How a €1 B digital‑engineering firm uses generative AI and agent tech to reinvent retail supply chains and CX.
18 000 engineers, €1 B revenue, 50+ patents—Nagarro’s Global CTO Rahul Mahajan explains how generative AI, vector databases and knowledge graphs are reshaping demand planning and personalization at scale.
⏱️ CHAPTERS
00:00 Intro: product‑to‑service mind‑set
00:22 Meet Rahul Mahajan & Nagarro overview
01:17 Missed NRF meetings + digital engineering culture
02:35 Diversified industries & complex problem solving
03:35 Rahul’s 50+ patents in retail AI
04:58 CPG use case: multi‑channel demand planning
06:49 SKU‑level AI forecasting & supply chain accuracy
07:32 “Humanizing personalization” patent explained
08:20 Ecosystem shift: partner products & services
09:29 Agent tech & zero‑downtime integration
10:16 From transactions to lifestyle services
12:08 Patenting novel data structures & AI models
13:19 Knowledge graphs + vectorized semantics
14:24 AI governance: tone, privacy, explainability
15:14 LLM interoperability (OpenAI, Anthropic, Gemini)
16:32 Why retailers must move before they’re disrupted
17:13 Contact Rahul & closing
🔑 KEY STATS
• 18 000 employees across 36 countries
• €1 B revenue; listed in Frankfurt
• >50 patents in AI, personalization & data science
• New patent filed on “Humanizing Personalization” (2025)
👇 CONNECT
• Nagarro ► https://nagarro.com
• Rahul on LinkedIn ► rahul.mahajan@nagarro.com
• Email ► rahul.mahajan@nagarro.com
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
And that's the opportunity for businesses to think differently
to also start exploring from a product provider, how do I
become an end to end extended product to a service providers
and stuff like that. Hello, and welcome to the Retail
(00:23):
Podcast. Now we're in the series of
meetings that should have happened, the NRF that didn't,
just because of the sheer scale of NRF.
I'm joined by Rahul Mahajan, whois Global CTO, Vice President at
Is it Nagaro? Is that right?
OK, fantastic. I'm winning.
(00:44):
So fun and a thumbs up. So we're going to have a, a
general discussion, but I I'm going to learn as much as you
are because sometimes I'll get an invite saying, Hey, Nagaro
doing this, this and this and AII think that's super
interesting. And, and I'll say let's get
let's get them on the show to have a, a chat.
So roll. So if you don't mind, tell us
why don't we start with you? What do you do at Nagaro?
(01:04):
And were you at NRF by the way? Yes.
So maybe I spend a minute about the about Nagaro, some of you
may know, some of you may not know and then I'll absolutely
come back to the question of that's OK, Alex.
Perfect. So Nagaro is a global digital
engineering organisation, 18,000people and a billion euro
(01:26):
revenue company listed in Frankfurt.
We are in the business where client perceive us for complex
engineering and especially clients love to work with us
across industry. We are fairly diversified.
So we work with surely retail, big part of what we do, but we
also work with the travel, banking and so on.
Clients love to work with Nagarowhen it comes to complex
engineering, when it comes to modern transformation, fast
(01:48):
moving problems, agile, fast moving stuff.
So we are perceived to be a company go with one, which is
cool in terms of its engineering, its culture and and
it's fast moving ways of workingon complex problems.
But that's a little bit about quick thing about Nagaro and,
and, and, and back to the stuff in, in NRF, we, we had a very
(02:10):
interesting event. We, we got to meet some amazing
industry leaders across the spectrum, retail, digital
transformation, consumer experiences.
This is big part of what we do. Also to briefly introduce
myself, I'm involved in the organisation as a technology
leader in the global CPO capacity, in the global CPO
capacity also. And hence I get to work with
(02:32):
some amazing clients, tier one clients, all kinds of clients.
But also I'm responsible from a business perspective for profit
and loss with some amazing strategic clients.
So I run the digital transformation portfolio across
the organisation as an horizontal and I get to work
with some amazing retailer, someamazing fashion companies,
commerce companies, banking companies, automobile companies,
(02:52):
CPG companies, and this always amazing stuff to learn from all
of these industries given the rate that these industries are
moving is different. So there's always stuff to
learn. So that's a quick introduction
about the company, about me individually, 15 odd patients.
A lot of this is in the topics that we could be of our interest
today in retail, in personalization data and in
(03:15):
marketing and statistics around sales and stuff.
And this is a reflection of how is a company from an innovation
perspective, where are we moving, what kind of solutions
are we creating? In fact, we just filed A
provisional application in India.
We'll talk about it around humanising personalization.
This was also a big theme for usin NRF.
We were there talking about how shifting personalizations are
(03:38):
happening. Humanising the whole
personalization aspect is huge part of what AI today can
deliver. This is also what the part of
what to do with some of our amazing year one planes.
I think with that, I'll give it back to you Alex for.
Well, what's the keeping it simple?
What's the number one outcome you feel you can help clients?
Where can you drive them? Like what?
(04:00):
What are the if I'm the CEO of whatever retailer your, your,
what outcome are you going to create for me?
Sure, it's, it's a, it's a heavyquestion, but let me take some
one or two examples and hopefully that should answer
your question. So we are working with one of
these amazing CPG companies in the larger beauty fashion space,
an amazing company. They are working with us on the,
(04:22):
on the, on the demand planning side, on the supply chain side.
And and the, the leadership cameto us, the ecosystems has
evolved. We are companies are not just
selling on retail outlets or notjust selling on commerce.
It's a fairly distributed eco system that companies to operate
in. So for the CPG company, they
have their own retail stores, buy brands, some of the brands
(04:44):
of their own stores, some of thebrands selling in 3rd party
outlets like 7 Elevens and so on.
They also sell through commerce.They first party commerce, but
they also sell through marketplaces, which are big
Amazons and then ebays and and then some focused industry
segment specific commerce players in different countries.
But they also sell through thirdparty retail, e-commerce and,
(05:06):
and, and, and it's an amazing complex stuff.
When you talk to these clients now managing demand and and
making sure on supply chain sideyou have right amount of orders
in this complex ecosystem is nottrivial.
Classically, companies would have gone to some big daddy
softwares. I don't want to name anyone
here, but but there are established existing
(05:28):
contemporary players. What Arrow came involved as kind
of now answering your question from a value perspective, how
you can use today AI to kind of look into demand?
Demand is an attribution of whatpromotions you are running.
Demand is as an attribution to what historical sales and the
classical stuff, purchase patterns and stuff like that.
(05:48):
Demand also has drivers coming in all the way from competition.
But now in case of marketplaces like Amazon's, suddenly there's
a price drop by a competition orthere's a season coming and you
see an A spike in your sales and, and, and their seasons
changing. There are influencers talking
about your products, about your skin care products, about your
(06:10):
fashion products on and and those influences messages change
your demand. In this kind of complex
scenario, surely you can go and run auto regressive forecasting
model and then create a fancy dashboard and live with it.
The problem is that the accuracyfor this tier one company was
(06:31):
fairly low and, and, and, and hence there was a need of by
SKU, by category. How can we look at demand in
very unique ways? And that's where the, the
promise with AI was that for every SKU, can we have a unique
model? Can we have these factors
dynamically picked in depending on what significance these
factors have on specific categories and so on.
(06:54):
So that's a big shift, a use case which nobody would have
talked about like 3 years back because the channel complexity
was not this complex. And then and, and trade planning
was primarily limited to trade management, largely the trade
optimization TPO was not tightlyintegrated into it.
Even the dealer signals were kind of missing.
(07:14):
But today, that's the opportunity.
And it's not about just deploying some AI models.
The another big opportunity, Alex, for us is how do we
humanise back to the NRF theme. Also, how do we humanise the
consumption? Can we make demand planners talk
to systems in natural languages?Can I go and talk to my system
using generative AI? Hey, just tell me what all SKU's
are ageing in my in my larger region and, and canny I come
(07:38):
back with to me have been a beautiful rendered widget.
Here are top five products whichare actually ageing.
And here are some great promotion strategies that you
can start considering all the way from Bobo to to price
discounts and stuff like that. And that's the opportunity.
A classical contemporary system just cannot do this in, in near
to real time. But that's on the B2B and
(07:59):
enterprise side. Let me shift my gears to
consumer facing experiences. The promise again is and then
one of the stuff that we have recently filed a patent for also
is on using generative AI to generate advisories. 2 big two
or three big trends that I'm observing in my leadership
conversations with some of thesebig clients.
(08:20):
One, it's not just about products anymore.
Definition of products is changing.
It's not just first party products.
Clients want to consider their partner products and not just
products, clients also want to consider the partner services.
So goal is to get embedded into the lifestyle beyond
transactions. Can you get embedded into the
lifestyle at an emotional level,at a humanising level?
(08:40):
So for example, if I'm buying anautomobile and I want to
customise it given the terrain and given the vacation, can AI
help me connect to my accessory partners, to my service
partners, to my insurance partners and services and create
a a very simple consumption viewout of it?
And that's the promise with the shifts around ecosystems of the
(09:04):
shift around services. How do we humanise this whole
thing? And you cannot do this, Alex,
without, for example, without having some of the new
capabilities like Agentech. In past, what we would have done
is typically go to service basedarchitecture written some
workflows. Today, if your ecosystems are
getting plugged in, insurance provider is coming in, a service
(09:25):
provider, a door service, a service provider at your
doorstep is coming in. How do you write these
workflows? You cannot write these
workflows. You cannot wire these workflows.
What you can rather do is today is go on agentic systems, use
agentic systems, deploy these workflows in a way that they can
on in runtime learn and adapt and stitch these additional new
(09:46):
services in a marketplace type of a pattern seamlessly.
So suddenly if you have a doorstep service provider coming
to your door to provide some services, automatically those
services get integrated into a workflow and that workflow then
gets converted to a humanised consumption format.
And, and you cannot take a, you cannot afford a downtime as a
(10:07):
client, as a retailer that let me take a downtime, let me
integrate those services, let mewrite those workflows, go back
and do the engineering for threemonths and come back.
These providers are coming in onthe fly again, technology do it.
That's the opportunity for all of us.
And that's exactly the kind of engineering shifts, Alex, we are
seeing with some of our amazing tier one clients.
(10:30):
These are things they're pushing, pushing for example, a
pharmacy company in retail context, they just don't want to
sell medicine anymore. They want to sell lifestyle
services, which mean diet services, Wellness services.
And imagine these kinds of contexts where suddenly from a
transactional product context, you shift to a lifestyle
emotional service provider in the larger ecosystem context.
(10:53):
For this kind of a context, you need a very different
architecture, very different kind of an AI and, and, and
hence of those things. Now these are the values that we
are promising, Alex, to your point to the leadership of some
of these organisation which are willing to cope with us and
become futureity before they getdisrupted by an online
marketplace or an online SAS provider.
(11:13):
So I mean, if we were going to break that down into its
components, obviously you, you'll build a unique AI model
for your, your client, because what comes across there is that
your ability to execute your engineering prowess is obviously
what you're strong in. Where does the, the, so the
patents that you hold or the patents that you're, you've
(11:36):
applied for, how does that come into this?
What are you actually patenting?Is it the AI model itself?
And then where does that AI model sit?
Is it tied to, I don't know, Microsoft or or ChatGPT or
Anthropic? What, what can you just expand
on that bit? Sure.
I'll be brief here because this gets super technical, but just
(11:59):
to talk about it, there's amazing opportunity, Alex, from
us for all of us, all of the engineering community and
solutions and consulting providers to think differently.
We went back to think of data structures from scratch because
AI can't consume structured data, video data, multimodal
data and give you advisories. So for example, if an influencer
(12:19):
is talking certain, giving some feedback on product, a good
feedback, let's say, or a certain, a certain way you have
to use the product or apply the product or, or, or around
product related scenarios. How can you bring that stuff
real time into your subsequent advisories, into loyalty and so
on, which means you need a different kind of data
structure. We are heavily using knowledge
(12:42):
draughts. Knowledge draughts has amazing
promise. I'll keep moving because
otherwise I'll keep speaking. So knowledge draughts is 1 big
amazing kind of a promising thing We are exploring.
Second and a huge thing, we are vectorizing data at an amazing
speed. Now data has been sitting
classically inside structured databases or or recently.
(13:03):
No SQL databases, big databases.But that's not something what
languages can consume directly. Language needs a semantic
substitution, so we are vectorizing the databases.
We are writing new semantic layers.
That's second big shift in the way we are now thinking of
solution, because language has capability to talk to data,
language has capability to even talk to AI.
(13:24):
Language has capability to talk to us as consumers.
So it has all that. You can use language to go
either ways, which means you need a strong semantic
catalogues to complement your architecture.
So that's point #2 in terms of where new solutions will have
novelty point #3 the governance.Now you need this big explain
(13:47):
ability around some big decisions happening in your
demand planning, all in terms ofadvisories.
We need to go back, double clickand see what model ran in, what
version of data ran in, what kind of observability was it,
who approved it, what data version, what lineage from a
master data management perspective and so on.
Who had the access right on thiskind of a data.
(14:08):
And then you have all data privacy, which is GDPR and CCPA
and and India data protection and so on.
So that's the third big opportunity around AI
governance. By the way, it's not just about
security and data privacy. How does AI even talk in a human
friendly format? There's a huge opportunity to
make AI not talk like a computeragent, computational agent, but
(14:30):
rather talk like how a marketingperson will talk.
For example, in beauty industry,a dry hair can be said as rough
hair, dull hair and so on. So brand and marketing talk in a
certain way, but computational agents, they typically would
just talk in in standard languages.
Now, how do you, Tony, how do you change the tone so that it
(14:52):
speaks like how a brand would like to talk?
So that's another part of the governance and that's governance
to me, the third big area of where novelties can be created.
So imagine the amount of noveltyopportunity that we have on.
And and then last but not the least, you talked about Entropic
LLMS from open AI clients today wants to build systems because
(15:13):
innovation is happening. Alex, we all know in A at
amazing speed in January, you could be seeing Gemini doing
better. In February it could be Entropic
doing better, in March it could be again GPD doing better.
And and who knows, in April some, some other company in some
other country may be doing better clients, at least when I
talk to them, they want to externalise their system so that
(15:34):
there's a certain amount of interoperability.
They can predict use X tomorrow,they can trace the same thing
with Y provider and Z provider and so on.
So externalisation means you can't just use the core APIs and
do stuff with them. You have to externalise the
vectors, you have to externalisethe governance, you have to get,
you have to do grounding in a way which is again common to all
(15:57):
of these raw providers and so on.
So in terms of creating novelties this huge and then you
don't get patent until unless you don't bring in the
application aspects on, on, on all of it, it cannot be a
problem, right. So that takes me back to what
kind of new application interfaces can build with this
kind of new, which I already touched in some ways.
(16:19):
And that's the opportunity for businesses to think, think
differently, to also start exploring from a product
provider, how do I become an into an extended product to a
service providers and stuff likethat.
There are companies, big CPG companies who are saying we just
don't want to be selling products.
Rather we want to get embedded aservice provider into the
consumer lifestyles at an emotional level.
(16:42):
And and you would have heard this from some amazing fashion
companies, beauty companies, sports companies and so on.
Alex But but easier said than done, The engineering for this
needs a very unique kind of an approach and that's the
opportunity for us to from a novelty point of view.
That's wonderful. Thank you.
Where can people find out more information, Raul?
(17:02):
So absolutely I'm I'm always accessible on on my LinkedIn, on
my, my e-mail idrahul.mahajan@nagaro.com can
reach out to Nagaro websites, reach out to us and then some of
us are actively available in different events and so on.
Lot of what I said by the way is, is is there on our, in, is
there on, on our and our marketing materials and
(17:24):
brochures. So we would be happy to talk to
any of our interesting clients. That's brilliant.
Thank you so much. I really appreciate.
It thank you Alex for the opportunity.
Thanks for inviting me. Thank you for your time.
And that's the opportunity for businesses to think differently
to also start exploring from a product provider, how do I
become an end to end extended product to a service providers
and stuff like that. Hello, and welcome to the Retail
(00:23):
Podcast. Now we're in the series of
meetings that should have happened, the NRF that didn't,
just because of the sheer scale of NRF.
I'm joined by Rahul Mahajan, whois Global CTO, Vice President at
Is it Nagaro? Is that right?
OK, fantastic. I'm winning.
(00:44):
So fun and a thumbs up. So we're going to have a, a
general discussion, but I I'm going to learn as much as you
are because sometimes I'll get an invite saying, Hey, Nagaro
doing this, this and this and AII think that's super
interesting. And, and I'll say let's get
let's get them on the show to have a, a chat.
So roll. So if you don't mind, tell us
why don't we start with you? What do you do at Nagaro?
(01:04):
And were you at NRF by the way? Yes.
So maybe I spend a minute about the about Nagaro, some of you
may know, some of you may not know and then I'll absolutely
come back to the question of that's OK, Alex.
Perfect. So Nagaro is a global digital
engineering organisation, 18,000people and a billion euro
(01:26):
revenue company listed in Frankfurt.
We are in the business where client perceive us for complex
engineering and especially clients love to work with us
across industry. We are fairly diversified.
So we work with surely retail, big part of what we do, but we
also work with the travel, banking and so on.
Clients love to work with Nagarowhen it comes to complex
engineering, when it comes to modern transformation, fast
(01:48):
moving problems, agile, fast moving stuff.
So we are perceived to be a company go with one, which is
cool in terms of its engineering, its culture and and
it's fast moving ways of workingon complex problems.
But that's a little bit about quick thing about Nagaro and,
and, and, and back to the stuff in, in NRF, we, we had a very
(02:10):
interesting event. We, we got to meet some amazing
industry leaders across the spectrum, retail, digital
transformation, consumer experiences.
This is big part of what we do. Also to briefly introduce
myself, I'm involved in the organisation as a technology
leader in the global CPO capacity, in the global CPO
capacity also. And hence I get to work with
(02:32):
some amazing clients, tier one clients, all kinds of clients.
But also I'm responsible from a business perspective for profit
and loss with some amazing strategic clients.
So I run the digital transformation portfolio across
the organisation as an horizontal and I get to work
with some amazing retailer, someamazing fashion companies,
commerce companies, banking companies, automobile companies,
(02:52):
CPG companies, and this always amazing stuff to learn from all
of these industries given the rate that these industries are
moving is different. So there's always stuff to
learn. So that's a quick introduction
about the company, about me individually, 15 odd patients.
A lot of this is in the topics that we could be of our interest
today in retail, in personalization data and in
(03:15):
marketing and statistics around sales and stuff.
And this is a reflection of how is a company from an innovation
perspective, where are we moving, what kind of solutions
are we creating? In fact, we just filed A
provisional application in India.
We'll talk about it around humanising personalization.
This was also a big theme for usin NRF.
We were there talking about how shifting personalizations are
(03:38):
happening. Humanising the whole
personalization aspect is huge part of what AI today can
deliver. This is also what the part of
what to do with some of our amazing year one planes.
I think with that, I'll give it back to you Alex for.
Well, what's the keeping it simple?
What's the number one outcome you feel you can help clients?
Where can you drive them? Like what?
(04:00):
What are the if I'm the CEO of whatever retailer your, your,
what outcome are you going to create for me?
Sure, it's, it's a, it's a heavyquestion, but let me take some
one or two examples and hopefully that should answer
your question. So we are working with one of
these amazing CPG companies in the larger beauty fashion space,
an amazing company. They are working with us on the,
(04:22):
on the, on the demand planning side, on the supply chain side.
And and the, the leadership cameto us, the ecosystems has
evolved. We are companies are not just
selling on retail outlets or notjust selling on commerce.
It's a fairly distributed eco system that companies to operate
in. So for the CPG company, they
have their own retail stores, buy brands, some of the brands
(04:44):
of their own stores, some of thebrands selling in 3rd party
outlets like 7 Elevens and so on.
They also sell through commerce.They first party commerce, but
they also sell through marketplaces, which are big
Amazons and then ebays and and then some focused industry
segment specific commerce players in different countries.
But they also sell through thirdparty retail, e-commerce and,
(05:06):
and, and, and it's an amazing complex stuff.
When you talk to these clients now managing demand and and
making sure on supply chain sideyou have right amount of orders
in this complex ecosystem is nottrivial.
Classically, companies would have gone to some big daddy
softwares. I don't want to name anyone
here, but but there are established existing
(05:28):
contemporary players. What Arrow came involved as kind
of now answering your question from a value perspective, how
you can use today AI to kind of look into demand?
Demand is an attribution of whatpromotions you are running.
Demand is as an attribution to what historical sales and the
classical stuff, purchase patterns and stuff like that.
(05:48):
Demand also has drivers coming in all the way from competition.
But now in case of marketplaces like Amazon's, suddenly there's
a price drop by a competition orthere's a season coming and you
see an A spike in your sales and, and, and their seasons
changing. There are influencers talking
about your products, about your skin care products, about your
(06:10):
fashion products on and and those influences messages change
your demand. In this kind of complex
scenario, surely you can go and run auto regressive forecasting
model and then create a fancy dashboard and live with it.
The problem is that the accuracyfor this tier one company was
(06:31):
fairly low and, and, and, and hence there was a need of by
SKU, by category. How can we look at demand in
very unique ways? And that's where the, the
promise with AI was that for every SKU, can we have a unique
model? Can we have these factors
dynamically picked in depending on what significance these
factors have on specific categories and so on.
(06:54):
So that's a big shift, a use case which nobody would have
talked about like 3 years back because the channel complexity
was not this complex. And then and, and trade planning
was primarily limited to trade management, largely the trade
optimization TPO was not tightlyintegrated into it.
Even the dealer signals were kind of missing.
(07:14):
But today, that's the opportunity.
And it's not about just deploying some AI models.
The another big opportunity, Alex, for us is how do we
humanise back to the NRF theme. Also, how do we humanise the
consumption? Can we make demand planners talk
to systems in natural languages?Can I go and talk to my system
using generative AI? Hey, just tell me what all SKU's
are ageing in my in my larger region and, and canny I come
(07:38):
back with to me have been a beautiful rendered widget.
Here are top five products whichare actually ageing.
And here are some great promotion strategies that you
can start considering all the way from Bobo to to price
discounts and stuff like that. And that's the opportunity.
A classical contemporary system just cannot do this in, in near
to real time. But that's on the B2B and
(07:59):
enterprise side. Let me shift my gears to
consumer facing experiences. The promise again is and then
one of the stuff that we have recently filed a patent for also
is on using generative AI to generate advisories. 2 big two
or three big trends that I'm observing in my leadership
conversations with some of thesebig clients.
(08:20):
One, it's not just about products anymore.
Definition of products is changing.
It's not just first party products.
Clients want to consider their partner products and not just
products, clients also want to consider the partner services.
So goal is to get embedded into the lifestyle beyond
transactions. Can you get embedded into the
lifestyle at an emotional level,at a humanising level?
(08:40):
So for example, if I'm buying anautomobile and I want to
customise it given the terrain and given the vacation, can AI
help me connect to my accessory partners, to my service
partners, to my insurance partners and services and create
a a very simple consumption viewout of it?
And that's the promise with the shifts around ecosystems of the
(09:04):
shift around services. How do we humanise this whole
thing? And you cannot do this, Alex,
without, for example, without having some of the new
capabilities like Agentech. In past, what we would have done
is typically go to service basedarchitecture written some
workflows. Today, if your ecosystems are
getting plugged in, insurance provider is coming in, a service
(09:25):
provider, a door service, a service provider at your
doorstep is coming in. How do you write these
workflows? You cannot write these
workflows. You cannot wire these workflows.
What you can rather do is today is go on agentic systems, use
agentic systems, deploy these workflows in a way that they can
on in runtime learn and adapt and stitch these additional new
(09:46):
services in a marketplace type of a pattern seamlessly.
So suddenly if you have a doorstep service provider coming
to your door to provide some services, automatically those
services get integrated into a workflow and that workflow then
gets converted to a humanised consumption format.
And, and you cannot take a, you cannot afford a downtime as a
(10:07):
client, as a retailer that let me take a downtime, let me
integrate those services, let mewrite those workflows, go back
and do the engineering for threemonths and come back.
These providers are coming in onthe fly again, technology do it.
That's the opportunity for all of us.
And that's exactly the kind of engineering shifts, Alex, we are
seeing with some of our amazing tier one clients.
(10:30):
These are things they're pushing, pushing for example, a
pharmacy company in retail context, they just don't want to
sell medicine anymore. They want to sell lifestyle
services, which mean diet services, Wellness services.
And imagine these kinds of contexts where suddenly from a
transactional product context, you shift to a lifestyle
emotional service provider in the larger ecosystem context.
(10:53):
For this kind of a context, you need a very different
architecture, very different kind of an AI and, and, and
hence of those things. Now these are the values that we
are promising, Alex, to your point to the leadership of some
of these organisation which are willing to cope with us and
become futureity before they getdisrupted by an online
marketplace or an online SAS provider.
(11:13):
So I mean, if we were going to break that down into its
components, obviously you, you'll build a unique AI model
for your, your client, because what comes across there is that
your ability to execute your engineering prowess is obviously
what you're strong in. Where does the, the, so the
patents that you hold or the patents that you're, you've
(11:36):
applied for, how does that come into this?
What are you actually patenting?Is it the AI model itself?
And then where does that AI model sit?
Is it tied to, I don't know, Microsoft or or ChatGPT or
Anthropic? What, what can you just expand
on that bit? Sure.
I'll be brief here because this gets super technical, but just
(11:59):
to talk about it, there's amazing opportunity, Alex, from
us for all of us, all of the engineering community and
solutions and consulting providers to think differently.
We went back to think of data structures from scratch because
AI can't consume structured data, video data, multimodal
data and give you advisories. So for example, if an influencer
(12:19):
is talking certain, giving some feedback on product, a good
feedback, let's say, or a certain, a certain way you have
to use the product or apply the product or, or, or around
product related scenarios. How can you bring that stuff
real time into your subsequent advisories, into loyalty and so
on, which means you need a different kind of data
structure. We are heavily using knowledge
(12:42):
draughts. Knowledge draughts has amazing
promise. I'll keep moving because
otherwise I'll keep speaking. So knowledge draughts is 1 big
amazing kind of a promising thing We are exploring.
Second and a huge thing, we are vectorizing data at an amazing
speed. Now data has been sitting
classically inside structured databases or or recently.
(13:03):
No SQL databases, big databases.But that's not something what
languages can consume directly. Language needs a semantic
substitution, so we are vectorizing the databases.
We are writing new semantic layers.
That's second big shift in the way we are now thinking of
solution, because language has capability to talk to data,
language has capability to even talk to AI.
(13:24):
Language has capability to talk to us as consumers.
So it has all that. You can use language to go
either ways, which means you need a strong semantic
catalogues to complement your architecture.
So that's point #2 in terms of where new solutions will have
novelty point #3 the governance.Now you need this big explain
(13:47):
ability around some big decisions happening in your
demand planning, all in terms ofadvisories.
We need to go back, double clickand see what model ran in, what
version of data ran in, what kind of observability was it,
who approved it, what data version, what lineage from a
master data management perspective and so on.
Who had the access right on thiskind of a data.
(14:08):
And then you have all data privacy, which is GDPR and CCPA
and and India data protection and so on.
So that's the third big opportunity around AI
governance. By the way, it's not just about
security and data privacy. How does AI even talk in a human
friendly format? There's a huge opportunity to
make AI not talk like a computeragent, computational agent, but
(14:30):
rather talk like how a marketingperson will talk.
For example, in beauty industry,a dry hair can be said as rough
hair, dull hair and so on. So brand and marketing talk in a
certain way, but computational agents, they typically would
just talk in in standard languages.
Now, how do you, Tony, how do you change the tone so that it
(14:52):
speaks like how a brand would like to talk?
So that's another part of the governance and that's governance
to me, the third big area of where novelties can be created.
So imagine the amount of noveltyopportunity that we have on.
And and then last but not the least, you talked about Entropic
LLMS from open AI clients today wants to build systems because
(15:13):
innovation is happening. Alex, we all know in A at
amazing speed in January, you could be seeing Gemini doing
better. In February it could be Entropic
doing better, in March it could be again GPD doing better.
And and who knows, in April some, some other company in some
other country may be doing better clients, at least when I
talk to them, they want to externalise their system so that
(15:34):
there's a certain amount of interoperability.
They can predict use X tomorrow,they can trace the same thing
with Y provider and Z provider and so on.
So externalisation means you can't just use the core APIs and
do stuff with them. You have to externalise the
vectors, you have to externalisethe governance, you have to get,
you have to do grounding in a way which is again common to all
(15:57):
of these raw providers and so on.
So in terms of creating novelties this huge and then you
don't get patent until unless you don't bring in the
application aspects on, on, on all of it, it cannot be a
problem, right. So that takes me back to what
kind of new application interfaces can build with this
kind of new, which I already touched in some ways.
(16:19):
And that's the opportunity for businesses to think, think
differently, to also start exploring from a product
provider, how do I become an into an extended product to a
service providers and stuff likethat.
There are companies, big CPG companies who are saying we just
don't want to be selling products.
Rather we want to get embedded aservice provider into the
consumer lifestyles at an emotional level.
(16:42):
And and you would have heard this from some amazing fashion
companies, beauty companies, sports companies and so on.
Alex But but easier said than done, The engineering for this
needs a very unique kind of an approach and that's the
opportunity for us to from a novelty point of view.
That's wonderful. Thank you.
Where can people find out more information, Raul?
(17:02):
So absolutely I'm I'm always accessible on on my LinkedIn, on
my, my e-mail idrahul.mahajan@nagaro.com can
reach out to Nagaro websites, reach out to us and then some of
us are actively available in different events and so on.
Lot of what I said by the way is, is is there on our, in, is
there on, on our and our marketing materials and
(17:24):
brochures. So we would be happy to talk to
any of our interesting clients. That's brilliant.
Thank you so much. I really appreciate.
It thank you Alex for the opportunity.
Thanks for inviting me. Thank you for your time.
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