SPEAKER_02
0:43
Welcome
in
to
our
Cargo
Rex
Guide
series
for
AI
use
cases
in
logistics.
And
we
are
joined
with
Tom
Curry.
He
is
the
president
over
at
Qt
and
we're
going
to
be
talking
about
the
issues
around
appointment
scheduling
and
how
Qt
aims
to
solve
that
problem.
So
Tom,
welcome
in.
SPEAKER_00
1:01
Yeah,
thanks.
I'm
so
glad
to
finally
connect.
I
know
we've
talked
about
doing
it
for
a
long
time.
So
we
did
it.
That's
a
victory
right
there,
right?
SPEAKER_02
1:08
Likewise.
Well,
you're
we
are
just
getting
started.
We
haven't
done
it
yet.
So
I'm
hoping,
you
know,
with
this
conversation,
we
will
knock
it
out
of
the
park
for
these
AI
use
cases
because
that's
where
you
know
the
industry
sort
of
finds
ourselves
in,
where
we
have
over
the
last
handful
of
years,
tech
has
entered
in
on
the
scene.
Not
entered
in
onto
the
scene,
but
it's
really
the
mass
adoption
of
it.
And
so
as
we
start
solving
some
of
these
problems,
more
problems
start
to
pop
up.
And
that's
where
a
company
like
QED
could
come
in
and
solve
those
problems.
So
give
us
a
general
landscape
of
the
main
issues
that
QED
is
is
created
to
solve.
SPEAKER_00
1:44
Yeah,
so
so
in
our
in
our
world,
all
we're
here
to
solve
is
appointment
scheduling.
Um
we're
fortunate
in
the
fact
that
we
became
very
committed
to
the
problem
as
opposed
to
committed
to
the
solution.
A
lot
of
these
players
that
come
out
here,
they
get
very
committed
to
the
solution
and
then
they
try
to
figure
out
which
problems
they
can
apply
it
to.
That's
not
our
approach.
Um,
Prasad
and
I
early
on,
we
were
very
committed
to
the
problem
of
appointment
scheduling.
So
all
we
focus
on
is
how
do
we
go
deeper
into
the
problem.
So
we
work
with
brokers,
we
work
with
cares,
we
work
with
shippers,
we
work
with
everyone
who
touches
appointments
at
some
level
so
that
we
can
get
connectivity
to
those
platforms
or
those
uh
locations,
but
we
can
actually
get
better
appointments.
Um,
there's
a
there's
a
lot
that
goes
into
that.
There's
a
lot
of
challenges,
there's
a
lot
of
things
that
we're
learning
on
the
dock
as
we
continue
to
get
deeper
into
that
problem.
But
all
we
do
is
appointment
scheduling.
And
so
the
nice
thing
for
me
is
when
people
say,
Can
you
tell
me
about
all
your
different
products?
I
can
say,
Yeah,
it's
appointment
scheduling.
That's
what
we
do.
SPEAKER_02
2:41
And
so
for
a
typical
is
there
a
typical
shipment
or
a
typical
load?
Let's
think
that
there
is
a
typical
load
or
a
typical
shipment.
How
many
appointments
are
are
regularly
scheduled
for
that
shipment?
SPEAKER_00
2:52
There's
a
lot
of
different
things
that
can
happen.
Most
of
the
time,
there's
a
pickup
on
both
sides.
Okay.
Every
now
and
then,
right,
you've
got
some
first
come,
first
serve
facilities
where
you
may
not
have
to
schedule,
but
you
might
have
to
give
them
a
heads
up
that
you're
gonna
be
coming
in
that
day.
Even
our
drop
trailer
scenarios,
um,
there's
typically
some
sort
of
notification
that
has
to
be
sent
out
that
says,
hey,
we're
gonna
drop
on
this
day.
So
we
typically
say
if
you're
talking
about
a
standard
shipment,
there's
usually
two
appointments.
Now,
where
it
gets
a
little
more
complex
is
of
course
in
the
multi-stop
or
the
consolidated
freight
world
that
we
do
a
lot
of.
But
the
other
side
of
it
is
how
often
team
members
actually
have
to
touch
a
single
appointment.
We'd
love
to
say
that,
hey,
you
schedule
an
appointment,
appointment
is
confirmed,
that's
the
end
of
it.
And
then
you
got
to
remind
everyone
we're
in
trucking.
Um,
it's
never
the
end
of
it.
Um,
there's
always
changes,
there's
always
updates,
there's
there's
things
that
happen
in
transit
that
you
have
to
engage
with
that.
And
so
our
customers
typically
tell
us
they
typically
touch
an
appointment
around
2.8
times.
So
it's
not
just
that
one
appointment,
right?
It's
that
it's
that
back
and
forth.
It's
oh,
I've
got
to
add
a
PO
now
to
that
appointment
because
the
the
PO's
changed
on
the
shipment
and
all
of
that
that
continues
to
stack
up.
And
that's
what
creates
so
much
of
the
bottlenecks
is
the
back
and
forth
on
all
these
different
changes
and
such.
SPEAKER_02
4:10
And
so,
how
do
you
tap
into
each
of
those
different
ICPs?
I
mean,
you
mentioned
brokers,
carriers,
shippers,
and
so
with
all
of
those
different
ICPs,
I
imagine
that
that's
different
processes
for
for
each
of
them.
SPEAKER_00
4:22
Yeah,
there
there
is.
There's
a
so
there's
a
few
different
things
that
we've
kind
of
always
said
to
our
team
is
that
we
need
to
know
more
about
our
problem
than
we
know
about
our
solution.
And
so
we
spend
a
lot
of
time
with
problems.
You
know,
when
I
talk
about
being
on
the
dock,
I
mean,
just
a
few
weeks
ago,
I
was
on
a
dock,
I
was
sitting
with
their
schedulers
who
were
who
are
handling
with
requests
from
carriers
and
brokers
and
and
private
fleets
who
are
who
are
bringing
these
inbound
in,
and
then
they're
figuring
out
where
do
I
want
to
place
them,
how
do
I
want
to
stack
my
slots
and
being
at
the
in
the
trenches,
like
right
where
they
are.
Like,
I'm
a
big
guy
about
being
in
the
trenches.
Like
the
the
closer
you
can
get
to
the
floor,
the
closer
you
can
get
to
what's
actually
happening,
the
better
you
are.
You
learn
so
much
more
about
the
people
who
do
that.
So
that's
how
we
stay
committed
to
that
is
because
we're
constantly
working
with
end
users,
not
always
the
C-suite
who
probably
hasn't
scheduled
an
appointment
in
30
years,
right?
But
but
literally
the
people
that
are
executing
on
complex
shipments
every
single
day,
those
are
the
people
you
want
to
talk
to
because
you
can
learn
more
about
the
problem.
And
then
you
can
talk
about
okay,
what
does
that
solution
really
look
like?
SPEAKER_02
5:32
And
that
was
one
of
your
case
studies
listed
on
the
CUDE
website
is
uh
uh
forgive
me
if
I'm
mispronouncing
this
or
if
it's
GIX
or
GIX.
Is
that
the
G
I
X,
yeah.
SPEAKER_00
5:42
Yeah,
yeah,
okay.
G
I
X,
yeah,
yeah,
yeah.
You're
good.
SPEAKER_02
5:46
But
you
saved
uh
the
study
showed
that
22
hours
were
saved
per
week
per
person.
Now,
is
is
that
a
best
case
scenario,
or
is
that
what
you're
seeing
across
different
ICPs?
SPEAKER_00
5:58
I
I
will
tell
you,
um,
that
was
uh
an
early
case
study
from
early
Q
days,
which
means
that's
conservative,
um,
which
is
great,
right?
Um,
and
so
most
people
will
say,
hey,
if
you
can
give
me
22
hours,
you're
crushing
out
of
the
ballpark.
Well,
most
of
our
customers,
we
see
90
plus
percent
adoption.
So
if
you
think
about
the
time
that
a
user
has
spent,
especially
if
you've
got
people
that
are
devoted
to
just
scheduling,
where
they
could
be
devoted
to
more
revenue-producing
work
or
impactful
or
getting
closer
to
the
customers
in
these
scenarios,
it
can
cause
a
pretty
significant
shift.
And
we're
talking,
you
know,
when
you
start
talking
about
90%
of
their
work
um
being
able
to
be
transitioned
through
connectivity
to
these
just
platforms,
it's
pretty
significant.
So
I've
always
been
a
uh
a
bit
of
a
uh
underpromise,
over-deliver
person.
And
so,
yeah,
that's
a
very
realistic
view
of
what
can
happen,
but
significantly
even
more
through
that,
through
across
our
customer
base
today.
SPEAKER_02
6:57
And
so
when
it
comes
to,
I
guess,
pitching
these
uh
this
efficiency,
what
does
uh
is
there
any
pushback
generally
from
this
in
the
trenches
staff
that
as
you
mentioned?
Is
it
is
it
is
there
uh
I
guess
a
hurdle
that
you
have
to
cross
to
get
people
to
kind
of
change
their
ways
or
to
get
them
to
think
about
things
a
little
bit
differently?
SPEAKER_00
7:20
Yeah,
I
think
if
I
was
a
really
effective
salesperson,
I
would
say,
no,
it's
always
really,
really
easy
and
everyone
loves
it
and
everyone
immediately
does
it.
And
so
it's
a
it's
a
huge
win.
But
I'd
be
lying
to
you,
right?
There,
there
are
there
are
certainly
pockets
of
that.
Um,
we
have
pockets
where
we
have
customers
that
they
have
team
members
that
have
been
doing
this
and
they
hate,
they
hate
spending
the
time
going
out
to
these
different
platforms,
trying
to
figure
out
how
to
schedule.
And
so
it
becomes
a
very,
very
quick
win,
easy
win.
There's
other
pockets
of
people
that
say,
I've
been
scheduling
appointments
for
20
years.
This
is
what
I
do,
this
is
what
I
know.
That
comes
with
a
lot
of
fear.
And
so
we
spend
a
lot
of
time
building
trust
with
users.
Um,
like
I
one
of
the
things
I'd
always
say
is
you
can't
trust
what
you
can't
see.
And
that's
typically
the
way
it
works
for
people
who've
been
doing
something
for
20
years.
They're
used
to
seeing
it,
they're
used
to
touching
it,
they're
used
to
feeling
it.
And
so
when
you
take
that
away
and
you
say,
no,
no,
no,
no,
trust
Q.
Cute's
gonna
automatically
do
it.
That's
not
there,
right?
They're
used
to
touching,
seeing,
feeling.
And
so
for
us,
we
try
to
do
a
lot
of
things
in
the
process
to
help
them
come
along
that
journey.
Sometimes
that
means
being
on-site
with
them.
I
was
just
planning
an
on-site
with
a
customer
here
for
next
month
where
we're
gonna
go
and
we're
gonna
sit
with
those
end
users
who
have
been
scheduling
for
20
plus
years
to
help
them
get
comfortable,
but
also
bring
in
executive
leadership
to
help
them
feel
very
comfortable
about
what
that
means
for
them
as
they
continue
to
adopt
this
new
piece
of
technology.
SPEAKER_02
8:42
Yeah,
because
that's
what
we
hear
for
a
lot
of
different
AI
use
cases
as
well.
I
don't
want
to
train
my
replacement.
I
don't
want
to
be
put
out
of
a
job.
And
I
think
in
in
some
industries,
that
fear
is
legitimate,
especially
on
the
you
know,
the
marketing
side
of
things.
But
on
in
this
area,
it
feels
like
this
is
something
that
nobody
really
wants
to
do
unless
it's
those,
you
know,
those
exceptions
that
have
been
doing
it
for
20
years.
And
and
how
do
you
handle
sort
of
the
the
conversations
to
approach
those
people
to
adopt
and
instead
of,
I
guess,
the
the
the
pushback
that
you
get.
Are
there
any,
I
guess,
maybe
common
questions
or
common
concerns
that
that
you've
seen
with
talking
to
customers
of
their
how
they
fear
this
technology
and
maybe
how
you're
overcoming
that
fear?
SPEAKER_00
9:27
So
one
of
the
things
that
we've
been
really
intentional
about
is
targeting
those
employees
first.
Um
it
seems
like
a
lot
of
companies,
when
they're
bringing
a
new
piece
of
technology,
they
want
to
take
their
poster
child,
their
champion,
and
oh
Jane,
Jane
loves
every
she
she
loves
trying
new
things,
she
loves
figuring
things
out.
That's
great.
But
what
about
Joe?
I
want
Joe.
I
want
Joe
who's
like,
I've
never
used
it,
I
don't
want
to
use
it.
Because
if
I
win
Joe
over,
guess
what?
Everyone
will
follow
what
Joe
does.
And
so
we've
kind
of
taken
the
approach
of
give
me
your
your
most
challenging
team
member
who
has
struggled
to
adopt
technology.
Let
me
win
them
over.
And
as
a
result,
the
whole
team
ends
up
coming
along,
right?
I'm
gonna
maybe
date
myself
a
little
bit.
There's
uh
there's
an
old
serial
commercial,
uh,
the
life
serial
commercial,
where
no
one
wants
to
try
it
and
they
they
pass
it
over
to
Mikey
and
Mikey
likes
it.
Oh
my
gosh,
Mikey
likes
it.
So
then
everyone
likes
it.
That's
my
approach,
right?
Like,
let's
find
the
people
who
actually
really
struggle
with
that.
And
we
have
customers
who
have
literally
said,
Tom,
I'm
not
gonna
lie,
this
is
gonna
be
very
difficult
for
you.
This
person
does
not
like
anything.
And
I'm
like,
please,
I
love
that
challenge.
And
if
you
can
win,
like,
look,
you've
got
to
build
a
layer
of
trust.
So
we
talked
about
what
are
your
fears?
What
are
your
concerns?
One
of
the
common
questions,
uh,
it's
almost
the
last
question.
Several
of
our
customers
have
probably
heard
me
ask
this
is
when
we're
on
a
call
with
the
ops
team
and
they're
first
looking
at
Q,
they're
trying
to
feel
it
out.
You
know,
I'm
I'm
the
one
that's
always
saying,
Hey,
please
get
me
to
your
ops
team
as
fast
as
possible.
I
appreciate
you
that
you
want
to
sign
and
this
sounds
great.
I
want
to
talk
to
your
ops
team
before
you
make
that
final
decision.
And
the
last
question
I
typically
ask
the
ops
team
is
I
say,
okay,
this
is
your
chance
to
tell
me
my
baby's
ugly.
So
tell
me,
tell
me
why
my
baby's
ugly
and
tell
me
why
this
won't
work
for
you.
And
that
breaks
down
and
we
have
an
honest
conversation
about
what
won't
work
and
why
it
won't
work.
And
then
we
can
be
honest
enough
to
be
able
to
say,
you're
right,
that's
going
to
be
super
challenging.
But
let
me,
can
we
dig
in
a
little
bit
more
so
I
can
understand
that?
Or
we
can
say,
hey,
good
news,
you're
the
fifth
person
to
bring
that
up.
And
let
me
show
you
what
we
did
with
these
other
five.
If
you
can
address
those
fears
as
quickly
as
they
come
up,
as
opposed
to
waiting
a
week
or
two
weeks
or
three
weeks,
it
just
changes
the
perspective
for
the
users
that
you're
working
with.
SPEAKER_02
11:59
And
so
you've
mentioned
uh
working
in
the
trenches.
You
mentioned
working
and
talking
to
the
executive
team.
I
I'm
curious,
before
the
conversation
ever
starts,
what
are
maybe
some
things
or
red
flags
that
a
company
can
notice
that
will
make
them
think
I
should
probably
reach
out
to
Q'd?
SPEAKER_00
12:18
I
think
one
of
the
things
that
we
typically
see
is
as
companies
continue
to
scale
and
grow,
um,
if
they
find
this
problem
where
they're
continuing
to
add
headcount
around
the
scheduling
problem.
We
have
customers
that
we've
worked
with
that
um
actually
some
of
the
the
moment
that
they
started
working
with
cued
was
because
they
had
a
team
member
that
leaved
or
a
team
member
that
was
being
promoted.
And
that
was
their
trigger
event
to
say,
okay,
I
don't
want
to
backfill.
I
want
to
fix
this
problem.
And
so
the
people
component
is
one
side
of
it.
The
bigger
component,
and
the
reason
why
a
lot
of
our
customers
are
coming
to
us
today
is
they're
getting
stuck
with
bad
appointments.
We
all
know
that
if
you
have
a
load
and
it's
a
Friday
for
a
Monday
delivery,
that's
wildly
different
than
a
Thursday
for
a
Friday
delivery,
right?
Those
are
two
very
different
shipments.
And
when
it
comes
down
to
the
RFPs
and
being
able
to
accept
lanes,
your
ability
to
secure
prime
appointments,
it
makes
or
breaks
these
shipments.
We
often
tell
people
you
can't
control
rates
as
much
as
we
want
to
believe
we
do,
you
can't
control
capacity
as
much
as
you
might
believe
you
do.
The
only
thing
you
typically
have
influence
on
on
a
shipment
is
the
appointment.
And
it's
wild
that
it's
one
of
the
things
that
we've
done
the
least
with
in
our
industry
is
that
we
really
haven't
touched
it.
We
really
haven't
evaluated,
we
haven't
tried
to
figure
out
how
do
we
solve
and
make
this
better.
And
so
if
you
find
yourself
struggling
with
some
of
these
shipments
that
you
see
your
competitors
moving,
you
have
to
figure
out
a
way,
how
do
I
secure
these
faster
than
the
way
my
team's
been
doing
it
today?
What
does
my
scheduling
duration
look
like?
Like,
how
long
is
it
in
my
TMS
before
I'm
actually
initiating
and
scheduling
these
appointments?
Because
your
customers
expect
you
to
be
able
to
get
appointments
on
an
RDD,
right?
They
expect
if
you
come
back
and
you
say,
I
can't
deliver
when
it's
supposed
to
because
there's
no
appointments,
and
they
say,
you've
had
this
shipment
for
three
days
and
you're
just
now
telling
me
this,
that's
a
tough
conversation
to
respond
to,
right?
But
if
you're
immediately
triggering
as
soon
as
the
stops
come
into
the
TMS,
well,
then
you've
got
a
pretty
solid
case
and
you
can
work
collaboratively
with
a
customer
to
come
up
with
a
long-term
solution.
SPEAKER_02
15:16
So
what
does
the,
I
guess,
the
the
next
phase
look
like?
So
you've
right,
I'm
a
I'm
a
customer,
I've
recognized
I
have
a
problem,
I
I
want
to
solve
this
problem.
What
does
maybe
the
onboarding
process
look
like?
What
do
I
have
to
prepare
on
my
end
of
things
in
order
to
hit
the
ground
running?
SPEAKER_00
15:31
Yeah,
so
we're
really
fortunate.
I
mean,
we've
integrated
with
the
majority
of
the
TMSs
that
are
out
there
to
date.
Um,
most
of
our
customers
can
actually
be
live
scheduling
within
a
week
or
two.
Um,
it's
not
a
very
long
process
for
us.
And
what
happens
is
on
our
side,
you
know,
we've
got
a
full
team
uh
devoted
to
customer
success.
And
that
group,
what
they
do
is
they
pair
up
and
they
they
analyze
your
entire
freight
network
and
they're
able
to
identify
where
are
you
scheduling,
how
often
are
you
scheduling,
which
platform
should
we
be
connecting
to,
where
are
there
gaps
in
your
scheduling
today
that
maybe
you
don't
even
recognize,
you
don't
even
realize
that
that's
it's
happening
every
single
day,
so
that
we
can
kind
of
aggressively
attack
those.
When
we
start
working
with
customers,
it's
uh
I
was
on
a
call
earlier
today,
and
you
know,
customer
went
live,
I
think
it
was
this
week,
and
they've
already
scheduled
hundreds
of
appointments
fully
automated.
And
the
speed
can
go
fast.
I
think
the
the
problem
that
we
have
in
technology
is
a
lot
of
people
don't
want
to
commit
to
a
solution.
I
think
when
we
find
people
that
are
committed
to
solving
problems
and
they
commit
to
a
solution,
they
they
fully
engage,
right?
They're
not
kind
of
like
it's
it's
not
a
side
project,
it's
not
something
they're
thinking
about
doing.
They're
they're
playing
with
it
as
they
have
time.
No,
no,
no.
They're
putting
resources
to
it.
They're
saying,
no,
no,
we
want
to
ramp
this
up,
we
want
to
get
the
full
value
out
of
this
product,
and
then
they
move
on
to
their
next
thing.
And
so
I
think
that's
what
we
continue
to
see
is
people
who
are
ready
to
engage,
they
can
really
ramp
up
at
a
much
higher
speed
than
someone
who
who
wants
to
um,
what's
the
word
I
would
use?
Uh
they
want
to
tinker,
right?
There's
there's
tinkers
who
like
to
play
with
things
and
do
things,
but
then
there's
doers
who
really
want
to
execute
and
want
to
fully
ramp
up
and
and
scale
their
organizations.
SPEAKER_02
17:17
Now,
for
a
lot
of
folks
who
have
used
you
know
LLMs,
ChatGPT,
Gemini,
Claude,
uh
all
of
those
main
players,
you
know
that
well,
historically
they
have
hallucinated.
Sometimes
they
they
make
up
things.
And
uh
and
so
when
people
hear
AI,
it's
either
the
fear
of
it's
gonna
get
something
wrong
or
it's
gonna
take
my
job.
Now,
when
I
hear
a
number
like,
you
know,
a
hundred
loads
or
the
a
hundred
appointments
that
they
they've
scheduled
in
a
week,
how
do
you
know
that
all
of
those
are
actually
efficient
and
are
gonna
get
the
job
done?
SPEAKER_00
17:51
So
one
of
the
things,
and
and
I
mentioned
this
earlier,
I'm
a
strong
believer
of
uh
users
can't
trust
what
they
can't
see.
Users,
um,
it's
the
same
thing
with
any
of
these
AI
platforms
that
exist
today,
is
that
there
is
an
element
of
trust
that
has
to
be
built,
but
it
has
to
be
earned.
Um,
think
about
a
new
employee
that
comes
in
your
team.
Um,
I
don't
immediately
trust
them
to
go
meet
with
my
largest
client,
right?
They
earn
that
respect
by
working
on
other
accounts
and
learning
the
business
well
enough
to
say,
yes,
this
person
should
go
in
front
of
this
client,
right?
That's
naturally
what
we
do.
And
so
the
way
you
learn
that
trust
is
you
see
them
doing,
you
see
them
performing,
you
see
them
acting.
You
don't
just
put
them
in
a
back
corner
and
just
wonder,
are
they
really
doing
what
they
do?
So
we
we
make
a
lot
of
this
stuff
visible
to
them
so
that
we
get
that
user
trust
up
front.
In
our
world,
um,
a
part
of
that
is
like
a
really,
really
successful
audit
log
where
you
can
see
every
single
screen,
every
single
digit,
every
single
button.
You
can
see
everything
that
happens
through
that
process.
And
this
is
also
a
nice
little
protection
for
our
customers
because
we've
all
been
there.
You
show
up
to
a
facility,
you
have
a
scheduled
appointment,
and
the
facility
looks
at
you
and
says,
You
don't
have
an
appointment
here.
Well,
nice.
I
I've
got
cued,
so
I
can
actually
go
back
and
show
you
the
entire
audit
log
and
every
screen
and
everything
that
we've
ever
done
to
be
able
to
confirm
those
appointments,
right?
And
then
they
say,
Okay,
yeah,
we'll
make
it
happen,
right?
Uh
and
so
like
we
have
to
we
have
to
help
people
trust
these
systems.
Um,
and
I
think
organizations
who
just
expect
blind
trust
from
people,
they
just
don't
know
how
people
work.
Um,
people
just
don't
do
that,
right?
They
have
to
that
you
have
to
earn
that
respect.
And
so
we
give
a
lot
of
visibility
to
that.
The
other
side
of
it
is
we
have
a
lot
of
validation
built
into
our
system.
So
imagine
an
appointment
comes
back
today,
it's
confirmed,
it's
dropped
in
the
TMS.
We
do
a
transit
calculation
on
every
single
appointment
we
get.
Um,
and
we
validate
and
say,
is
it
feasible
for
a
driver
to
be
able
to
accomplish
this?
And
we
have
customers
that
have
been
able
to
prevent
service
failures
because
of
that
transit
validation,
right?
That
transit
validation
is
gonna
say,
whoa,
whoa,
whoa,
whoa,
whoa,
you
got
the
appointment
that
maybe
you
wanted,
but
there's
no
way
this
driver's
gonna
be
able
to
execute
on
this.
And
here's
why.
The
users
can
make
an
impact
on
that
before
that
there's
an
actual
service
failure,
which
then
costs
you
revenue
and
all
the
things
that
come
with
that.
So
uh
for
us,
it's
all
about
you
you've
got
to
present
the
information
to
users
so
that
they
can
have
that
level
of
trust.
Otherwise,
in
the
back
of
their
mind,
they're
always
gonna
wonder
is
Qt
really
doing
what
it's
supposed
to
be
doing.
SPEAKER_02
20:29
Walk
me
through
what
the,
I
guess
the
the
user
process
looks
like
through
a
user
dashboard.
So
you
you've
onboarded,
you're
you're
already
a
customer,
you
you've
set
some
appointments.
What
how
are
you
managing
exceptions?
How
are
you
managing
maybe
new
features?
What
does,
I
guess,
sort
of
the
the
tab
or
the
dashboard
look
like
if
I'm
a
user?
SPEAKER_00
20:48
So
here's
what's
what
we
love
about
what
we've
tried
to
do
in
this
space.
We
are
not
trying
to
be
the
portal
of
all
portals.
If
you
pull
a
hundred
uh
transportation
professionals
and
you
ask
them,
how
many
of
you
would
like
another
portal?
The
hands
aren't
going
to
be
like
waving
that
yet,
right?
And
so
we
actually
integrate
completely
into
the
TMS.
The
users
work
where
they
work.
Uh,
I'm
a
big
believer
of
stop
trying
to
send
users
to
another,
another
dashboard,
another
widget,
another
login,
another
thing,
right?
We
don't
need
more
things.
We
just
need
to
be
able
to
execute,
right?
And
so
what
we've
done
is
we've
built
our
workflow
to
exist
100%
within
that
TMS.
So
it
feels
very
natural.
It
almost
feels
like
we're
a
part
of
the
TMS.
I
mean,
think
about
EDI.
You
don't
go
to
anyone
today
and
say,
hey,
show
me
your
EDI.
What
am
I
gonna
show
you,
right?
It's
all
it's
it's
in
back
into
the
system,
all
that
data's
flowing
through.
So
that's
the
way
that
we've
tried
to
build
it.
So
it's
right
in
the
TMS
workflow
for
users.
You're
gonna
have
exceptions,
right?
Those
exceptions
can
be
handled
in
a
couple
different
ways.
Sometimes
they're
directly
within
the
TMS.
We
have
some
customers
that
handle
their
exceptions
through
Slack
or
through
Teams
or
through
email
or
through
some
other
sort
of
chat
functionality
that
they
like
to
use.
So
we
really
believe
in
like
go
where
the
users
are.
Um,
we
don't
want
to
say,
hey,
we're
gonna
send
you
here.
We
like
to
go
to
them
and
say,
well,
where
do
you
go
today?
What
do
you
do
today?
What
does
that
look
like?
Because
if
we
can
find
the
most
natural
process
flow
for
them
and
we
can
just
fit
right
into
that
same
natural
flow,
user
adoption
goes
through
the
roof
because
they're
not
they're
not
fighting
some
new
process
and
trying
to
learn
some
other
things.
Oh,
it's
it's
just
like
I
was
already
doing
it.
It's
just
instead
of
me
jumping
and
doing
these
other
16
steps,
it's
just
one
button
or
one
trigger
right
here
on
my
TMS.
SPEAKER_02
22:37
And
so
how
does
that,
I
guess,
sort
of
uh
how
does
the
AI
layer
filter
on
top
of
that?
Because
I'm
imagining
a
dashboard
where
I
can,
you
know,
view
the
exceptions,
I
can
view
maybe
some
improvements
or
some
things
to
think
about
a
little
bit
differently,
all
of
the
successes,
which
is
great,
maybe
the
failures
as
well.
How
does
AI
layer
into
that
to
make
me
more
efficient
on
an
ongoing
basis?
SPEAKER_00
23:02
So
it
does
a
few
different
things.
The
first
layer
is
it
handles,
so
in
our
world,
it's
not
just
scheduling
in
web
portals,
right?
You're
scheduling
via
email,
you're
scheduling
via
phone
call.
You're
every
possible
mode
of
scheduling
is
what
we
do.
So
in
that
scenario,
right,
we
are
leveraging
AI
to
automatically
form
emails
and
then
respond
to
requests
and
respond
to
details,
read
those
emails
and
push
them
back
into
TMS.
So
all
that
back
and
forth
that
a
user
would
do,
like
a
typical
A
typical
appointment
will
require
about
four
to
six
emails
to
get
confirmed.
And
I
count
those
like
thanks
emails
because
guess
what?
Your
team's
clicking
at
a
thanks
email
and
they're
moving
it
somewhere
and
they're
what
what
it's
still
work.
It's
it's
it's
wasted
work,
but
it's
work.
So
all
of
that
our
AI
is
able
to
do
in
the
background.
Uh
all
that
communication,
all
that,
all
that,
all
those
updates.
The
other
side
is,
of
course,
a
voice
making
those
phone
calls,
right?
We
have
voice
agents
that
actually
can
go
out
and
schedule
appointments.
We
do
it
multi-stop
deliveries
all
day,
15,
16
stop
loads
that
we're
scheduling
for
right
now.
The
other
side
is
using
your
data
to
inform
what's
really
the
best
appointment.
Because
today,
the
way
it
has
always
worked
is
Jane
tells
me
I
always
want
a
9
a.m.
appointment
at
this
facility.
And
so
whoever
Jane
trains
to
the
day
they
die,
they
want
9
a.m.
9
a.m.
is
all
they've
got
in
their
head.
I
want
9
a.m.,
9
a.m.
Well,
what
they
don't
know
is
that
9
a.m.,
there's
actually
a
shift
change
that
takes
place.
And
that
9
a.m.
truck
actually
takes
30
minutes
longer
to
unload
than
the
truck
at
9:30.
So
you're
better
off
taking
the
9:30,
or
you're
better
off
taking
the
8:30,
or
whatever
it
is.
So
we
learn
these
patterns.
And
so
instead
of
just
automatically
taking
9
a.m.,
where
maybe
you've
had
a
little
more
service
failures
or
maybe
you've
had
a
little
bit
more
delays,
we're
able
to
adjust
that
so
that
we're
actually
confirming
for
the
right
time,
not
just
the
time
you
thought
was
right.
SPEAKER_02
24:57
And
then
how
are
those,
I
guess,
that
that
feedback
delivered?
Is
it
in
the
app
of
choice
or
is
it
maybe
some
summary
reports?
How
how
do
they
know
what
to
get
better?
SPEAKER_00
25:07
What
happens
for
us
is
our
system
can
automatically
do
that
within
a
tolerance
range.
So
our
customers,
they
set
tolerance
ranges
with
us
and
they
say,
hey,
this
is
an
appointment
I
want,
but
really
I
want
within
X
amount
of
range
of
that.
Well,
that
range,
what
it
does
is
allows
us
to
dial
in
their
appointment
selection
without
users
having
to
get
involved.
So
you
think
about,
you
know,
we've
got
customers
that
we're
scheduling,
you
know,
hundreds
of
thousands
of
appointments
for.
If
we
were
always
triggering
them
to
say,
hey,
you
really
should
have
thought
about
930
instead
of
nine,
they're
not,
they're
not
gonna
tap
into
that,
right?
That's
gonna
be
way
too
much
work,
way
too
much
user
engagement.
So
that's
why
we
use
that
tolerance
band
that
we
are
able
to
say,
okay,
we
can
do
whatever
we
need
to
within
this
ban
as
long
as
we
determine
what's
the
best
appointment
for
them
based
off
of
the
identity
of
that
shipment.
SPEAKER_02
25:53
And
so
as
you're
thinking
about,
you
know,
sort
of
what
comes
next
in
your
platforms,
is
it
is
it
really
just
coming
directly
from
the
customer
itself,
or
is
it
kind
of
the
overall
sort
of
AI
landscape
of,
oh,
this,
you
know,
Chat
GPT
just
dropped
a
new
model,
maybe
we
want
to
integrate
that?
Or
how
does
the,
I
guess,
the
speed
of
the
adoption
and
of
the
evolution
of
AI
fit
into
the
the
cued
roadmap?
SPEAKER_00
26:19
Yeah.
So
one
of
the
one
of
the
biggest
things
that
we've
done
with
our
customers
that
we
just
rolled
out
in
the
last
month
is
our
ability
to
leverage
natural
language
prompting
to
create
customized
rules.
So
if
you
think
about
transportation,
there's
a
lot
of
complexity
in
the
appointment
scheduling
process.
I
think
uh
the
reason
why
a
lot
you
don't
see
a
lot
of
competition
in
the
space
is
because
it
is
crazy
complex.
Most
of
these
tech
companies
that
are
VC
backed,
they
want
a
simple,
repeatable
process
that
they
they
can
rubber
stamp
this
thing
and
then
push
it
all
the
way
out.
Well,
appointment
scheduling
doesn't
look
like
that.
Um,
and
so
what
we've
done
in
the
way
that
we've
built
our
product
is
we've
allowed
users
to
use
natural
language
to
build
customized
rules.
So,
for
example,
we
have
a
customer
that
every
time
you
go
to
schedule
at
this
Costco
facility,
what
you
have
to
do
is
you
have
to
look
at
the
commodity.
And
if
the
commodity
uh
is
blank,
then
we're
gonna
automatically
assume
that
it's
meat.
If
it's
not
blank
and
it's
and
the
PO
starts
with
a
1052,
then
we're
gonna
mark
it
as
a
palletized
load
1052.
That
sounds
stupid,
right?
That's
a
lot,
that's
the
stuff
that
people
are
looking
at
every
single
day.
And
so
in
the
past,
what
would
happen
is
if
you
were
trying
to
automate
scheduling
like
this,
you'd
have
to
reach
out
to
a
developer
and
you'd
say,
Hey,
developer,
here's
this
really
custom
rule.
We
would
detail
it
all
out,
it
would
go
to
them,
it
would
go
into
a
sprint,
takes
a
couple
weeks,
gets
on
the
back
end
of
the
sprint,
gets
deployed.
Then
you
take
it
out
to
the
user
and
the
user
gets
to
test
it,
and
they
say,
Yes,
this
works.
So
you're
talking
three
weeks
or
so
to
be
able
to
get
a
rule
like
that
deployed.
Now
our
customers
actually
have
the
ability
where
in
our
back-end
system,
they
can
just
type
a
rule
the
way
I
just
said
it.
Every
time
I
get
this
load,
if
the
commodity
is
blank,
we
want
to
we
want
to
schedule
for
a
meet
door.
If
it's
not
meet
and
the
PO
starts
with
1052,
market
has
a
palletized
load,
1052.
They
hit
test
and
it
immediately
writes
the
code
to
create
the
rule,
and
it
can
be
deployed
in
a
matter
of
minutes,
right?
So
this
the
way
that
we're
able
to
scale
scheduling
for
customers,
that's
the
real
problem
that
we're
solving.
People
can
build
automation
to
automatically
send
an
email.
They
can
write
it
write
an
RPA
to
be
able
to
go
out
and
automatically
say
what
they
can't
do
is
scale
it,
and
that's
what
we
really
bring
when
it
comes
to
how
do
you
fix
scheduling?
You
have
to
be
able
to
scale
it
across
your
organization.
And
leveraging
AI
to
be
able
to
write
these
natural
language
prompts
to
then
code
into
the
system
is
a
significant
shift
in
how
we
can
deploy
and
speed
up
this.
SPEAKER_02
28:52
With
all
the
companies
that
you
speak
with,
what
does,
I
guess,
sort
of
the
modern
scheduling
department
look
like
when
they're
using
Q'd?
And
then
how
do
you
layer
in
all
of
the
different
cost
structures
that
seem
to
fluctuate?
There's
some
examples
of
what
I'm
hearing
where
developers
are
asking
for
a
salary
and
then
they're
asking
for
a
certain
amount
of
token
budget.
How
are
companies,
in
in
your
experience,
kind
of
adjusting
their
hiring
for
those
different
economic
units?
SPEAKER_00
29:24
So
there's
a
few
things
that
we're
saying.
First
off,
a
lot
of
these
employees
are
switched
to
exception
handling
work,
right?
You're
really
focused
on
the
exceptions,
is
what
you're
trying
to
do.
Um,
and
that
that
changes
the
conversation
you're
having.
What
we
what
we
see
a
lot
of
appointment
schedules
getting
engaged
with
now
is
let's
say
because
they're
not
having
to
go
out
and
manually
schedule
all
these
anymore,
they
have
a
time
to
actually
now
look
at
your
your
scheduling
book.
Uh,
which
locations
are
your
problem
child?
You
know,
we
can
identify,
hey,
here's
a
location
that
every
time
you
submit
an
appointment
request,
it
takes
up
to
32
hours
to
get
a
response
back.
Well,
those
are
the
things
you
want
to
bring
into
a
QBR
with
your
customer,
right?
You
want
to
be
able
to
sit
down
with
your
customer
and
say,
hey,
look,
we're
trying
to
be
really
aggressive
about
this
lane.
We
like
this
lane,
it
works
well
for
our
network.
The
problem
is
we
have
such
a
turn
time
on
this
specific
location.
How
could
we
get
these
appointments
back
faster?
Right.
These
we're
taking
them
into
deeper
layers
of
conversation
around
scheduling
that
they've
never
had
before,
and
to
the
point
where
a
lot
of
our
large
customers
are
now
inviting
us
to
come
to
their
customer
advisory
meetings
and
saying,
no,
no,
no,
we
want
you
to
talk
about
this
with
them
so
that
they
understand
more
about
this.
They
understand
where
we
want
to
take
this.
And
so
it
really
is
all
about
um
creating
uh
additional
visibility
to
scheduling
that
before
you
didn't
have
because
you're
just
executing.
You're
right,
you're
just
trying
to
keep
up
with
the
madness
of
what
is
going
on
every
single
day.
Now
you're
able
to
actually
look
at
how
does
my
network
work
and
then
where
where
are
there
gaps
on
the
scheduling
side
that
could
make
my
network
a
lot
stronger
for
my
carrier
base.
SPEAKER_02
30:58
And
then
that
way
that
that
translates
into
what
the
company
can
deem
as
appropriate
for
the
positions
that
they're
gonna
hire,
whether
it's
an
AI
ops
role
or
only
dedicated
to
exception
management.
Um,
so
it
sounds
like
it
you
guys
are,
you
know,
kind
of
at
the
front
lines
of
of
building
what
that
new
department
looks
like
inside
of
logistics
companies.
SPEAKER_00
31:18
Yeah,
and
look,
I
think
um
in
our
industry,
we're
gonna
constantly
be
learning
about
that,
right?
I
mean,
I've
been
I've
been
in
the
space
for
20
years,
and
I
I
would
like
to
say
that
you
know,
we
reach
a
point
that
we
figured
it
out,
but
I'm
sure
in
the
next
20
years
I'll
still
be
saying
the
same
things
that
we're
learning,
we're
figuring
out,
we're
trying
to
understand
how
this
all
works.
Like
that
it's
just
it's
a
part
of
that.
I
think
the
thing
that
matters
most
is
that
a
lot
of
this
comes
from
experience
of
doing.
That's
why
I
talk
so
much
about
being
on
the
front
lines,
being
directly
with
users,
understanding
their
rural
workflow,
not
creating
things
in
a
vacuum,
because
when
when
you
can
engage
at
that
level,
you
can
solve
problems.
Um,
if
we
left
our
engineering
team
in
a
back
room
to
solve
these
problems,
what
would
be
delivered
to
the
transportation
industry
wouldn't
necessarily
fit
the
mold.
And
that
that's
what's
really
important
for
us
for
as
you
look
at
these
tools
and
you
look
at
different
different
products
that
exist
in
the
space,
you
have
to
figure
out
how
close
are
you
gonna
be
able
to
be
to
the
end
users
who
have
control
over
that
roadmap
so
that
you
can
make
sure,
hey,
is
this
problem
gonna,
is
this
problem
gonna
come
back
in
a
few
months
when
my
users
don't
want
to
engage?
Or
is
it
gonna
get
stronger?
Where's
it
gonna
be
and
how
how
much
am
I
gonna
end
up
depending
on
it
going
forward?
SPEAKER_02
32:33
Now
that
that
that's
really
well
said
and
and
sheds
a
lot
of
light
on
a
problem
that
I
didn't
know
was
this
extensive,
which
I
I
imagine
for
a
lot
of
your
customers,
they
are
finding
that
out
as
well.
All
right,
Tom,
uh
anything
else
that
you
feel
is
important
to
mention
that
we
haven't
already
talked
about?
SPEAKER_00
32:50
No,
I
think
um
look,
I
think
that
um
as
people
are
evaluating
new
pieces
of
technology,
one
of
the
things
that
that
I
always
encourage
you
is
make
sure
that
you're
investing
in
things
that
impact
your
customers.
When
I
was
running
a
brokerage,
when
I
ran
a
fleet,
um
it's
it's
it's
very
easy
to
get
distracted
and
all
the
different
AI
that's
coming
at
you
and
all
the
different
technologies
that's
coming
at
you
and
try
to
figure
out
how
to
prioritize.
I
always
encourage
people
to
go
talk
to
your
customers
and
ask
them,
hey,
we're
thinking
of
investing
in
another
layer
of
technology
for
our
team.
Here's
some
of
the
things
that
we're
looking
at.
Which
of
these
are
interesting
to
you?
And
when
you
talk
to
your
shippers
about
that,
if
there's
products
that
excite
them,
then
you're
coming
to
them
as
a
partner,
you're
you're
coming
to
them
as
someone
who
understands
their
business
and
values
their
input.
And
that
becomes
incredibly
valuable.
And
it's
it's
not
uncommon
for
me
to
be
on
a
call
with
a
potential
customer
or
a
customer
and
their
shippers
that
they're
working
with
because
they
want
to
have
this
collaborative
conversation
around
what
would
this
do
if
we
did
this
for
your
business?
And
so
just
don't
lose
sight
of
your
customer
in
the
journey
of
trying
to
leverage
technology.
SPEAKER_02
33:59
That's
a
mic
drop
moment
for
the
conversation.
And
so,
Tom,
where
can
folks
connect
with
you,
learn
more
about
Q'd?
We
can
put
Qed
uh
com
in
the
show
notes
just
to
make
it
easy
for
folks,
uh,
but
anywhere
else
we
can
send
them.
SPEAKER_00
34:12
Yeah,
yeah.
Feel
free
to
hit
up
our
website
um
or
find
me
on
LinkedIn,
shoot
me
a
message
on
LinkedIn.
Um
I'm
usually
uh
uh
pretty
busy
out
there
as
well.
SPEAKER_02
34:21
Awesome.
Or
on
the
conference
circuit,
as
as
we
discussed
before
we
started
recording.
Thank
you
so
much,
Tom.
SPEAKER_00
34:27
Thank
you.
SPEAKER_01
34:33
Thanks
for
tuning
in
to
another
episode
of
Everything
Is
Logistics
where
we
talk
all
things
supply
chain
for
the
thinkers
in
freight.
If
you
like
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episode,
there's
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Everything
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Logistics.
And
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We
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Additionally,
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All
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