Inside HEAVY.AI's Startup Journey with Dcode | The Pair Program Ep39

In this episode, we dive into the world of commercial innovation within regulated industries, particularly defense and national security. Our guests, Jon Kondo, CEO of HEAVY.AI, and Rebecca Gevalt, Managing Partner at Dcode Capital, unpack the challenges and opportunities of navigating the federal sector.HEAVY.AI specializes in data analysis, offering an intelligence platform that empowers organizations to extract insights from vast datasets, crucial for making time-sensitive decisions. Jon shares insights into how their technology addresses the government's struggle with data analysis on a massive scale.

Rebecca sheds light on Dcode's role in helping startups enter the government market, facilitating their entry into the federal arena.Together, they discuss Dcode's pivotal assistance in HEAVY.AI's journey towards federal adoption, highlighting the importance of collaboration between innovative startups and established players in the government space.Tune in to discover how these trailblazers are reshaping the landscape of govtech and driving impactful change within regulated sectors.

About Jon Kondo: Jon is CEO of HEAVY.AI. Jon has 30 years of general management, sales, and marketing experience building both global enterprise software companies and SaaS-based start-ups. Prior to HEAVY.AI, Jon was SVP of global sales and marketing for Appen, an ASX company, providing data services and technology for AI/ML. Before that he was co-founder and CEO of OpsPanda, a leading application for sales resource management acquired by Xactly. Jon’s additional leadership roles include Chief Revenue Officer at Replicon, CEO of Host Analytics, Group Vice President at Oracle, and SVP & GM, Americas at Hyperion. Jon received his BA from UC Santa Barbara and his MBA from the Thunderbird School of Global Management.

About Rebecca Gevalt: Rebecca Gevalt is Managing Partner at Dcode Capital where they invest alongside leading venture firms in commercially-successful, high-growth technology companies that can dramatically improve the federal government. Prior to Dcode Capital, Rebecca worked at the CIA for more than a decade, including with In-Q-Tel to bring novel tech startups into the national security space.

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Welcome to The Pair Program from hatchpad, the podcast that gives you

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a front row seat to candid conversations

with tech leaders from the startup world.

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I'm your host, Tim Winkler,

the creator of hatchpad,

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Mike Gruen: and I'm your

other host, Mike Gruen.

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Tim Winkler: Join us each episode

as we bring together two guests to

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dissect topics at the intersection of

technology, startups, and career growth.

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So hey everyone, welcome

back to The Pair Program.

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I'm Tim Winkler, Mike

Gruen joining me per usual.

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Uh, Mike, I messaged you about this

earlier, but I read a, I read this stat,

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um, regarding like the new Grand Theft

Auto game that's coming out next year.

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And, uh, the trailer just came out

yesterday and it received, you know,

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millions and millions of views.

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Um, but it, it created this

chain reaction to where a Tom

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Petty song, love is a long road.

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Spiked by the number was

36, 000 percent in streams.

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Uh, I just think that's fascinating

how just like one little thing

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leads down to that next big spike.

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Um, when, when, when something goes

a bit viral, I've done this before.

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Uh, with HBO's series, the last of us,

um, they played this Linda Ronstadt

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song called long, long time, and I was

obsessed and just had it on repeat.

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So I looked up that that was

actually another top performer.

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That was 4, 900.

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Percent and then stranger things was

another one that had something like

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that's like, is there anything that

caused you to like from a series of

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movie or a game that caused you to like,

just start streaming an audio track?

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Mike Gruen: So what's funny is I can

remember the audio, but I can't remember

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like what cause I can't remember what show

or movie or whatever it was that I caught.

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But there was something that like brought

Dolly Parton back into, like, into, into,

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like, like into my playlist a little bit.

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And, um, just a phenomenal performer.

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I'm not a big country fan.

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Uh, it's like well on my list

of like genres I listen to.

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Um, so I don't know what caused that.

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Um.

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Tim Winkler: The dog is

trending right now as well.

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That's funny.

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Yeah, she is.

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Yeah, that's

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Mike Gruen: funny.

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Cause that was, she's back.

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That was a couple of years ago even.

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Um, but yeah, I think it's like

when, uh, when, if I see a modern

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show that set back like, like that,

like whether it's stranger things

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or something that will sort of bring

some stuff back into my like rotation.

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Um, there was something that happened,

I think real relatively recently that

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brought REM playlists and stuff like that.

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And that's stuff that like I listened to

mostly cause my sister listened to it.

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Tim Winkler: So.

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Yeah, it's exactly, that's a good

point because it's usually, um,

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older songs that they bring back

to be current and they, they spike.

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So Linda Ron said, obviously, right.

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Uh, classic older, um,

artists, but all right, cool.

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Good talk.

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Um, all right, so, uh, let's, let's, uh,

let's, let's talk about today's episode.

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I'm, I'm excited to, to share

this one with our listeners.

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So, um, particularly, you know, those

that might be curious about, you know, how

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commercial startups can serve areas that

are high priority within the government.

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Uh, and again, this is a theme

that we've been building on here,

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uh, across the hatchpad community.

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Uh, this concept of commercial innovation

within, you know, regulated industries

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like defense and national security.

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Uh, today we're going to break

down an actual use case of how

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this is, how this is being done.

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Uh, we've got some

excellent guests joining us.

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We've got John Kondo, the CEO of

Heavy AI, uh, an analytics and

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location intelligence platform.

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Uh, they serve both commercial

and public, uh, sector customers.

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And we have Rebecca Gavalt,

uh, managing partner of Decode

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Capital, a company that helps tech

companies enter the federal market.

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Uh, John, Rebecca, thank you

both for spending some time with

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us here on The Pair Program.

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Thanks.

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Happy to be

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Jon Kondo: here.

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Same.

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Oh, I don't want to date myself with the

songs you were naming, but some of those

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are not necessarily old songs for me.

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Tim Winkler: all right.

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Fair.

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Yeah.

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I mean, Hey, everybody loves a little

Tom Petty, Linda Ronstadt and, uh,

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timeless, timeless, uh, artist.

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Um, before we dive in, we're going

to kick things off with, uh, with

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our favorite segment, uh, pair me up.

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Um, here's where we kind

of go around the room.

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Mike, you kick us off, uh, lead us

with, uh, your, your pairing for, uh,

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Mike Gruen: for the day.

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Yeah.

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So, uh, I gave it some thought.

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Um, one of the things, uh, as.

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Anyone who listens regularly

probably knows, uh, my, one

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of my sons plays baseball.

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Uh, I get involved in some of the coaching

and one of the things that a number of

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his coaches have brought up with regard

to baseball is how important it is when

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you make a mistake to have a short memory.

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And I've always thought

that was interesting.

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Right?

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Like you, okay.

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You made a mistake, whatever.

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Like you got to put it behind you.

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Baseball's like a game

of like small things.

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Like don't, don't like focus in don't get.

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Beat yourself up because you know,

you struck out or you missed a, a, a,

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a defensive play or whatever it is.

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And why I brought, why I bring it up is

because I think, like when I think about

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careers and profe being professional,

like, you know, when you make a mistake,

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you wanna make sure you learn from it.

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But I think it's also important to not

beat yourself up about it, to have a

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short memory, to be like, you know what?

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I'm going to take that

one, put it behind me.

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Let's focus, let's look

ahead and so on and so forth.

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So, um, I thought that was

like a good life lesson at like

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a little kid baseball level.

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That's just, it's an important part of

life and, um, make mistakes, put it behind

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you, have a little bit of a short memory,

take what you can from it, move on.

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So that's my, that's my

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Tim Winkler: pairing.

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It's solid.

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Yeah.

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I think it's so important to get, get

the, those lessons in for kids at early

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ages, you know, especially through sports.

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It's a great channel to do so.

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Um, I mean, we deal with startups all day.

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We talk to startups all day.

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I mean, let's call it what it is.

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You know, it's like a

90 percent failure rate.

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Uh, I was reading a really

interesting article about this.

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Where it's just not, it's not talked

about much of like the, the failure,

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you know, the failure stories.

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And.

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Uh, some of them were, were really,

um, you know, uh, I don't know,

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they kind of hit to a core, uh, when

they kind of show, you know, show

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a little bit of vulnerability of

like, Hey, this is a failure story.

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This is what happened could

have sold at 25 million.

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Instead.

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We, we, we went bankrupt, right?

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We doubled down and yeah, you know,

and it's sometimes all timing, but, um,

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anyways, failure, you know, and, and,

you know, not, uh, not letting that hit

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you so hard, but rather learning from

it, I think is such a great lesson.

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Um, cool.

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I dig that.

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Um, all right.

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Well, I'm gonna really go

lighthearted here with a food pairing.

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Uh, I haven't done a food pairing

in a while, so I'm gonna hit it.

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I'm gonna hit y'all with the,

um, ketchup and horseradish.

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Um, so obviously just a brilliant

combination of sweet, tangy, spicy.

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Uh, it's one of my favorite

condiments, cocktail sauce.

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Um, and so obviously like going into

the swing of The holidays, uh, you know,

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everybody's gonna, you know, spike their,

their shrimp consumption here real soon.

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I'm sure it's some, some parties.

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Um, but if you don't have like a really

good cocktail sauce on the table, um,

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that shrimp's just not going to, it's

not going to hit like it should hit.

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So, uh, I'm giving a big shout out

to the staple continent for any yeah.

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Yeah, any seafood restaurant or like

home, home kitchen cocktail sauce can't

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be prepared without ketchup and I like

a hot horseradish, like a little really

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spicy one, um, gives it a good burn.

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So that's, that's what I'm gonna go with.

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Um, ketchup and horseradish sauce.

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So, that's, uh, that's the food pairing.

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Pass it over to Rebecca.

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What, uh, quick intro and, and,

uh, your pairing for the day or

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Mike Gruen: feel free to trash

Tim's if that's what you want to do.

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Tim Winkler: That's not very nice.

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Mike.

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Rebecca Gevalt: It's just disgusting.

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There's really, there's a lot

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Tim Winkler: of judgment.

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I could eat it by the spoonful.

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I'm being real.

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I love it so much.

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Rebecca Gevalt: Yeah.

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I mean, I didn't know this

before I agreed to come on.

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Tim Winkler: Hey, this is a

no judgment zone, Rebecca.

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It's an

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Rebecca Gevalt: indicator

of questionable decision

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Tim Winkler: making.

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Maybe I failed, but I'll learn from it.

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You

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Rebecca Gevalt: know,

it's great to be here.

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So I am, uh, one of three managing

partners of Decode Capital.

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Which is a venture capital firm,

um, and very excited to be here.

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And so my pairing, uh, when you

emailed me to tell me to be prepared

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for this, I looked around and said,

well, what am I doing right now?

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Um, so I was sitting in front of the

fireplace working later at night, uh, and

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I had a mezcal old fashioned, and if you

haven't had a mezcal old fashioned, highly

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recommend best type of old fashioned.

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And a fireplace and

ideally not doing work.

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That was the bad part of the

pairing, but the rest of it was good.

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Tim Winkler: Nice.

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Great.

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I do like it.

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Fire.

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So I haven't had an old fashioned.

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I definitely do a Mezcal Marg,

um, but the old fashioned.

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It's a really good, yeah,

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Rebecca Gevalt: it's more

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Tim Winkler: wintry.

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Yeah.

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A little smoky.

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Mm hmm.

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Throw a little cocktail

sauce on the, on the rim.

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No.

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You should not do that.

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You should not do that.

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Um, any specific type of

mezcal that you're, you're

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drawn to, or is it just any,

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Rebecca Gevalt: um, Yeah.

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So there's, there's, um,

Iligal is pretty good.

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The Anejo.

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And then, um, there's the one that's

the four rabbits, but rabbits is

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in Spanish and I can't come up with

the word for rabbits right now.

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And I don't want to embarrass

myself when this gets put out, but

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Tim Winkler: We'll plug

it in the show notes.

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We'll, we'll, we'll Google it.

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Um, I'm taking, I'm taking notes

because I, I love a good, a good

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recommendation on a bottle of booze.

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Um, awesome.

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Well, yeah, thanks again for joining

us and we'll pass it to John.

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John, quick intro and, and your pairing.

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Jon Kondo: Well, thanks

and, uh, happy to be here.

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I'm John Condo, uh, CEO of Heavy ai.

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Um, been on board for about,

uh, two and a half years.

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Um, and it's funny, Rebecca,

you're pairing, because when I

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started to think about it too.

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You know, outside of, you know, but a very

typical pairing would be a bourbon and a

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cigar, but that's, you know, I thought I

could be a little bit more deep than that.

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And, uh, so then it was like, okay, a

bourbon and email, because every now and

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then I am working late and do an email.

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And there's nothing like winding down

with email and a glass of bourbon.

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But I didn't really, you know,

what, what, what I came up with was.

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A good bottle of red wine and

cooking and it doesn't matter what

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I'm cooking, but just having that.

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I think the important part of

that is really, it's, it's not so

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much the pairing of the flavors

and things along those lines.

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It's kind of the mood that it puts you in.

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And so, you know, I, I have a pretty

busy life during the week and, uh, on

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the weekends and such, both with work and

with family and things along those lines.

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But cooking is my getaway, right?

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It is, uh, it's my time.

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It's my thing.

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If I've got a good glass of wine going

with it, uh, try not to try and do

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all the chopping before I do that,

but, uh, you know, doing that, so

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it's a good parent, that's, that's

my kind of, I think, favorite parent,

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but, uh, you know, Rebecca, next time

we're sending emails late at night,

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I'll think of you drinking a mezcal

fast and you can think of me with it.

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Tim Winkler: Just sitting

there, make it stop.

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strong, strong, uh, alcohol

showing on this round, guys.

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I, that's, uh, kudos to everyone.

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Uh, John, what is the, um, what's

your, the last meal that you cooked?

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What, what, what were you cooking?

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Jon Kondo: Geez.

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What is the last meal I cook?

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We've been, you know, I barbecue a lot.

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Uh, just this week, what did I make it?

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Uh, and then this week

I did a, uh, I did a.

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Cause it was during the week meal,

so I had to do it pretty quick with

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salmon, um, you know, made some homemade

Mongolian barbecue sauce, marinated

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that, seared on both sides and then

put in the oven for a little bit

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and stuff that came out really well.

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So no cocktail sauce.

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No cocktail

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Tim Winkler: sauce.

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The cocktail sauce.

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Save it for the shrimp.

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I do

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Jon Kondo: love cocktail sauce and I

love really fresh, good horseradish.

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But I'm not sure about the

ketchup and horseradish.

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Yeah, ketchup and

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Mike Gruen: horseradish.

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I'm not, I didn't, that's not, anyway.

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But we can move on.

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Tim Winkler: I mean, that's

how cocktails made, but.

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I know it is, but you know.

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Can't have one without the other.

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It's just when you say

ketchup, it just, you know.

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I know.

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It actually used to be a punishment.

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Uh, when we were kids, we would,

you know, you lose, you have

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to eat a spoonful of ketchup.

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It's a punishment still.

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It's torturish.

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All right, moving on.

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Um, let's get to the heart

of the discussion here.

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So this is how I kind of envision

the conversation flowing.

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Uh, I'd like to have like each

of you just kind of give a brief

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breakdown of your, of your companies.

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John, I'll actually start with you just

to hear a little bit more about the

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product you're building at, at heavy.

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ai, the mission, some of the types of

customers that your technology is serving,

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then we'll pass it over to you, Rebecca,

to shed some light on how decode capital

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operates and then from there, I'll just

have you just go straight into how.

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Heavy AI was introduced, um, you know,

went going through the decodes program.

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Um, and then we can

kind of riff from there.

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So John, why don't you begin and just,

you know, hear, hear, hear a little

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bit more about, you know, what, what

you guys are doing over there at heavy.

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ai.

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Sure.

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Jon Kondo: Happy to.

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And, and I think, you know, one of

the things that I think is always an

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interesting story is how we were founded.

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Uh, and I, and I like to start there

and then I'll talk a little bit about

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what we're doing currently and stuff.

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But Todd Mosack, who's our founder,

uh, and now our CTO Todd, uh, was

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uh, one of those brilliant minds in

the world that was, uh, running his

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dissertation at Harvard on the influence

of social media on the Arab Springs.

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Not necessarily a traditional place

to start a database company, but

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when harvested, this is about 10

years ago, when harvested, you know,

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hundreds of millions of tweets that

were in most of them are geolocated

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trying to do analysis on those.

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And as he loaded it up, found

that everything he loaded that

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much data into just came to a

screeching halt and thought.

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You know, so in his spare time, I thought,

Hey, maybe I can write a program to

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do this, spend some time, had a little

bit of background starting to start

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hanging out with MIT computer lab.

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They said, people started to see it.

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They said, geez, if you can do

this, you know, what can you do?

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And his, his notion then was, you

know, which today looks very obvious,

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but back then was to use GPUs.

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And if you think back 10 years.

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I go, GPOs are really thinking

of just really for gaming, right?

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And not for analytics or certainly

not for AI and what it is today.

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So, you know, fast forward, what

we've developed is a GPU accelerated

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platform that helps people do

in primarily geospatial types of

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analysis, but it's where there is.

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You know, there's a few critical

things that come to mind, right?

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You have to have a moment, you

know, you have to be able to make

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a decision within a time period.

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So many of our applications

are, we need to make a decision.

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We have an impending

deadline, whatever that is.

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There's, you know, some investment

needs to be made or decision.

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You know, a mission critical decision

that needs to be made and we have

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to do as much analysis as we can,

but that decision can't be put off.

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Right?

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And so to be able to take as much

data, different types of data,

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often geospatial data, satellite,

LIDAR, things along those lines.

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Bring that together and be able to get

quick answers doing that quickly has

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allowed us to be really successfully

in some of the big heavy industry.

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So you think about telecommunications

when you're looking at, you

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know, uh, call records and such.

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And there's billions of those

and looking at where you have

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network issues and stuff.

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We're thinking about where to put

your next power to improve service.

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Um, you've got things in the federal

government that are looking a lot of geo

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intelligence and stuff in the intelligence

community and such seeing some, you know,

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great use cases in the energy space,

oil and gas and renewables and such.

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So there's.

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You know, where there's big data sets,

lots of different types of data and

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decisions that have a window in which

they need to be made, you know, we do

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Tim Winkler: really well there.

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Yeah.

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Very interesting.

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Yeah.

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It certainly seems like a lot of use

cases popping up, um, with the innovations

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happening in space tech, right?

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You know, a lot more

satellites being shot out.

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Uh, into orbit and, um, you know, the

amount of data that's being aggregated

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as a result of that, certainly,

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Jon Kondo: as you know, there's all

kinds of stats, but it just shows

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that, you know, we always talked

about forever more is law, right?

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The doubling of compute power.

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Now you look at it and your data is

quadrupling almost every month, right?

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The amount of data being collected.

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You know, if you think about the

amount of video streaming that is

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collected, or just all the types

of data, you know, not getting into

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all the privacy issues and all those

things, but just satellites, right?

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You look at the stats are incredible.

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The number of satellites that are being

launched and then your ability to intake

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that ingested, analyze it, make decisions.

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You know, and that's where we do really

well is we help you do that really fast.

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Mike Gruen: Yeah.

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It's interesting the, um, that

you say that cause like the,

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I was at a talk a while ago.

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Uh, it was, um, it was an ISP,

essentially the provider to, to our,

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um, to part of the campus, whatever,

talking about like the large amount

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of data that they're pushing through

their networks and how they needed

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data science and scientists to actually

start looking at the network trap.

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Like they're like the things that

you guys are doing, you guys are

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all, these are huge scientific.

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Projects it's billions of, of

records traveling over our network.

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If you got, you know, the data, data

generates, generates data type of

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concept of like, now we just need you

guys to also apply some of that same

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stuff to some of the network stuff that

we're doing just to figure out how we

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can optimize and there's just so much.

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Um, and it's funny, the Moore's law aspect

of it as well, where data just keeps

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generating more data, which generates

other data and so on and so forth.

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So

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Jon Kondo: cool.

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Yeah, absolutely.

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I mean, it's, it's, it's, it's

just, it's a growing problem, right?

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Tim Winkler: Yeah.

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Well, I'd love to expand more on, on how,

you know, decode and, and, uh, heavy.

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ai is kind of led more down

this, this pathway for national

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security and defense customers.

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So before we talk about that, Rebecca,

yeah, let's, let's hear a little

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bit more about, you know, what,

what's going on at decode capital.

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And then you can kind of just

segue that right into, you know,

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how you got introduced with heavy.

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ai.

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Rebecca Gevalt: Yeah, sure.

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Happy to.

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So, um, do you go capital?

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Our tagline is that we're the

VC firm that knows federal.

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So that is the space that we plan.

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And we invest in, um, what's

called dual use companies.

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So, companies that have large plays in

the private sector that want to either

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launch or expand into the Fed market.

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Um.

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So our, our thesis there is, you know,

the U S government should really stop

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paying some of the large primes to

rebuild what already exists in the

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private sector, um, just fix your

acquisition processes and, and how you're

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engaging and take advantage of the tech

that's better and already out there,

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um, you know, particularly in the very

innovative startup space that the U S has.

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So, um, that's where we invest.

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We invest at the growth

stage, I should say.

415

So we're at the late A to C range.

416

Um, and then we actually launched

the fund from an advisory firm that

417

we've been running for about 7 years.

418

And that's how we worked with with heavy.

419

Um, so the advisory firm, it's,

uh, you can think about it

420

as a dual facing marketplace.

421

So, on 1 side of the firm, we work

directly with the federal government.

422

So we have contracts with.

423

Primarily, though, we are government

wide, and we're providing them

424

training and advisory services

on how to find and contract with

425

innovative companies like heavy.

426

So we've trained now about

427

And a lot of times people would

think, well, why is an outside

428

company training the government on

their own acquisition processes?

429

But.

430

Um, it's pretty complex stuff,

and that's what I don't specialize

431

in it, but the team does.

432

And so we help the government, you

know, figure themselves out to be able

433

to engage with companies like heavy.

434

And then on the other side

of the business, we work

435

directly with tech companies.

436

So.

437

Um, we've worked with about 160

over the years, and what we're

438

doing is sort of teaching the

fundamentals of the federal market.

439

And making sure that companies, once

they decide they want to go into federal,

440

well, 1st, we help them decide if they

even want to do it because it's painful.

441

Um, and then once they decide that they

want to do it, what we try to do is.

442

Equip them with the knowledge and know how

they need to actually stay in the market,

443

be successful and hit revenue faster

than if they were just sort of doing it.

444

Um, on their own.

445

Um, the, we actually, the, the

founder story from the advisory

446

firm, I just think it's cool.

447

It's super fast.

448

My partner, um, serial entrepreneur

herself, always in the GovCon,

449

GovContracting space, GovCon space.

450

And she wrote an email to all the

big VC firms in Silicon Valley and

451

said, um, I can help your portfolio

companies figure out the federal market.

452

Are you interested?

453

And she got like a 40, 40 or 60,

which 1 it is percent hit rate.

454

Um, and she launched the company

from there because there was such.

455

Everybody knows that

the Fed market is big.

456

It's sticky.

457

There's a lot of money there, but it's

also pretty opaque and pretty hard.

458

And so, um, that's how

we were, we're founded.

459

So we found heavy, um, back in 2018.

460

So we've been working

together for a while.

461

Um, so back in 2018, the way the decode

program worked back in the day, it's

462

different now, but back in the day,

what we did is we took applications.

463

Um, for classes, we ran classes 4

times a year, um, around specific

464

tech areas, and then each class was

about 2 month long, 2 months long.

465

And during those, you know, classes,

there were 2 in person weeks in DC, but

466

otherwise it was, you know, virtual,

virtual learning and, um, us providing

467

a lot of, um, you know, instruction on

all the different aspects of the market,

468

marketing, pricing, use case mapping.

469

All the different contract

vehicles, things like that so

470

heavy had applied to the program.

471

And then my partner, Megan

actually went out to California

472

to get a demo of the tool.

473

I'm sort of blown away.

474

Um, at what they were able to

do, but also, you know, our

475

perspective is always okay.

476

This technology is really cool.

477

We can see the private sector use

cases, but can the government use it?

478

Um, and it was huge to be able to see.

479

The sheer amount of data that

they were able to manipulate

480

and inside the government.

481

It's actually illegal

for them to erase data.

482

So it is a growing and continually

growing problem for them to be

483

able to analyze the data sets that

they are constantly ingesting.

484

So it was a very clear fit between what

heavy was doing in the private sector

485

and what they could do in the government.

486

So it was a pretty easy decision on our

part that we wanted them in the program.

487

So then, um, there were some personnel

changes that happened in the interim.

488

And so eventually when they

joined the program, they had a

489

really phenomenal fed salesperson,

um, who we loved working with.

490

And we just really started

the relationship from there.

491

Um, and then I personally had

worked with heavy separately.

492

I had worked with In Q Tel for a bit.

493

Um, and In Q Tel is the venture capital

arm of the intelligence community.

494

And so I had been working with

them and had seen the amazing

495

things that Heavy could do.

496

Um, again, working within the IC.

497

And so it was a pretty easy

decision for us to see the value.

498

And then it was, you know,

Heavy clearly saw the value of

499

getting into the Fed market.

500

So it was pretty much a great match.

501

Yeah, that's

502

Tim Winkler: really interesting.

503

So it's, I'm curious on

your, your background.

504

Um, what, what were you

doing before decode capital?

505

Rebecca Gevalt: Yeah, sure.

506

So, um, I was in the government actually.

507

So I was at the CIA for about 13 years.

508

Um, within the CIA, I primarily

worked in counterterrorism.

509

So, um, I was working on Taliban,

Al Qaeda issues in Afghanistan.

510

Pakistan, um, did that for several years.

511

Went out to Afghanistan a few times,

and then I worked a bit with the Israeli

512

Mossad on some other terrorist groups.

513

They were most interested in and,

um, really the way that I describe

514

it to people is that I was the

end user of what is theoretically

515

the best tech in government.

516

Right?

517

So this is post 9 11.

518

A lot of the government budgets were

going toward the counterterrorism

519

mission and the use cases.

520

There are really finding

a needle in a haystack.

521

So you're, you're inundated

with information gathered from

522

across the intelligence community

agencies, whether it be N.

523

S.

524

A.

525

N.

526

G.

527

A.

528

So N.

529

S.

530

A.

531

to cell phones and G.

532

A.

533

does geospatial overhead.

534

Um, so you're, you're gathering all

this kind of data and you have to

535

find the bad people in that data.

536

And so, um, the tools that, that we

were able to, that the tools that

537

we had were honestly not that great.

538

Um, and so then I, when I left, I did

that for about 10 years and then decided

539

to go over and work with In Q Tel, um,

because what they were doing was going

540

out to find the best technologies.

541

And I wasn't alone at CIA with

having these problems right

542

across the intelligence community.

543

People are struggling

to manipulate data sets.

544

And manage significant data overload

and try to derive insights from it.

545

And so working with you tell, for those

2 and a half years was fascinating

546

to be able to see all the tools that

were out there in the private sector

547

that we're doing similar things.

548

And the parallel use cases,

whether the use cases in.

549

The financial sector or the

telecommunications sector

550

was a similar use case.

551

It was just different data sets.

552

And so working with and seeing what

heavy could do is sort of mind blowing.

553

And then when I ultimately joined

decode, heavy had joined decode as well.

554

So, for me, personally, I had been able

to work with heavy when they were with and

555

then also when they were, um, with decode.

556

So able to see, to work with them both

places and has been super rewarding.

557

Tim Winkler: Yeah, really neat.

558

How that's all kind of, how that all

kind of came about and are connected,

559

uh, through some past lives there.

560

But, um, so I am curious, like,

um, you know, when we first

561

connected, you, you kind of pointed

out like, you know, we're not.

562

I don't know.

563

We're not like an accelerator.

564

Um, you know, we're not like a, not

like a Y combinator or something here.

565

Um, and just kind of like pulling on that

thread a little bit, because I want to

566

just kind of expand a little bit more on,

you know, how decode capital, uh, and your

567

consulting arm or advisory arm is unique.

568

So do you all have a

certain number of companies?

569

Like, do you have like a,

you know, a cohort or is it

570

kind of hand selecting one?

571

By one and giving this kind of like white

glove experience, uh, per engagement.

572

Rebecca Gevalt: Yeah.

573

So we've, we've iterated over the years

in terms of how we provide that support.

574

And the driver for us is, you know,

from a business perspective, um,

575

you know, we wanted to make sure it

was scalable on our side, but more

576

importantly, we wanted to lower the

barrier to entry for tech companies.

577

So when heavy came through the program,

it was, um, classes four times a year,

578

eight week intensive sort of stuff.

579

Um, and.

580

Now what we've what we've and it

was more expensive, quite frankly,

581

it was probably 3 times more

expensive than what it is now.

582

No more than that.

583

4 times.

584

I can do math.

585

I can do math.

586

It's 40.

587

It was 4 times more

expensive than what it is

588

Tim Winkler: now.

589

So, just to expand on that, we don't

talk specifics of the numbers, but

590

you charge you charge these companies

a fee to to get that advisory.

591

Rebecca Gevalt: Okay, exactly.

592

And so what we're doing

is and that's why you.

593

Whenever people try to call us an

accelerator, I would try to correct it

594

because when people hear accelerator, they

think you're taking investment rights.

595

You're taking equity in

return and your early stage.

596

And that's not what we're doing.

597

So we are a fee for service model.

598

We don't require an equity

or investment rights.

599

Um, when I was working with tell.

600

You know, they require investment

rights to work with them.

601

And for a variety of reasons,

companies at any given time might

602

not be raising, or it might not

be the right transaction for them.

603

And so we would miss out on working

with really great companies and we

604

didn't want to replicate that at decode.

605

So it's always been a fee for

service model and then it's

606

not necessarily early stage.

607

You know, the companies that we work

with, like heavy have solid product

608

market fit in the private sector,

because what they're trying to do

609

is expand into a different market.

610

So we're experts on the market.

611

We're not requiring companies to give

up equity and we're not requiring any

612

sort of modifications to technology to

be in order to work in the government.

613

We're trying to take the

companies where they are.

614

So where the pro so the program was more

intensive and a little bit more expensive.

615

Previously, where we are now.

616

We really wanted to, um, again, like

lower the hurdle to work with us.

617

So we now have a bootcamp that we

run quarterly because that just makes

618

sense for staffing for us to know

it repetitively where it's going.

619

Um, but we run a bootcamp that's,

um, about an hour or 2 per week.

620

And it's 8 weeks long and each week

covers the fundamentals of the Fed market.

621

So again, that marketing pricing,

use case mapping, budget.

622

All the different contract vehicles,

and then we have an online platform.

623

So we're always sticky with these

companies if they ever need help.

624

We're there.

625

Um, it's full of vendors as well.

626

So if they need a proposal writer,

compliance experts, or anything

627

like that, you might need for the

Fed network or for the Fed market.

628

It's sort of at your fingertips.

629

And then we have events twice

per month, and companies can

630

go to that in perpetuity.

631

And those events are hour long

sessions with the Air Force or the

632

Navy, where they come in and they

talk about different opportunities

633

that are great for companies.

634

And so the price point for

that is pretty inexpensive.

635

Um, and then it's, you know, as many seats

as the company want wants for 1 price.

636

So, um, we're trying to hit as many

companies as we can that that models

637

ultimately more scalable for us.

638

And honestly, more beneficial for for

tech companies at their earlier stage.

639

Inexpensive cost to figure out, do I

really want to go into the Fed market

640

before I make some very expensive

mistakes, hiring a lobbyist, hiring

641

sales people before you need to.

642

And if you're a growth stage company,

you know, a CRO or VP of sales trying

643

to figure out why is my fed sales team

telling me what they're telling me?

644

Um, I don't quite understand the market.

645

So that's really where we're trying

to help the most companies we can.

646

I think one of the

647

Mike Gruen: other things I worked at 2

companies that got In Q Tel investment.

648

Um, and one of the things I saw was

sort of this, like, it was great.

649

It opened some doors.

650

I'm not going to say

that, like, it wasn't.

651

beneficial in some ways, but at the

same time, some of the doors they

652

opened, it was like us just trying

to fit a square peg in a round hole

653

or, you know, whatever was like,

great, we have this introduction.

654

We have like, there was just this

pressure on us to do these things because

655

of the In Q Tel relationship and how

we got into the IC and other things.

656

And.

657

I think it took us off mission.

658

I think it, it sort of took us from, like,

you know, we had this product that has a

659

commercial use, it has a government use

and it sort of bifurcated those things in

660

some cases, because we were sort of being

pushed by In Q Tel into, into areas that

661

maybe weren't the best, you know, for our

product or for where we wanted to take it.

662

So I really liked that fee for, you

know, that the model that you guys have.

663

Um,

664

Tim Winkler: interesting.

665

So I, you know, I understand too,

though, you know, you all partner

666

with venture capital, right?

667

So there's this strategic partnership

that comes into play where if that's

668

from a lead source, um, you know, they

generating, you know, deals and leads, um,

669

or is there an actual You know, financial

piece to, uh, what decode capital is

670

doing that is helping with maybe, you

know, really earlier stage companies that

671

could use a little bit of a, you know,

a check or something is that, is that

672

something that is cafeteria style or it

gets brought up on occasion, or it sounds

673

like it wasn't applicable for heavy dot

AI, but I'm just generally curious on

674

how that partnership with the VCs work.

675

Rebecca Gevalt: Yeah.

676

So we, we only launched the

fund a couple of years ago.

677

So companies that have been going

through the program again would

678

come in for fee for service.

679

But ultimately what happened was a

number of companies would ask us for

680

investment because they wanted that

federal expertise on their cap table.

681

Um, as a board observer, you know,

they wanted that kind of help

682

and we didn't have any money.

683

So we grew out an angel network

partnered with a couple of VCs

684

to invest in a few companies that

had been through the program.

685

So we made 6 investments, um,

pre fund before we decided to go

686

ahead and launch a fund and it

got it that exact problem, right?

687

Like companies were coming through.

688

They really wanted, they really

wanted, um, to give us equity,

689

obviously in return for money.

690

So, um, we tested, we tested

those waters and then ultimately

691

decided to launch our own fund.

692

So, you know, we've been running as

an advisory firm for about 8 years

693

and the fund is only the past 2 years.

694

So now what we do is, um, we are investing

our own money now into companies that

695

we have known through, um, the advisory

firm or actually we recently invested

696

in a company that has not gone through

the firm, but it's going through now.

697

And so heavy, we actually

made an investment and we were

698

Super thrilled to do that.

699

Um, love what they're doing.

700

Love the team.

701

Obviously, that's why john and I are here.

702

Um, we've been friends for a while.

703

And so now decode.

704

So companies go through decode, they're

in the pipeline for decode capital.

705

It's not a given.

706

It's not a requirement.

707

It's not, you know, it's it's We

make a bet on ourselves that will be

708

enough value adds of the companies

that they will want us to invest.

709

Um, and I'm always very upfront

and honest with companies that if

710

they're joining our bootcamp, a decode

capital investment is not a given.

711

We've invested in, um, 10

companies over our lifespan and

712

160 have gone through the program.

713

So where are you really using it

as an opportunity to invest in,

714

in great companies and continue to

support them, particularly the ones

715

like heavy, where we're able to

help them win government contracts.

716

They've got solid.

717

Product market fit in both sectors.

718

And it just, it made financial

sense to us and to them, um, to

719

increase the partnership that way.

720

Tim Winkler: Very, very

interesting, very unique.

721

I haven't heard much, much like it, uh,

out there in the market and John, I'd

722

love to hear your, and your two thoughts,

uh, your two cents on this as well as

723

somebody that's kind of been a part

of the program and worked closely with

724

decode capital and, um, you know, after

maybe graduating from the program, right.

725

What, what, what did you, what did you

see as a, you know, a value add, you

726

know, going through it and then like

even post program, like what are, what

727

are you still seeing from it today?

728

Jon Kondo: Yeah, it's interesting.

729

So, as I mentioned up front, you know,

I've been on board for about 2 and a half.

730

Uh, I guess a little more than that.

731

Now, it seems like it goes by fast.

732

Um, and, uh, you know, when I came in, I

had, you know, I'd run other companies and

733

such, and I'd had some federal experience.

734

And so this was obviously, uh, or this

company had the deepest relationships in

735

the, in the IC community of any company.

736

And so there was a lot more.

737

Yeah.

738

Intricacies to, uh, you know, working

with that because I don't have

739

clearances, so I can only, you know, I

get told what my team tells me and such.

740

And so to Rebecca's point,

sometimes, you know, you're like,

741

so why are they telling me this?

742

And what are these things?

743

And you learn a lot about

3, 3 letter acronyms and.

744

But I think 1 of the things that was super

refreshing from decode, I got introduced,

745

you know, as kind of my early introduction

is joining the company to run the company.

746

And Todd had done a great job running

the company, they had gone through the

747

program, you know, our head of federal

at the time, uh, you know, spoke super

748

highly of decode and such and things.

749

And, you know, candidly, we've gotten a

couple successful contracts, you know,

750

with the reference through through decode.

751

So, um, and I think what's developed

and I think what ultimately led to

752

us, you know, taking the investment

and them investing us was.

753

That building relationship.

754

And I think that's what you want to

call it an accelerator or be on a call,

755

you know, and it's not an accelerator

as the cure definition, but one of

756

those where you're going and learning,

you know, as a small, uh, as a small

757

technology company, the most valuable

resource, you know, obviously cash is

758

super important, but it's time, right.

759

And it's, it's, it's focused.

760

Uh, we don't have a ton of time or

resources to, you know, spread thin.

761

So you want to do that.

762

So being able to have

somebody that you can call.

763

And, you know, I talked to

Rebecca, her 2 partners all the

764

time and, you know, about, hey,

what do you guys think about this?

765

Or this is what happened.

766

You know, we got.

767

Um, you know, quite candidly, we

got kicked in the shins a little

768

bit this year with a surprise.

769

They were great.

770

Right?

771

They were great on.

772

Hey, here's how we should think about it.

773

Here's what we're going to do it.

774

And thanks.

775

And so they're a great sounding

board because you're talking to

776

people who understand the space.

777

You know, I can talk to.

778

I have a, uh, I have a great strong

board of directors, big institutional

779

VCs and such, and they're great.

780

When it comes to federal stuff, you

know, Rebecca and team are who I call.

781

We just, uh.

782

You know, how to transition in our, in

our federal leadership, they were part of

783

the interview team and a big part of that.

784

So, um, I think it's, it's, you know,

1 of the things that I would say to

785

any of the companies that have either

are going through or gone through is.

786

Leverage that network leverage

the network that the code brings

787

to you and stuff like that.

788

You have to invest in it.

789

Right, I mean, I think that it's

something, but the federal piece is

790

an important part of our business.

791

Uh, we're excited about

what the prospects are.

792

We're finally getting to some.

793

A couple of programs, some really strong

maturity where we're going to move to.

794

Program a record on a couple of

things and such, and when you do

795

that, you really get instantiated.

796

Um, but, uh, you know, having

the help of going, okay, so what,

797

what they're asking us to do this,

they're asking us to do this, and

798

there's this term and what's it mean?

799

And all those things that having

that help is, is super important.

800

Yeah, I think, uh, it's kind of

funny to talk to every now and then.

801

Tim Winkler: So, yeah, that's

really what you're paying the fee

802

for really is a little comic relief

when you get kicked in the shins.

803

And a lot of companies got

kicked in the shins this year.

804

And, you know, when you think about,

I love the point you make about time.

805

Yeah.

806

Because the amount of time it takes to

tap into a market like the Fed space,

807

if you're not familiar with it, um, you

know, it's, it's, it's very important to

808

have some realistic expectations of what

it's going to take to, to start to seize.

809

Jon Kondo: Yeah, sorry, but the other

thing too, is it's re if you apply a

810

commercial mindset to the federal space.

811

You will wind up driving yourself

crazy, uh, just because you're

812

like, look, they used our stuff.

813

They, they, they accomplished a really

important mission critical thing.

814

They can't not use this.

815

Why aren't they buying it?

816

Right.

817

I mean, it's like you guys cure

cancer with our stuff, but yet,

818

oh, you're, you didn't pass, you

know, this score on this thing.

819

And so it can really, like,

it can drive you nuts.

820

And to have somebody that's

like, calm, this is normal.

821

You're not the only person

that this has happened to.

822

This is, you know, here's

what you got to do.

823

Yes.

824

The fact that you have that

strong omission case will help

825

you get through all of that stuff.

826

But doing that, but having, you know,

and I think those, especially that

827

have started in commercial going to

federal, I can't tell you how valuable

828

Tim Winkler: that is.

829

Yeah, that's, that's well said.

830

And, um, you know, one of the things

that's fascinating or interesting about

831

the program, Rebecca is like the, the

events, uh, because for folks that maybe

832

aren't familiar with how a lot of business

is done in defense or, um, areas of, of,

833

you know, DOD is, you know, these events

are very important to meet folks, uh, in

834

person, shake hands, like this is, you

know, sometimes a little bit of an old

835

school style of how business is done.

836

And Very, it could be very

different from commercials.

837

So the fact that you all are

facilitating this meet and greet

838

scenario where it's like, Hey,

here's, here's folks from, from Navy.

839

Here's folks from the army.

840

I think it's really, uh,

uh, a really smart move.

841

Um, but I am curious too, cause you know,

we, we also deal a lot with recruiting,

842

um, as a part of our, our business.

843

And it seems like an organic almost

add on for you all, uh, as a way to,

844

you know, Help with the break into the

federal space for folks that maybe don't

845

have that federal person intact just yet.

846

Have you ever helped in other areas

of like operations of it be like?

847

Staffing, recruiting, uh, you know,

in, in injecting, you know, uh, actual,

848

like human capital to help them break

in because, you know, this person is

849

well plugged into X, Y, Z agencies.

850

Rebecca Gevalt: Yeah, for sure.

851

I mean, one of the.

852

One of the things that I, you know,

the reason, one of the reasons that

853

I decided to jump from the government

to work for decode is because of

854

the mission that was still there.

855

Like, that was still very important

to me to, to fix the government,

856

to make sure, you know, I still

had to focus on national security.

857

And one of the best ways to get the

government to use something is to

858

introduce them to other people in

the government who are using it.

859

So it's a much better sale if

you can get 2 different agencies

860

to talk to each other about how

they're using a particular product.

861

And this is 1 of the ways that

we were able to get heavy.

862

Some additional contracts

across the government.

863

Because they had so many champions

in other agencies that were willing

864

to say, oh, my gosh, this tool is

amazing is helping us do stuff.

865

We would not be able to do otherwise.

866

And so from a, from a human

capital perspective, what we,

867

what we've been able to do is.

868

Leverage the relationships that

you're talking about across the D O D.

869

So across the Navy to the air

force, and then over, you know,

870

to DHS to say, Hey, this is how

this, this group is using heavy.

871

Here's how you could be using it too.

872

Let's connect you guys and

then bring heavy in after that.

873

So we do that.

874

We also, you know, as John mentioned,

we helped him with his interview

875

process for his fed salesperson.

876

Um, we see a lot of fed salespeople.

877

There's a lot of good ones.

878

There's a lot of bad ones.

879

Um, and so your debt, there's definitely

certain things that you look for

880

and certain answers you look for.

881

Um, and then, you know, we had a, this is

this did not happen to happen to heavy.

882

This is a horror story

from another company.

883

But, um, we ended up, we invested in

the company, but after they went through

884

this, so they were going after a contract

with the air force and the air force

885

has certain rules around ownership.

886

In order to qualify for certain contracts,

so how much what percentage of your

887

company is owned by VCs and the company

closed on an investment round 2 days

888

before they were going to be awarded

millions of dollars from the Air Force

889

that they were now no longer able to get

because the ownership structure of the

890

company changed 2 days before they were

going to sign this Air Force contract.

891

Hey, you know, the Fed sales guys,

you know, that's a lot of money

892

for him that he was going to get

in variable comp that he just lost.

893

Um, and so, you know, he was, can you

please talk to my C suite, help them

894

to understand this, talk to the board.

895

You know, there is some significant.

896

There are significant effects on.

897

Um, revenue and, and how you go after

the Fed market with decisions that you're

898

making solely in the private sector.

899

Um, when you're thinking about your

private sector business, so we, we, we

900

try to help with those things as well.

901

You know, if you make a decision to

move to a purely SAS product, this is

902

what will happen to your gov contracts.

903

You know, there's all sorts of things

that that companies don't think about

904

because they haven't had to, um,

with what happens in the Fed space.

905

Tim Winkler: I'm, I'm curious about

like from an acceptance rate of, you

906

know, you all, you know, wanting to, you

know, to collaborate or partner with a

907

company that's, you know, looking to,

to go through the decode, uh, capital

908

program is, um, do you turn away a

lot of, a lot of companies, I mean,

909

I, that, that think they have this as

Dual use, uh, scenario, but you know,

910

you're, you're going to save them a lot

of time by not going down that path.

911

Or, you know, I'd imagine

like a little bit differently.

912

It's a, it's a, you know,

paid for service, you know?

913

So I'm just curious to know like how

much work you're trying to turn it away.

914

And the truth of like, this is really

not going to be a good path for you.

915

Rebecca Gevalt: Yeah, and

this, this honestly gets to

916

how we iterated the business.

917

So when heavy went through

the program, um, it was a

918

competitive application process.

919

So, um, we selected heavy because

they were so amazing at what

920

they do, um, where we are now.

921

What we, with the, the bootcamp that

we have, what we're trying to hit is

922

as many companies as we possibly can.

923

So we're not turn.

924

And 1 of the use cases that we want

companies to have when they come to us is.

925

Do I want to get into

the Fed market at all?

926

So, I mean, I'll talk numbers because

it's relevant for this conversation.

927

But the price for our

928

So that's a very inexpensive

way for companies to figure out.

929

I'm thinking about the Fed market.

930

Do I really want to go into it?

931

Should I hire?

932

You know, somebody who recently

left the government and retired and

933

is claiming to have a good Rolodex

short answer is no, do not do that.

934

Um, but, uh, you know, what, what do I do?

935

And so we want them come to us.

936

As long as you're a product

company, we don't do services.

937

As long as you're a product company,

come to us, take the bootcamp.

938

It's inexpensive.

939

And then you can make an informed decision

on what you do next and who you hire next.

940

Tim Winkler: That's awesome.

941

That's great.

942

Yeah.

943

That, that 9, 500 is a drop in the bucket

compared to what you'd be spending on your

944

opportunity costs of figuring it all out.

945

Like, Oh, we're the wrong person.

946

I,

947

Mike Gruen: the whole, Oh,

this person has a good Rolodex.

948

That's a very common, terrible mistake.

949

Rebecca Gevalt: Or hiring

a lobbyist immediately.

950

Like that, that's not how you get,

that's not how you make sales, right?

951

Tim Winkler: Yeah, well, that's great.

952

I, I appreciate the, um, you know,

just kind of like that guiding light,

953

uh, mentality because, you know, a

lot of folks would just, you know,

954

take the money to take the money.

955

Right.

956

Um, you're really trying to

make sure it's a good fit.

957

And, uh, for that reason, it's,

you know, it's priced the way it

958

is, but it's also, um, You know,

it's not, it's not forced on them.

959

It's not, you know, opt in kind of course.

960

Um, so yeah, that's great.

961

Um, I guess, uh, just kind of putting

a bow on the conversation before we

962

transitioned to our final final segment,

any, um, any closing remarks or advice

963

that you have for those companies out

there, Rebecca, that are looking to maybe

964

make that leap in, you know, some things

that those, those leadership teams might.

965

Want to ask themselves to, to see if it

makes sense, uh, or to, to consider, you

966

know, uh, a program like decode capitals.

967

Rebecca Gevalt: Yeah.

968

I mean, I would say if you, if you

were, if you want to go directly

969

into the government space and that's

going to be your prime customer,

970

then, then you make that decision.

971

If you want to be a dual use company,

build your company in the private

972

sector first, build it, get the money,

get good customers, build it there.

973

And then if you can do something creative

and lightweight in the government, which

974

is going to be rare, there's a couple

of programs inside DOD where you can,

975

but make sure it's in line with what

you want to do in the private sector.

976

But we tend to tell companies if you're

launching a company, if you're early

977

stage, Figure it out in the private sector

1st, because you're more likely to survive

978

than if you go after a Fed contract

too early in your in your lifespan.

979

Um, that's

980

Tim Winkler: what I'd recommend.

981

Very nice.

982

And then John, I guess, you know,

without getting too specific, but any

983

exciting, uh, you know, avenues that

you all are, are channeling going

984

into defense or national security

985

Yeah, we're, we're

986

Jon Kondo: excited about, uh, you

know, the process we've had a couple.

987

You know, I think, you know, Mike, you

were talking about all the new satellites

988

and things that are being launched.

989

There's just tons of new

sensors that are being deployed.

990

Um, and, you know, we're part of some

of those programs and the response we're

991

getting is overwhelmingly positive.

992

You know, the fact that, you know,

they've been able to 1 of our, 1 of the.

993

Our largest competitors, they were able to

put a day's worth of data in and another

994

1, they could put a weeks in and ours,

they can analyze over a year, right?

995

In faster time.

996

Right?

997

So the fact that we're able

to help do that, it's good.

998

Uh, you know, it's

delivering on the mission.

999

And, you know, I think 1 of the things

too, that I would say a couple of things

00:46:31,020 --> 00:46:32,430

that is, I think about the Fed space.

00:46:32,980 --> 00:46:39,770

Um, you know, to kind of tag on, uh,

Rebecca is, you know, number one, believe

00:46:39,950 --> 00:46:43,130

you got to believe in the mission, by

the way, because you need people who

00:46:43,130 --> 00:46:46,020

actually believe in the mission of

what we're, what is trying to be done.

00:46:46,420 --> 00:46:48,960

The other thing is, you got to be patient.

00:46:49,475 --> 00:46:52,715

Uh, because it just it's 1 of

those things that I wouldn't

00:46:52,865 --> 00:46:54,525

build a business plan built on.

00:46:54,875 --> 00:46:55,205

Okay.

00:46:55,205 --> 00:46:57,695

We're going to get this deal

by this time by these things.

00:46:57,945 --> 00:47:00,475

You have to look at it and say,

yes, we will make an incremental.

00:47:00,815 --> 00:47:02,705

And by the way, you're going

to get early indications.

00:47:02,705 --> 00:47:04,445

You'll get put on to a research contract.

00:47:04,840 --> 00:47:08,110

You know, we've made it

still got to find a home.

00:47:08,110 --> 00:47:12,910

So there's a lot of things that need to

happen, but it can be a really fulfilling.

00:47:13,020 --> 00:47:15,940

Um, it can be a really

fulfilling piece of business.

00:47:16,030 --> 00:47:19,660

And, you know, I think it's, it's

dual uses the right way to go.

00:47:19,970 --> 00:47:24,650

Um, but to Rebecca's point, go build

your private stuff 1st, your commercial

00:47:24,650 --> 00:47:25,940

stuff 1st, and then go do that.

00:47:26,895 --> 00:47:27,625

Tim Winkler: Yeah, no doubt.

00:47:27,625 --> 00:47:30,835

I mean, it's, it's an area that

we are seeing more and more of.

00:47:30,895 --> 00:47:34,755

Uh, it's just, it's a smart,

if, if there's a play there,

00:47:34,755 --> 00:47:36,855

it's very smart to diversify.

00:47:36,935 --> 00:47:41,305

Um, and in today's market, it's extremely

tough to, to raise capital right now.

00:47:41,405 --> 00:47:46,635

Um, and call it what it is, you know, in

times like this, when there's instability,

00:47:46,635 --> 00:47:49,145

people lean into stable verticals.

00:47:49,585 --> 00:47:52,675

Um, Maybe, you know, it doesn't

always seem like it from the outside

00:47:52,675 --> 00:47:55,015

in, but the , the government's

got a lot of money to spend.

00:47:55,015 --> 00:47:55,945

Uh, he does.

00:47:55,945 --> 00:47:59,385

There's, when they spend, they spend

and there, and there's a, a clear call

00:47:59,385 --> 00:48:01,695

to, to a need to revamp our defense.

00:48:01,695 --> 00:48:05,895

And, you know, in, in today's time,

when, when, when war is sadly prevalent.

00:48:05,895 --> 00:48:10,755

So, um, certainly a need for, you

know, the heavy do AI use case and, and

00:48:10,755 --> 00:48:14,295

certainly a, a need for, uh, what you,

you all are doing at Decode Capital.

00:48:14,295 --> 00:48:18,695

So, uh, kudos to both you all and, um,

let's, let's kind of wrap it up with some.

00:48:19,055 --> 00:48:20,495

Uh, rapid fire Q and a.

00:48:20,495 --> 00:48:23,235

So we're gonna, we're gonna switch

to our, our last segment here

00:48:23,275 --> 00:48:24,675

called the five second scramble.

00:48:25,035 --> 00:48:28,635

Um, this is, you know, try to keep

your answers within five seconds.

00:48:28,775 --> 00:48:31,965

If you don't, you know, you're not going

to get, you know, the air horn boot.

00:48:32,365 --> 00:48:35,705

Um, but, um, you know, some, some

business, some personal, we're

00:48:35,705 --> 00:48:36,945

not getting too personal here.

00:48:37,325 --> 00:48:41,655

Um, I'm going to start with, uh, you

Rebecca, and then I'll jump to John.

00:48:41,695 --> 00:48:43,405

So are you, uh, you ready?

00:48:44,145 --> 00:48:44,685

Ready.

00:48:44,685 --> 00:48:44,974

Okay.

00:48:44,975 --> 00:48:45,365

Okay.

00:48:46,215 --> 00:48:51,165

What would you say is unique

about the culture at Decode

00:48:51,165 --> 00:48:51,655

Rebecca Gevalt: Capital?

00:48:53,775 --> 00:48:55,945

Um, there's a significant

focus on having fun.

00:48:55,955 --> 00:48:58,075

If you check out our website,

everybody had to put up gifts

00:48:58,255 --> 00:49:01,225

underneath their photos, and it's

actually hilarious how well it

00:49:01,265 --> 00:49:02,815

matches up with people's personality.

00:49:02,905 --> 00:49:03,565

It's amazing.

00:49:03,565 --> 00:49:04,725

I was actually looking at it last night.

00:49:05,105 --> 00:49:07,865

Because all the new people join and they

have to pick one, and it's really great.

00:49:07,985 --> 00:49:08,255

That's

00:49:08,255 --> 00:49:08,795

Tim Winkler: so good.

00:49:08,915 --> 00:49:11,735

I mean, gifs is a can't,

can't lose scenario.

00:49:11,735 --> 00:49:13,115

Everybody's got something

that's a little worried.

00:49:13,120 --> 00:49:13,530

Is it gif?

00:49:13,535 --> 00:49:13,625

Is

00:49:13,625 --> 00:49:14,075

Rebecca Gevalt: it gif?

00:49:14,075 --> 00:49:15,090

I don't know what you guys

00:49:15,430 --> 00:49:15,650

Tim Winkler: gif.

00:49:15,665 --> 00:49:16,355

I go gif.

00:49:16,355 --> 00:49:16,770

I go gif.

00:49:16,990 --> 00:49:17,530

But yeah,

00:49:17,765 --> 00:49:20,375

Mike Gruen: I know what the owner or

the inventor wants, but I don't care.

00:49:22,205 --> 00:49:22,595

,

Tim Winkler: John.

00:49:22,600 --> 00:49:23,705

John, you need to settle this.

00:49:23,705 --> 00:49:23,945

gif.

00:49:23,945 --> 00:49:24,125

Gif

00:49:25,470 --> 00:49:25,590

.

Jon Kondo: I.

00:49:25,595 --> 00:49:26,255

I go.

00:49:26,615 --> 00:49:28,415

But that's, you know, I.

00:49:34,795 --> 00:49:35,825

Tim Winkler: All right, moving on.

00:49:35,825 --> 00:49:39,175

So, uh, what can folks be

most excited about with decode

00:49:39,175 --> 00:49:41,365

00:49:42,195 --> 00:49:42,885

Rebecca Gevalt: more investments?

00:49:43,015 --> 00:49:43,705

We're excited.

00:49:43,855 --> 00:49:48,545

Um, you know, we've raised more money and

we are excited to be deploying it into.

00:49:48,965 --> 00:49:52,375

Uh, great companies, and then I

personally am very excited to continue

00:49:52,375 --> 00:49:54,955

helping heavy out with, uh, some of

the work that they're doing in the I.

00:49:54,985 --> 00:49:55,195

C.

00:49:55,195 --> 00:49:55,445

it is.

00:49:55,865 --> 00:49:57,625

Beyond pool and super rewarding.

00:49:58,970 --> 00:50:00,250

Tim Winkler: Uh, morning routine.

00:50:00,290 --> 00:50:02,610

So what, what's the first thing

that you do when you wake up?

00:50:03,690 --> 00:50:04,380

Rebecca Gevalt: Are we still on me?

00:50:04,380 --> 00:50:05,950

Or is we, when, when do we go to John?

00:50:06,330 --> 00:50:08,130

Tim Winkler: After you've

got, you've got 10 questions.

00:50:08,340 --> 00:50:11,080

So you've got, yeah, you've

got, you're on your third.

00:50:11,700 --> 00:50:14,470

Rebecca Gevalt: Uh, first thing

I do, I mean, after like going

00:50:14,470 --> 00:50:15,760

to the bathroom, I walk the dog.

00:50:16,060 --> 00:50:16,390

Yeah.

00:50:18,750 --> 00:50:21,990

Tim Winkler: Um, what, uh,

aside from your phone, what's

00:50:21,990 --> 00:50:24,200

one tech gadget that you can't

00:50:24,210 --> 00:50:24,910

Rebecca Gevalt: live without?

00:50:26,680 --> 00:50:27,470

Honestly, that's it.

00:50:27,730 --> 00:50:30,010

So my husband kept trying to get

me to wear the Apple watch and I

00:50:30,010 --> 00:50:31,730

cannot do all the notifications.

00:50:31,730 --> 00:50:32,560

I just can't.

00:50:32,905 --> 00:50:33,365

Deal with it.

00:50:33,365 --> 00:50:34,765

I don't watch a ton of TV.

00:50:35,195 --> 00:50:38,465

Um, I would like to throw my laptop

out the window most of the time.

00:50:39,385 --> 00:50:43,525

So I'm not a big, I know I'm in BC and

I should say I'm a big tech person, but

00:50:44,465 --> 00:50:44,705

Tim Winkler: yeah.

00:50:45,105 --> 00:50:46,005

I love that answer.

00:50:46,045 --> 00:50:46,685

That's the same way.

00:50:46,685 --> 00:50:49,085

I'm, I'm anti, uh, Apple watch as well.

00:50:49,085 --> 00:50:51,765

Just get me away from the notifications.

00:50:51,765 --> 00:50:52,485

Don't boost them.

00:50:53,805 --> 00:50:56,395

Uh, you rather work from

home or in the office.

00:50:57,450 --> 00:50:57,800

Um,

00:50:58,220 --> 00:51:00,570

Rebecca Gevalt: just had a conversation

about this in the office, but in

00:51:00,570 --> 00:51:05,230

the office as office culture was pre

COVID post office culture is tough

00:51:06,280 --> 00:51:07,520

because nobody's actually there.

00:51:07,530 --> 00:51:08,180

People are in and out.

00:51:08,180 --> 00:51:11,690

But I loved because at the CIA, you

have to be in the office, right?

00:51:11,690 --> 00:51:12,300

There's no option.

00:51:12,420 --> 00:51:13,770

Otherwise you're you get arrested.

00:51:14,170 --> 00:51:17,160

So if you're looking at classified

information outside the office, you

00:51:17,520 --> 00:51:18,570

have a large problem on your hands.

00:51:19,675 --> 00:51:22,585

Um, love, love and

office culture pre COVID.

00:51:22,745 --> 00:51:24,915

Tim Winkler: Yeah, that's

a good, good answer.

00:51:25,525 --> 00:51:29,345

Uh, what is a charity or a corporate

philanthropy that's near and dear to you?

00:51:30,495 --> 00:51:33,445

Rebecca Gevalt: Uh, my in laws, uh,

helped found, uh, puppies behind bars.

00:51:33,885 --> 00:51:37,845

Um, very cool organization where

they use, um, inmates to train.

00:51:38,340 --> 00:51:42,990

Uh, dogs that then become, uh, seeing

eye dogs or bomb sniffing dogs.

00:51:43,480 --> 00:51:46,830

And actually when I was in Afghanistan,

one of the dogs that they trained, um, was

00:51:46,830 --> 00:51:48,370

actually at the gate sniffing for bombs.

00:51:48,580 --> 00:51:51,320

So it was a cool, very

full circle kind of moment.

00:51:52,000 --> 00:51:52,460

Tim Winkler: Very neat.

00:51:53,510 --> 00:51:55,230

What does your go to dessert?

00:51:56,350 --> 00:51:57,730

Rebecca Gevalt: Anything

chocolate, anything that's

00:51:57,730 --> 00:51:58,910

not chocolate is not dessert.

00:52:01,600 --> 00:52:05,360

Tim Winkler: Do you have a favorite

quick stress relief activity?

00:52:08,630 --> 00:52:09,460

Rebecca Gevalt: Going for a walk.

00:52:09,460 --> 00:52:11,740

So that whatever I'm about to

write doesn't actually get sent.

00:52:13,710 --> 00:52:15,750

Tim Winkler: Mixed with the

mezcal margarita afterwards.

00:52:20,580 --> 00:52:24,200

If you could time travel, which

period would you want to visit first?

00:52:24,810 --> 00:52:25,100

Can I go

00:52:25,100 --> 00:52:25,630

Rebecca Gevalt: to the future?

00:52:26,070 --> 00:52:26,450

Yeah.

00:52:26,860 --> 00:52:27,060

Yeah.

00:52:27,060 --> 00:52:27,710

That's where I would go.

00:52:27,890 --> 00:52:30,710

I would go to wherever Star Trek,

the next generation took place.

00:52:31,010 --> 00:52:32,070

Tim Winkler: Oh, very nice.

00:52:33,080 --> 00:52:33,950

And then last question.

00:52:33,950 --> 00:52:35,590

What's one thing that's on your bucket

00:52:35,590 --> 00:52:36,120

Rebecca Gevalt: list?

00:52:38,180 --> 00:52:40,990

Oh, I try not to think like that because

otherwise, why am I not doing it now?

00:52:43,040 --> 00:52:43,680

Tim Winkler: Well played.

00:52:43,690 --> 00:52:46,300

Well, you just flipped

that question on his head.

00:52:47,060 --> 00:52:47,280

Awesome.

00:52:47,310 --> 00:52:47,770

All right.

00:52:47,770 --> 00:52:49,910

You, you are all through John.

00:52:49,980 --> 00:52:50,840

Uh, you're up.

00:52:50,840 --> 00:52:51,270

Are you ready?

00:52:51,930 --> 00:52:52,270

All right.

00:52:52,620 --> 00:52:53,210

Sure.

00:52:53,890 --> 00:52:54,160

All right.

00:52:54,870 --> 00:52:57,660

Um, explain heavy, a heavy.

00:52:57,660 --> 00:52:59,820

ai to me as if I were a five year old.

00:53:03,800 --> 00:53:06,140

Jon Kondo: If you want to find a

needle in a haystack and the haystack

00:53:06,140 --> 00:53:09,130

is an immense assortment of hay.

00:53:09,650 --> 00:53:13,000

Or geospatial data, customer

data, all those kinds of things.

00:53:13,020 --> 00:53:13,770

We help you find it.

00:53:15,170 --> 00:53:15,870

What's your favorite

00:53:15,870 --> 00:53:17,860

Tim Winkler: part about

the culture at heavy.

00:53:18,430 --> 00:53:18,630

ai?

00:53:19,840 --> 00:53:22,930

Jon Kondo: Um, it is an amazing amount.

00:53:22,970 --> 00:53:25,380

Everyone that's here has

an amazing amount of grit.

00:53:25,490 --> 00:53:27,660

And I think the other thing that's

kind of cool is they're all a

00:53:27,660 --> 00:53:30,180

bunch of geospatial, uh, just

00:53:30,190 --> 00:53:30,720

Tim Winkler: freaks.

00:53:32,710 --> 00:53:35,800

We kind of touched on this earlier,

but what, what can folks be most

00:53:35,800 --> 00:53:37,740

excited about for, um, heavy.

00:53:37,740 --> 00:53:39,660

00:53:42,220 --> 00:53:46,040

Jon Kondo: I think the product continues

to evolve and I think more importantly,

00:53:46,040 --> 00:53:49,710

the product and us understanding where

it fits in the world, both in the

00:53:49,710 --> 00:53:54,140

commercial sector and in the public

sector is really coming into focus.

00:53:54,200 --> 00:53:58,170

And I think what I look at next

year is our ability to then really

00:53:58,170 --> 00:54:00,570

go expose that expose that fit.

00:54:01,010 --> 00:54:05,620

And have people see it, you know, I

think our issue has been, um, it took a

00:54:05,620 --> 00:54:07,180

while for people to see and have that.

00:54:07,250 --> 00:54:08,120

Oh, my moment.

00:54:08,600 --> 00:54:13,530

Um, we, we call it something else

internally, but, uh, the, uh, the

00:54:13,530 --> 00:54:17,730

old what moment, you know, and now we

want to be able to have that to be a

00:54:17,860 --> 00:54:20,360

much broader, uh, so people see that.

00:54:21,270 --> 00:54:21,510

Yeah,

00:54:21,510 --> 00:54:22,850

Tim Winkler: I mean,

this is an R rated show.

00:54:22,850 --> 00:54:25,800

You can say, oh, shit, if you want

to, but we'll, we'll, we'll take, oh,

00:54:25,800 --> 00:54:27,190

my, we'll take the, oh, my moment.

00:54:27,840 --> 00:54:30,430

Um, yes or no to pineapple on pizza.

00:54:31,075 --> 00:54:31,615

No.

00:54:34,135 --> 00:54:35,495

Oh, Rebecca disagrees.

00:54:36,225 --> 00:54:45,615

Um, what's the last, what's the

last, um, uh, video movie, uh,