of,
you know, understanding that not everybody's as lucky as us,
so we the Samaritan Fund is one that we we
also are big contributors to.
Speaker 2 (24:37):
So we we try to do what we.
Speaker 1 (24:39):
Can across those different dimensions and you know, try to
instill that in our kids as well.
Speaker 3 (24:45):
Understanding. Thank you for all that, Pat, I really appreciate that,
and also your generosity you or your wife and your family.
I did want to do a couple things before we
let you go and talk about AI and what you
do as a whole in the future. I know you
and your team very smart people, always looking two, five,
ten years, and while we can't predict some things, you
can predict other things out there. How does it look
(25:08):
for the future of what you're doing with your clients
out there?
Speaker 1 (25:10):
Well, I think that the goal right now, for let's
say the next ten years, is to increasingly build what
I would call autonomous, fully autonomous technology where you can
put you can put these programs, these reasoning engines out
into space, under sea in untenable environments that a human
(25:35):
could not tolerate, but they have the full reasoning capability
needed to carry out the job in a way that
is completely in accordance with you know, the designer of
the product's wishes and the customers wishes. So think about
deep space factories on the moon, humanoid you know systems
(25:58):
that all of them will require their own special model
that will operate them. As an example, you know, a
humanoid in a factory of the future, not too far away.
Maybe building products in space or on Mars or the
Moon will will need multiple dimensions to it, not just
(26:19):
the ability to think about something, but to move attenuators, arms, legs,
think of them, or to be able to use other
kinds of capabilities. So all of that, you know, information
to be built today and put into those models to
sort of advance everything you know here on Earth. I
(26:40):
think is A is a big goal for us.
Speaker 2 (26:43):
You know.
Speaker 3 (26:43):
I'm sure you've seen it throughout your career. Anytime there's
a shiny new object, everybody likes to hop on board
and try it out. And I think that's happened with AI.
And I think just in the news as a layman,
I watch where some companies have gone in all AI
then realizing they actually need people to do certain jobs,
so they've had kind of step back a little bit.
With that said, the future of it people AI, I'm curious,
(27:05):
just as a general public, what do we get wrong
about AI? But what excites you about AI moving forward?
Speaker 1 (27:11):
Well, I think what people get wrong about IT is
that they give it ay, they give it a personality
where or where they say it's good or it's evil,
and it's not either of those it is. It is
what is programmed into it or it and it does
the task it's programmed into.
Speaker 2 (27:33):
But like anything, it can be misused.
Speaker 1 (27:35):
I mean the guy that the guy that builds steak
knifes doesn't mean for somebody to use it, you know,
to stab somebody, right, It's the same thing with AI.
You're building it to solve a particular problem. If someone
misuses it, it's not that the machine or the program
is good or evil, it's the people they use it.
Speaker 2 (27:51):
So I think we get that wrong.
Speaker 1 (27:53):
I think the part we that really excites me is that,
you know, throughout human history there have always been advances
in technology, whether it's the wheel or whether it's electricity
or nuclear power or whatever, that humans have figured out
how to harness, and it's always increased, you know, the
quality of life. And I think that's AI is just
(28:13):
basically another technology that has appeared that when that humans
will harness and it will deliver more value, you know,
new drug discovery, new ways to build products, new experiences
that people can have. It will be an enhancement as
opposed to a detriment. I'm standing and I think you can.
(28:34):
I can agree, and my tongue is in my cheek
on this one. We just don't want the machines to
take over, right Pat.
Speaker 2 (28:39):
Now, they won't. They will only take over things you
want them to, right.
Speaker 3 (28:44):
That's right. Well, listen, I want to get some final
thoughts from you. We want to give the website. By
the way, your people in the website's gorgeous, it's easy
to navigate, its beautiful, and there's a lot of information
out there. Let's just kind of recap what we talked
about about secret technologies. Pat, the floor is yours.
Speaker 1 (28:59):
So the things that I would remind everybody is number
one that you need to be able to defend the output,
and by defending the output, you need to have a
fully explainable way of telling your customer or telling a
client that the results that occurred can be traced at
(29:20):
are transparent all the way back to the very beginning.
So provenance, lineage, and intent are the three key things
that I would say. When you're building an AI model
and you're using Seeker, you get all of that. You
don't get that anywhere else. And I think the second
thing is you can deploy it anywhere, whether you want
to deploy it in a cloud, on a premise or
(29:43):
on a remote sensing operation in space. It's irrespective of
way you want to deploy it.
Speaker 3 (29:49):
All right, let's give everybody that fantastic QRL to the
website Seeker dot com, all right, and mo a spell
Seeker for them so they get that right too, seekr
dot com. There we go. Hey, Pat, it's a pleasure
to talk to you. I know that you're based on
just a little bit down the street from me, that
you're in Western Virginia and that's where you're a headquarters.
So listen, you're doing incredible work out there. I know
(30:11):
a lot of the things that you do you can't
share with us, but I appreciate that people like me
and families out there can sleep well at what you do.
With all the companies out there. It's fantastic work and
I'm just in awe of what you and your team do.
So thank you for all that you do. We appreciate it,
and I'm glad we could spotlight you on CEOs. You
should know we really appreciate it. Continued success.
Speaker 2 (30:31):
Yeah, thank you very much. Thank you
Chelmsford, Massachusetts, small town right outside of Boston, about twenty
miles east of that west Sorry, And I went to
school in Chelmsford and then went on to college at
Suffolk University in Boston. Lived in the Boston area for
a while, New Hampshire for a couple of years, and
then married in nineteen eighty four, and then moved to Washington,
(00:23):
d c.
Speaker 2 (00:23):
In nineteen ninety five.
Speaker 3 (00:25):
And you still have a little bit of that fantastic
Boston accent because I've lived up there and it's one
of my favorites. And listen, I'm curious. You know, everybody's
got all these incredible paths in this series. When we
talked to CEOs about what they want to do coming
out of school, what they thought they were going to do.
As you were coming out of school, what did you
want to do?
Speaker 2 (00:42):
You know, it's a great question. You know.
Speaker 1 (00:44):
I graduated with a degree in finance and math, and
it was generally worthless at the time I graduated, but
I ended up getting hired at Northrop Grumming and they
put me on a project that involved building a guidance
system for kind of the next generation missile program that
(01:07):
was taking place, and I just became fascinated with this
whole idea of a navigation system, and so, you know,
since that point in time, I've always been in something
related to navigation, whether it's pure navigation or search technology,
and kind of where I am with Seeker is sort
of an extension of both, which is really interesting. So,
(01:30):
but I had never set out to do that. I
hadn't even thought about it.
Speaker 3 (01:33):
Well, I got to tell you, you know, it's always fascinating
the journey that we get on here, and I'm always
in awe of people that start their own companies. It
doesn't matter if it's for profit nonprofit. I've talked to
so many different leaders out there, but one of the
most fascinating things is people like you have got a
great idea. They see a hole in the industry, and
it's not necessarily that they want to be their own boss,
but they have something special that they think they can
(01:55):
add to the world out there, which you've done for
five and a half years now. But I would love
to hear the origin story of Seeker. What was the
impetus behind it, the idea, the origin about why you
wanted to start this company.
Speaker 2 (02:06):
Yeah, so I had in the mid nineties.
Speaker 1 (02:09):
You know, I had just completed working at a large company,
Digital Equipment Corporation, and I was approached by a New
York banking firm, Allen and Company in New York, and
they asked me if I would be interested in taking
a look at a small company that they had, and
it had it involved search technology, and I said, sure,
I'll go ahead and do that. And since that point
(02:32):
in time, so now, geez, over thirty something years, I've
always built and had my own company because I'm a
very curious person and I read a lot and I
kind of look ahead at the future and I say,
is this something there that I could build or that
I could be part of that would really make a difference.
And so my career has really always been built around that.
(02:54):
So the first company that I started was really in
the national security and fence area, where the analysts at
NSA were trying to figure out how to aggregate as
much information in the world as they.
Speaker 2 (03:09):
Could for national security.
Speaker 1 (03:10):
They didn't have a good way to search through all
the different volumes of data, and so we invented that,
created a company that did it, and then took it
public and I sold it to Intel for one point
two billion in nineteen ninety nine, and then post nine
to eleven, they needed a company that could help target
and track down terrorists and all of the different threats
(03:33):
that we had post nine to eleven. So I built
a second company that did that and took that public
as well, and ultimately ended up selling that. And then
for the last fifteen years, I've recognized a couple of
different areas where the unique search technologies can play a
role in solving certain kinds of problems, whether it would
(03:54):
be an advertising or telecommunications business, and so forming Seeker,
it was sort of a kind of a natural, kind
of confluence of all the things I had learned over
the last thirty years and in search and Seeker was
really put together because in somewhere around the six fifteen
(04:15):
and sixteen you began to hear the words things like
polarized nation and people that were so disagreed on every
every major issue. And I kept saying to myself, why
is this the case? Like what is happening? And so
we built a team to analyze news, social media blogging,
(04:37):
and what we discovered was that there was a huge
amount of nation state influence that was taking place in
our news in our social media there were the confluence
of things like poor journalism to generate you know, sensationalism
and advertising, coupled with cognitive biases that were built into
(05:00):
the stories or angles that wanted to be built in,
all sort of amplified by large nation states, and we said,
this is really going to drive us further and further
apart as more and more people look to the news
or look to social media for information, and this idea
of these quick sound bites and these quick headlines, that's
(05:22):
all people were looking at. And as a result, every
major issue that the United States face was fought out
in this division, in this divisive way. So we started
a company Seeker that took a look at what were
the reasons, the causes, what's the underlying root of this,
and ultimately it grew into the platform that we have today,
(05:45):
which allows anybody in the enterprise business or government business
to trust the inputs that.
Speaker 2 (05:51):
Are going in, follow how the data.
Speaker 1 (05:54):
Is transformed, and then transparently deliver a result, an AI
result that can be immediately traced back to the origins
of the data, so that everyone can essentially trust the
input and defend the output standing well.
Speaker 3 (06:10):
Listen before we get into some of your products and
into the weeds and all the incredible things that you
do with your team. Pat, I hope you can indulge
me in something because I get this fortuitous opportunity to
talk to people like you that have had multiple companies.
And I'd love to use an analogy from my standpoint
of being in sports radio for almost thirty years. Every
time I've left a radio station where I left it
(06:33):
in great shape for the next person takeover, I've always
been worried about what they're going to do with it,
even though I've got nothing to do with it anymore.
As you sell these companies, and whether you stayed on
or not, you always worry about this incredible product that
you've come up with and then you hand it over
to somebody. Yeah, you're getting a nice payoff, but it
was your baby. Can you give any advice? And what
(06:53):
was it like to have these companies, develop them from
a start, incubate them, leave them in a really good spot,
then sell them off to somebody. What's that like us?
Speaker 1 (07:03):
The first thing is you have an instinct that says
that you want to qualify the buyer, and all buyers
are not equal, so you want to look at somebody
that's buying you for a particular reason that is important
to you. So many of the companies I've had and
the products I've done have been really important to the nation,
(07:23):
and I wanted to make sure that they were in
the right hands. And so some of the blue chip
companies like a Microsoft or a lockheater or an Intel
that have purchased companies from me, that was my first
priority was to make sure that that that they were
the right kind of companies, and then second that they
take care of the people, because the people are really
(07:45):
why the company is successful, and you want everybody to
feel good about something like this and to be rewarded
for it.
Speaker 2 (07:53):
In many ways. It's not just financial, but it.
Speaker 1 (07:56):
Could be the quality of work, it could be the location,
it could be a whole host of things. So you
look for I think intrinsically, you look for those kind
of values to be able to you know, before you
hand it off, and then I think you can hand
you can do it in good conscious.
Speaker 3 (08:12):
All right, well, listen, it's not going to surprise you.
But every leader and CEO we talked to and founder
team is everything the people. Sometimes the staff can be
smarter than you, or they're in a lane that you're
not good at. And it is always a reoccurring theme.
So I'm glad that you mentioned your team Pat. We'll
talk more about them a little bit later, but I
didn't want to and you were kind of leading into this.
But I'd love for the people that are being introduced
(08:34):
to Seeker Technologies for the first time ever. If you
were to give a thirty thousand foot view about what
exactly you do, what would you tell everybody?
Speaker 2 (08:42):
So we are we.
Speaker 1 (08:43):
Are a generative AI platform that allows you to build
a model or launch a model within thirty minutes or
less that can completely change the way that you were
doing a particular operation within your company.
Speaker 2 (09:04):
Everything is everything.
Speaker 1 (09:07):
About it ties together this concept of explainability. So from
start to finish, you know where the data came from,
you know how it was transformed, and you know how
you got the result. And should you come into any
scenario where there's a question on the answer, we can
provide the full kind of package around how the answer
(09:29):
was derived and you could defend that answer in any scenario,
whether it's legal, financial, national security, Should any of the
question the result of your answer, and that is completely
unique in the world of AI today.
Speaker 3 (09:43):
Tell me a little bit about your mission and vision
as well.
Speaker 1 (09:46):
So our mission really is to challenge some of the
more difficult ideas out there about AI, where you've got hallucinations, bias, inaccuracies,
cyber attacks, personal informa. All of these things are question
marks that people have about deploying large scale AI. We
(10:07):
address those in the Seeker products so that all of
that is covered upfront.
Speaker 2 (10:13):
And you can be assured that those points are covered.
Speaker 3 (10:16):
Tell me a little bit about the products. After a
Seeker Flow, You've got the Seeker Guard, the Seeker Intel,
and the Seeker GEO. Tell me about the products.
Speaker 1 (10:26):
So built on top of the Seeker Flow platform are
those three.
Speaker 2 (10:31):
Products that we kind of own and operate.
Speaker 1 (10:34):
Seeker Guard allows you to evaluate models, So if you're
looking for a model that has particular characteristics, we can
evaluate that model for you. Break down the guardrails, understand
whether there are any concepts in there, or biases or
potential foreign influences that you do not want to be
(10:55):
deployed within, say your data center environment.
Speaker 2 (10:58):
The Seeker Intel is all about discovering.
Speaker 1 (11:02):
New information, so whether it could be financial fraud, it
could be targeting and tracking for particular assets, say within
national security, and it utilizes AI to be able to
look at all the different possible possibilities that exist for
something to be present that that was non obvious, let's say.
(11:27):
And then in the geo environment, it's all about extending
the model to go beyond text, so visual identification of objects, images, videos, audio,
anything that is in text that gets extended out in
the visual models.
Speaker 3 (11:43):
Pat, I know you're in a super secret business, so
you don't have to mention any of your clients. But
as you started this company and where you are five
and a half years later, did you want you spread
your net just domestically with government organizations or even private
sectors that worked on high level security stuff or is
this more of a worldwide thing you had.
Speaker 1 (12:00):
Planned to Yeah, we started commercially actually to build out
improve the model, and so we began to, you know,
build it for banks. We have telecom companies, we had
supply chain, and it took about two years for us
to get all of the relevant clearances, the security requirements,
everything that we needed to really pursue classified business or
(12:25):
even unclassified in certain areas. And so now we have
a pretty balanced company, which is about fifty percent commercial
fifty percent government, and we like to keep it that
way because it allows us it's the same common product,
but the learnings from each of these different you know, verticals,
strengthen the product because they're they're common. So the same
(12:49):
reason the government needs explainability is the same reason a
health insurance company would so that they don't deny a
drug or a loan denial system.
Speaker 2 (12:58):
You know, within a bank. You know, you can explain
how that happened.
Speaker 1 (13:01):
All of these things, So they're common requirements, they're just
different implementations.
Speaker 3 (13:05):
Pat without any assumptions on my end that there's any
other company in the world that does exactly what you
and your team do. With that said, if there are
similar companies out there that are competing for the same
type of business, how do you differentiate yourself from them?
Speaker 1 (13:19):
So the primary differentiator is the fact that we have
built this thread through our entire platform called context attribution,
data attribution, and model attribution.
Speaker 2 (13:32):
Those three concepts.
Speaker 1 (13:34):
Are very unique. We have thirty patents built around them.
They separate us from everyone. Someone can do one or
two of those, but all three as a package are
unique in the world. And it's those three that are
increasingly becoming necessary to compete. In government business, many of
(13:55):
the executive orders that have just been written by the
President all address governance and guardrails and explainability, so we
are completely.
Speaker 2 (14:04):
Compliant and fully down the road there.
Speaker 1 (14:07):
And then in international business they have something called Sovereign
AI which gets at protection of privacy of information of
confidential systems, and it also gets at the explainability, So
we are fully down the road and further ahead than
anybody in both of those dimensions which are increasingly becoming
(14:30):
you know, part of the fabric of implanting AI in
corporations or governments.
Speaker 3 (14:35):
Well, Pat, this is a good segue. I'm going to
make an educated guess at about ninety eight percent of us,
which I'm included in that, really don't know too much
about AI except either chat, GPT or claude as I phone,
and we use it for simple things. It's a part
of you and your team's life daily. I'm curious about
when AI was introduced in your life, where it was
then to where it is now and how helpful it is.
(14:57):
And also, you know, what's the future of A because
I know it's so fluid and it's moving so quickly
out there.
Speaker 1 (15:03):
Companies like mine, people like myself, you know, we've seen
AI very early on in our careers. I mean I
started out where you know, robotics and machine vision were
all driven by AI. So AI was not an uncommon
thing and has always been in search technology, in a
variety of other guidance systems things of that nature. But
(15:26):
what really I think exploded AI was the fact that
for the first time, when something like chat GBT arrived,
the average person could communicate with the with the system
and get it to do get a computer to do
things that it could never have done before. And it
took out all the need to be a software programmer
(15:48):
to be able to use the power of you know,
that kind of compute in capacity.
Speaker 2 (15:54):
And when that occurred, it opened up I think a.
Speaker 1 (15:58):
Lot of windows into AI and directionally where it could go.
And my first thought was that, boy, the quality of
that information that CHATGBT and others are trained on is
extremely poor. That reasoning model is going to be very deficit.
But the average person may accept that because of the
(16:21):
novelty of it and the use of it and the
consequences of it for an individual, but a corporation or
a government could never accept those hallucinations, those inaccuracies, those
potential biases that are built in. So I immediately knew
that the direction that we had to head in and
(16:41):
what that looked like given the acceptance rate that we
saw on the consumer side, Well.
Speaker 3 (16:47):
That makes a lot of sense. I did want to
ask you. We do joke in this series that it's
not always unicorns and rainbows all the time for everybody
that runs companies. There are some challenges out there. What
kind of challenges present you in your team today?
Speaker 1 (16:58):
Well, I think the biggest challenge of all is the
right level of technical people.
Speaker 2 (17:05):
That you can find to you know, for your company.
Speaker 1 (17:10):
That challenge, you know, is well documented on the West Coast.
I mean you can see it, you know, with some
of the proclamations that are made around the cost to
hire an engineer, the team swapping that's going on, where
you know, the lead for Google goes to the lead
for Meta, who goes to the lead to Apple. I
(17:30):
mean you can see all this these dynamics taking place,
and I think that you know, we're a we as
a small company, have been successful at bringing people on
because of you know, the promise, the work that's done,
the location, the culture.
Speaker 2 (17:45):
But as you get bigger, that becomes more challenging.
Speaker 1 (17:47):
And I think that's going to be the biggest challenge
for any company is there are only so many people
in the world that know this, that are experts at it, that.
Speaker 2 (17:56):
Can lead these things.
Speaker 1 (17:57):
You know, over time it'll change, but right now it's
a rare commodity, and that's the biggest challenge.
Speaker 3 (18:02):
You know, this is a great segue because I wanted
to talk to you about leadership, and I could also
ask you ten million questions about leadership and your company,
your team, but specifically to what you're talking about. I'd
love to really get inside your brain and what you
think about this. Loyalty is not what it used to be.
I think you can I agree just in general terms,
because you talked about switching companies, and I think for
(18:23):
young people that are talented, sometimes it's can I work
at home all the time? Can I get the biggest salary.
There's a lot of different reasons why people move around,
as opposed to thirty years ago when somebody a man
or woman would stay at a company for twenty or
thirty years and then retire. It's just a different world.
Right now, you and I can agree on that. With
that said, can you speak a little bit more about
(18:45):
not only your experiences in your company, but loyalty, people
working off site and all the things that come without
the salary, about how you manage that and continue to
retain people and why we're living in the world keep
on going in that direction.
Speaker 1 (19:01):
Yeah, So it is a challenge to shape let's say,
historical beliefs with current, you know, actualities. I don't think
anybody saw COVID coming and what that really meant. And
that's a true anomaly that you know, has never probably
occurred in the last fifty sixty years. There hasn't probably
(19:22):
been any event like that. Maybe the Vietnam War was
something as disruptive as that in terms of how people
thought about work and their loyalty. But what we tried
to do here is, at first, you know, we accepted
that reality of work at home and that kind of thing,
and but what we tried to do here is drive
(19:43):
the level of capability and the level of curiosity on
building new and interesting things and how to solve difficult
problems which naturally people gravitate to. That that are of
the mindset that you want to ultimately we build your
company is they're always interested in solving something really difficult,
(20:05):
or building kind of a next generation type of a product,
or being a leader in a certain space, because that's
what gets the engineering technical people excited.
Speaker 2 (20:16):
They don't want to be you know, they want to
be inventive.
Speaker 1 (20:20):
They want to they want to be important to the
product they build. So we try to build a culture
around that, and as a result, we've had more and
more people wanting to come into work, wanting to be
around that kind of dynamic environment, want to get involved
in really interesting new technologies. And so we've had to
(20:44):
drive it that way as opposed to mandates like you'll
come in one day a week.
Speaker 2 (20:48):
Or two days or three days, because those don't I
don't think work.
Speaker 1 (20:52):
I mean maybe in some verticals they do, but in
ours they don't. It's got to be driven by the
leadership that says we are pushing towards new, exciting, never
been ford doone kind of technologies.
Speaker 2 (21:05):
You should come along with us.
Speaker 1 (21:07):
That's how Elon Musk got all his people on SpaceX, right.
These people stayed there because they had a belief that
they were doing something special.
Speaker 2 (21:15):
Something unique, something earth shattering.
Speaker 1 (21:18):
Right, And I think that is the kind of leadership
that I look for that I try is we that's
what gets people up in the morning and want to
come to work and want to sit with their boss
or their teammate and say, yeah, I want to this
is incredible. We're solving some of the greatest problems for
national security or something, you know, those kind of things,
(21:40):
and that's how I think, you know, that's how I'm
leading the company, and I look for people that can
do that. I've been very lucky to have several people
that have been with me for a long time, so
kind of like again SpaceX, he had people that started
eighteen years ago and kind of believed in him, and
I have been lucky enough to have that. But that
kind of enthusiasm and loyalty sort of spreads down the chain,
(22:04):
and that's why that's how we run the company.
Speaker 3 (22:07):
Well, I appreciate you sharing all that and what I'm
gathering from you and you and I found out that
we're big sports fans offline when we were chatting a
little bit, and I learned culture through sports and you
can apply that to the business world. You and I
know that and I think what you're also saying too,
with the culture that you have there at the company
at Secret Technologies, simple acknowledgment with your people and also
(22:31):
listening to them too. I don't think enough leaders do that.
Would you agree now?
Speaker 1 (22:34):
I think listening is a real is a skill that
people don't think about, but it's one of the most
important skills because you learn a lot when you listen.
Speaker 2 (22:45):
You also.
Speaker 1 (22:47):
Giving that other person this signal that you know you're
interested in, that you're you know, a polite, thoughtful person,
and that usually gets you.
Speaker 2 (22:58):
Further than the kind of behavior.
Speaker 1 (23:02):
And so being a good listener to your team and
people are really important. That's That's how I've learned the
most is to listen to a wide diversity of people
from different areas in the company telling me something.
Speaker 2 (23:18):
Yep. My job is to synthesize it and make a decision.
But I can't do it.
Speaker 1 (23:22):
With poor data. And the only way I'm going to
get that data is to listen.
Speaker 3 (23:26):
All right, thank you for all of that.
Speaker 2 (23:27):
Listen.
Speaker 3 (23:28):
I want to put a pin in work just for
a second, if we could and talk about philanthropic and
charity work. I know you're super busy with secret technology.
But when you do have time to be a part
of anything, what do you like to spend it with.
Speaker 1 (23:38):
Yeah, so my wife and I really sort of thought
about a couple of things.
Speaker 2 (23:43):
One is, we certainly appreciate everything.
Speaker 1 (23:47):
To do with veterans, and you know, my father was
in the military. Her she has the same kind of
heritage in her family and so we wanted to return
back to them. So we really appreciate some of these
veterans associations, Veritae funds, tunnels to towers, anything that sort
of returns back to people that serve the country in
(24:09):
some way and you know, are disadvantaged at this point
in their life.
Speaker 2 (24:14):
Second area is.
Speaker 1 (24:15):
That illnesses that we have had in our family that
we would love to you know, donate to, So things
like the Als Charity and the Hard Fund and things
like that we do. And then we're also part of,
you know, understanding that not everybody's as lucky as us,
so we the Samaritan Fund is one that we we
also are big contributors to.
Speaker 2 (24:37):
So we we try to do what we.
Speaker 1 (24:39):
Can across those different dimensions and you know, try to
instill that in our kids as well.
Speaker 3 (24:45):
Understanding. Thank you for all that, Pat, I really appreciate that,
and also your generosity you or your wife and your family.
I did want to do a couple things before we
let you go and talk about AI and what you
do as a whole in the future. I know you
and your team very smart people, always looking two, five,
ten years, and while we can't predict some things, you
can predict other things out there. How does it look
(25:08):
for the future of what you're doing with your clients
out there?
Speaker 1 (25:10):
Well, I think that the goal right now, for let's
say the next ten years, is to increasingly build what
I would call autonomous, fully autonomous technology where you can
put you can put these programs, these reasoning engines out
into space, under sea in untenable environments that a human
(25:35):
could not tolerate, but they have the full reasoning capability
needed to carry out the job in a way that
is completely in accordance with you know, the designer of
the product's wishes and the customers wishes. So think about
deep space factories on the moon, humanoid you know systems
(25:58):
that all of them will require their own special model
that will operate them. As an example, you know, a
humanoid in a factory of the future, not too far away.
Maybe building products in space or on Mars or the
Moon will will need multiple dimensions to it, not just
(26:19):
the ability to think about something, but to move attenuators, arms, legs,
think of them, or to be able to use other
kinds of capabilities. So all of that, you know, information
to be built today and put into those models to
sort of advance everything you know here on Earth. I
(26:40):
think is A is a big goal for us.
Speaker 2 (26:43):
You know.
Speaker 3 (26:43):
I'm sure you've seen it throughout your career. Anytime there's
a shiny new object, everybody likes to hop on board
and try it out. And I think that's happened with AI.
And I think just in the news as a layman,
I watch where some companies have gone in all AI
then realizing they actually need people to do certain jobs,
so they've had kind of step back a little bit.
With that said, the future of it people AI, I'm curious,
(27:05):
just as a general public, what do we get wrong
about AI? But what excites you about AI moving forward?
Speaker 1 (27:11):
Well, I think what people get wrong about IT is
that they give it ay, they give it a personality
where or where they say it's good or it's evil,
and it's not either of those it is. It is
what is programmed into it or it and it does
the task it's programmed into.
Speaker 2 (27:33):
But like anything, it can be misused.
Speaker 1 (27:35):
I mean the guy that the guy that builds steak
knifes doesn't mean for somebody to use it, you know,
to stab somebody, right, It's the same thing with AI.
You're building it to solve a particular problem. If someone
misuses it, it's not that the machine or the program
is good or evil, it's the people they use it.
Speaker 2 (27:51):
So I think we get that wrong.
Speaker 1 (27:53):
I think the part we that really excites me is that,
you know, throughout human history there have always been advances
in technology, whether it's the wheel or whether it's electricity
or nuclear power or whatever, that humans have figured out
how to harness, and it's always increased, you know, the
quality of life. And I think that's AI is just
(28:13):
basically another technology that has appeared that when that humans
will harness and it will deliver more value, you know,
new drug discovery, new ways to build products, new experiences
that people can have. It will be an enhancement as
opposed to a detriment. I'm standing and I think you can.
(28:34):
I can agree, and my tongue is in my cheek
on this one. We just don't want the machines to
take over, right Pat.
Speaker 2 (28:39):
Now, they won't. They will only take over things you
want them to, right.
Speaker 3 (28:44):
That's right. Well, listen, I want to get some final
thoughts from you. We want to give the website. By
the way, your people in the website's gorgeous, it's easy
to navigate, its beautiful, and there's a lot of information
out there. Let's just kind of recap what we talked
about about secret technologies. Pat, the floor is yours.
Speaker 1 (28:59):
So the things that I would remind everybody is number
one that you need to be able to defend the output,
and by defending the output, you need to have a
fully explainable way of telling your customer or telling a
client that the results that occurred can be traced at
(29:20):
are transparent all the way back to the very beginning.
So provenance, lineage, and intent are the three key things
that I would say. When you're building an AI model
and you're using Seeker, you get all of that. You
don't get that anywhere else. And I think the second
thing is you can deploy it anywhere, whether you want
to deploy it in a cloud, on a premise or
(29:43):
on a remote sensing operation in space. It's irrespective of
way you want to deploy it.
Speaker 3 (29:49):
All right, let's give everybody that fantastic QRL to the
website Seeker dot com, all right, and mo a spell
Seeker for them so they get that right too, seekr
dot com. There we go. Hey, Pat, it's a pleasure
to talk to you. I know that you're based on
just a little bit down the street from me, that
you're in Western Virginia and that's where you're a headquarters.
So listen, you're doing incredible work out there. I know
(30:11):
a lot of the things that you do you can't
share with us, but I appreciate that people like me
and families out there can sleep well at what you do.
With all the companies out there. It's fantastic work and
I'm just in awe of what you and your team do.
So thank you for all that you do. We appreciate it,
and I'm glad we could spotlight you on CEOs. You
should know we really appreciate it. Continued success.
Speaker 2 (30:31):
Yeah, thank you very much. Thank you
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