Implementing AI In The Real World — With Kyndryl's Antoine Shagoury

Channel: Alex Kantrowitz

Published at: 2025-01-31

YouTube video id: hYB7Yfy2kiw

Source: https://www.youtube.com/watch?v=hYB7Yfy2kiw

kindrell Chief technology officer antoan
shagui joins us to talk about
implementing AI in the real world
covering everything from Agents to open
source in a YouTube exclusive brought to
you by kindrell Antoine great to see you
welcome to the show thanks Alex
appreciate the time today oh I'm so
excited to speak with you mainly because
you're actually seeing what it's like to
implement AI in the real world and we
hear often companies talking about AI as
a philosophy or AI as a distant dream AI
3 to 5 years from now but you're doing
it uh and so let's talk immediately
about what you're seeing in the agent
world we just had Mark Benny off on the
show recently talking about how they're
bringing agents uh into into reality uh
connecting it with the Salesforce data
um I still can't quite tell what the
reality is for everybody else and
exactly you know what that means so you
are doing this in the real world you're
helping companies Implement agents um
what can you tell us about that one the
first thing to reflect on it it's it's
absolutely amazing at the rate and Pace
in which you know the the conversation
has grown the opportunity discussions
you know have continued to kind of
Advance um but one of the things that we
we we'd love to kind of talk about is
especially with agents it's it's it's
you don't start there it doesn't start
with an agent there's a whole different
process that kind of gets us into that
set of capabilities and one of the more
difficult things to break when you go
from what the expectation may be to how
do you want to approach it really starts
to lay the groundwork on what we think
is the right pattern and practice that
that help makes it work and when I the
way I refer to that is almost and um you
may laugh me for saying it it's kind of
that Journey message so you kind of have
to take that with a little bit of grain
of salt um but as we start to unpack the
business a bit that demystifying um it
sounds strange how do we demystify a
business we start with understanding the
information the data sets available how
do we really understand what we're
working with and um often we get into a
little bit into demonstrating what we
can do with simple AI introductions some
simple automations to step through do we
have the right data sets based on what
the client's looking to do and as we
build into that that's when we start to
build the pattern of information and we
go from simple automations to predictive
capabilities and that is really at the
first time there's signs to put an
opportunity to how we can deploy an
agent to demonstrate going from a
predictive opportunity to something
prescriptive so we know enough about a
business process enough about A System's
operation to really an agent step in to
orchestrate maybe manage a state of an
application a failover process and that
really starts to drive it and as as much
as it sounds a little bit defeating it
really demonstrates that you know we we
can really change the framework on how
clients approach it and I I'll put one
last comment to the answer too which
which sometimes gets a raised eyebrow
for me ultimately we're saying look how
do you want to become autonomous so
agents are really just another step
getting closer to to really automated
systems and automated processes and
that's how we really look start to look
at and approach it yeah and that's what
I wanted to ask you is Agent just a new
fancy word for automation I mean we've
been doing so the the tech world has
been doing automation for a long time so
is there anything new and different
about this agentic moment as every
research house is telling us about that
should let us think hey this is
something that's actually going to work
this time or is this just like we're
automating a little bit more and it's
rebranded to give you the word answer it
can be both I oh no that makes total
sense like it's kind of like the
proverbial Rat Hole a little bit or the
rabbit hole um yeah we can we can go and
really just apply it in simple
automations but that's not the benefit
it's it's really the granularity that we
can start to map into to help the
business to help kind of provide the
outcome and I'd actually look at um this
is this is going to make sound strange
too it's almost like the impact radius
like the impact area there's so many
things that agents can impact whether
it's looking at a business process I'm
looking at you know human involvement
like who's involved like how many people
have to be involved in a say a business
process a service or a transaction so it
has the opportunity to really get very
deep into can it be the better
orchestrator right of an event can it be
the better analyst to understand the
permutations of a situation can it be a
better distributor or a a run agent to
distribute when something should run so
it has a phenomenal opportunity and we
are seeing use cases where the clients
are able to get deeper into and that's
why I use that term demystifying
business a little bit as we get deeper
into how business runs it really exposes
Legacy systems human intermediation
across different processes applications
even partners and agents allow us really
to start to decompose that in a way in
which we can focus on what really
matters There's an opportunity no
there's definitely an opportunity can
you give me like one concrete example of
how like you've helped uh and then we're
going to get a little bit into I know
we're like talking a lot about what you
do I want to talk a little bit about
what the company is uh but but before we
get there just one concrete example of
how you've helped a company uh Implement
an agent or sure we stop there yeah no
it's a good one and maybe simple's best
sometimes but you know uh and I'll touch
on you know AI coding or AI assisted
coding for example we've we've be able
to use our our AI agent framework to
deploy not only codee assist a agents
but also into test and deployment agents
so how we can also shorten the time in
which where we see errors in coding to
be returned back and be refined so we've
deployed that across a client for
example um International client in Telco
space on how they improved the code life
cycle the code deployment life cycle and
the in the Improvement life cycle so
there was enough specifics in what they
wanted to deliver within this code
Factory that the agents were able to
really overlay it in a way in which
which we improve quality improve quality
that means lower defects klock type of
analogy and how we can improve how we
deploy more effective releases better
functionality faster to the client base
so it's it's specific it's it's it's not
like it's not dominating and changing
the world day one but it does provide
efficiencies and a lot of the early
gains going back to the basics can I
provide more
efficiencies right you know back into
the business and that's one of the
perfect use cases yes so let's just for
audience tell them a little bit about
what kindrell is because sure you have a
pretty interesting history oh yeah we we
spun out from IBM a little over three
years ago um and the way I can best
represent what we were is we were the uh
implementers we were the integrators and
we were the operators for managed
services and the infrastructure in the
systems as a part of IBM and their
client base um and when we spun off it's
a phenomenal Heritage to base our
business on so when you think about what
we do we are experts in running large
complex Global Mission critical systems
and as we left IBM we went from being
you know say you know running their
portfolio and their Integrations and we
expanded in our Partnerships so we
became again close partners with
Microsoft AWS Google Dell now Nvidia
think about those time examples sap I
mean Oracle relationships so we can now
really really extend our integration
implement ation experience really to the
benefit of our client you know from that
community so we really become like what
I call this ecosystem player but we are
rooted in manage services right and it's
these are the companies that we speak
about on the show every week so you have
pretty good visibility into them I might
ask you an Nvidia question later in fact
I probably will on the implementation
side I think this is one of the big
areas of interest for me and for our
audience and for anybody working in Tech
which is uh in AI in particular there
are so many proofs of concept everybody
has built some AI po as they call it and
then it kind of sits there um and
sometimes they make it out but most of
them don't I think like something like
10 or 20% of proof of Concepts in AI
have haven't really been proofs at all
and no sorry only 10 or 20% have made it
out the door and 80 or 90% haven't
really been proofs of at all they've
just been sort of prototypes and you
know they look nice you go to a
management meeting everyone cheers and
they never see the light of day on the
implementation side why is it so hard to
get AI projects out of the door um wow
this is I got to be careful on how I
answer this one right I'm not sure I'm
going to help the numbers or statistics
in the conversation the
um I'll probably reflect first a little
indirectly and then directly so
indirectly it's it's no different than
the rate and pace of solutions hunting
for a problem I mean the market is
amazing that way and the investment is
is phenomenal so we have so many things
pent up in the opportunity side
um the best part we're starting to see
is the willingness to adopt the
willingness to try the pocs have
actually gone up exponentially they
haven't gone down they haven't died down
at all in that scenario but we often get
into is the expectation the expectation
that AI is going to naturally solve the
problem where we haven't really defined
it yet so there's a lot of I'll say
missed expectations and although yeah
80% failure rate is not not uncommon or
call it you know getting thrown on the
shelf right first of a kind is last of a
Kind type of scenario um the the the
approach we often get into is yeah we we
we worked on the POC but it's actually
trying to find out what's missing and we
continually work on so a lot of the
effort we bring is in the approach so
how do we understand what you're looking
for what's the business problem and we
often find many of the poc's turn into
they don't require complex AI new model
development and things that space they
require simple automations so they
require more or more data right or more
programmatic changes in how the
application is operating or the business
is operating so a lot of it is just the
I guess the gap between the
understanding of what technology is and
what it can do versus how you need to
deliver that value and and we see that
and and as much as um as much as we talk
about it and it sounds almost like in a
negative way it it is a part of the
discovery process and the the one thing
we always want to encourage though is
probably what the business is intending
to do how do I uncover how do I evolve
what's actually happening here and
that's that's usually the best way to
keep the focus and actually recraft the
thesis and we try and do that more so I
think as much uh and I'll I'll I'll
leave on this one strange kind of um
point with you we are also getting
better at what we need to do in the POC
requests so how do we help shape it and
that's where I think you see a lot of
maturity happening not just with
kindrell I think you see a lot of us in
the market now maturing how we're
engaging them right so if I'm reading
what you're saying the right way there
was Chad CHP came out November 2022
everyone says we need an AI strategy
they throw AI at every problem they have
and it makes sense for some folks but
for a lot of companies what you're
saying is basically hey slow down uh you
might not need a generative AI solution
here you might be able to fix this with
uh more standard automation yeah it's
it's it's often the case to start now
again the journey is important and I do
like the chat GB example I mean and that
became even in itself a great
opportunity for us to show how agents
can help continue to refine and filter
responses
even sources but everyone thought it
would have everything they needed just
trying the next the next staunch or the
next opportunity with with the product
so now that we've established that who
is generative AI working
for I mean what are the 10% that this
actually makes sense for um you know
we're seeing you know and outside I was
going to laugh and and say not but
there's some really interesting things
that that are maturing so um and
depending I'm you know happy to go into
more detail on some of this depending on
your questions but if I look at things
like some of the Telecom clients that we
have um a lot of the work in how they're
approaching AI approaching information
gathering approaching the analysis um is
working very well and how we start to
apply again gen looking at procedurals
product you know capabilities um you
know selling opportunities so how do you
go into upsell so I think there's a huge
kind of gain that we're seeing in
certain industries so they're seeing
benefits in that so now they've evolved
into cart generation so how do I
understand what may be better targeted
for you personalization so they've gone
from understanding what they built
within the workflow they built the the
the efficiencies within the workflow the
product life cycle and now they're using
you know gen to assist within that sale
process and now agents involved in
personalizing baskets and and shopping
carts for you so reducing the time in
which to actually get you to sign a
contract for a new Service Automotive a
little bit differently interesting
though um big push on understanding
personalization so harvesting a lot of
the surveys the interactions the chats
and really directing that through so
they've really started to leverage
models and now micro models to really
specialize to Target audiences better
and that's influencing supply chain
quality so I think the I think there may
be a really different dichotomy in where
the industries
are right you know based on the access
to information how they can start to
address that and how targeted they were
either addressing a client need or in
theory like for example a an opportunity
to a committed contract type of life
cycle right and so everyone's scrambling
and they're like where's the ROI and Ai
and we see the models getting better why
isn't that immediately translated and I
think what I'm hearing from you is that
each industry is going to have to find
the specific ways that it makes sense
for them and that's a discovery process
yeah absolutely well said now you've
given a couple interesting examples and
you offered to drill down a little bit
deeper so I'm going to take you up on it
okay you said it's helping in the sales
process for telecom companies can you
talk a little bit about what that looks
like in practice yeah it's a little bit
of a double click in and you know kind
of through the process in itself um
think about even I mean we're all
customers right of Telos in some some uh
realm or another one as you start to
call in you know getting almost the
immediate uh analysis or information on
our profiles what services do we have
how long have we had the service things
like a person would normally talk to you
when they're trying to upsell you on a
new contract or a contract renewal but
it starts to build that knowledge base
of information the system can very
quickly we start to identify
opportunities hey we can reduce your
bill you know by X because you're not
using these services so can I improve
your experience all of a sudden
suggestions start to come in and you
don't need the same human interaction
associated with it and as you start to
get into that you can now give scenarios
to show you this can reduce your price
so instead of you going through this
human process and I'm not sure everybody
is but if like I'm I'm a customer of one
large TCO in the United States they'll
call me every year with different
scenarios how do I reduce that I wind up
having to do the analysis myself they've
closed the Gap and many of them are
starting to close the gap on how they
approach and demonstrate the information
of your usage how they can improve your
experience improve again the economics
associated with your service and how
fast they can actually change the
service for you they really shorten the
life cycle of opportunity and the agents
in the process are collecting so I gave
you the example earlier around specific
types of agents that generate or help
lead to Value so the orchestration agent
type of concept can now start to break
up or Shard the the query to start to
pull the information the analysis in so
where are you what's your demographic
what's your usage pattern what's your
bills associated with your usage it
brings it to the analysis almost
immediately then from orchestration you
have the calculations of the opportunity
agent how do I give scenarios that may
equate to your demographic right your
income range right opportunity say
family size so we can start now put
opportunity analysis in play and then we
have presentation agents how do I
represent that to you in a language in a
presentation not so the personalization
becomes very apparent so it really it it
it is a um an accelerant and it's an
augment to helping to really improve the
service right or elongate your
willingness to keep the service with
them the annuity that they have with you
and their in their company I don't if
that helped characterize enough for you
no it's definitely did and uh it's
fascinating how it's just going to exist
along every single step and yeah it's
going to be I mean if this is what we're
seeing today I just imagine that as the
models get better it's going to be
pretty impressive both for companies who
are trying to sell to us and maybe for
us as well to be able to you know send
our agents out in the world and be like
all right what's everybody offering and
how do I get the best deal oh man and
you probably shouldn't comment it's like
the agent War goes in my head it's like
the war of the agents in the backg going
to negotiate with each other they really
are
yeah I agree go ahead oh no no I was
going to say I I I made the comment
before that impact kind of statement
there's so many impacts that it's
there's such a different Evolution like
there's a statement I which I I you I
probably get t for saying but we are
seeing the greatest evolution of
business in our lifetime fundamentally
we are and it's it's changing business
it's changing how we develop product
it's changing how we experience you know
Services it's changing job roles right
there's so many things that this has
implications to and it just it's it's
mindboggling in concept most definitely
I I agree with you 100% I don't think
you're going to get in trouble for that
statement I think I agree with it for
sure and in fact in the in the book you
see behind my shoulder here always day
one which was about um basically how all
the big tech companies use AI when I was
reporting on Amazon I found that they
were using automation systems to
negotiate with the vendors who were
supplying their fulfillment centers and
my point of the book was like write this
book because big tech companies are
going to know what's going on before
everybody else does does and if you see
what they're doing then you'll have a
heads up for what comes for the rest of
us and the AI negotiating with vendors I
I was like that's crazy that's never
going to come for everybody else but as
you're saying we're getting close which
is amazing absolutely so let's talk a
little bit more about some of the things
that might happen upon implementation
and what you're going to have to deal
with on a um on a CTO or technology
level or even if you're just like you
know working in the trenches and trying
to implement and there are some
considerations that you need to have and
I think the first thing and you sort of
have hinted at this a couple of times
when you talk about how you prepare
companies to go about this journey and
what you need to get in order is data
because there I mean my mind has been
blown about all the different scenarios
that uh might occur if you don't take
care of your data um could your AI agent
or chatbot spit out data it shouldn't
could employees you know there've been
examples of employes seeing the CEO's
emails so talk about from your
perspective how data uh privacy data I
guess segmentation is important yeah I
think
um I I probably can't underscore
important enough it's Paramount it's
it's one of those scenarios where um
it's probably the investment area that's
the most you know unspoken where the
proper grammar is right it's the least
spoken about but it becomes the most
pivotal right CU one you know data
inventory data harvesting classification
data access really becomes you know
critical um if you can tell by my
hairline I grew up in highly regulated
businesses I mean you couldn't touch a
piece of information without 10 people
watching it in that type of scenario but
you'd be surprised how many Industries
don't have that rigor so understanding
data lineage within the process becomes
absolutely fundamental so understanding
the auditability of how information has
been one generated collected propagated
been manifested that's been somewhat you
know what I call that Foundation element
within that securing the information
within it so there's been a growth and I
think probably almost too quiet but a
growth within new technologies that are
allowing us to tokenize information and
uh in theory you know even you know even
protect data in in uh in its construct
so how do we actually start to look at
data in the true sense that it is so we
go from this data inventory data lineage
management then how do I understand now
how do I can actually protect the
information or components of it so
understanding so may need to protect
your name but I may not need to protect
your your race or your gender I may need
to protect your your geography but I
don't need to protect your usage so even
that's now growing within that construct
um and the security in itself in and
around that has grown exponentially so
how we look at that kind of data pattern
and access the the next area that we
often that we often see into is and this
is where it becomes another opportunity
on how we build automation but you know
how do we build transparency within the
data usage for models for the
analytics and for the usage themselves
so it not only puts the visibility
within how the data has been used it
also then puts a tremendous like say
kind of I say magnifying glass on how
it's been adopted so a lot of those
things are really started to mature also
as we're going through that space But
it's a part of it it it also lends
itself and demonstrates and exposes a
lot of the issues with Legacy so you
know the Legacy Tech or the tech that
situation we're dealing with um and I
don't want to say I could probably spend
10 years on the data issues but the the
other we get into that's concerning know
it doesn't it right it's a hint htin
right worth another podcast maybe with
someone with more hair than me but the
the thre you get into is data
duplication like data manipulation a lot
of the this is could be a say strange
phrase um but data has become so
mutable in the environments from made
business because the applications are
fragile so what they've done is they
layer application services but they
don't touch them and what they do is
they keep modifying Source data and when
you start to see that that is a Cascade
so when you when you're looking at
hallucinations you're looking at errors
in calculations these are often the
sources models models can be adapted
data if you start with a bad ingredient
you're going back to square one in
regard to the analysis the Assumption
right and the and and the metrics
associated with it is that the biggest
lift for companies trying to work in the
AI space right right now getting their
data in in order um I think it's the
biggest hurdle yeah it's the biggest
hurdle not even a lip it's the biggest
hurdle to get past or be able to
demonstrate there's enough of an area to
prove it because a lot of the areas need
to be proven still so boards uh a lot of
the SE Suite it's this is there's a
tremendous visibility not just on tech
for tech anymore but can you know can
the technology enable a business
opportunity or an outcome so that
visibility is the first you know really
large speed bump right the the the next
lift is scale scale is the real lift the
way like how do I if I proved it how do
I scale it right so how do you do that
um so this is where uh this is where
it's kind of the concept of you know uh
how do I break it up in a way in which
become affordable it's very expensive by
the way so getting deep in analysis deep
in processing continual real-time model
operations become very expensive so a
lot of things we talk about is what can
be what we can do to either run that
right through Partners so how do you
come more more Cloud friendly more on
demand more scale or El
capability um even the examples of our
Nvidia work is you know how do we can be
better at purposely driven workloads so
how do we understand quality of service
on a on on a workload does it need the
highest performance gpus can it run can
it run a different type of memory
environments can it run on a slow burn
so a lot of things we do is we also will
size shape and estimate how we can
optimize or architect right the analysis
and what data is needed and how to run
it because I'm sorry cost matters right
the taxi can be fairly large right in
the over process I've heard some horror
stories absolutely some really bad ones
um so talk a little bit about who makes
the decisions about uh whether to go
forward with AI is it the tra is it a
traditional Tech decision or are there
new folks in involved in it great
question oh it's a phenomenal question I
um I can tell you without much
hesitation there is a material shift
we're seeing a lot more Business Leaders
involved in the decision process and I
think that comes in a couple of
different uh a couple different reasons
or I think environmental changes and I
kind of made that Evolution comment
before um but the consumption model is
evolving too there's a lot less appetite
for build there's a lot less appetite
you know for uh creating Solutions into
the business it's it's a needed now
scenario so we're seeing more and more
so the analysis at least we feel it
being we feel it in the sense of we're
seeing large the majority of investment
being directed through Business Leaders
and how we're driving into it um it also
shows there's a lot less patience in
drift and a lot less patience in a
thesis being proven wrong but it is it
is a shift um within that within that uh
within that operating environment yeah
my theory is this is coming from the CEO
often who's reading about it in the
press and seeing the magic and saying we
need to harness that and that's and it's
driven from the very top of the company
usually I don't know if you just to kind
of help compliment like like a year ago
we started to look at just for fun
the growth of AI being mentioned you
know in company announcements right so
we all started playing that we saw it in
the Martin news and everything else and
it just it just went exponential I mean
it literally went exponential and then
when we went from it being in the news
to how many meetings did we have with a
a seite that discussed what the strategy
benefits could be and we start you it
went from you know heads of
infrastructure data management people an
you know an analyst teams things that
then it started to get into Coos and
then the CFOs started to ask questions
on looking at you know like operating
Effectiveness scary stuff for
technologist you don't want to ever open
up the curtain right um you know and
then to your point yeah CEOs became
engaged it's like how do I there was a
great conversation um probably earlier
uh early last year and then to Middle
last year a CEO basically said I want to
understand the pulse of my
business yeah right and and and these
are the tools that they want to deploy
so it's actually on the one side it's
exciting it it's but it's a very
different audience it's a very different
appetite and it is is turning that
directional tide definitely so on the
build uh the build I guess you said
build is important or what what you say
about build people don't have patience
for that anymore yeah there's a there's
a lot there's a lot less appetite to
start building Homegrown Solutions
anymore but this is what I need to ask
you though because you're you're CTO um
there's a debate between you go off the
shelf or you go open source so I mean
open source gets you kind of half the
way there but it's still building on top
of it so what do you think is the most
effective way to to implement AI is it
the off the-shelf stuff or the open
source models I um I'm going to
underwhelm you and it's the worst it's
it's a little bit of both you have to
kind of balance what the what the use
case is there's tremendous opportunities
and and I do think there's an
interesting split um in the market
there's like and and and I'm sorry I
haven't really thought about this too
much detail like from an explanation
standpoint but there's the embedded
tooling so like like the things like the
Salesforce conversations or the service
now conversations there's a lot of AI
That's embedded within the workflows and
the information being gathered and a lot
of that you're not necessar going to
replace that fundamentally so there's
there's a combination what you start to
get into it but infusing and I'll use
that word a little bit differently right
but infusing different capabilities
within your interaction either with data
you know data classification the model
development even in how you're starting
to create micro models so small language
model capabilities to Target your
business that's where the I think that's
where the I think the real linkage
starts to come so
it's not as if you're creating again
your own model your own infrastructure
your own analytics the question is is
there enough around how do I create the
benefit to both and the area that we
see a great amount of energy is in the
um
integration okay so how how can I
leverage can I leverage and we did we
did some work on on someone to see or
compare or or arbitrate the large
language models mhm it's like okay well
let's let's understand what you're
looking for and let's see you know what
parameters they need within the models
and you know can you now devolve or
basically create a micro model that's
very targeted to your business that
basically was a recipe so how do I look
at what's happening within you know
within say chat TP versus Gemini um how
do I start looking at llama differently
and that wasn't as if it was it was
brought from scratch but it was a recipe
that kind of created that that
integration layer and that's where I
think that I think the good energy I
think there good energy good results
that happened from that definitely no I
love I think it's the right way to to
look at it definitely 100% okay I I
don't want to go without talking to you
about your Nvidia partnership sure um
you guys are partners with Nvidia um
talk a little bit about the nature of
the partnership and then also what's it
like working with a company like Nvidia
they are they are fascinating the way
that they operate uh yeah it's um I
think we learned something new every
every turn I I uh so one you you can't
hide they are driving a material growth
in the market um and when we look at you
know kind of the partnership
opportunity you kind of first people
think initially oh it's all about
Revenue it's all about kind of growth
turnover in sales but one you know to
our Delight working with them it was
really around how do we accelerate
opportunity how do we identify and
co-create adoption work and and I I I
see your smile sortly because even I was
kind of like wait this sounds too good
to be true I mean I'm not I'm this is
there's usually a hook that's going to
get you somewhere around the corner um
but you know from a I'll I'll probably
give you from perspective it is probably
one of the best engineering Partnerships
that that demonstrates you can colearn
you can you know develop and Target
opportunities and you can you really can
think about how you co-create so a lot
of the things that we've started to do
and the partnership was based on was can
we start to natively extend our
capabilities to use their for example Ai
and their agent Frameworks can we
develop it faster can we use like their
interfaces like the Nim interface kind
of scenarios where we can speed to
market right opportunity and can we
Shield our customers from some of the
complexity so a lot of that was really
at the at the root of the partnership
that we're working with them so there's
a learning part of that partnership
There's an opportunity to kind of uh
extend into their platform so like the
Nemo concepts with them how do we make
available to the environments and we
even came back and uh we announced a few
months back we had a partnership between
Dell and Nvidia on private AI
infrastructure so can we actually plan
build deploy Sovereign kind of
controlled environments and it just
became a natural extension so it's
really an enabler type of a contract and
relationship yeah no I was smiling
because uh with Nvidia when you talk
about Nvidia you hear the word
accelerate within the first 10 words and
you mentioned it so I said okay there
you go this is working I uh okay so
let's go back to basics it is it is kids
in the candy shop right right it just
one the compute capability the power
that you start to to see we're also can
I I'm not sure how old we all are I'm
definitely old given my my appearance in
hairline but it's kind of like you know
we're not just the president we're also
a client right so you know a lot of our
things that we do is manage services we
you we manage um exabytes of information
across our client base we manage you
know thousands of customers a lot of
things we do around how do we improve
operations so again even ourselves we're
looking how to improve that when we
start to Advantage our workloads our
models on their environment
it's an exponential Improvement right in
time and and turnover velocity a
different term but how do we improve and
optimize the models and the and the
input accuracy so it it really becomes
really interesting it's kind of like
throwing a lot of octane in your car
really fast yeah you it's interesting
right yeah yeah for me someone in my
position I'm always asking how real is
this yeah it's real it's very real very
real so anine if folks want to work
together with you or work together with
kindrell what's the best way to get in
touch um so one uh we can do it multip
ways so one we're operating in most
countries so we have a we we definitely
have an ability to directly connect to
most operating countries our operating
environments and we can forward you uh
for something later on the interview you
can come to kel.com we can kind of do
that kind of capability right and just
reagg we use our own tools to help
direct right you know clients the right
way I won't say AI tools but it's kind
of fun when you have one in that space
but uh but we we we are operate we we
are operating in most countries that way
and happy to engage directly with our
customers awesome well Antoine great to
speak with you hopefully the first of
many conversations I learned a ton and
as I said before it's real so thanks
again for coming on the on the show
great session Alex it's really
appreciate it thanks a lot thank you
anine and thanks everybody for watching
we'll be back on the channel soon