April 8, 2025 • 37 mins
Fighting fraud and improving visibility is paramount for freight brokers, carriers and shippers. GenLogs’ nationwide network of sensors collects 50,000 images of trucks every five minutes. This data and its artificial intelligence-powered platform can help brokers better vet carriers by visibly inspecting them and their equipment. In this Talking Transports podcast, GenLogs’ co-founder and CEO Ryan Joyce joins Lee Klaskow, Bloomberg Intelligence’s senior transportation & logistics analyst, to discuss how technology can help create more efficient and safer supply chains. Royce also talks about expanding outside of the US, battling fraud and theft as well as his journey as an entrepreneur tracking terrorists to now tracking trucks.
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Episode Transcript
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Speaker 1 (00:07):
Hi everyone, this is Lee Clascow and we're Talking Transports.
Welcome to the Bloomberg Intelligence Talking Transports podcast. I'm your host,
Lee Klaskows, Senior freight transportation logistics Analysts at Bloomberg Intelligence,
Bloomberg's in house research arm of almost five hundred analysts
and strategists around the globe. Before diving in a little
public service announcement, your support is instrumental to keep bringing
(00:30):
great guests and conversations to you, our listeners, and we
need your support. So please, if you enjoy this podcast,
share it, like it, and leave a comment. Also, if
you have any ideas for future episodes or just want
to talk transports, please hit me up on the Bloomberg terminal,
on LinkedIn or on Twitter at Logistics. Lee. Now on
to the episode. We're delighted to have Ryan Joyce with
(00:52):
us today. He's the CEO of Genlogs. Ryan previously spent
over fifteen years in the US intelligence community conducting counter
terrorism operations throughout the Middle East. Ryan holds a BA
from James Madison University, So go dukes. Ryan. Thanks so
much for being on the podcast with us today.
Speaker 2 (01:11):
Lee, It's great to join. Thanks so much for having me.
Speaker 1 (01:14):
All right, So you were in the intelligence industry and
now you're tracking trucks. So for those that don't know,
can you just give us a little background about what
gen logs is all about.
Speaker 2 (01:26):
Absolutely, we are a truck intelligence platform, which really means
that we track all the patterns and locations of trucks
as they move around North America. Now there's a number
of reasons why we would do that. First and foremost,
there's one hundred and sixty billion dollar freight brokerage industry
that's that intermediary between shippers and the truck fleets or
(01:46):
carriers around the country, trying to make sure that they
can match with each other more efficiently. But there's just
never been one place that you can go to figure
out which truck operates in certain regions with the right equipment.
And that's where gen l comes online. We take what
historically used to have to be done through numerous phone
calls and a lot of manpower. We now automate that
(02:07):
so it's all in one place, right.
Speaker 1 (02:10):
And I got to meet you guys at an event.
I'm trying to think it it was a manifest conference, Yeah,
and it was I found out to be very intriguing
just your your overall business. You are a new company,
an emerging company, and I think the technologies was pretty interesting.
So you know, just before we go into the technology
(02:32):
and a little more detail, can can you talk about
the ownership like and how you got on board on
gen Logs.
Speaker 2 (02:41):
Sure, we founded the company back in April of twenty
twenty three, so we're coming up on our two year
anniversary next month with myself and two co founders, Blake
Balch and Joe Sherman. We all had worked together at
a previous company when we had the idea for gen Logs,
and we have raised venture capital through a few successive
rands about twenty one million dollars total the Genlogs. The
(03:04):
we're still controlling of the company and the board, although
we have fantastic partners when it comes to some of
our investors on our cap table. Two of our board
members from Venrock and Steel Atlas have been wonderful as well.
Speaker 1 (03:18):
Okay, great, and I guess you're in the building mode,
so I guess the goal is to just continue to
build out. Are you guys in the market for investors
now or you're kind of happy where you are right now?
From a financial standpoint.
Speaker 2 (03:36):
We're pretty happy where we are. We have some capital
to deploy, and we do have somewhat of a capital
intensive business in the sense of the way we track
trucks as we deploy sensors all over North America along
major interstates, freight lanes, highways. Each one of these have
cameras that collect on the front side and rear of
(03:56):
trucks as they pass by. So that takes some capital
expenditure just to get this network built. But given we
just closed our last round of funding in December of
last year, we're still in good shape for a while,
and we'll start having investor conversations again around Q three
of this year.
Speaker 1 (04:14):
All right, let's talk about the network. So are you
So I'm assuming you're not all over the country yet,
because you're still building it out roughly, Where are you
in the buildout in the US, because I know you
were also eyeing other places in North America.
Speaker 2 (04:31):
Absolutely, I would say right now we're about seventy percent
of the targeted build out, and by that our full
buildout in the United States, the lower forty eight is
really to get to the point that we are collecting
on more than ninety percent of trucks that are on
the roads each day, we're seeing them two or more times.
And the way we kind of targeted trying to redeploy
(04:52):
this network is we took essentially a map of how
trucks move around the United States and volumes on different roads.
We targeted the most are the highest traffic volume first
and then are moving out to the edges. And so
we're seventy percent of trucks on the road each day
we do see in our data, and we're now pushing
that closer to that ninety to hopefully, you know, eventually
(05:14):
one hundred percent level, while also eyeing expansion down into
Mexico and up into Canada soon.
Speaker 1 (05:20):
So currently you're not in Mexico or Canada that the
plan is to get to get there.
Speaker 2 (05:24):
We have locations that we've secured to get our sensors
out there. We're working a little bit. There's it's quite
a lot that goes into making sure that the data
of which we're collecting terabytes today flowing through these cameras
can be processed at the edge and the data brought back,
and it's that data back hall that we're just looking
(05:45):
to solve. We've solved that throughout the United States and
looking to solve that in Canada and Mexico as well,
so that we can actually make sure that once we
deploy these sensors out there, we can get the data
back reliably and in real time.
Speaker 1 (05:57):
So before we talk about the software, I'm just so
intrigued about the hardware. So are these like video cameras?
Are they like just taking Is there a sensor that
just takes a picture when something moves by? How does
the hardware work, Who maintains the hardware? How do you
figure out where to go? And kind of what I
guess what are those leasing arrangements like? And are you
(06:19):
on like billboards? Are you where are you?
Speaker 2 (06:22):
Yeah? We started out with a theory that if we
could get enough density of sensors throughout the United States
on private property, then public entities that want access to
the same data to do counter human trafficking, counter narcotics
or fentanyl work would also then work with us to
deploy in additional areas. And that theory has played out
in the sense that we did start exclusively on private property,
(06:45):
so we are on the sides of warehouses or barns,
or on telecommunications structure wherever there's a structure within a
certain proximity to a highly truck trafficked lane, that's where
we look to deploy. Now, we have high requirements with
regards to the clear lines of sight to trucks as
they approach the side and then depart the area. We
(07:06):
need certain power requirements, a certain amount of data back
hall capabilities. So it takes the forty six thousand miles
of U in a center state and actually makes it
a very small subset of that that would work for
our purposes. And when it does work for our purposes,
we will go out and approach private property owners to
pay a monthly lease to host our sensors. These things
are nothing more than a giant ring cam, essentially, in
(07:29):
the way that a ring camera might sit at the
front of your house, overlooking the road in front of
your house, we're sitting on property overlooking public roadways and
collecting the data there. It's not motion sensing in the
sense that there are a classic emotion sensor that stands
apart to trigger the image collection we record twenty four
(07:50):
to seven, But we deploy truck detecting models or AI
computer vision models at the edge that work hand in
hand with our cameras to filter out all private vehicles
and only store images where trucks are seen present in
the image, and then pull that data back for additional processing.
Speaker 1 (08:10):
And what's the average length of these leases that you
have with these private property owners.
Speaker 2 (08:15):
We'd love to do them as long as possible and
as exclusive as possible. So we're on decades long leases
often with many of these property owners.
Speaker 1 (08:23):
And you know, obviously with anything that's technology, something could
go wrong, whether you know, I guess a bug gets
squished on the lens, or weather or whatnot. How are
you maintaining these sensors?
Speaker 2 (08:39):
As they say hardware is hard, and we would have
to agree with them. We've had, you know, different issues
with whether it's tornadoes coming through certain areas that bring
them down, We've had power outages in the areas, and
you're right, we have had certain issues with bird scat
on some of the lenses too. We have a multiple
(09:00):
field technician teams that are crisscrossing the country each and
every day with bucket trucks that go and both install
these sensors as we deploy them, but then also maintain
them as well. We have pretty rigorous standards for our
uptime both internally as a company and what we want
to deliver for our customers, and so we are often
(09:21):
making tweaks to some of the mechanics within these sensors
to ensure that that uptime can be maintained.
Speaker 1 (09:28):
And you said it's kind of like a ring camera.
Is that actually the size of it? Is it like
smaller than a shoe box?
Speaker 2 (09:34):
No, it's actually quite a bit bigger. And each one
of these cameras are HD cameras that collect ten frames
per second. I mean, they're your maybe security camera looking
type type sensors that we have now they do as
a result. You know, they could be on the side
of a warehouse and you would never know that the
(09:56):
purpose of them is actually to be looking at some
of the truck patterns nearby. And actually, our partners are
real estate partners, enjoy and prefer those cameras because it
does do the double duty of deterring theft in the area.
When it does look like security cameras attached, We kind
of tuck away all of the parts into a smaller
box off to the side that does a lot of
(10:18):
the processing and the data xfilm gotcha.
Speaker 1 (10:22):
And roughly how many employees do you have at gen Lux.
Speaker 2 (10:26):
We have about fifty employees now when you count some
of our part time employees too that work with us,
but on the whole in terms of both W two
and ten ninety nine, we're about fifty now.
Speaker 1 (10:37):
Okay, And is it like spill like half people in
the field and half programmers and management.
Speaker 2 (10:43):
It makes me breaks down at about thirds. A third
is our sensor deployment team, and that includes both those
that are sourcing the leases for us to get our
sensors out, planning for the sensor deployments, and then the
field technician crews that are going and actually installing and maintains.
Another third of the team are engineers and data scientists
(11:04):
taking all of that data that's coming off of those sensors,
fusing it with other third party data sets that we
go out and collect, and then building the actual product
that we put in our customers hands. And then the
last third is a mixture of our sales and our
customer success folks, ensuring that the product hits the market
or exceeds in all cases what customers are expecting to get.
Speaker 1 (11:26):
Gotcha, And I'm assuming that most of the equipment you
guys are buying it from third parties and putting it
together per your own specs.
Speaker 2 (11:35):
This is all US manufacturers. We have multiple manufacturers that
we work with to take the concept of what we
needed to accurately collect on trucks as they fly by
seventy miles per hour in a road, especially when some
of the data we collect off them only need to
be about by law about one inch high some of
the US Department of Transportation numbers on the sides and
other marketings. So in order to get that, we had
(11:57):
to really ensure that we were working hand in hand
and with manufacturing partners to ensure that the entire process
could actually collect the data at that high speeds. And
so we do work with with US manufacturers to get
these built and then deployed.
Speaker 1 (12:13):
All right, So I guess that's a good transition. Let's
talk about the technology. You know, you did mention a
nice buzzword AI. You know you put that in your
emission statement. You probably get a couple turns on your
valuation in today's market, but it truly is machine learning
AI enabled. You can you talk about, you know, what
(12:35):
you're looking for and the kind of use cases for genalogs.
Speaker 2 (12:41):
Yeah, I would say we're starting to service a number
of different vertocols. We started with just focused on that
freight brokerage industry in the United States. Very quickly we
got connected with some very large shippers throughout the United
States that have trailers or different other assets themselves that
were missing, and we demonstrated early on on our ability
to find those in the wild where maybe a trailer
(13:04):
full of toys or electronics had been stolen and these
shippers were trying to track down who the thieves were.
There was no recourse for that back in the day.
Now Genlogs has the ability to set an alert on
a specific trailer that you're looking for by number, by logo,
and when that is seen anywhere in the United States,
you'll get an instant alert that also tells you what
(13:25):
the truck that is carrying that right now, where it is,
the direction. So that was another use case that has
expanded in terms of this asset location and recovery. Beyond that,
we are now dealing with the commercial auto insurance industry
helping them with better underwriting, continuous monitoring, as well as
claims investigations, and we are looking for a further expansion
(13:46):
into the federal and state government space as well as
financial institutions. So there's a lot of different use cases
for understanding the truck activity in the United States. If
you think about how goods flow to and from the
United States, they might be flown, they might be on ships,
but at some point they touch a truck. And that's
really why we've doubled down on the truck intelligence aspects
(14:09):
of what we're doing. Fourteen trillion dollars of goods filters
through the US economy each year with goods on trucks,
and that's why we focus there specifically.
Speaker 1 (14:20):
So if I'm a freight broker like a sh Robinson
or an RXO, what is the use case? So, am
I is it just for tracking?
Speaker 2 (14:29):
It's actually tracking might come later right now. When we
work with freight brokerages, there's kind of three use cases
we focus on. First is we focus on these carrier
sales process. So traditionally, these freight brokerages exist because they
can see the long tail of the market that involves
them employees armies of young professionals to pick up the
(14:50):
phone every day call lists of truck carriers and ask
them a few basic questions like how many trucks do
you have, where do you like to operate? Where are
your trucks? Now, what type of a equipment do you
have with those trucks? We, by just nature of seeing
everything that's happening on the roads, know the answers to
all those questions, so we can absolutely cut down on
the number of people that need to be making those
(15:11):
calls or in the number of calls that have to
be made each day. So essentially going from a very
opaque process of understanding where trucks are to now a
transparent process for a H. Robinson for example, to fined.
So that's that carrier sales side. We then fuse the
data we collect on trucks out there in the roads
with other additional data sets in order to map back
to where those trucks came from their origins to their destinations. Now,
(15:35):
when you do that at scale, you get to understand
the shipper patterns and networks in the United States. So
we can take a Coca Cola, for example, and understand
where all of their suppliers are and where the trucks
come from from their suppliers to a Cocoa Cola bottling
plant for example, and then where the trucks go from
that Coco Cola bottling plant to then deliver to the
(15:57):
end locations. Grocery stores can be stores, So that's data
that we also have aggregated together with our AI. So
we then give h Robinson or other types of third
party logistics or freight brokerages insights into the shipper networks
and therefore those are the sh those are the customers.
And then lastly there's a compliance piece to this all
(16:18):
that we can validate that a truck carrier that says
that it operates on the roads actually does have the
equipments that they claim, almost making sure that the digital
footprint or what is claimed matches the physical footprint of
the actual ground truth. And so that's the compliance we
look when we deal with freight brokerages, we look at
carrier customers in compliance and we have a holistic solution
(16:41):
for them.
Speaker 1 (16:42):
And so tracking might be down the road. Can you
just expand on what you meant by that.
Speaker 2 (16:47):
I think there's a little bit has to do with
censored density. We're not at the point yet where, like
I said, our goal is to get to the point
that over ninety percent of the trucks on the roads
we're seeing two or more times, three, four, five times.
There's just going to be some additional sensors we need
to deploy. There's going to be additional work we need
to do to make sure that we can take the
(17:07):
different types of data points that we collect on a
truck and tie that down to the vin level so
that we know that it's not just acme trucking writ
large that we saw at a certain place, but it's
this specific truck or specific trailer that is underload that
is traveling from Dallas to Chicago that we see it
outside the confines of Dallas, midway in Missouri or Illinois
(17:30):
heading into Chicago. I think once we get to that point,
we will be in a position to automate a lot
of the tracking that occurs that currently necessitates providing the
actual permission of the electronic logging device or some other
type of cell phone tracking that has to be manually
enabled by a driver or fleet. We will be able
(17:51):
to automate that across all roads without needing that manual enabling.
And really, what we've found is that people don't really
want breadcrumbs. It's nice to have when you see something
every five minutes pinging on a road, but people want
to know two things are there. Is there shipment going
to be late or is it going in the wrong
(18:12):
direction potentially stolen. And if we can get to the
point that we can actually say, hey, your shipment is
on time, it's en route uh, and then say that
again a few hours down the road, and again and
we can do that all without needing any type of
enabling or the manual sharing, then I think that's where
we want to be. It's just going to take a
little bit more time for us, right.
Speaker 1 (18:33):
And does the just the essence of what you guys do,
does it bring up any privacy concerns? You know, because
I guess you are filming the highways and you know,
some people may may or may not like that.
Speaker 2 (18:50):
Great question. It's one we consider deeply before we started
gen logs, and we always wanted to make sure that
anything we did was taking privacy kind of first and
foremost in mind. I think it's important to keep in
mind that there are certainly cameras all over the roads already.
And if you can go to any Department of Transportation
website right now, the five to one to one dot
orgs of Virginia or Texas or Florida, you can see
(19:13):
cars on the roads and you don't know who's actually
driving those cars. You just know that a car is there,
and we kind of took that mentality, I think, thinking
it further. You know, we talked about the ring cam example,
like all these ring cams are overlooking the roads as well.
But if you think about every Tesla on the road
right now having like eight to ten cameras on board
recording twenty four to seven, almost every truck on the
(19:35):
road now has a ford facing dash cam. And if
you actually look at Google Earth and specifically looking at
the Google street View, you can zoom in on trucks
and actually see the same data points that we pull
that Google is now putting that out there on the
road and the time that it was collected that image.
So we treat every truck as if it's autonomous, meaning
(19:57):
we don't know if there's a driver, and we do
not care if it's a driver, because we're not collecting
on the person. We're collecting on the inanimate commercial object
known as this truck that just happened to be at
a time and place. And when we collect that data
over time, you get a really rich understanding of how
let's say Walmart's fleet moves around or Pepsi's Newburn fleet
(20:17):
moves around the roads without knowing the actual people that
we're driving at that time.
Speaker 1 (20:23):
He kind of mentioned that there's cameras everywhere, and there's
even cameras on the road right now. Your network is
you know, stationary. Is it possible for you guys to
tap into those networks, whether it's like, you know, you
want to partner with a large trucking company that has
cameras in their cabs, either you know, forward facing and
road facing cameras. Are are you guys able to tap
(20:46):
into that?
Speaker 2 (20:47):
We are in some discussions to do that. I think
long term we want gen logs to be transformed into
kind of the the all things sensors of trucks on
the roads and however that might be. And I think
we're also looking at in the future, how do we
factor satellites into collecting on trucks that we confuse with
the data that we're collecting at the ground level, so
(21:07):
that we know what is seen by overhead matches, what
we're collecting with higher fidelity from the ground, and then
also looking at some of those existing networks like the
traffic camera networks, which we did evaluate early on in
our journey. The resolution just wasn't good enough to collect
the types of data points we need off the trucks.
But there is potentially, with more R and D, the
(21:28):
ability for us to uniquely fingerprint a truck on one
of our own deployed sensors and then see that same
truck two miles down the road on an existing Department
Transportation network sensor and know that those two are the
same truck, so that we can get closer to that
breadcrumb's type look of truck patterns without needing to deploy
(21:49):
sensors ourselves in all of these different areas.
Speaker 1 (21:53):
Right, Okay, and you know you mentioned there's a use
case for law enforcement, and I'm assuming when someone uses
you it's an enterprise subscription. It's not just like a
one off find my trailer.
Speaker 2 (22:05):
Right, That's correct. Although for anyone in the industry and
anyone listening to this, if you do have a trailer missing,
please do you can go to our website genlogs dot io.
On the upper right. We have a find my Lost
asset button. You can click that submit your details. We
will investigate it for you, and if we find it
we will send it over to you where that was
last scene so you could take action, and we will
(22:27):
do that for anyone in the industry for free for
a number of times, just to essentially give back and
demonstrate the value. I think long term, we hope that
people see that and say, well, I want to make
sure I get ahead of that theft. Let me make
sure I'm dealing with the real trusted carriers that are
out there in the roads. That's what Genlogs can bring
to the table. And if anything ever does go wrong,
(22:49):
we can help you recover it. And but we do
package all of that up on our enterprise subscription model
so that anyone in an organization you mentioned H Robinson earlier,
for example, like AH Robinson, their carrier folks could be
using the platform, their customer facing folks, as well as
their compliance and recovery folks, and all of that could
(23:10):
be done there together. You mentioned law enforcement, it's an
area we're exploring there. We've had law enforcement interest comes
in when there's truck related crimes that they just simply
don't have great data to figure out, especially because trucks
by nature often interstate and might be in Texas one day,
(23:30):
but then not to be seen again for a while,
and trying to understand where that truck came from and
where it's going, especially if you have limited data on
the markings on that truck and are trying to figure out, Hey,
who was the actual registered carrier that was involved in
either this hit and run or this theft or this
fentanyl related type of situation. We can now get involved
(23:53):
in a help law enforcement track down specific trucks.
Speaker 1 (23:56):
And I'm just curious this might be too, just like
a little bunch in the weeds. But like so, if
a shipperd does use you guys to find a lost trailer,
and it's a customer and you find it, it's the
first call to the shipper, it's a law enforcement how
do you know?
Speaker 2 (24:14):
We try to do it simultaneous. We had an incident
of this happened the other day in California where there
was a pet food distribution center. We had an unfortunate
case where a freight broker was involved and was not
using gen logs at the time and therefore tendered the
load to a fictitious carrier that came with an unmarked
truck to go pick up the act. The full truckload
(24:38):
of pet food and stole it. And essentially it turns
out that the trailer that they used was also a
stolen trailer from one of our other customers. So our
investigations team was quickly able to put tunes together. We
were able to find that truck on the roads, find
the trailer that was associated with it, called the actual
(24:59):
trailer owner and say, hey, can you tell us where
this trailer is right now? Because they had tracking on
that trailer, and then we were able to direct law
enforcement to go find where that trailer was and they
got to it too, fine the trailer at that time,
So that'd be an example where multiple parties were involved.
Gen Logs did the quarterbacking of it to help someone
(25:19):
that wasn't even our customer at the time, and since
then they've now signed up to VA gen Laws customer
that broker because they see, hey, we could have helped
them avoid that, but if something does go wrong then
we can help them get to the bottom of it
rather quickly.
Speaker 1 (25:33):
Got ya, And I guess you know from a from
a carrier standpoint, they're using it primarily similar to the
brokerage use case, where you're kind of tracking and looking
at hippers as well.
Speaker 2 (25:53):
The carrier use cases currently are for much larger carriers
that have kind of a larger trailer to truck race,
so they have unsecured trailers in certain areas that people
are stealing or misusing all the time. We're helping them
find get to the bottom of those misuses, although we
long term we do want to help carriers and kind
of go down market to help the smaller carriers. As
(26:15):
you know, ninety seven percent of the truck market is
made up of companies with ten or fewer trucks. And
what we've noticed is an unfortunate uptick in cases in
which bad actors, fraudsters, or thieves are posing as legitimate
truck carriers. So they will pull up to a distribution
(26:35):
center posing as ACME Trucking with a US Department of
Transportation number correctly put on the outside of their truck
that corresponds to ACME Trucking. It just so happens that
they're fraudster and they actually aren't hauling for ACME Trucking,
and a theft will occur, and either the shipp or
the freight broker will put in a negative report on
(26:57):
that carrier when it wasn't them involved. And so there's
a future state in which Genlogs is kind of doing
overwatch for smaller carriers to alert them in real time
whenever someone is posing as them and not actually part
of their fleet and giving them a heads up that, hey,
all of your trucks are on the East coast right now,
(27:19):
but we just saw someone on the West coast that
has your Department of Transportation number on the outside of
their truck that doesn't seem to be associated, and let's
get ahead of that before they do damage to your brand.
Speaker 1 (27:29):
And I guess is the biggest competitor or risk to
the model is that there's a sensor in every truck
at every trailer.
Speaker 2 (27:37):
Is that well, I would say that that already exists.
And back in twenty twelve, Congress mandated that all trucks
in America have this electronic logging device in their trucks
that are collecting their location, speed and all of that
all the time. Now, all of that data is stovepiped
within those trucking companies. It's not mandated to be shared publicly.
(27:58):
It just has to be stored and be able to
records turned over to authorities upon request. So I would
almost say that almost every large fleet and even small
fleet has trackers on their trucks, so the data is
out there, it's just not required to be in one place.
And that's been why genlogs emerged in the scene and
has had the growth we've seen, is because because that
(28:20):
data is all stovepipe, our ability to collect it on
all trucks breaks down those stovepipes and we get full
visibility and transparency into the market without needing anyone to
share data with us.
Speaker 1 (28:32):
And could you just talk about the genesis of genlogs
between you and your co founders, like, you know, you
guys playing poker and someone said, hey, you know what
we should do? Like how did the idea come about?
Speaker 2 (28:43):
Well, we talked about my background the US intelligence community,
and the other two co founders have similar backgrounds where
they worked at times with the intelligence community. I myself
spent a spell of my career at CIA conducting counter
terarism operations, and my job was to fruit sources in
these foreign terists networks to give us information. Now before
(29:04):
we could validate or vet that information or act on it,
we would see what they told us and how that
looked about what we were seeing from sensors, satellites, or
even hearing from other sources. So we called this an
all source intelligence approach to vetting the new information that
we received and then when we could actually validate that,
then we could act on it. And so it was
(29:24):
a process by which we took data from disparate sources
stitched it all together. In fact, we were doing what
pollunteer does now for the US government. We had kind
of pioneered that before Pollunteer came onto the scene, and
as we were starting to think about, hey, where is
there an application for what we were doing in the
counter terrorism field to potentially in private industry. It was
at a time where I opened up I think it
(29:45):
was the Economist or potentially in Bloomberg and saw something
about the increase in truck fraud and theft, and there's
thirty billion dollars of cargo theft going on in the world,
but specifically to trucking, there was this upswing of that
was leading the theft known as double brokering, and I
just went down a rabbit hole there and it was
just so crystal clear to me that the methods that
(30:07):
we had used in the counter terrorism space could now
be applied and kind of this all source intelligence gathering
and stitching to ensure that what people were telling us
could actually be validated, and that led us down this road,
and we kind of liked to joke at Genlogs we
used to track terrorists and now we track trucks. And
it's essentially just taken the methodology that we did for
(30:27):
two decades at the Global War and tear and applying
it domestically on trucking.
Speaker 1 (30:32):
Got youa So you've worked both in the private and
public sector during your career, obviously you know very different
kinds of organizations. Can you talk a little bit about
what you enjoy most about your current role at Genlogs.
Speaker 2 (30:46):
Yeah, I think there's a lot to love. First of all,
it's the people that have joined Genlogs from of the
most brilliant data scientists, engineers, operation folks that are working
on a frankly, a really tough problem. I think a
lot of investors thought we were kind of crazy. Early
on we talked about getting this nationwide network of hardware
deployed in order to collect on trucks and yet censored
(31:07):
by censor by sensor. We've deployed that, so one is
just a privilege to be able to work with incredibly
talented and bright people, but number two, to see that
data that we collect be transformed into insights that is
truly driving customers business forward. I think that is where
I just love that each and every day is when
we customers on their own accord write to us by
(31:29):
email say I just want to let you know I
love your platform, and it helped us do X, Y
or Z that resulted in this recovery of this stolen trailer,
this new customer opportunity that was a six figure opportunity
that was only sourced directly to you or hey. Our
ability to now see trucks on the roads with the
most niche equipment to carry heavy haul, or you know,
(31:52):
satellites for SpaceX or whatever it might be that they
need to get from point A to point B that
before they were kind of stuck, but now they have
actual data to make those decisions. Like I just love
hearing that from our customers.
Speaker 1 (32:06):
O got ya, And I'm just going curious, you know,
because you know we were talking about the use cases,
So is the brokerage community currently your largest market.
Speaker 2 (32:16):
That's been where we've focused and that's where we've seen
the most traction we've We've we just have ramped up
completely on the brokerage community. We now have more than
fifty percent of the top twenty brokerages are actively on
our platform right now. We continue to expand out through
the rest of the top one hundred and beyond. The
traction's been quick there. We've just started to look at
(32:39):
what happens beyond there and breaking into the commercial auto
insurance space. Now we have two of the larger commercial
auto insurance companies that are our customers as well, that
are using the data that we're collecting in the roads
via APIs to automate their underwriting process. Given that we
are able to deliver into their hands about one thousand
(33:00):
times more data points than they historically had on trucks
gathered through US Department of Transportation inspection data. So for them,
it's a completely huge shift in terms of orders of
magnitude of more data that they can make better underwriting decisions.
Speaker 1 (33:17):
Right and there are a lot of trucking companies like
you know, jbon Knight Warner that had their own brokerage units.
So if one of those large players are using the platform,
are they using across their businesses or is it kind
of just rank fenced around the brokerage business.
Speaker 2 (33:33):
We often will go in start through the brokerage business,
and once the brokerage business gets to see what we're
able to do, then they'll bring in their their asset
tracking folks, they'll bring in their customer sales folks. And
within some of those organizations you mentioned, we are in
with them. Some of them have been acknowledged, like Werner
and others that are using us across the spectrum of
(33:54):
their operations now great, and this.
Speaker 1 (33:56):
Is a question I always like to kind of end
the conversation with. I'm always interested to hear what people
are reading, and I know our listeners like to seek
out books that they might have not heard before. You
Do you have any favorite books on either transportation, business
or leadership that's kind of close to your heart?
Speaker 2 (34:14):
You know one on transportation that I read recently that
was kind of maybe outside your normal transportation topics was
a book called Long Haul, written by an Associate Deputy
Director of the FBI, Frank Fabuzi I believe his name is,
and he talks about the fact that there are over
eight hundred serial murders that they believe are linked to
(34:35):
long haul trucking over the last two decades, and they
believe there are some active, unfortunately cases ongoing that the
FBI just never had a response. They formed a task
force to try to track it down, but they lacked
the data to actually act on this. And that's part
of what Genlog's mission is is to keep the bad
actors out so that the good actors can flourish, because
(34:56):
ninety nine percent of the truck drivers on the roads
and nine percent of brokerages out there are just trying
to do great work and a few bad actors are
spoiling it and making it more difficult and more costly
for them. And that was an eye opening book to
just read. Frankly, it brought us through what the lens
of someone that does long haul trucking is. So the
author hops in with a long haul trucker and follows
(35:17):
him around and then talks through the law enforcement eyes
of what they're working on as well. So that was
an interesting read and I give that out to all
new employees that join gen logs. Yeah, so I'll kind
of leave it there.
Speaker 1 (35:31):
All right, that's great. I'ven't heard about that one, so
I'll definitely check that out and you know, I know
as an entrepreneur and you know, a co founder early
on in the company, the job is more or less
a twenty four hour, seven day a week kind of job.
But you know, we do have some some rye in time,
we'll call it. What do you like to do?
Speaker 2 (35:51):
You know, I got a farm out in the Shenandoah
River in Virginia. That's my pride and joye. So I
like to just spend time out there away from a
computer chopping, would enjoy nature. And so it's very few
and far between that I do get that that these days,
but that's where I love to spend my time.
Speaker 1 (36:09):
So you say a farm, is it a working farm?
Speaker 2 (36:11):
It's not a working farm per se. It used to be.
Right now it's farm for hay. But it's absolutely peaceful
and we'll see once again if we could turn it
into a little bit more of a working farm in
the future. I like to like to say that beyond
genlogs and beyond doing counter terrorism, I would love to
just be a peaceful farmer someday.
Speaker 1 (36:29):
Just like George Washington.
Speaker 2 (36:30):
You got it all right, Well.
Speaker 1 (36:32):
Thanks so much for your time. I thought the conversation
was great and it was really interesting to learn more
about genlog, So thanks for that. Ryan.
Speaker 2 (36:40):
Yeah, thanks Lee, thanks for having me all.
Speaker 1 (36:42):
Right, and I want to thank you for tuning in.
If you liked the episode, please subscribe and laver review.
We've lined up a number of great guests for the podcast,
so please check back to your conversations with C suite executives, shippers, regulators,
and decision makers within the freight markets. Also, if you
want to learn more about the freight transportation market, check
out our work on the Bloomberg Terminal at big or
(37:03):
on social media. Take care of let's keep those supply
chains moving. Thanks
Hi everyone, this is Lee Clascow and we're Talking Transports.
Welcome to the Bloomberg Intelligence Talking Transports podcast. I'm your host,
Lee Klaskows, Senior freight transportation logistics Analysts at Bloomberg Intelligence,
Bloomberg's in house research arm of almost five hundred analysts
and strategists around the globe. Before diving in a little
public service announcement, your support is instrumental to keep bringing
(00:30):
great guests and conversations to you, our listeners, and we
need your support. So please, if you enjoy this podcast,
share it, like it, and leave a comment. Also, if
you have any ideas for future episodes or just want
to talk transports, please hit me up on the Bloomberg terminal,
on LinkedIn or on Twitter at Logistics. Lee. Now on
to the episode. We're delighted to have Ryan Joyce with
(00:52):
us today. He's the CEO of Genlogs. Ryan previously spent
over fifteen years in the US intelligence community conducting counter
terrorism operations throughout the Middle East. Ryan holds a BA
from James Madison University, So go dukes. Ryan. Thanks so
much for being on the podcast with us today.
Speaker 2 (01:11):
Lee, It's great to join. Thanks so much for having me.
Speaker 1 (01:14):
All right, So you were in the intelligence industry and
now you're tracking trucks. So for those that don't know,
can you just give us a little background about what
gen logs is all about.
Speaker 2 (01:26):
Absolutely, we are a truck intelligence platform, which really means
that we track all the patterns and locations of trucks
as they move around North America. Now there's a number
of reasons why we would do that. First and foremost,
there's one hundred and sixty billion dollar freight brokerage industry
that's that intermediary between shippers and the truck fleets or
(01:46):
carriers around the country, trying to make sure that they
can match with each other more efficiently. But there's just
never been one place that you can go to figure
out which truck operates in certain regions with the right equipment.
And that's where gen l comes online. We take what
historically used to have to be done through numerous phone
calls and a lot of manpower. We now automate that
(02:07):
so it's all in one place, right.
Speaker 1 (02:10):
And I got to meet you guys at an event.
I'm trying to think it it was a manifest conference, Yeah,
and it was I found out to be very intriguing
just your your overall business. You are a new company,
an emerging company, and I think the technologies was pretty interesting.
So you know, just before we go into the technology
(02:32):
and a little more detail, can can you talk about
the ownership like and how you got on board on
gen Logs.
Speaker 2 (02:41):
Sure, we founded the company back in April of twenty
twenty three, so we're coming up on our two year
anniversary next month with myself and two co founders, Blake
Balch and Joe Sherman. We all had worked together at
a previous company when we had the idea for gen Logs,
and we have raised venture capital through a few successive
rands about twenty one million dollars total the Genlogs. The
(03:04):
we're still controlling of the company and the board, although
we have fantastic partners when it comes to some of
our investors on our cap table. Two of our board
members from Venrock and Steel Atlas have been wonderful as well.
Speaker 1 (03:18):
Okay, great, and I guess you're in the building mode,
so I guess the goal is to just continue to
build out. Are you guys in the market for investors
now or you're kind of happy where you are right now?
From a financial standpoint.
Speaker 2 (03:36):
We're pretty happy where we are. We have some capital
to deploy, and we do have somewhat of a capital
intensive business in the sense of the way we track
trucks as we deploy sensors all over North America along
major interstates, freight lanes, highways. Each one of these have
cameras that collect on the front side and rear of
(03:56):
trucks as they pass by. So that takes some capital
expenditure just to get this network built. But given we
just closed our last round of funding in December of
last year, we're still in good shape for a while,
and we'll start having investor conversations again around Q three
of this year.
Speaker 1 (04:14):
All right, let's talk about the network. So are you
So I'm assuming you're not all over the country yet,
because you're still building it out roughly, Where are you
in the buildout in the US, because I know you
were also eyeing other places in North America.
Speaker 2 (04:31):
Absolutely, I would say right now we're about seventy percent
of the targeted build out, and by that our full
buildout in the United States, the lower forty eight is
really to get to the point that we are collecting
on more than ninety percent of trucks that are on
the roads each day, we're seeing them two or more times.
And the way we kind of targeted trying to redeploy
(04:52):
this network is we took essentially a map of how
trucks move around the United States and volumes on different roads.
We targeted the most are the highest traffic volume first
and then are moving out to the edges. And so
we're seventy percent of trucks on the road each day
we do see in our data, and we're now pushing
that closer to that ninety to hopefully, you know, eventually
(05:14):
one hundred percent level, while also eyeing expansion down into
Mexico and up into Canada soon.
Speaker 1 (05:20):
So currently you're not in Mexico or Canada that the
plan is to get to get there.
Speaker 2 (05:24):
We have locations that we've secured to get our sensors
out there. We're working a little bit. There's it's quite
a lot that goes into making sure that the data
of which we're collecting terabytes today flowing through these cameras
can be processed at the edge and the data brought back,
and it's that data back hall that we're just looking
(05:45):
to solve. We've solved that throughout the United States and
looking to solve that in Canada and Mexico as well,
so that we can actually make sure that once we
deploy these sensors out there, we can get the data
back reliably and in real time.
Speaker 1 (05:57):
So before we talk about the software, I'm just so
intrigued about the hardware. So are these like video cameras?
Are they like just taking Is there a sensor that
just takes a picture when something moves by? How does
the hardware work, Who maintains the hardware? How do you
figure out where to go? And kind of what I
guess what are those leasing arrangements like? And are you
(06:19):
on like billboards? Are you where are you?
Speaker 2 (06:22):
Yeah? We started out with a theory that if we
could get enough density of sensors throughout the United States
on private property, then public entities that want access to
the same data to do counter human trafficking, counter narcotics
or fentanyl work would also then work with us to
deploy in additional areas. And that theory has played out
in the sense that we did start exclusively on private property,
(06:45):
so we are on the sides of warehouses or barns,
or on telecommunications structure wherever there's a structure within a
certain proximity to a highly truck trafficked lane, that's where
we look to deploy. Now, we have high requirements with
regards to the clear lines of sight to trucks as
they approach the side and then depart the area. We
(07:06):
need certain power requirements, a certain amount of data back
hall capabilities. So it takes the forty six thousand miles
of U in a center state and actually makes it
a very small subset of that that would work for
our purposes. And when it does work for our purposes,
we will go out and approach private property owners to
pay a monthly lease to host our sensors. These things
are nothing more than a giant ring cam, essentially, in
(07:29):
the way that a ring camera might sit at the
front of your house, overlooking the road in front of
your house, we're sitting on property overlooking public roadways and
collecting the data there. It's not motion sensing in the
sense that there are a classic emotion sensor that stands
apart to trigger the image collection we record twenty four
(07:50):
to seven, But we deploy truck detecting models or AI
computer vision models at the edge that work hand in
hand with our cameras to filter out all private vehicles
and only store images where trucks are seen present in
the image, and then pull that data back for additional processing.
Speaker 1 (08:10):
And what's the average length of these leases that you
have with these private property owners.
Speaker 2 (08:15):
We'd love to do them as long as possible and
as exclusive as possible. So we're on decades long leases
often with many of these property owners.
Speaker 1 (08:23):
And you know, obviously with anything that's technology, something could
go wrong, whether you know, I guess a bug gets
squished on the lens, or weather or whatnot. How are
you maintaining these sensors?
Speaker 2 (08:39):
As they say hardware is hard, and we would have
to agree with them. We've had, you know, different issues
with whether it's tornadoes coming through certain areas that bring
them down, We've had power outages in the areas, and
you're right, we have had certain issues with bird scat
on some of the lenses too. We have a multiple
(09:00):
field technician teams that are crisscrossing the country each and
every day with bucket trucks that go and both install
these sensors as we deploy them, but then also maintain
them as well. We have pretty rigorous standards for our
uptime both internally as a company and what we want
to deliver for our customers, and so we are often
(09:21):
making tweaks to some of the mechanics within these sensors
to ensure that that uptime can be maintained.
Speaker 1 (09:28):
And you said it's kind of like a ring camera.
Is that actually the size of it? Is it like
smaller than a shoe box?
Speaker 2 (09:34):
No, it's actually quite a bit bigger. And each one
of these cameras are HD cameras that collect ten frames
per second. I mean, they're your maybe security camera looking
type type sensors that we have now they do as
a result. You know, they could be on the side
of a warehouse and you would never know that the
(09:56):
purpose of them is actually to be looking at some
of the truck patterns nearby. And actually, our partners are
real estate partners, enjoy and prefer those cameras because it
does do the double duty of deterring theft in the area.
When it does look like security cameras attached, We kind
of tuck away all of the parts into a smaller
box off to the side that does a lot of
(10:18):
the processing and the data xfilm gotcha.
Speaker 1 (10:22):
And roughly how many employees do you have at gen Lux.
Speaker 2 (10:26):
We have about fifty employees now when you count some
of our part time employees too that work with us,
but on the whole in terms of both W two
and ten ninety nine, we're about fifty now.
Speaker 1 (10:37):
Okay, And is it like spill like half people in
the field and half programmers and management.
Speaker 2 (10:43):
It makes me breaks down at about thirds. A third
is our sensor deployment team, and that includes both those
that are sourcing the leases for us to get our
sensors out, planning for the sensor deployments, and then the
field technician crews that are going and actually installing and maintains.
Another third of the team are engineers and data scientists
(11:04):
taking all of that data that's coming off of those sensors,
fusing it with other third party data sets that we
go out and collect, and then building the actual product
that we put in our customers hands. And then the
last third is a mixture of our sales and our
customer success folks, ensuring that the product hits the market
or exceeds in all cases what customers are expecting to get.
Speaker 1 (11:26):
Gotcha, And I'm assuming that most of the equipment you
guys are buying it from third parties and putting it
together per your own specs.
Speaker 2 (11:35):
This is all US manufacturers. We have multiple manufacturers that
we work with to take the concept of what we
needed to accurately collect on trucks as they fly by
seventy miles per hour in a road, especially when some
of the data we collect off them only need to
be about by law about one inch high some of
the US Department of Transportation numbers on the sides and
other marketings. So in order to get that, we had
(11:57):
to really ensure that we were working hand in hand
and with manufacturing partners to ensure that the entire process
could actually collect the data at that high speeds. And
so we do work with with US manufacturers to get
these built and then deployed.
Speaker 1 (12:13):
All right, So I guess that's a good transition. Let's
talk about the technology. You know, you did mention a
nice buzzword AI. You know you put that in your
emission statement. You probably get a couple turns on your
valuation in today's market, but it truly is machine learning
AI enabled. You can you talk about, you know, what
(12:35):
you're looking for and the kind of use cases for genalogs.
Speaker 2 (12:41):
Yeah, I would say we're starting to service a number
of different vertocols. We started with just focused on that
freight brokerage industry in the United States. Very quickly we
got connected with some very large shippers throughout the United
States that have trailers or different other assets themselves that
were missing, and we demonstrated early on on our ability
to find those in the wild where maybe a trailer
(13:04):
full of toys or electronics had been stolen and these
shippers were trying to track down who the thieves were.
There was no recourse for that back in the day.
Now Genlogs has the ability to set an alert on
a specific trailer that you're looking for by number, by logo,
and when that is seen anywhere in the United States,
you'll get an instant alert that also tells you what
(13:25):
the truck that is carrying that right now, where it is,
the direction. So that was another use case that has
expanded in terms of this asset location and recovery. Beyond that,
we are now dealing with the commercial auto insurance industry
helping them with better underwriting, continuous monitoring, as well as
claims investigations, and we are looking for a further expansion
(13:46):
into the federal and state government space as well as
financial institutions. So there's a lot of different use cases
for understanding the truck activity in the United States. If
you think about how goods flow to and from the
United States, they might be flown, they might be on ships,
but at some point they touch a truck. And that's
really why we've doubled down on the truck intelligence aspects
(14:09):
of what we're doing. Fourteen trillion dollars of goods filters
through the US economy each year with goods on trucks,
and that's why we focus there specifically.
Speaker 1 (14:20):
So if I'm a freight broker like a sh Robinson
or an RXO, what is the use case? So, am
I is it just for tracking?
Speaker 2 (14:29):
It's actually tracking might come later right now. When we
work with freight brokerages, there's kind of three use cases
we focus on. First is we focus on these carrier
sales process. So traditionally, these freight brokerages exist because they
can see the long tail of the market that involves
them employees armies of young professionals to pick up the
(14:50):
phone every day call lists of truck carriers and ask
them a few basic questions like how many trucks do
you have, where do you like to operate? Where are
your trucks? Now, what type of a equipment do you
have with those trucks? We, by just nature of seeing
everything that's happening on the roads, know the answers to
all those questions, so we can absolutely cut down on
the number of people that need to be making those
(15:11):
calls or in the number of calls that have to
be made each day. So essentially going from a very
opaque process of understanding where trucks are to now a
transparent process for a H. Robinson for example, to fined.
So that's that carrier sales side. We then fuse the
data we collect on trucks out there in the roads
with other additional data sets in order to map back
to where those trucks came from their origins to their destinations. Now,
(15:35):
when you do that at scale, you get to understand
the shipper patterns and networks in the United States. So
we can take a Coca Cola, for example, and understand
where all of their suppliers are and where the trucks
come from from their suppliers to a Cocoa Cola bottling
plant for example, and then where the trucks go from
that Coco Cola bottling plant to then deliver to the
(15:57):
end locations. Grocery stores can be stores, So that's data
that we also have aggregated together with our AI. So
we then give h Robinson or other types of third
party logistics or freight brokerages insights into the shipper networks
and therefore those are the sh those are the customers.
And then lastly there's a compliance piece to this all
(16:18):
that we can validate that a truck carrier that says
that it operates on the roads actually does have the
equipments that they claim, almost making sure that the digital
footprint or what is claimed matches the physical footprint of
the actual ground truth. And so that's the compliance we
look when we deal with freight brokerages, we look at
carrier customers in compliance and we have a holistic solution
(16:41):
for them.
Speaker 1 (16:42):
And so tracking might be down the road. Can you
just expand on what you meant by that.
Speaker 2 (16:47):
I think there's a little bit has to do with
censored density. We're not at the point yet where, like
I said, our goal is to get to the point
that over ninety percent of the trucks on the roads
we're seeing two or more times, three, four, five times.
There's just going to be some additional sensors we need
to deploy. There's going to be additional work we need
to do to make sure that we can take the
(17:07):
different types of data points that we collect on a
truck and tie that down to the vin level so
that we know that it's not just acme trucking writ
large that we saw at a certain place, but it's
this specific truck or specific trailer that is underload that
is traveling from Dallas to Chicago that we see it
outside the confines of Dallas, midway in Missouri or Illinois
(17:30):
heading into Chicago. I think once we get to that point,
we will be in a position to automate a lot
of the tracking that occurs that currently necessitates providing the
actual permission of the electronic logging device or some other
type of cell phone tracking that has to be manually
enabled by a driver or fleet. We will be able
(17:51):
to automate that across all roads without needing that manual enabling.
And really, what we've found is that people don't really
want breadcrumbs. It's nice to have when you see something
every five minutes pinging on a road, but people want
to know two things are there. Is there shipment going
to be late or is it going in the wrong
(18:12):
direction potentially stolen. And if we can get to the
point that we can actually say, hey, your shipment is
on time, it's en route uh, and then say that
again a few hours down the road, and again and
we can do that all without needing any type of
enabling or the manual sharing, then I think that's where
we want to be. It's just going to take a
little bit more time for us, right.
Speaker 1 (18:33):
And does the just the essence of what you guys do,
does it bring up any privacy concerns? You know, because
I guess you are filming the highways and you know,
some people may may or may not like that.
Speaker 2 (18:50):
Great question. It's one we consider deeply before we started
gen logs, and we always wanted to make sure that
anything we did was taking privacy kind of first and
foremost in mind. I think it's important to keep in
mind that there are certainly cameras all over the roads already.
And if you can go to any Department of Transportation
website right now, the five to one to one dot
orgs of Virginia or Texas or Florida, you can see
(19:13):
cars on the roads and you don't know who's actually
driving those cars. You just know that a car is there,
and we kind of took that mentality, I think, thinking
it further. You know, we talked about the ring cam example,
like all these ring cams are overlooking the roads as well.
But if you think about every Tesla on the road
right now having like eight to ten cameras on board
recording twenty four to seven, almost every truck on the
(19:35):
road now has a ford facing dash cam. And if
you actually look at Google Earth and specifically looking at
the Google street View, you can zoom in on trucks
and actually see the same data points that we pull
that Google is now putting that out there on the
road and the time that it was collected that image.
So we treat every truck as if it's autonomous, meaning
(19:57):
we don't know if there's a driver, and we do
not care if it's a driver, because we're not collecting
on the person. We're collecting on the inanimate commercial object
known as this truck that just happened to be at
a time and place. And when we collect that data
over time, you get a really rich understanding of how
let's say Walmart's fleet moves around or Pepsi's Newburn fleet
(20:17):
moves around the roads without knowing the actual people that
we're driving at that time.
Speaker 1 (20:23):
He kind of mentioned that there's cameras everywhere, and there's
even cameras on the road right now. Your network is
you know, stationary. Is it possible for you guys to
tap into those networks, whether it's like, you know, you
want to partner with a large trucking company that has
cameras in their cabs, either you know, forward facing and
road facing cameras. Are are you guys able to tap
(20:46):
into that?
Speaker 2 (20:47):
We are in some discussions to do that. I think
long term we want gen logs to be transformed into
kind of the the all things sensors of trucks on
the roads and however that might be. And I think
we're also looking at in the future, how do we
factor satellites into collecting on trucks that we confuse with
the data that we're collecting at the ground level, so
(21:07):
that we know what is seen by overhead matches, what
we're collecting with higher fidelity from the ground, and then
also looking at some of those existing networks like the
traffic camera networks, which we did evaluate early on in
our journey. The resolution just wasn't good enough to collect
the types of data points we need off the trucks.
But there is potentially, with more R and D, the
(21:28):
ability for us to uniquely fingerprint a truck on one
of our own deployed sensors and then see that same
truck two miles down the road on an existing Department
Transportation network sensor and know that those two are the
same truck, so that we can get closer to that
breadcrumb's type look of truck patterns without needing to deploy
(21:49):
sensors ourselves in all of these different areas.
Speaker 1 (21:53):
Right, Okay, and you know you mentioned there's a use
case for law enforcement, and I'm assuming when someone uses
you it's an enterprise subscription. It's not just like a
one off find my trailer.
Speaker 2 (22:05):
Right, That's correct. Although for anyone in the industry and
anyone listening to this, if you do have a trailer missing,
please do you can go to our website genlogs dot io.
On the upper right. We have a find my Lost
asset button. You can click that submit your details. We
will investigate it for you, and if we find it
we will send it over to you where that was
last scene so you could take action, and we will
(22:27):
do that for anyone in the industry for free for
a number of times, just to essentially give back and
demonstrate the value. I think long term, we hope that
people see that and say, well, I want to make
sure I get ahead of that theft. Let me make
sure I'm dealing with the real trusted carriers that are
out there in the roads. That's what Genlogs can bring
to the table. And if anything ever does go wrong,
(22:49):
we can help you recover it. And but we do
package all of that up on our enterprise subscription model
so that anyone in an organization you mentioned H Robinson earlier,
for example, like AH Robinson, their carrier folks could be
using the platform, their customer facing folks, as well as
their compliance and recovery folks, and all of that could
(23:10):
be done there together. You mentioned law enforcement, it's an
area we're exploring there. We've had law enforcement interest comes
in when there's truck related crimes that they just simply
don't have great data to figure out, especially because trucks
by nature often interstate and might be in Texas one day,
(23:30):
but then not to be seen again for a while,
and trying to understand where that truck came from and
where it's going, especially if you have limited data on
the markings on that truck and are trying to figure out, Hey,
who was the actual registered carrier that was involved in
either this hit and run or this theft or this
fentanyl related type of situation. We can now get involved
(23:53):
in a help law enforcement track down specific trucks.
Speaker 1 (23:56):
And I'm just curious this might be too, just like
a little bunch in the weeds. But like so, if
a shipperd does use you guys to find a lost trailer,
and it's a customer and you find it, it's the
first call to the shipper, it's a law enforcement how
do you know?
Speaker 2 (24:14):
We try to do it simultaneous. We had an incident
of this happened the other day in California where there
was a pet food distribution center. We had an unfortunate
case where a freight broker was involved and was not
using gen logs at the time and therefore tendered the
load to a fictitious carrier that came with an unmarked
truck to go pick up the act. The full truckload
(24:38):
of pet food and stole it. And essentially it turns
out that the trailer that they used was also a
stolen trailer from one of our other customers. So our
investigations team was quickly able to put tunes together. We
were able to find that truck on the roads, find
the trailer that was associated with it, called the actual
(24:59):
trailer owner and say, hey, can you tell us where
this trailer is right now? Because they had tracking on
that trailer, and then we were able to direct law
enforcement to go find where that trailer was and they
got to it too, fine the trailer at that time,
So that'd be an example where multiple parties were involved.
Gen Logs did the quarterbacking of it to help someone
(25:19):
that wasn't even our customer at the time, and since
then they've now signed up to VA gen Laws customer
that broker because they see, hey, we could have helped
them avoid that, but if something does go wrong then
we can help them get to the bottom of it
rather quickly.
Speaker 1 (25:33):
Got ya, And I guess you know from a from
a carrier standpoint, they're using it primarily similar to the
brokerage use case, where you're kind of tracking and looking
at hippers as well.
Speaker 2 (25:53):
The carrier use cases currently are for much larger carriers
that have kind of a larger trailer to truck race,
so they have unsecured trailers in certain areas that people
are stealing or misusing all the time. We're helping them
find get to the bottom of those misuses, although we
long term we do want to help carriers and kind
of go down market to help the smaller carriers. As
(26:15):
you know, ninety seven percent of the truck market is
made up of companies with ten or fewer trucks. And
what we've noticed is an unfortunate uptick in cases in
which bad actors, fraudsters, or thieves are posing as legitimate
truck carriers. So they will pull up to a distribution
(26:35):
center posing as ACME Trucking with a US Department of
Transportation number correctly put on the outside of their truck
that corresponds to ACME Trucking. It just so happens that
they're fraudster and they actually aren't hauling for ACME Trucking,
and a theft will occur, and either the shipp or
the freight broker will put in a negative report on
(26:57):
that carrier when it wasn't them involved. And so there's
a future state in which Genlogs is kind of doing
overwatch for smaller carriers to alert them in real time
whenever someone is posing as them and not actually part
of their fleet and giving them a heads up that, hey,
all of your trucks are on the East coast right now,
(27:19):
but we just saw someone on the West coast that
has your Department of Transportation number on the outside of
their truck that doesn't seem to be associated, and let's
get ahead of that before they do damage to your brand.
Speaker 1 (27:29):
And I guess is the biggest competitor or risk to
the model is that there's a sensor in every truck
at every trailer.
Speaker 2 (27:37):
Is that well, I would say that that already exists.
And back in twenty twelve, Congress mandated that all trucks
in America have this electronic logging device in their trucks
that are collecting their location, speed and all of that
all the time. Now, all of that data is stovepiped
within those trucking companies. It's not mandated to be shared publicly.
(27:58):
It just has to be stored and be able to
records turned over to authorities upon request. So I would
almost say that almost every large fleet and even small
fleet has trackers on their trucks, so the data is
out there, it's just not required to be in one place.
And that's been why genlogs emerged in the scene and
has had the growth we've seen, is because because that
(28:20):
data is all stovepipe, our ability to collect it on
all trucks breaks down those stovepipes and we get full
visibility and transparency into the market without needing anyone to
share data with us.
Speaker 1 (28:32):
And could you just talk about the genesis of genlogs
between you and your co founders, like, you know, you
guys playing poker and someone said, hey, you know what
we should do? Like how did the idea come about?
Speaker 2 (28:43):
Well, we talked about my background the US intelligence community,
and the other two co founders have similar backgrounds where
they worked at times with the intelligence community. I myself
spent a spell of my career at CIA conducting counter
terarism operations, and my job was to fruit sources in
these foreign terists networks to give us information. Now before
(29:04):
we could validate or vet that information or act on it,
we would see what they told us and how that
looked about what we were seeing from sensors, satellites, or
even hearing from other sources. So we called this an
all source intelligence approach to vetting the new information that
we received and then when we could actually validate that,
then we could act on it. And so it was
(29:24):
a process by which we took data from disparate sources
stitched it all together. In fact, we were doing what
pollunteer does now for the US government. We had kind
of pioneered that before Pollunteer came onto the scene, and
as we were starting to think about, hey, where is
there an application for what we were doing in the
counter terrorism field to potentially in private industry. It was
at a time where I opened up I think it
(29:45):
was the Economist or potentially in Bloomberg and saw something
about the increase in truck fraud and theft, and there's
thirty billion dollars of cargo theft going on in the world,
but specifically to trucking, there was this upswing of that
was leading the theft known as double brokering, and I
just went down a rabbit hole there and it was
just so crystal clear to me that the methods that
(30:07):
we had used in the counter terrorism space could now
be applied and kind of this all source intelligence gathering
and stitching to ensure that what people were telling us
could actually be validated, and that led us down this road,
and we kind of liked to joke at Genlogs we
used to track terrorists and now we track trucks. And
it's essentially just taken the methodology that we did for
(30:27):
two decades at the Global War and tear and applying
it domestically on trucking.
Speaker 1 (30:32):
Got youa So you've worked both in the private and
public sector during your career, obviously you know very different
kinds of organizations. Can you talk a little bit about
what you enjoy most about your current role at Genlogs.
Speaker 2 (30:46):
Yeah, I think there's a lot to love. First of all,
it's the people that have joined Genlogs from of the
most brilliant data scientists, engineers, operation folks that are working
on a frankly, a really tough problem. I think a
lot of investors thought we were kind of crazy. Early
on we talked about getting this nationwide network of hardware
deployed in order to collect on trucks and yet censored
(31:07):
by censor by sensor. We've deployed that, so one is
just a privilege to be able to work with incredibly
talented and bright people, but number two, to see that
data that we collect be transformed into insights that is
truly driving customers business forward. I think that is where
I just love that each and every day is when
we customers on their own accord write to us by
(31:29):
email say I just want to let you know I
love your platform, and it helped us do X, Y
or Z that resulted in this recovery of this stolen trailer,
this new customer opportunity that was a six figure opportunity
that was only sourced directly to you or hey. Our
ability to now see trucks on the roads with the
most niche equipment to carry heavy haul, or you know,
(31:52):
satellites for SpaceX or whatever it might be that they
need to get from point A to point B that
before they were kind of stuck, but now they have
actual data to make those decisions. Like I just love
hearing that from our customers.
Speaker 1 (32:06):
O got ya, And I'm just going curious, you know,
because you know we were talking about the use cases,
So is the brokerage community currently your largest market.
Speaker 2 (32:16):
That's been where we've focused and that's where we've seen
the most traction we've We've we just have ramped up
completely on the brokerage community. We now have more than
fifty percent of the top twenty brokerages are actively on
our platform right now. We continue to expand out through
the rest of the top one hundred and beyond. The
traction's been quick there. We've just started to look at
(32:39):
what happens beyond there and breaking into the commercial auto
insurance space. Now we have two of the larger commercial
auto insurance companies that are our customers as well, that
are using the data that we're collecting in the roads
via APIs to automate their underwriting process. Given that we
are able to deliver into their hands about one thousand
(33:00):
times more data points than they historically had on trucks
gathered through US Department of Transportation inspection data. So for them,
it's a completely huge shift in terms of orders of
magnitude of more data that they can make better underwriting decisions.
Speaker 1 (33:17):
Right and there are a lot of trucking companies like
you know, jbon Knight Warner that had their own brokerage units.
So if one of those large players are using the platform,
are they using across their businesses or is it kind
of just rank fenced around the brokerage business.
Speaker 2 (33:33):
We often will go in start through the brokerage business,
and once the brokerage business gets to see what we're
able to do, then they'll bring in their their asset
tracking folks, they'll bring in their customer sales folks. And
within some of those organizations you mentioned, we are in
with them. Some of them have been acknowledged, like Werner
and others that are using us across the spectrum of
(33:54):
their operations now great, and this.
Speaker 1 (33:56):
Is a question I always like to kind of end
the conversation with. I'm always interested to hear what people
are reading, and I know our listeners like to seek
out books that they might have not heard before. You
Do you have any favorite books on either transportation, business
or leadership that's kind of close to your heart?
Speaker 2 (34:14):
You know one on transportation that I read recently that
was kind of maybe outside your normal transportation topics was
a book called Long Haul, written by an Associate Deputy
Director of the FBI, Frank Fabuzi I believe his name is,
and he talks about the fact that there are over
eight hundred serial murders that they believe are linked to
(34:35):
long haul trucking over the last two decades, and they
believe there are some active, unfortunately cases ongoing that the
FBI just never had a response. They formed a task
force to try to track it down, but they lacked
the data to actually act on this. And that's part
of what Genlog's mission is is to keep the bad
actors out so that the good actors can flourish, because
(34:56):
ninety nine percent of the truck drivers on the roads
and nine percent of brokerages out there are just trying
to do great work and a few bad actors are
spoiling it and making it more difficult and more costly
for them. And that was an eye opening book to
just read. Frankly, it brought us through what the lens
of someone that does long haul trucking is. So the
author hops in with a long haul trucker and follows
(35:17):
him around and then talks through the law enforcement eyes
of what they're working on as well. So that was
an interesting read and I give that out to all
new employees that join gen logs. Yeah, so I'll kind
of leave it there.
Speaker 1 (35:31):
All right, that's great. I'ven't heard about that one, so
I'll definitely check that out and you know, I know
as an entrepreneur and you know, a co founder early
on in the company, the job is more or less
a twenty four hour, seven day a week kind of job.
But you know, we do have some some rye in time,
we'll call it. What do you like to do?
Speaker 2 (35:51):
You know, I got a farm out in the Shenandoah
River in Virginia. That's my pride and joye. So I
like to just spend time out there away from a
computer chopping, would enjoy nature. And so it's very few
and far between that I do get that that these days,
but that's where I love to spend my time.
Speaker 1 (36:09):
So you say a farm, is it a working farm?
Speaker 2 (36:11):
It's not a working farm per se. It used to be.
Right now it's farm for hay. But it's absolutely peaceful
and we'll see once again if we could turn it
into a little bit more of a working farm in
the future. I like to like to say that beyond
genlogs and beyond doing counter terrorism, I would love to
just be a peaceful farmer someday.
Speaker 1 (36:29):
Just like George Washington.
Speaker 2 (36:30):
You got it all right, Well.
Speaker 1 (36:32):
Thanks so much for your time. I thought the conversation
was great and it was really interesting to learn more
about genlog, So thanks for that. Ryan.
Speaker 2 (36:40):
Yeah, thanks Lee, thanks for having me all.
Speaker 1 (36:42):
Right, and I want to thank you for tuning in.
If you liked the episode, please subscribe and laver review.
We've lined up a number of great guests for the podcast,
so please check back to your conversations with C suite executives, shippers, regulators,
and decision makers within the freight markets. Also, if you
want to learn more about the freight transportation market, check
out our work on the Bloomberg Terminal at big or
(37:03):
on social media. Take care of let's keep those supply
chains moving. Thanks
Host
Lee Klaskow
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