Oana Jinga
0:05
Probably
the
one
thing
I
wanted
to
touch
on
is
from
our
perspective
.
I
mean
,
the
robot
is
there
to
just
collect
information
,
but
what
you
can
do
with
that
information
,
then
it's
pretty
much
kind
of
endless
and
limitless
,
because
you
can
kind
of
extract
data
and
different
points
,
you
can
compare
it
with
other
data
sources
,
you
can
kind
of
crunch
it
,
you
can
use
it
for
simulation
,
for
forecasting
and
so
on
.
So
normally
the
way
we
kind
of
approach
things
,
our
product
at
the
end
of
the
day
is
the
data
rather
than
the
robot
itself
,
versus
some
of
the
other
solutions
that
I
was
mentioning
earlier
,
that
you
referenced
,
where
the
actual
robot
is
the
one
that's
actually
doing
the
job
.
Blythe Brumleve
0:42
Welcome
into
another
episode
of
Everything
is
Logistics
,
a
podcast
for
the
thinkers
in
freight
.
We
are
proudly
presented
by
SPI
Logistics
and
I
am
your
host
,
Blythe
Brumleve
,
now
today
on
the
show
.
I'm
happy
to
welcome
in
Oana
Jenga
.
She
is
the
co-founder
,
chief
commercial
and
product
officer
over
at
Dexory
,
and
we
are
going
to
be
talking
about
robotics
,
data
and
technology
inside
of
warehouses
.
So
this
is
a
very
timely
discussion
considering
everything
that
,
especially
around
the
explosion
of
e-commerce
orders
and
just
the
adoption
of
tech
overall
within
logistics
.
So
,
Oana
,
welcome
to
the
show
.
Oana Jinga
1:16
Hello
,
thank
you
so
much
for
having
me
and
,
yeah
,
I
really
regret
to
talk
about
all
these
topics
because
obviously
they're
very
,
very
dear
to
my
heart
.
Blythe Brumleve
1:23
Now
I
want
to
take
it
back
to
you
know
,
before
you
got
into
logistics
,
you
spent
I
think
what
was
it
six
years
at
Google
,
so
you
spent
a
long
time
there
.
And
so
I'm
wondering
what
was
the
catalyst
from
working
at
Google
to
saying
I
want
to
start
my
own
company
?
Oana Jinga
1:41
Yeah
,
and
I
think
it
probably
was
way
back
when
I
kind
of
had
that
initial
thought
in
my
mind
.
I
don't
know
why
,
but
even
kind
of
growing
up
I
always
had
this
idea
that
one
day
I'll
have
my
own
company
.
I
don't
think
at
the
time
I
would
ever
think
what
exactly
in
which
sector
,
like
what
exactly
the
company
will
do
and
why
,
but
I
think
always
kind
of
had
this
inkling
and
kind
of
looking
back
.
I
don't
really
know
where
it
came
from
,
because
my
family
it's
kind
of
very
traditional
,
like
doctors
and
lawyers
and
everything
else
and
like
you
have
to
have
a
very
stable
profession
and
all
of
that
,
and
kind
of
went
a
little
bit
against
the
flow
.
But
I
think
my
first
step
into
technology
was
actually
one
of
my
first
jobs
.
Oana Jinga
2:19
I
used
to
work
at
Telefonica
,
so
Telco
,
working
on
some
of
the
digital
products
very
much
in
line
with
,
I
mean
,
all
the
typical
apps
that
we
have
today
,
like
your
chat
apps
and
your
video
calls
apps
.
Oana Jinga
2:32
At
the
time
when
I
started
there
they
were
just
very
much
in
their
infancy
,
so
I
was
part
of
that
initial
kind
of
kickoff
and
boom
of
communication
apps
.
And
then
from
there
I
moved
over
to
Google
,
like
you
said
,
where
I
had
a
chance
to
learn
from
some
fantastic
people
again
going
back
to
building
products
,
taking
them
to
market
and
understanding
what
people
want
and
kind
of
feeling
that
one
with
technology
.
So
it
did
kind
of
prepare
me
very
,
very
well
to
having
the
skills
and
knowledge
and
the
desire
to
build
something
from
scratch
myself
.
But
probably
due
to
mention
,
it's
not
just
me
,
we're
three
co-founders
and
the
other
two
co-founders
are
actually
a
lot
techier
than
me
.
They
are
kind
of
software
developers
,
engineers
and
have
a
very
kind
of
strong
background
into
that
.
So
they
kind
of
came
with
the
angle
of
robotics
and
I
kind
of
came
with
the
angle
of
,
okay
,
robotics
,
so
what
,
and
who's
going
to
pay
and
buy
this
?
And
that's
kind
of
how
Dexory
kind
of
came
together
.
Oana Jinga
3:22
And
that's
kind
of
how
Dexory
kind
of
came
together
,
and
so
I'm
curious
as
to
what
were
some
of
those
,
I
guess
,
lessons
that
you
learned
at
Google
that
still
stick
with
you
to
this
day
.
I
think
I
was
talking
about
that
the
other
week
with
somebody
in
the
team
and
there's
many
,
many
different
things
.
I
mean
some
good
and
some
bad
as
well
.
I
mean
,
google
is
an
extraordinary
company
to
work
with
,
but
sometimes
you're
also
thinking
like
,
oh
,
I
probably
don't
want
to
do
that
in
my
own
company
because
it
wouldn't
work
.
Oana Jinga
3:50
Um
,
but
um
,
starting
from
just
what
I
was
saying
earlier
around
that
idea
on
how
do
you
even
start
when
you
want
to
build
something
,
like
,
where
is
the
insight
?
Oana Jinga
3:58
How
does
that
come
from
the
,
the
customer
,
and
um
,
google
has
this
kind
of
mantra
around
like
know
the
customer
,
know
the
product
and
then
connect
the
two
and
make
magic
.
Basically
,
um
,
so
,
um
,
really
understanding
how
to
to
read
kind
of
people
,
needs
and
wants
,
like
obviously
,
like
all
the
,
the
tools
to
to
bring
that
together
,
but
then
also
trying
to
figure
out
how
you
turn
that
into
into
a
solution
and
the
product
was
probably
like
the
,
the
biggest
learning
.
Um
,
but
then
also
a
lot
of
different
things
around
how
to
manage
big
teams
technology
teams
as
well
versus
like
business
teams
,
and
how
everyone
kind
of
operates
a
little
bit
differently
.
Having
systems
like
well
OKRs
,
like
objectives
and
key
results
in
place
to
be
able
to
guide
everyone
towards
the
same
directions
of
a
company
so
multitude
of
things
.
Yeah
,
going
across
all
the
different
areas
of
a
business
so
a
multitude
of
things
going
across
all
the
different
areas
of
a
business
.
Blythe Brumleve
4:45
And
so
when
you
are
first
coming
up
with
sort
of
the
initial
concept
of
Dexory
,
what
were
some
of
those
,
I
guess
,
those
key
customer
insights
that
you
were
trying
to
solve
for
them
?
Oana Jinga
4:56
So
,
funnily
enough
,
we
actually
didn't
start
in
logistics
.
So
the
background
of
Dexory
is
actually
retail
.
So
we
started
with
having
an
autonomous
robot
that
would
do
stock
checking
and
inventory
taking
inside
retail
stores
.
This
was
just
before
the
pandemic
.
I
mean
,
it
was
going
really
really
well
and
it's
kind
of
like
booming
at
the
time
and
then
hey
ho
,
over
2020
,
everything
kind
of
closed
.
Oana Jinga
5:16
So
we
actually
had
our
retail
customers
from
back
then
ask
us
well
,
obviously
the
stores
are
closed
,
but
we
do
have
a
massive
problem
in
the
warehouse
.
Obviously
,
at
the
time
we
couldn't
even
kind
of
get
people
in
and
they
had
to
be
distanced
and
everything
else
.
So
can
you
actually
do
what
you're
doing
in
the
stores
but
on
the
warehouse
side
and
help
us
keep
track
of
inventory
and
our
stock
?
So
it
started
with
a
customer
ask
and
I
think
ever
since
,
everything
we've
been
building
has
always
kind
of
been
on
top
of
a
customer
need
or
a
customer
question
um
,
yeah
,
our
customer
interest
.
So
it's
always
kind
of
very
important
to
to
try
and
identify
the
early
days
so
you
mentioned
that
the
the
almost
the
robotics
focus
shifted
from
2019
to
2020
.
Blythe Brumleve
5:59
Has
it
evolved
since
then
or
has
it
kind
of
stayed
the
same
since
2020
?
Yeah
,
it's
,
it's
constantly
evolving
.
I
mean
,
at
the
essence
it's
,
it's
pretty
much
kind
of
stayed
the
same
since
2020
?
Oana Jinga
6:04
Yeah
,
it's
constantly
evolving
.
I
mean
,
at
the
essence
,
it's
pretty
much
kind
of
the
same
in
the
sense
that
we
use
an
autonomous
robot
to
collect
information
and
data
inside
warehouses
,
we
digitize
that
into
our
digital
twin
platform
and
obviously
present
our
customers
with
a
lot
of
insights
and
information
.
But
the
way
we
collect
that
data
,
the
type
of
data
we
collect
,
how
we
deconstruct
it
,
how
we
present
it
,
all
of
that
pretty
much
changes
,
I
think
,
week
to
week
,
as
we
keep
evolving
the
product
and
keep
learning
more
from
our
users
.
So
it's
a
constant
kind
of
trial
and
error
and
understanding
of
the
market
.
Blythe Brumleve
6:36
So
how
does
?
I'm
curious
because
,
for
I
just
,
I
got
introduced
to
Dexory
at
the
Manifest
Conference
Future
of
Logistics
and
Supply
Chain
,
or
Future
of
Supply
Chain
and
Logistics
and
I
stopped
by
the
booth
because
you
guys
had
this
amazing
,
you
know
,
Lego
kit
,
which
was
really
creative
marketing
,
and
I
had
to
get
my
hands
on
it
.
So
I
was
one
of
the
lucky
few
to
get
my
hands
on
it
and
I'm
keeping
it
in
the
bag
.
I'm
scared
to
build
it
because
I
don't
want
to
mess
it
up
.
I
feel
like
it's
going
to
be
a
collector's
item
.
So
was
Manifest
sort
of
maybe
one
of
the
first
sort
of
coming
out
moments
for
Dexory
,
or
were
you
on
the
trade
show
sort
of
circuit
before
then
?
Oana Jinga
7:14
It's
a
very
,
very
good
point
because
we
actually
so
maybe
kind
of
for
the
listeners
we
are
based
in
Europe
,
so
we're
based
in
the
UK
,
just
kind
of
outside
London
.
We
have
our
kind
of
big
facility
for
manufacturing
and
commissioning
robots
and
bringing
them
together
,
but
we
do
have
customers
around
the
world
.
So
Manifest
for
us
was
a
great
opportunity
to
actually
introduce
our
product
or
solution
and
our
team
to
the
US
market
in
particular
.
We
did
have
like
a
couple
of
customers
already
live
at
the
time
,
but
we
had
never
really
done
something
very
big
for
the
US
market
.
However
,
kind
of
going
back
to
your
question
,
we
have
done
some
of
the
bigger
kind
of
trade
shows
and
events
in
Europe
for
probably
the
past
kind
of
two
,
three
years
now
,
Slowly
of
course
,
kind
of
growing
that
presence
as
the
company
evolved
as
well
.
Blythe Brumleve
7:56
But
Manifest
was
kind
of
our
first
Blythe Brumleve
7:57
step
across
the
pond
.
I'm
curious
as
to
what
the
differences
between
.
Are
there
any
differences
between
the
trade
shows
and
events
in
Europe
versus
the
United
States
?
Oana Jinga
8:05
are
there
any
differences
between
the
trade
shows
and
events
in
Europe
versus
the
United
States
?
Oana Jinga
8:11
It's
a
very
good
question
because
I
think
last
well
,
this
kind
of
like
last
quarter
,
when
we
started
with
Manifest
,
we
then
moved
over
to
,
I
think
we
had
Modex
and
we
had
Logimat
in
Europe
and
then
we
had
one
in
the
UK
as
well
Interlogistics
so
it
was
very
interesting
because
the
core
team
was
pretty
much
the
same
going
from
one
to
the
other
and
at
the
end
I
mean
like
,
oh
my
god
,
this
experience
couldn't
have
been
different
and
I
think
it's
a
.
Oana Jinga
8:28
It's
a
very
big
cultural
difference
around
how
,
um
,
I
mean
,
I
don't
the
american
culture
just
embraces
innovation
and
ask
questions
and
they're
very
curious
and
want
to
know
more
,
um
,
more
kind
of
central
european
,
a
little
bit
more
skeptical
and
technical
,
and
go
into
a
lot
of
technical
details
,
so
that
the
people
that
you
speak
to
react
to
very
different
things
,
which
is
not
something
we
had
thought
about
before
,
but
definitely
like
a
big
,
big
learning
after
this
season
,
as
we
call
it
,
because
now
kind
of
things
calm
down
in
logistics
for
a
while
,
which
is
very
,
very
strange
.
You
do
a
lot
of
beginning
of
the
year
and
then
nothing
for
a
few
months
.
Blythe Brumleve
9:01
Yeah
,
it's
definitely
.
You
have
your
trade
show
peak
seasons
and
we
definitely
just
ended
,
I
think
,
for
a
good
majority
of
it
.
You'll
have
a
few
over
the
summer
,
but
then
the
fall
is
going
to
pick
right
back
up
and
I
think
Manifest
kind
of
kicks
that
off
,
at
least
in
the
North
American
market
.
I've
had
somebody
on
the
show
previously
from
Pitney
Bowes
and
they
were
talking
about
the
differences
from
warehouse
operations
in
Europe
versus
the
United
States
and
I'm
curious
if
you've
seen
you
know
sort
of
a
similar
issues
or
maybe
challenges
that
go
on
,
because
for
a
lot
of
,
from
what
I
understand
,
for
a
lot
of
European
customers
,
the
way
that
they're
dealing
with
warehousing
is
that
it's
a
lot
smaller
of
facilities
or
a
lot
smaller
buildings
,
retail
shops
,
things
like
that
,
and
so
the
facilities
and
the
delivery
from
that
point
A
to
B
has
to
be
more
efficient
in
a
smaller
way
in
Europe
versus
the
United
States
.
Are
you
seeing
some
of
those
similarities
as
well
?
Oana Jinga
9:57
I
mean
yes
,
yes
and
no
.
I
would
say
it
kind
of
depends
where
in
the
United
States
and
then
what
in
Europe
as
well
.
Because
,
um
,
I
mean
,
if
we
go
back
to
the
basics
,
the
one
thing
we've
definitely
noticed
,
like
in
the
us
you
have
warehouses
that
are
much
larger
horizontally
,
whereas
in
europe
,
and
especially
the
uk
,
people
are
building
vertically
a
lot
more
because
of
,
yeah
,
the
cost
of
real
estate
.
So
you
would
have
warehouses
that
are
18
,
20
meters
in
the
uk
I
mean
,
most
of
the
us
,
let's
say
they're
about
,
like
I
don't
know
,
12
,
14
,
even
that
would
be
a
tall
one
.
So
there's
like
an
interesting
kind
of
element
of
infrastructure
to
start
with
.
The
other
element
,
of
course
,
because
of
space
,
and
you
will
have
more
kind
of
smaller
,
let's
say
,
distribution
centers
.
I
wouldn't
necessarily
kind
of
call
them
more
warehouses
,
because
some
of
them
will
be
be
quite
small
,
like
you
said
,
it's
like
the
back
of
a
retail
store
and
people
are
distributing
from
there
,
um
.
But
on
the
other
hand
,
I
mean
there
is
a
lot
of
trade
and
commerce
and
movement
of
goods
happening
across
europe
,
so
there
are
also
massive
,
massive
parks
that
have
literally
kind
of
come
up
,
um
,
over
the
past
couple
of
years
and
they
keep
kind
of
expanding
and
growing
.
So
most
of
the
motorways
around
cities
,
like
you'll
see
huge
industrial
parks
with
really
big
warehouses
as
well
.
Oana Jinga
11:03
Um
,
so
I
wouldn't
say
it's
necessarily
a
trend
.
From
what
we're
experiencing
,
there's
definitely
a
lot
of
like
really
big
sites
as
well
.
Like
the
biggest
site
we're
actually
deployed
in
has
110,000
pallet
locations
,
about
1
million
square
feet
.
It's
in
actually
Eastern
Europe
.
So
,
yeah
,
it's
a
site
with
Maersk
.
I
can
kind
of
like
very
openly
talk
about
that
and
they're
distributing
their
goods
across
kind
of
Eastern
and
Central
Europe
.
And
then
also
we
have
sites
in
the
US
which
are
much
smaller
.
So
it's
yeah
,
it's
not
necessarily
a
trend
,
but
the
element
of
real
estate
,
I
think
,
is
what
kind
of
dictates
a
little
bit
the
possibilities
of
where
you
can
build
over
here
versus
in
the
US
.
Blythe Brumleve
11:40
Yeah
,
great
point
.
Because
when
I
was
at
Manifest
the
location
that
it
was
at
before
it's
not
going
to
.
It's
going
to
be
at
the
Venetian
for
next
year
,
but
the
location
it
was
for
this
year
there
were
some
folks
that
were
on
the
trade
show
floor
that
could
not
build
their
booths
as
high
because
the
ceilings
were
or
chandeliers
were
kind
of
getting
in
the
way
of
them
,
showing
off
you
know
,
their
different
sort
of
robots
.
That
were
robots
that
were
picking
and
packing
and
things
like
that
,
and
so
the
next
phase
is
going
to
be
the
higher
ceiling
.
So
that
was
interesting
from
a
logistics
perspective
of
those
types
of
people
that
are
setting
up
their
boots
.
So
it's
really
interesting
that
it's
almost
the
same
thing
for
warehouse
locations
as
well
.
Blythe Brumleve
12:20
Are
you
in
freight
sales
with
a
book
of
business
looking
for
a
new
home
,
or
perhaps
you're
a
freight
agent
in
need
of
a
better
partnership
?
These
are
the
kinds
of
conversations
we're
exploring
in
our
podcast
interview
series
called
the
Freight
Agent
Trenches
,
sponsored
by
SPI
Logistics
.
Now
I
can
tell
you
all
day
that
SPI
is
one
of
the
most
successful
logistics
firms
in
North
America
,
who
helps
their
agents
with
back
office
operations
such
as
admin
,
finance
,
it
and
sales
.
But
I
would
much
rather
you
hear
it
directly
from
SPI's
freight
agents
themselves
,
and
what
better
way
to
do
that
than
by
listening
to
the
experienced
freight
agents
tell
their
stories
behind
the
how
and
the
why
they
joined
SPI
?
Hit
the
freight
agent
link
in
our
show
notes
to
listen
to
these
conversations
or
,
if
you're
ready
to
make
the
jump
,
visit
spi3plcom
.
You've
mentioned
a
couple
times
the
digital
twin
environment
.
How
do
robotics
sort
of
sync
up
or
complement
a
digital
twin
environment
?
Are
they
the
ones
scanning
the
warehouse
and
reporting
back
the
data
?
Is
that
kind
of
a
summation
of
how
they
play
together
?
Oana Jinga
13:27
So
from
our
perspective
,
the
robots
are
there
to
just
collect
information
and
then
they
digitize
everything
and
on
the
edge
,
on
the
actual
unit
,
and
push
the
data
back
into
a
cloud
platform
where
you
kind
of
have
that
digital
twin
.
And
when
we
say
digital
twin
,
I
think
maybe
it's
worth
just
clarifying
what
the
definition
is
,
especially
from
our
perspective
.
So
sometimes
people
call
a
digital
twin
any
kind
of
representation
of
a
physical
object
in
a
digital
format
,
any
kind
of
representation
of
a
physical
object
in
a
digital
format
,
so
a
CAD
file
or
,
let's
say
,
like
a
construction
model
of
a
warehouse
.
A
lot
of
times
people
say
that's
a
digital
twin
but
it's
not
really
.
It's
a
model
,
so
it's
been
done
once
.
It
kind
of
like
never
changes
and
you
kind
of
go
back
to
it
from
time
to
time
to
try
and
figure
out
where
the
fire
escape
is
or
something
like
that
,
because
that
will
not
move
.
But
the
way
we
look
at
a
digital
twin
is
like
it's
a
dynamic
and
living
and
breathing
digital
object
,
so
it
needs
to
be
able
to
kind
of
show
you
at
any
point
in
time
what's
the
latest
that's
happening
in
the
physical
world
,
how
that's
evolving
,
where
the
factors
are
influencing
that
change
.
So
from
our
perspective
.
When
we
see
a
digital
twin
of
a
warehouse
,
we
literally
mean
like
this
digital
environment
that
changes
continuously
as
the
warehouse
changes
and
that
you
can
also
then
use
in
the
future
,
like
,
if
you
do
want
to
simulate
and
try
different
scenarios
.
You
kind
of
use
it
to
to
see
how
that
reaction
obviously
will
will
evolve
over
time
based
on
whatever
kind
of
happens
to
it
.
Oana Jinga
14:44
So
in
in
our
perspective
,
we
use
the
robot
to
get
data
continuously
from
warehouses
to
push
them
into
that
digital
twin
and
show
as
close
to
reality
as
possible
what's
going
on
.
Oana Jinga
14:54
But
moving
over
to
other
types
of
robotics
as
well
,
the
one
element
that
digital
twin
helps
the
one
that
we
create
is
that
we
can
then
feed
this
data
back
to
other
types
of
robotics
and
help
them
best
kind
of
operate
and
orchestrate
within
a
warehouse
.
Oana Jinga
15:08
So
let's
say
,
if
you
have
picking
robots
and
they
would
go
to
a
location
to
get
,
obviously
,
a
product
,
but
actually
that
location
is
empty
because
it
hasn't
been
replenished
,
I
mean
we
flag
that
pretty
much
kind
of
real
time
and
make
sure
that
there
is
enough
product
there
so
that
the
picking
robot
goes
to
the
right
location
.
Similarly
for
autonomous
forklifts
,
like
the
3D
environment
that
we
have
and
the
information
we
have
about
pallets
and
goods
can
feed
into
their
kind
of
data
points
to
know
where
they're
going
,
what
they're
picking
,
where
they're
putting
things
as
well
.
So
it's
kind
of
two
ways
.
Obviously
,
from
strictly
our
perspective
,
we
build
the
digital
twins
and
obviously
we
enable
customers
to
get
information
from
them
,
but
then
from
us
to
other
robotic
systems
as
well
.
They
become
that
kind
of
orchestration
layer
and
platform
for
any
kind
of
technology
.
Blythe Brumleve
15:50
That's
super
interesting
that
your
data
is
syncing
with
other
robots
or
robotics
within
the
warehouse
itself
.
Now
where
is
that
data
being
stored
?
Is
it
within
your
system
?
Is
it
within
the
WMS
,
or
are
they
two
different
,
complementary
systems
?
Oana Jinga
16:06
So
they're
two
different
,
complementary
systems
in
the
sense
that
WMS
has
a
very
different
purpose
,
so
it's
being
obviously
designed
to
hold
information
about
suppliers
,
dates
,
things
come
in
,
when
they
move
,
how
much
quantity
has
to
go
where
,
whereas
what
we
do
is
,
rather
than
kind
of
going
horizontally
across
kind
of
all
these
different
types
of
data
points
about
goods
,
we're
also
going
kind
of
vertically
within
,
really
inside
the
warehouse
,
so
really
understanding
kind
of
racking
structures
,
occupancy
levels
,
quantity
and
volumes
of
every
single
kind
of
data
point
.
So
all
of
that
information
,
together
with
,
like
I
don't
know
,
high
resolution
images
,
for
example
,
you
kind
of
suddenly
put
them
back
into
the
WMS
because
it
hasn't
been
designed
to
store
and
keep
that
kind
of
information
.
So
our
system
kind
of
works
very
much
in
conjunction
to
WMS
and
becomes
kind
of
that
one
source
of
truth
across
the
warehouse
.
Blythe Brumleve
16:53
And
because
the
shape
of
a
Dexory
robot
is
very
vertical
and
so
,
with
other
sort
of
robotics
within
the
warehouse
,
it
feels
like
from
the
people
I've
talked
to
and
the
things
that
I've
seen
is
that
robotics
within
the
warehouse
are
doing
a
very
specific
job
.
Is
that
accurate
?
Oana Jinga
17:14
Yeah
,
and
I
think
that's
probably
I
mean
,
sometimes
we're
being
asked
about
,
like
,
what
competitors
do
you
have
there
in
the
robotic
space
?
Oana Jinga
17:21
And
I
think
the
answer
I
usually
use
is
if
you
kind
of
go
into
your
kitchen
,
you
have
a
lot
of
different
appliances
and
they're
all
kind
of
designed
and
meant
to
do
different
things
.
Oana Jinga
17:28
Like
you
would
not
use
a
dexory
robot
to
pick
something
or
to
move
a
pallet
,
the
way
you
wouldn't
use
,
like
a
locus
robot
to
scan
racks
up
like
50
meters
in
the
air
,
because
they're
not
designed
to
do
that
.
Oana Jinga
17:40
So
I
feel
like
the
importance
of
kind
of
the
shape
factor
and
the
way
like
a
unit
is
built
has
to
always
kind
of
keep
into
account
exactly
what
the
purpose
of
that
unit
is
and
what
it's
meant
to
be
doing
.
Hence
why
,
like
yeah
,
we
took
the
approach
of
having
kind
of
these
really
tall
units
that
can
scan
warehouses
pretty
much
kind
of
top
to
bottom
in
one
go
.
So
with
that
we
can
do
about
10,000
to
15,000
padded
locations
an
hour
,
because
it
kind
of
acts
like
a
giant
scanner
,
if
you
think
about
it
,
kind
of
moving
around
the
warehouse
,
and
we
have
to
be
able
to
kind
of
moving
around
the
warehouse
and
we
have
to
be
able
to
kind
of
get
to
that
height
to
keep
that
speed
up
and
then
the
consistency
of
the
data
.
But
of
course
,
like
,
if
you
don't
have
to
go
up
there
for
other
use
cases
,
then
you
don't
need
a
tall
robot
.
Blythe Brumleve
18:21
Yeah
,
I
was
always
wondering
what
I
would
imagine
,
because
the
theory
that
I've
heard
is
that
,
you
know
,
robotics
are
very
efficient
and
very
good
for
one
specific
task
,
and
so
you
see
a
lot
of
different
types
of
robots
within
a
trade
show
floor
,
for
example
,
and
so
I'm
always
curious
as
to
how
do
you
make
those
different
decisions
on
what
to
get
or
what
to
invest
in
.
And
that
makes
a
whole
lot
of
sense
that
you
would
get
this
certain
kind
to
do
this
specific
job
and
you
would
get
this
other
kind
to
do
another
specific
job
,
because
warehouse
would
get
this
other
kind
to
do
another
specific
job
,
because
,
you
know
,
warehouse
labor
is
incredibly
challenging
to
fulfill
.
I
think
it
was
amazon
that
said
a
couple
years
ago
that
they
were
,
in
the
us
at
least
,
that
they
were
in
danger
of
actually
running
out
of
people
to
hire
because
it's
such
a
demanding
job
and
there's
such
a
you
know
,
a
high
turnover
rate
.
I
see
you
shaking
your
head
,
that's
,
it
seems
like
you
agree
with
a
lot
of
those
statements
.
Oana Jinga
19:13
And
what's
funny
is
that
,
I
mean
,
a
lot
of
our
customers
are
telling
us
that
the
problem
is
that
Amazon
is
taking
all
the
people
.
So
if
Amazon's
struggling
,
then
you
can
imagine
how
everyone
else
is
who
can't
necessarily
afford
to
kind
of
keep
throwing
money
at
the
problem
and
,
yeah
,
kind
of
restrained
by
other
types
of
resources
as
well
.
Oana Jinga
19:29
So
absolutely
,
and
I
think
,
kind
of
going
back
to
your
point
a
little
bit
earlier
around
kind
of
different
use
cases
,
probably
the
one
thing
I
wanted
to
touch
on
is
from
our
perspective
.
I
mean
,
the
robot
is
there
to
just
collect
information
,
but
what
you
can
do
with
that
information
,
then
it's
pretty
much
kind
of
endless
and
limitless
,
because
you
can
kind
of
extract
data
and
different
points
,
you
can
compare
it
with
other
data
sources
,
you
can
kind
of
crunch
it
,
you
can
use
it
for
simulation
,
for
forecasting
and
so
on
.
Oana Jinga
19:54
Um
,
so
normally
the
the
way
we
kind
of
approach
things
.
So
our
product
at
the
end
of
days
is
the
data
rather
than
the
robot
itself
,
versus
some
of
the
other
solutions
that
I
was
mentioning
earlier
that
you
referenced
,
where
the
actual
robot
is
the
one
necessarily
doing
the
job
.
Um
,
because
we
talked
about
picking
and
carrying
and
so
on
,
I
mean
you
,
the
robot
is
the
one
kind
of
performing
that
task
and
that's
kind
of
where
it
ends
.
Of
course
it's
a
lot
of
complexity
behind
the
technology
,
but
for
us
capturing
the
data
is
just
step
one
.
It's
like
what
you
can
do
with
it
afterwards
is
where
the
magic
kind
of
happens
I
was
saying
earlier
,
and
where
the
most
value
can
be
extracted
from
.
Blythe Brumleve
20:27
Well
,
could
you
elaborate
a
little
bit
on
some
of
those
actionable
tips
,
because
I
think
it's
one
thing
to
get
data
,
but
what
do
I
do
with
that
data
next
,
after
I
see
it
?
Oana Jinga
20:37
Exactly
exactly
.
Oana Jinga
20:38
And
I
think
the
most
basic
element
around
,
like
we
,
I
mean
,
at
the
core
of
our
technology
is
the
fact
that
we
can
instantly
find
discrepancies
versus
warehouse
management
system
data
or
other
kind
of
systems
data
.
So
the
first
step
is
like
okay
,
once
you
kind
of
see
a
discrepancy
,
what
are
you
going
to
do
about
it
?
Is
it
a
physical
movement
of
a
good
or
is
it
actually
like
a
system
change
that
you
have
to
do
?
But
then
it
gets
a
lot
more
complex
and
interesting
because
,
like
,
if
you
do
have
,
for
example
,
multiple
locations
with
the
same
product
,
what
we
do
is
we
kind
of
recommend
consolidating
those
product
locations
and
kind
of
making
more
space
in
the
warehouse
.
Oana Jinga
21:10
If
your
distance
from
a
pick
to
reserve
location
is
too
long
and
that
kind
of
might
lead
further
on
to
delays
,
we
can
also
kind
of
recommend
bringing
them
together
based
on
the
occupancy
levels
that
we
see
around
that
pick
location
.
So
it's
again
kind
of
correlating
different
data
points
across
the
warehouse
and
being
able
to
kind
of
make
recommendations
to
our
users
to
actually
know
what
to
do
about
it
.
Because
,
like
you
said
,
if
you
just
kind
of
present
the
report
at
the
end
of
the
day
it's
like
okay
,
fine
,
somebody
like
,
then
has
to
get
their
head
around
,
like
,
what
does
it
actually
mean
?
Whereas
we
take
the
next
step
around
,
like
we
actually
interpret
that
and
we
tell
customers
okay
,
if
you
do
X
,
y
,
z
,
then
this
is
the
benefit
that
you're
going
to
see
and
potentially
would
it
be
worth
it
or
not
in
the
end
to
take
the
action
.
Blythe Brumleve
21:50
Oh
,
interesting
,
and
I'm
curious
are
there
any
other
sort
of
data
points
,
maybe
like
purchasing
habits
or
purchasing
flows
that
go
out
,
you
know
,
throughout
the
year
?
Are
those
impacting
some
of
the
data
decisions
that
Dexory
is
making
?
Oana Jinga
22:05
Absolutely
,
and
we're
kind
of
like
mixing
,
I
think
,
different
kind
of
data
points
in
terms
of
like
external
factors
,
like
you
were
saying
,
like
is
it
peak
season
,
is
it
kind
of
Christmas
approaching
and
we
kind
of
see
different
items
kind
of
potentially
being
in
higher
needs
.
We
can
take
information
that
already
exists
in
kind
of
ERP
systems
or
even
the
WMS
or
simple
things
.
Like
one
of
the
use
cases
we
have
for
the
food
industry
is
if
a
product
kind
of
approaches
the
best
before
kind
of
the
date
,
then
you
could
kind
of
highlight
them
they
haven't
moved
in
a
while
to
potentially
kind
of
be
the
first
ones
to
be
taken
out
of
the
warehouse
.
So
it's
a
combination
of
factors
that
we
take
into
account
.
Some
of
them
might
be
completely
external
and
seasonal
,
some
of
them
might
just
be
quite
specific
about
a
particular
item
or
good
in
the
warehouse
.
Blythe Brumleve
22:47
And
you've
mentioned
Maersk
a
couple
times
here
.
They're
one
of
the
customers
of
Dexory
.
What
does
that
relationship
sort
of
entail
?
You
mentioned
a
warehouse
in
Eastern
Europe
.
I
also
was
listening
to
an
interview
where
it
was
mentioned
that
they
wanted
to
have
stacking
or
sort
of
storage
optimization
.
Can
you
break
that
down
for
us
?
Oana Jinga
23:09
Yeah
,
and
it's
fantastic
because
they've
been
a
great
kind
of
partner
to
us
and
we
have
numerous
kind
of
slides
live
with
them
.
We're
all
in
all
of
their
locations
in
the
UK
.
Like
I
mentioned
Eastern
Europe
,
we're
kind
of
in
conversations
of
launching
in
the
US
very
soon
as
well
.
So
it's
kind
of
grown
very
quickly
and
I
think
that's
kind
of
testament
of
our
collaboration
and
the
fact
that
we've
been
kind
of
very
open
to
explore
the
use
cases
that
you
discussed
as
well
with
them
.
Oana Jinga
23:30
So
,
um
,
I
think
,
yeah
,
where
that
comes
from
is
kind
of
different
angles
.
Um
,
one
is
a
little
bit
towards
I
was
saying
earlier
.
I
mean
,
they're
very
much
embracing
,
using
that
data
to
then
also
make
decisions
around
how
to
best
organize
their
space
and
be
much
more
efficient
with
um
,
with
with
the
resources
that
they
have
at
hand
.
Like
,
yeah
,
they
can
kind
of
bring
in
multiple
customer
goods
different
times
and
making
sure
that
they
also
are
able
to
report
back
to
that
customer
um
what
stock
situation
they
have
at
hand
.
Like
,
yeah
,
they
can
kind
of
bring
in
multiple
customer
goods
different
times
and
making
sure
that
they
also
are
able
to
report
back
to
that
customer
what
stock
situation
they
have
.
Oana Jinga
23:57
But
we're
also
like
and
we're
going
to
announce
this
in
a
little
bit
more
detail
soon
.
We
are
moving
away
from
just
obviously
racking
to
all
the
other
types
of
kind
of
racking
configuration
as
well
sorry
,
goods
configuration
,
because
it's
not
racking
like
things
on
the
floor
,
pick
faces
and
smaller
mezzanines
and
so
on
.
So
I
think
also
being
able
to
have
that
capability
of
doing
different
types
of
sites
and
even
if
a
site
has
different
types
of
configuration
,
kind
of
covering
both
angles
and
being
able
to
suggest
moving
goods
from
one
part
to
the
other
,
was
something
they
were
very
interested
in
as
well
.
So
,
yeah
,
very
excited
about
that
collaboration
and
they're
fantastic
at
asking
for
things
,
which
is
good
for
us
because
we
learn
and
then
we
have
us
kind
of
providing
back
to
them
.
Blythe Brumleve
24:36
Do
you
find
that
what
Maersk
is
experiencing
on
,
you
know
,
sort
of
the
enterprise
level
is
a
lot
of
the
same
issues
that
maybe
SMBs
you
know
small
business
,
small
to
medium
sized
businesses
are
experiencing
as
well
,
just
at
a
different
scale
?
Oana Jinga
24:53
well
,
just
at
a
different
scale
.
Oana Jinga
24:54
To
a
large
extent
,
yes
,
I
think
what
MERS
kind
of
has
the
advantage
of
is
really
understanding
the
other
parts
outside
of
the
warehouse
obviously
the
transportation
side
and
obviously
shipping
there's
no
need
to
kind
of
explain
that
so
they're
probably
a
little
bit
more
in
tune
and
connected
to
what
happens
outside
of
the
warehouse
to
be
able
to
then
be
much
more
efficient
inside
the
warehouse
,
which
obviously
some
of
the
smaller
players
because
they'll
be
very
much
focused
on
the
contract
logistics
angle
or
just
the
site
itself
probably
wouldn't
have
the
same
level
of
expertise
.
Oana Jinga
25:20
But
in
essence
the
problems
themselves
would
be
very
similar
.
They're
also
kind
of
struggling
to
attract
talent
and
also
kind
of
like
onboard
people
as
quickly
as
possible
,
retain
people
and
then
from
there
making
sure
that
obviously
their
side
data
is
as
accurate
and
as
good
as
possible
,
because
without
having
kind
of
people
doing
the
checks
and
keeping
on
top
of
that
and
also
with
a
high
level
of
retention
,
you
will
have
a
lot
of
mistakes
and
issues
that
naturally
happen
and
that
will
be
across
the
board
,
whether
there's
a
smaller
or
large
company
.
Blythe Brumleve
25:47
Brokering
success
demands
a
battle-ready
strategy
.
Thai
TMS
equips
freight
brokers
with
the
ultimate
battle
station
for
conquering
a
tough
market
.
With
Tai
,
brokers
gain
access
to
a
comprehensive
platform
where
rate
intelligence
and
quote
history
converge
on
a
single
screen
.
It's
not
just
a
page
,
it's
a
strategic
command
center
designed
to
help
brokers
win
.
Tai
equips
your
team
with
all
of
the
data
they
need
to
negotiate
with
confidence
and
allows
them
to
communicate
directly
with
carriers
and
customers
from
a
simple
control
base
.
Revolutionize
the
way
your
brokers
perform
by
giving
them
a
competitive
advantage
with
Tye
TMS
.
For
more
info
,
go
to
tai-softwarecom
.
Blythe Brumleve
26:27
Backslash
battle
stations
and
we
also
have
a
link
for
you
in
the
show
notes
to
sign
up
for
a
demo
.
Backslash
Battlestations
and
we
also
have
a
link
for
you
in
the
show
notes
to
sign
up
for
a
demo
.
There
is
I
heard
a
stat
the
other
day
that
only
7%
of
US-based
warehouses
have
robotics
integrated
into
their
system
and
into
their
building
.
Based
on
how
you've
worked
with
customers
,
I'm
curious
as
to
why
that
adoption
rate
is
so
low
.
Is
it
something
that
is
growing
,
because
it
is
sort
of
fairly
new
?
And
then
,
what
are
the
challenges
of
you
know
,
sort
of
onboarding
or
making
that
decision
that
you
know
it's
time
to
invest
in
robotics
?
Oana Jinga
27:06
I
think
it's
yeah
,
it's
multifaceted
,
it's
multifaceted
.
The
one
interesting
element
from
our
perspective
is
,
a
lot
of
the
times
we're
one
of
the
first
robots
to
come
in
because
we
actually
require
very
little
to
do
upfront
,
there's
very
few
demands
from
our
customer
.
All
we
need
is
a
power
plug
and
then
we
can
pretty
much
be
on
our
way
.
We
don't
even
need
a
Wi-Fi
connectivity
if
that's
not
available
,
and
also
we
do
not
without
going
into
too
much
detail
,
but
our
business
model
is
robot
as
a
service
,
so
we
don't
require
a
massive
upfront
investment
to
kind
of
bring
a
big
automation
in
which
makes
it
a
lot
kind
of
seamless
and
quicker
to
get
customers
comfortable
to
start
their
journey
in
robotics
actually
Oana Jinga
27:46
with
us
.
Because
now
going
back
to
your
second
part
of
the
question
,
I
think
number
one
is
people
fear
making
a
massive
investment
that
will
then
kind
of
have
a
pretty
kind
of
long
term
to
provide
a
return
on
that
.
So
if
you
have
to
bring
in
a
I
don't
know
significant
automation
system
for
sortation
or
like
conveyor
belts
or
whatever
it
is
,
to
kind
of
get
things
from
the
door
to
the
racks
,
that
will
be
potentially
in
the
hundreds
of
thousands
or
maybe
even
millions
to
get
started
on
.
So
you
have
to
be
very
,
very
sure
that
you
want
that
in
,
and
especially
if
we're
talking
3PLs
,
like
third-party
logistics
providers
,
they'll
have
to
be
confident
that
whatever
customer
they
have
on
site
will
stay
enough
to
be
able
to
then
see
the
returns
of
that
investment
.
So
that
upfront
cost
is
a
big
hurdle
.
And
then
the
second
part
is
I
feel
like
people
did
kind
of
make
the
decision
that
they
want
automation
.
It's
just
like
having
the
confidence
again
to
start
and
really
understand
that
again
.
Oana Jinga
28:44
Technology
has
evolved
a
lot
and
it's
probably
much
easier
and
quicker
and
less
kind
of
intense
from
an
internal
resource
perspective
to
actually
get
some
of
the
technology
out
than
they
think
.
So
there's
some
misconceptions
around
,
like
it
will
take
months
and
months
of
integration
and
I'll
need
20
different
partners
to
work
together
.
Yeah
,
I
need
to
kind
of
revisit
my
staffing
and
everything
.
So
there's
a
lot
of
kind
of
misconceptions
around
that
.
And
I
think
most
of
the
companies
that
have
evolved
in
the
industry
are
way
beyond
that
and
they
can
get
up
and
running
much
quicker
with
very
few
resources
.
So
yeah
,
it's
kind
of
like
time
and
money
at
the
end
of
the
day
,
which
kind
of
makes
decisions
a
little
bit
harder
.
But
I
do
think
,
again
,
from
my
knowledge
,
that
the
US
is
actually
quite
far
ahead
versus
some
other
markets
.
Of
course
,
if
you
go
more
into
Asia
,
some
of
them
might
be
more
advanced
,
especially
when
it
comes
to
manufacturing
rather
than
logistics
.
But
yeah
,
I
think
others
are
even
more
behind
.
Blythe Brumleve
29:30
Yeah
,
I
think
others
are
even
more
behind
.
Yeah
,
I
imagine
that
if
you're
facing
that
,
that
might
be
the
catalyst
for
these
folks
to
adopt
robotics
,
because
they
face
so
many
different
hiring
challenges
over
the
last
few
years
and
it's
probably
not
going
to
be
going
away
anytime
soon
.
When
somebody
does
make
that
decision
,
OK
,
I'm
going
to
invest
in
robotics
.
What
does
that
sort
of
onboarding
initial
you
know
,
couple
weeks
,
maybe
a
couple
months
look
like
From
?
Oana Jinga
29:56
our
perspective
it's
very
,
very
seamless
,
like
we
can
be
up
and
running
in
about
five
to
seven
days
,
depending
a
little
bit
on
the
size
of
the
site
and
,
like
I
said
,
all
we
need
is
a
power
plug
.
Oana Jinga
30:04
So
it's
a
very
,
very
kind
of
fast
journey
.
So
normally
what
we
do
is
we
,
depending
on
where
we
kind
of
start
a
conversation
with
the
customer
,
because
a
lot
of
our
customers
obviously
have
multiple
sites
and
it
might
be
like
a
regional
conversation
or
directly
a
local
conversation
with
a
site
.
We
try
to
get
our
heads
around
operations
very
quickly
,
understand
what's
happening
on
the
ground
,
get
an
idea
of
the
layout
like
where
would
the
robots
can
,
how
would
it
go
around
,
what
would
be
the
routes
,
and
then
we
pretty
much
kind
of
ship
a
unit
.
It
comes
in
two
boxes
and
it
takes
like
just
an
hour
to
put
together
like
that
lego
block
.
You
should
time
yourself
,
by
the
way
,
when
you
build
a
lego
,
because
there
is
a
bit
of
a
competition
going
oh
,
wow
quicker
if
you
can
do
that
quicker
than
our
team
can
put
together
an
actual
robot
.
Oana Jinga
30:48
Um
yeah
,
so
it
comes
into
into
the
boxes
and
then
,
like
yeah
,
they
put
it
together
and
in
a
couple
of
hours
we
do
a
map
of
the
site
by
literally
just
driving
the
robot
around
.
Using
the
,
the
sensors
on
the
robot
,
it
kind
of
captures
its
kind
of
environment
in
3d
and
2d
and
from
there
we
just
run
a
couple
of
kind
of
tests
to
make
sure
that
we're
capturing
all
the
kind
of
barcodes
and
types
of
boxes
or
whatever
it
is
.
Do
some
training
with
the
site
team
and
you're
pretty
much
kind
of
ready
to
go
.
So
it's
a
very
,
very
fast
process
.
Again
,
I
can
only
speak
for
our
perspective
and
our
technology
,
because
we
focus
so
much
on
trying
to
make
this
as
easy
as
possible
for
the
end
user
so
that
they
can
very
quickly
then
get
access
to
the
data
that
we
collect
and
actually
see
the
impact
of
that
.
Oana Jinga
31:30
Obviously
,
some
other
systems
and
solutions
might
take
a
bit
longer
because
they
might
require
reconfiguring
of
spaces
or
just
even
kind
of
workflows
being
a
little
bit
different
,
because
I
referenced
kind
of
picking
robots
.
I
mean
,
from
there
you
have
to
kind
of
understand
a
little
bit
how
that
will
work
with
the
people
around
and
who's
going
to
maneuver
the
robots
and
obviously
put
goods
in
or
whatever
kind
of
system
you're
using
and
so
on
.
So
we
don't
touch
on
any
of
that
because
from
our
perspective
,
if
no
one
looks
at
,
touches
the
robot
,
then
that's
perfect
.
It
means
we're
doing
a
good
job
and
it's
working
in
the
background
and
it's
collecting
that
information
.
Blythe Brumleve
32:02
So
when
you
a
typical
warehouse
,
it
sounds
like
they
would
,
I
say
,
a
typical
warehouse
.
I'm
not
even
sure
if
like
a
typical
warehouse
exists
,
but
for
most
,
I
guess
,
warehouses
.
Do
you
find
that
it
only
takes
one
Dexory
robot
to
sort
of
map
the
entire
system
,
or
is
it
kind
of
you
know
a
suggested
amount
per
square
footage
?
Oana Jinga
32:20
Yeah
,
yeah
,
so
it
depends
a
little
bit
on
how
often
and
how
much
data
the
customer
wants
,
but
on
most
of
our
sites
we
only
have
one
robot
.
Like
I
was
saying
earlier
,
we
do
a
huge
amount
of
kind
of
data
collection
an
hour
.
So
within
a
couple
of
hours
even
the
biggest
sites
you
will
have
a
full
wall-to-wall
kind
of
inventory
check
and
data
collection
from
the
racking
.
If
you
have
sites
that
are
very
fast
moving
and
this
high
velocity
of
the
goods
,
then
you
might
kind
of
make
sense
to
have
two
units
so
that
when
one
is
kind
of
charging
the
other
ones
out
and
vice
versa
.
But
apart
from
that
,
like
even
the
biggest
of
spaces
will
still
be
able
to
cover
at
least
once
in
24
hours
,
which
for
most
of
our
customers
is
enough
,
considering
that
currently
probably
takes
them
weeks
,
even
months
,
to
actually
get
the
accuracy
and
visibility
of
data
.
Blythe Brumleve
33:03
Now
we've
made
it
30
minutes
,
close
to
31
minutes
,
into
the
interview
and
we
haven't
mentioned
AI
once
,
which
I
think
is
sort
of
the
perfect
time
to
start
talking
about
it
.
I'm
trying
not
to
no
,
now
we
got
to
ask
,
now
we
got
to
talk
about
it
.
I
imagine
that
AI
machine
learning
.
How
do
you
define
AI
and
its
implementation
with
in
Dexory
?
Oana Jinga
33:28
Yeah
,
it
comes
at
different
Oana Jinga
33:29
levels
.
So
maybe
,
if
I
break
down
the
technology
a
little
bit
,
obviously
on
the
robotic
side
there's
a
lot
of
kind
of
machine
learning
and
computer
vision
behind
the
robot
navigating
in
this
massive
,
massive
spaces
with
,
like
I'm
saying
,
no
markers
on
the
floor
,
no
beacons
or
anything
like
that
to
guide
them
around
,
just
literally
kind
of
using
the
racking
,
the
ceiling
and
the
floor
,
so
that
in
itself
,
from
a
robotics
perspective
,
there's
loads
of
different
kind
of
tools
and
resources
that
we're
using
to
really
kind
of
make
that
as
seamless
as
possible
.
It
then
moves
into
how
we're
collecting
data
,
so
we
take
continuous
pictures
of
the
racks
and
then
we
deconstruct
those
and
can
use
computer
vision
and
again
some
elements
of
machine
learning
to
see
what
we're
seeing
in
that
data
.
And
then
it
moves
into
the
digital
twin
and
how
can
you
actually
kind
of
use
all
kinds
of
formulas
and
mathematical
understanding
of
the
space
to
be
able
to
optimize
that
space
and
move
forward
.
Oana Jinga
34:19
And
there's
quite
a
few
other
things
that
are
coming
soon
that
I
can't
like
go
into
too
much
detail
on
.
That
I
can't
go
into
too
much
detail
on
,
but
we're
bringing
a
lot
more
tools
and
understanding
on
that
Because
at
the
end
of
the
day
.
I
mean
,
we
get
a
ridiculous
amount
of
data
from
every
single
site
because
of
just
the
nature
of
these
kind
of
warehouses
being
very
,
very
big
,
and
we
get
that
on
a
daily
basis
.
So
there's
a
lot
of
kind
of
foundation
there
to
start
building
and
analyzing
and
extracting
insights
and
information
that's
useful
for
our
customers
from
that
data
,
which
currently
they
wouldn't
be
able
to
get
because
they
just
do
not
have
the
data
in
the
first
place
.
So
it's
a
very
,
very
exciting
kind
of
topic
and
area
for
us
because
,
yeah
,
at
the
end
of
the
day
I
mentioned
like
data
is
our
bread
and
butter
and
what
the
product
is
and
how
you
can
make
that
more
useful
for
for
users
,
key
to
our
success
and
next
steps
.
Blythe Brumleve
35:08
Do
you
find
that
large
language
models
?
You
know
that
everybody
talks
when
they
mention
AI
today
,
sort
of
the
casual
will
typically
be
referring
to
the
large
language
models
ChatGPT
,
you
know
,
and
the
like
.
Does
that
play
a
role
in
robotics
yet
,
with
in
Dexory
yet
,
or
is
that
maybe
coming
?
Oana Jinga
35:27
uh
it
does
and
it
will
continue
to
evolve
.
Um
,
I
think
,
um
and
I
mean
we're
not
kind
of
playing
the
technical
expert
here
because
I've
got
people
in
the
team
that
are
kind
of
much
closer
to
to
the
technical
details
here
but
I
think
a
lot
of
the
things
that
you're
seeing
at
the
moment
out
in
the
market
are
very
much
kind
of
focused
on
consumer
use
cases
or
still
barely
kind
of
scratching
the
surface
around
.
You
know
,
kind
of
creating
memes
and
videos
and
and
obviously
um
,
fake
,
um
,
homework
for
for
taking
back
to
school
.
So
I'm
not
saying
they're
obviously
kind
of
simple
technology
,
because
what's
behind
it
is
insane
,
but
in
terms
of
the
use
case
it's
still
a
little
bit
kind
of
superficial
in
terms
of
,
like
,
exactly
what
the
full
potential
potential
is
.
So
what
we're
seeing
over
the
past
kind
of
I
would
say
,
six
to
eight
months
,
is
a
huge
,
huge
focus
on
,
like
,
what
does
it
actually
mean
for
b2b
use
cases
?
Oana Jinga
36:13
And
from
our
perspective
,
I
mean
,
if
you
think
about
it
in
kind
of
pure
essential
terms
,
um
,
for
us
kind
of
the
language
is
actually
what
exactly
is
happening
inside
the
site
and
how
that
site
is
evolving
and
based
on
what
.
So
what
is
influencing
it
?
How
,
like
what
exactly
kind
of
external
factors
,
like
you
were
asking
me
earlier
,
um
can
drive
to
different
behavior
.
So
it
doesn't
have
to
be
language
in
the
pure
sense
of
literally
speaking
words
.
It
can
be
like
the
language
and
the
environment
of
a
particular
kind
of
customer
or
site
,
and
how
can
you
then
extract
more
from
that
and
then
understand
how
we
will
potentially
react
to
different
types
of
kind
of
factors
in
the
future
?
So
I
think
that's
where
kind
of
the
focus
is
moving
now
.
When
it
comes
to
AI
,
in
terms
of
like
,
okay
,
we
are
looking
at
the
basics
,
but
what
does
that
mean
in
particular
industries
,
sectors
,
and
how
can
you
define
that
?
Blythe Brumleve
37:03
based
on
it
.
Yeah
,
it's
definitely
such
a
rapidly
evolving
landscape
in
a
variety
of
different
ways
,
but
you've
hit
home
on
a
few
different
notes
that
really
ring
true
for
me
,
and
that's
making
data
actionable
.
It's
one
thing
to
feed
people
a
bunch
of
information
,
but
being
able
to
take
those
next
steps
and
what
does
that
look
like
for
sort
of
the
common
warehouse
worker
that's
going
to
be
using
these
systems
From
the
lens
of
you
know
we've
talked
about
starting
a
company
.
I'm
curious
as
to
you
know
,
what
you
envision
for
growing
the
company
.
Is
it
more
on
the
software
side
of
things
?
Oana Jinga
37:45
talking
about
just
product
and
,
yeah
,
that
that's
one
of
the
biggest
kind
of
focus
areas
that
we
have
at
the
moment
.
Oana Jinga
37:48
Um
is
like
what
else
can
we
Dexory
doing
on
the
digital
twin
platform
to
really
kind
of
make
that
more
and
more
useful
and
take
it
to
the
next
level
?
Oana Jinga
37:54
Um
,
there's
obviously
a
lot
of
different
angles
to
growing
a
company
.
It's
like
from
growing
the
team
to
like
going
to
new
markets
to
um
,
as
obviously
we
talked
earlier
on
the
product
side
as
well
,
expanding
the
product
range
,
and
for
us
it's
been
an
insane
kind
of
past
18-24
months
,
I
would
say
ever
since
we
kind
of
raised
our
seed
round
.
Then
we
moved
into
a
series
A
and
the
team
has
pretty
much
kind
of
quadrupled
in
less
than
a
year
.
So
there's
all
kinds
of
interesting
challenges
and
opportunities
that
come
from
that
.
But
we're
just
very
grateful
to
actually
be
able
to
provide
a
lot
of
value
to
multiple
customers
globally
and
learning
from
them
and
understand
,
like
,
how
the
different
operations
they
have
vary
.
So
I
think
a
lot
of
the
growth
will
come
from
us
just
being
more
and
more
established
and
having
more
insight
into
our
market
,
which
will
then
obviously
feed
back
into
the
product
.
But
it's
multifaceted
,
I
would
say
.
Blythe Brumleve
38:44
Oh
for
sure
.
Yeah
,
definitely
,
there's
a
lot
of
uncontrollable
situations
and
then
controllable
situations
and
you
got
to
navigate
both
of
those
different
paths
,
especially
as
a
co-founder
.
And
last
couple
of
questions
here
as
a
co-founder
in
logistics
and
technology
,
what
advice
would
you
give
to
somebody
else
who
may
be
thinking
about
,
you
know
,
either
entering
logistics
or
starting
their
own
company
,
adopting
technology
?
What
would
be
sort
of
that
general
overview
of
advice
?
Oana Jinga
39:14
it
goes
back
to
where
we
started
the
conversation
in
terms
of
knowing
the
user
and
the
problem
.
We
spent
a
lot
of
time
on
warehouse
floors
shadowing
people
,
trying
to
understand
why
they're
doing
certain
things
,
asking
a
million
questions
to
really
kind
of
get
our
heads
around
their
environment
and
their
challenges
and
even
outside
of
kind
of
the
use
cases
that
we
do
,
to
really
be
able
to
build
something
that
then
answers
those
elements
.
And
as
our
team
kind
of
grew
,
I
mean
we
now
have
a
kind
of
product
team
that
looks
after
that
and
then
so
on
.
But
obviously
they're
still
a
bit
kind
of
newer
to
the
sector
than
myself
and
my
two
co-founders
were
when
we
actually
kind
of
tapped
into
it
and
we
were
there
spending
the
time
on
the
warehouse
floor
.
And
we're
still
coming
back
to
them
and
say
I
actually
don't
think
that
makes
sense
because
operationally
this
is
what
people
are
doing
and
all
the
time
they're
like
how
do
you
know
?
That's
like
well
,
because
I
I
was
like
I
was
there
and
I
kind
of
spent
the
time
and
I
took
my
time
to
ask
the
questions
.
Oana Jinga
40:05
So
that
would
be
like
the
number
one
advice
is
kind
of
really
really
know
your
user
and
spend
as
much
time
as
you
can
with
them
.
Oana Jinga
40:11
And
then
it
goes
into
like
very
practical
kind
of
entrepreneur
type
of
advice
,
like
just
make
sure
you
don't
follow
kind
of
guidance
from
people
that
you
don't
feel
like
they're
where
you
want
to
be
in
a
few
years
time
.
So
like
always
kind
of
follow
the
ones
that
you
want
to
follow
their
footsteps
as
well
,
and
other
elements
around
like
hire
very
,
very
good
people
.
As
the
team
is
growing
,
you
have
to
have
some
fantastic
kind
of
individual
around
you
to
be
able
to
support
that
growth
,
and
especially
as
a
founder
,
when
so
we're
just
over
like
well
,
over
a
hundred
people
now
.
So
making
sure
that
we
have
a
strong
leadership
team
that
we
can
start
delegating
things
to
and
letting
some
of
the
things
go
is
fundamental
,
because
otherwise
you
can't
scale
Like
you
can't
just
be
.
Everything
has
to
go
through
one
person
.
So
there
are
loads
,
loads
of
points
of
advice
there
,
but
it
all
goes
back
to
like
knowing
your
user
very
well
.
Blythe Brumleve
40:59
Yeah
,
that's
great
advice
that
is
applicable
to
anyone
who
is
running
a
business
and
also
learning
how
to
delegate
.
That
is
applicable
to
anyone
who
is
running
a
business
and
also
learning
how
to
delegate
.
That
is
so
challenging
as
a
founder
to
sort
of
let
it
go
and
trust
that
somebody
else
is
going
to
care
as
much
as
you
do
and
you
know
just
.
Oana Jinga
41:22
Lastly
,
is
there
anything
else
that
you
feel
is
important
to
mention
that
we
haven't
already
talked
about
?
I
mean
,
I'm
always
kind
of
passionate
about
the
topic
,
so
I
will
bring
it
up
.
Obviously
,
I
am
a
woman
in
tech
and
robotics
field
,
and
also
kind
of
in
logistics
,
which
is
becoming
a
lot
more
diverse
than
when
I
kind
of
started
to
look
at
it
a
couple
of
years
ago
.
So
that's
fantastic
and
there's
so
many
initiatives
to
support
that
.
So
I
think
,
yeah
,
that
probably
is
another
angle
kind
of
going
back
to
your
question
earlier
around
like
advice
angle
.
Kind
of
going
back
to
your
question
earlier
around
,
like
advice
.
Oana Jinga
41:47
Um
,
I
love
that
.
I'm
kind
of
seeing
more
entrepreneurs
and
more
kind
of
women
,
especially
in
the
logistics
sector
,
um
trying
to
disrupt
the
market
,
come
up
with
ideas
to
all
kinds
of
amazing
things
,
um
,
so
,
uh
,
yeah
,
definitely
wanted
to
say
always
kind
of
here
for
anyone
who
wants
to
have
a
conversation
about
that
.
I'm
also
,
like
,
always
very
open
to
uh
speak
to
,
to
people
that
could
be
mentors
for
myself
as
well
.
So
,
very
,
very
kind
of
key
to
myself
and
also
to
us
at
Exory
.
Blythe Brumleve
42:10
Yes
,
very
,
very
well
said
.
I
used
to
joke
about
the
fact
that
the
line
,
the
bathroom
line
at
conferences
and
logistics
used
to
be
very
short
,
oh
yeah
.
Oana Jinga
42:20
Oh
yeah
,
but
now
it's
getting
longer
,
which
is
nice
to
see
.
Yeah
,
absolutely
,
and
I
think
that's
probably
the
number
one
thing
that
you
notice
versus
any
other
type
of
event
that
you
go
to
.
Blythe Brumleve
42:32
It
was
funny
.
At
one
of
my
first
logistics
conferences
there
was
a
break
after
a
big
talk
and
the
bathroom
line
for
the
men
was
so
long
.
But
then
on
the
girls'
side
,
I
just
walked
right
in
and
I
was
like
wow
this
is
unusual
.
Oana Jinga
42:45
I
was
like
this
is
nice
,
right
?
Blythe Brumleve
42:46
I'm
like
Hmm
,
I
was
like
this
is
nice
,
but
also
maybe
not
,
maybe
,
maybe
we
need
to
do
something
about
this
.
So
uh
kudos
to
you
for
for
leading
that
charge
and
being
and
and
setting
the
example
.
Um
Ohana
,
is
there
anything
else
,
uh
,
where
folks
can
maybe
follow
your
work
?
Uh
,
sign
up
for
a
demo
,
withxery
.
All
that
good
stuff
.
Oana Jinga
43:06
Sure
,
I
mean
,
the
number
one
kind
of
channel
that
we
use
is
LinkedIn
,
so
you'll
see
everything
that
we're
doing
over
there
.
Definitely
kind
of
reach
out
to
anyone
on
the
team
or
kind
of
drop
us
a
message
Always
very
happy
to
show
people
the
product
,
regardless
of
what
kind
of
work
angle
or
sector
they're
coming
at
it
from
.
So
,
yeah
,
just
give
us
a
follow
and
you'll
be
able
to
stay
on
top
of
all
the
different
news
backstory
wise
,
appreciate
it
.
Blythe Brumleve
43:28
Thank
you
so
much
.
This
was
fascinating
conversation
.
Thank
you
so
much
for
having
me
Absolutely
.
Blythe Brumleve
43:38
I
hope
you
enjoyed
this
episode
of
Everything
is
Logistics
,
a
podcast
for
the
thinkers
in
freight
,
telling
the
stories
behind
how
your
favorite
stuff
and
people
get
from
point
A
to
B
.
Subscribe
to
the
show
,
sign
up
for
our
newsletter
and
follow
our
socials
over
at
everythingislogisticscom
.
And
in
addition
to
the
podcast
,
I
also
wanted
to
let
y'all
know
about
another
company
I
operate
,
and
that's
Digital
Dispatch
,
where
we
help
you
build
a
better
website
.
Build
a
better
website
.
Now
.
Blythe Brumleve
44:03
A
lot
of
the
times
,
we
hand
this
task
of
building
a
new
website
or
refreshing
a
current
one
off
to
a
co-worker's
child
,
a
neighbor
down
the
street
or
a
stranger
around
the
world
,
where
you
probably
spend
more
time
explaining
the
freight
industry
than
it
takes
to
actually
build
the
dang
website
.
Blythe Brumleve
44:27
Well
,
that
doesn't
happen
at
Digital
Dispatch
.
We've
been
building
online
since
2009
,
but
we're
also
early
adopters
of
AI
automation
and
other
website
tactics
that
help
your
company
to
be
a
central
place
to
pull
in
all
of
your
social
media
posts
,
recruit
new
employees
and
give
potential
customers
a
glimpse
into
how
you
operate
your
business
.
Our
new
website
builds
start
as
low
as
$1,500
,
along
with
ongoing
website
management
,
maintenance
and
updates
starting
at
$90
a
month
,
plus
some
bonus
freight
marketing
and
sales
content
similar
to
what
you
hear
on
the
podcast
.
You
can
watch
a
quick
explainer
video
over
on
digitaldispatchio
.
Just
check
out
the
pricing
page
once
you
arrive
and
you
can
see
how
we
can
build
your
digital
ecosystem
on
a
strong
foundation
.
Until
then
,
I
hope
you
enjoyed
this
episode
.
I'll
see
you
all
real
soon
and
go
Jags
.
