May 14, 2025 • 12 mins
Daniel Wagner, CEO of London-based Rezolve Ai, explains how his company uses artificial intelligence to improve and personalize the shopping experience across a variety of product categories.
Rezolve is among the first publicly traded, pure-play AI companies, featuring a proprietary Large Language Model specifically designed for the retail sector.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2 (00:07):
You're listening to Bloomberg BusinessWeek with Carol Masser and Tim
Steneveek on Bloomberg Radio and Video. And AMD will supply
semiconductors to Saudi Arabian AI company Humane for a massive
data center project, adding under a Trump administration initiative that
lifts restrictions on delivering advanced tech to the region. In Vidia,
(00:28):
just looking at what they ended up closing out today,
Carol mass they were really surging earlier in the session,
driving the entire index. Hire in Vidia finished higher, AMD
was hired as well. In Video was up five point
six percent today.
Speaker 3 (00:41):
Yeah, big bump up, no doubt about it makes sense, right.
So that's when I ended the AI spectrum, and certainly
one that gets a lot of attention from investors. On
a different part of that spectrum is the AI that
customers deal with, and that's where Daniel Wagner comes in,
and he's chairman CEO of the publicly traded enterprise software
as a service company, Resolve AI. It's got a market
cap of about five hundred million dollars based in London.
(01:03):
Company has a suite of AI powered products that is
meant to help companies with customer retention, engagement, loyalty and support.
They work with a lot of companies that you know,
shares are down about forty percent so far this year.
He joins us Daniel that is in the Bloomberg Interactive
Broker Studio. It is nice to have you here with us.
Tell us a little bit more about your company and
the environment right now.
Speaker 4 (01:24):
Sure, so you know, I'm a multi decade entrepreneur in
the tech business and Resolve Ai is the culmination of
my career in many respects, having built market leaders in
e commerce and search. The development of Genai as a
technology is an enabler and we use it to really
enhance the digital experience for consumers. Right now, when you
(01:47):
go onto an e commerce site, it's a very perfunctory experience,
and it's actually very ineffectual because seven out of ten
people who go into a physical store end up buying
a product and seven out of ten people who go
into a digital store and not not buying a product.
And the reason for that is because I believe they
don't get the answers to their questions that they need.
You know, if my wife wanted to go and buy
me a mobile phone. She couldn't do it online because
(02:09):
she couldn't get She doesn't know what iOS is or Android.
She doesn't know what a mega bite is or a megapixel.
But if she went into the AT and T store
on Lexington Avenue, the salesman.
Speaker 1 (02:18):
Would help her through that process.
Speaker 4 (02:20):
So what we're doing with Jenai is we're changing, you know,
e commerce into a conversational commerce. We've created a language
model that is the best salesman on the planet with
deep product knowledge, empathy.
Speaker 3 (02:32):
So with your wife, what might so do with her?
Where you say?
Speaker 1 (02:37):
She doesn't have that?
Speaker 3 (02:37):
So she would go I'm in that camp too, which
is why my husband buys all of our phones. But
I'm just saying, like, how would that help her?
Speaker 4 (02:43):
So she would be able to go to a website
now and say, hey, I need to buy my husband
a mobile phone. What you know, what would you recommend?
And the answer would be verbally or typed up on
the screen. Well, tell me a little bit more about
your husband. Does he does he use a PC or
a MAC? Is he a business person or a creative?
Does he use his phone all day?
Speaker 1 (03:03):
Or you know?
Speaker 4 (03:04):
And the answers to those questions will will guide her
to a phone with a long lasting battery or a
phone with you know, an Android or an iOS and
she would result probably with buying a phone from that
digital site.
Speaker 2 (03:17):
Do you use your own LM that you've trained yourself,
or are you using a cloud or We.
Speaker 4 (03:23):
Started developing this in twenty sixteen, so a lot before
it became fashionable. I come from as an information services
and as I mentioned before, commerce and retail, so we
understood about data structure, We understood the implications of the
algorithms that the foundational algorithms for GENAI. So we started
building a language model initially that could replicate the best
(03:44):
salesman on the planet, and then we built products that
sit on top of it that allow our customers to
deploy it really as a plug into their existing sites.
Speaker 1 (03:51):
So it's a very easy deployment from it.
Speaker 2 (03:53):
But if it's a plug, and then how does it
know if you're shopping? How is it an expert in
shoes and running shoes, for example, versus an expert in fune.
Speaker 4 (04:01):
So think about our LLLM as a sort of blank
sheet of paper when we when we approach a customer,
it doesn't have the world's knowledge. It just has conversational skills.
Speaker 1 (04:11):
And then what we do is we.
Speaker 4 (04:12):
Import the product catalog of that retailer, so it could
be shoes or whatever, then there would be context around that.
Speaker 2 (04:18):
So you still have to train after a company side.
Speaker 1 (04:21):
Really no, it's.
Speaker 4 (04:23):
Already training you ingest. We've already trained the model to
be a salesman. The training quote unquote of its product
catalog is literally a matter of minutes, probably hours at most,
and then the everything else about the language, model and
the technology is that it is a very proficient salesperson.
Speaker 1 (04:41):
So if you take a digital information, right, let's.
Speaker 4 (04:45):
Just take a physical analogy. Okay, if I've run it.
If I'm running a camera camera store and I need
a new sales guy, and new sales guy or girl
joins my retail store, the first thing I'm gonna do
is say, hey, here's our products.
Speaker 1 (04:58):
You better learn about the product. Do you know anything
about photography?
Speaker 4 (05:01):
You know? You know, so that the next time somebody
walks in, they can sell a camera. Now we're able
to do that piece in a matter of minutes. And
then from that point forward, all of the best sales
capabilities are inherently built into our model.
Speaker 2 (05:15):
Where did you train it.
Speaker 1 (05:16):
Where did we train it?
Speaker 2 (05:18):
What information do you do we use?
Speaker 4 (05:19):
We needed the Canadian book Repository, which is two hundred
million books, open source books, Wikipedia for language and context,
and then so it has three hundred billion token data
sets as the foundational.
Speaker 1 (05:32):
Model to it.
Speaker 4 (05:32):
We did this over a period of years, so unlike
many others, we didn't throw bucket loads of cash, you know,
into a big black hole. We trained it over period
using a much more efficient model, and so it took us,
you know, six or seven years, but then we ended
up with the foundational model that has been recognized by
Microsoft and Google, who are partnered with us as one
of the leaders in the field.
Speaker 2 (05:52):
One of the examples you use on your website is
saying you have any dinner party you want it vegetarian?
You have six people coming, Yeah, more or less? Give
me some recipes or what should I make? I could
use chatch ept for.
Speaker 4 (06:05):
That, yes, now, but why you couldn't do it on
Deagasino's website or on you know, a target. So we're
setting to retailers. We're not selling, We're not providing you know, Google,
we're providing best Bard But is.
Speaker 1 (06:18):
It a huge? Sorry? Go ahead?
Speaker 3 (06:20):
So if your wife was looking for a phone, she
would go to let's say Apple, Well.
Speaker 1 (06:26):
She'd go to Google.
Speaker 4 (06:26):
She'd go to Google and end up probably on AT
and T side or Charison or something else.
Speaker 3 (06:32):
But with yours, she's gotta either start with Verizon or
start with Apple or Samsung or something.
Speaker 1 (06:36):
Right, No, not necessarily, I mean, because how do you compare?
Speaker 3 (06:39):
How do you get the like you've.
Speaker 4 (06:40):
Got to You've got to remember, right, there are millions
of retailers out there driving traffic to their sites all
the time. When you get to that site, seven out
of ten people drop out. So what we're doing is
that when you arrive at that site, you're welcomed, you
get answers to your questions, you get detailed information about
the products you're looking at, so that you and you're
guided through the sales process. So if I went to
Mace's today and I said, Hey, I got to buy
(07:02):
gift for my niece for her twenty first birthday, It's
not gonna give you an answer. I'm gonna have to
navigate through lots of categorism try and figure out do
I want to get her a candle?
Speaker 1 (07:11):
Do I want to get it? You know, I don't know.
Speaker 4 (07:13):
But if I was able to ask that question of
the salesperson in MACES, they say, hey, we've got lots
of ideas for you.
Speaker 1 (07:17):
So they.
Speaker 4 (07:20):
We're leveling up comments from what was a poor experience
to a very rich, engaging experience, which we believe and
have shown results in better conversion, which has a material impact.
Speaker 3 (07:30):
Change in the conversion.
Speaker 4 (07:31):
Well, it's only been going, you know, a short period
of time, but we announced only a few weeks ago
that we've seen twenty five percent uplifting conversion, which is massive.
Speaker 1 (07:40):
Massive.
Speaker 2 (07:41):
What's to stop a company like chat g ept from
just selling directly to target Ordagostino and saying, hey, this
is the this is the this is what's going to
power it. Yeah, come to us because we have this
data set.
Speaker 4 (07:53):
So they're a generalist, right, So the issue with that
is that they already think they know everything, and that
can eminates the model. Okay, so if you ingest a
whole load of cosmetics and fragrances, they're going to have
names like savage and beast, and their descriptions are going
to be things like BlackBerry notes with sandalwood and barbecue.
(08:13):
Right now, a GENAI model like open AI doesn't understand
that well and will hallucinate thinking you're talking about.
Speaker 1 (08:19):
A beast in the wood. We don't.
Speaker 4 (08:21):
We have a clean slate. We're ingesting fragrances. We tell
the model it's just fragrances you're ingesting, and then it
knows how to sell that. So it's a different approach.
And it's why Microsoft and Google partner with us because
they recognize, both of them with significant investments in AI,
recognize that we have really, you know, focused on this
vertical to make this the most appropriate solution.
Speaker 3 (08:42):
Well, it sounds like each one because you're dealing with
a particular retailer. It's their kind of.
Speaker 4 (08:47):
Closed data sets in some respect, it is, right, yeah.
Speaker 3 (08:49):
And so you just kind of whittle it down. All right,
I pull up the FA function. I know you're a
young company, you just started, So what are your expectations
in terms of revenue growth? Like, where do you see
this company?
Speaker 1 (09:01):
We're getting it out of the park straight away. Well,
we started at the.
Speaker 3 (09:05):
Beginning last I mean, it's easy around next year.
Speaker 4 (09:07):
We had nothing beginning of this year. You know, we
announced couple of weeks ago, forty one million consumers have
our technology on their phones. I mean, it's like almost
instant we're going to exit this year with one hundred
million dollars of arr and we are. And we signed
a deal only two or three weeks ago in partnership
with Google, which was a ten million dollar annual deal
with Liverpool in Mexico, a.
Speaker 1 (09:26):
Very large department.
Speaker 3 (09:27):
When is that partnership with Google? Because that's fascinating, right,
they're doing their own thing. They're worried about potentially being
replaced in terms of a search engine, which is why
they're doing Gemini. But they're watching very carefully right in
terms of how people are now communicating online to find
a soft.
Speaker 4 (09:42):
And Google both wanted to do an exclusive deal with me.
I rejected both of those advances and we ended up
doing a non exclusive deal with both. It demonstrates our
position in the market and the valuable asset that we
have in this vertical Google.
Speaker 3 (09:55):
So what's the deal with them? Tell me how that
was Google.
Speaker 4 (09:58):
Google are introducing our technology to their retail customers as
a solution which ties in those customers to Google Cloud
and to us, So they see us as a sticky
enhancement to Google Cloud. Microsoft are doing exactly the same
thing and see us as a sticky enhancement to AZOR.
Both Azor and Google Cloud are competitive cloud services that
(10:19):
are really it's a you know, you can move from
onto the other very easily, but with our technology sort
of embedded in those customer accounts. They see us as
you know, retaining those customers when they come up for renewal,
and as a result, they're pushing us out to ninety
percent of the retailers on the planet because those two
companies basically deal with pretty much everybody.
Speaker 2 (10:37):
You've got a few different products in the suite of
software as a service products. How do you know that
consumers are okay with this at this point?
Speaker 4 (10:46):
Well, I think it's Look, I'm an innovator. All the
products and services I've created in my career, which have
all gone on to become market leaders, have always been very,
very innovative. The first company I set up in ninet
eighty four was a pioneer and online information. In fact,
Mike Bloomberg licensed my content newspapers for the Terminals back
in eighty six. So we were pioneering stuff back then.
We're pioneering stuff in the late nineties, and we're doing
(11:08):
it again now. I started this in twenty sixteen. We're
nine years in and now we're productizing it and going
to market. We're way ahead of anybody else in the space,
I believe, and I think it's being shown by the
takeup we're seeing in the last few months that we're.
Speaker 1 (11:20):
On to a winner.
Speaker 4 (11:21):
This is a this is a step up for commerce,
and right now I believe the environments and the interfaces
that we're using in commerce are so out of date,
they're forty years old, that they're due a complete refresh.
Speaker 3 (11:34):
Just really quickly. Thirty seconds. I haven't heard Amazon. Are
you talking with Amazon?
Speaker 4 (11:38):
No? Obviously Amazon is the big competitor to every retailer
on the planet.
Speaker 1 (11:41):
They do a great job.
Speaker 4 (11:42):
We're not going to try and sell to them, and
we're certainly not going to be partnering with them. You know,
our customers are everybody else fascinating.
Speaker 3 (11:50):
Come back in six months, twelve months, that's coursings are going.
I really enjoyed it. Daniel Wagner, he's the chairman CEO
of the publicly traded enterprise software as a service company.
We're talking about Reserve AI joining us right here in
our Bloomberg Interactive Broker studio of Taniel thank you so much,
really appreciate it,
Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2 (00:07):
You're listening to Bloomberg BusinessWeek with Carol Masser and Tim
Steneveek on Bloomberg Radio and Video. And AMD will supply
semiconductors to Saudi Arabian AI company Humane for a massive
data center project, adding under a Trump administration initiative that
lifts restrictions on delivering advanced tech to the region. In Vidia,
(00:28):
just looking at what they ended up closing out today,
Carol mass they were really surging earlier in the session,
driving the entire index. Hire in Vidia finished higher, AMD
was hired as well. In Video was up five point
six percent today.
Speaker 3 (00:41):
Yeah, big bump up, no doubt about it makes sense, right.
So that's when I ended the AI spectrum, and certainly
one that gets a lot of attention from investors. On
a different part of that spectrum is the AI that
customers deal with, and that's where Daniel Wagner comes in,
and he's chairman CEO of the publicly traded enterprise software
as a service company, Resolve AI. It's got a market
cap of about five hundred million dollars based in London.
(01:03):
Company has a suite of AI powered products that is
meant to help companies with customer retention, engagement, loyalty and support.
They work with a lot of companies that you know,
shares are down about forty percent so far this year.
He joins us Daniel that is in the Bloomberg Interactive
Broker Studio. It is nice to have you here with us.
Tell us a little bit more about your company and
the environment right now.
Speaker 4 (01:24):
Sure, so you know, I'm a multi decade entrepreneur in
the tech business and Resolve Ai is the culmination of
my career in many respects, having built market leaders in
e commerce and search. The development of Genai as a
technology is an enabler and we use it to really
enhance the digital experience for consumers. Right now, when you
(01:47):
go onto an e commerce site, it's a very perfunctory experience,
and it's actually very ineffectual because seven out of ten
people who go into a physical store end up buying
a product and seven out of ten people who go
into a digital store and not not buying a product.
And the reason for that is because I believe they
don't get the answers to their questions that they need.
You know, if my wife wanted to go and buy
me a mobile phone. She couldn't do it online because
(02:09):
she couldn't get She doesn't know what iOS is or Android.
She doesn't know what a mega bite is or a megapixel.
But if she went into the AT and T store
on Lexington Avenue, the salesman.
Speaker 1 (02:18):
Would help her through that process.
Speaker 4 (02:20):
So what we're doing with Jenai is we're changing, you know,
e commerce into a conversational commerce. We've created a language
model that is the best salesman on the planet with
deep product knowledge, empathy.
Speaker 3 (02:32):
So with your wife, what might so do with her?
Where you say?
Speaker 1 (02:37):
She doesn't have that?
Speaker 3 (02:37):
So she would go I'm in that camp too, which
is why my husband buys all of our phones. But
I'm just saying, like, how would that help her?
Speaker 4 (02:43):
So she would be able to go to a website
now and say, hey, I need to buy my husband
a mobile phone. What you know, what would you recommend?
And the answer would be verbally or typed up on
the screen. Well, tell me a little bit more about
your husband. Does he does he use a PC or
a MAC? Is he a business person or a creative?
Does he use his phone all day?
Speaker 1 (03:03):
Or you know?
Speaker 4 (03:04):
And the answers to those questions will will guide her
to a phone with a long lasting battery or a
phone with you know, an Android or an iOS and
she would result probably with buying a phone from that
digital site.
Speaker 2 (03:17):
Do you use your own LM that you've trained yourself,
or are you using a cloud or We.
Speaker 4 (03:23):
Started developing this in twenty sixteen, so a lot before
it became fashionable. I come from as an information services
and as I mentioned before, commerce and retail, so we
understood about data structure, We understood the implications of the
algorithms that the foundational algorithms for GENAI. So we started
building a language model initially that could replicate the best
(03:44):
salesman on the planet, and then we built products that
sit on top of it that allow our customers to
deploy it really as a plug into their existing sites.
Speaker 1 (03:51):
So it's a very easy deployment from it.
Speaker 2 (03:53):
But if it's a plug, and then how does it
know if you're shopping? How is it an expert in
shoes and running shoes, for example, versus an expert in fune.
Speaker 4 (04:01):
So think about our LLLM as a sort of blank
sheet of paper when we when we approach a customer,
it doesn't have the world's knowledge. It just has conversational skills.
Speaker 1 (04:11):
And then what we do is we.
Speaker 4 (04:12):
Import the product catalog of that retailer, so it could
be shoes or whatever, then there would be context around that.
Speaker 2 (04:18):
So you still have to train after a company side.
Speaker 1 (04:21):
Really no, it's.
Speaker 4 (04:23):
Already training you ingest. We've already trained the model to
be a salesman. The training quote unquote of its product
catalog is literally a matter of minutes, probably hours at most,
and then the everything else about the language, model and
the technology is that it is a very proficient salesperson.
Speaker 1 (04:41):
So if you take a digital information, right, let's.
Speaker 4 (04:45):
Just take a physical analogy. Okay, if I've run it.
If I'm running a camera camera store and I need
a new sales guy, and new sales guy or girl
joins my retail store, the first thing I'm gonna do
is say, hey, here's our products.
Speaker 1 (04:58):
You better learn about the product. Do you know anything
about photography?
Speaker 4 (05:01):
You know? You know, so that the next time somebody
walks in, they can sell a camera. Now we're able
to do that piece in a matter of minutes. And
then from that point forward, all of the best sales
capabilities are inherently built into our model.
Speaker 2 (05:15):
Where did you train it.
Speaker 1 (05:16):
Where did we train it?
Speaker 2 (05:18):
What information do you do we use?
Speaker 4 (05:19):
We needed the Canadian book Repository, which is two hundred
million books, open source books, Wikipedia for language and context,
and then so it has three hundred billion token data
sets as the foundational.
Speaker 1 (05:32):
Model to it.
Speaker 4 (05:32):
We did this over a period of years, so unlike
many others, we didn't throw bucket loads of cash, you know,
into a big black hole. We trained it over period
using a much more efficient model, and so it took us,
you know, six or seven years, but then we ended
up with the foundational model that has been recognized by
Microsoft and Google, who are partnered with us as one
of the leaders in the field.
Speaker 2 (05:52):
One of the examples you use on your website is
saying you have any dinner party you want it vegetarian?
You have six people coming, Yeah, more or less? Give
me some recipes or what should I make? I could
use chatch ept for.
Speaker 4 (06:05):
That, yes, now, but why you couldn't do it on
Deagasino's website or on you know, a target. So we're
setting to retailers. We're not selling, We're not providing you know, Google,
we're providing best Bard But is.
Speaker 1 (06:18):
It a huge? Sorry? Go ahead?
Speaker 3 (06:20):
So if your wife was looking for a phone, she
would go to let's say Apple, Well.
Speaker 1 (06:26):
She'd go to Google.
Speaker 4 (06:26):
She'd go to Google and end up probably on AT
and T side or Charison or something else.
Speaker 3 (06:32):
But with yours, she's gotta either start with Verizon or
start with Apple or Samsung or something.
Speaker 1 (06:36):
Right, No, not necessarily, I mean, because how do you compare?
Speaker 3 (06:39):
How do you get the like you've.
Speaker 4 (06:40):
Got to You've got to remember, right, there are millions
of retailers out there driving traffic to their sites all
the time. When you get to that site, seven out
of ten people drop out. So what we're doing is
that when you arrive at that site, you're welcomed, you
get answers to your questions, you get detailed information about
the products you're looking at, so that you and you're
guided through the sales process. So if I went to
Mace's today and I said, Hey, I got to buy
(07:02):
gift for my niece for her twenty first birthday, It's
not gonna give you an answer. I'm gonna have to
navigate through lots of categorism try and figure out do
I want to get her a candle?
Speaker 1 (07:11):
Do I want to get it? You know, I don't know.
Speaker 4 (07:13):
But if I was able to ask that question of
the salesperson in MACES, they say, hey, we've got lots
of ideas for you.
Speaker 1 (07:17):
So they.
Speaker 4 (07:20):
We're leveling up comments from what was a poor experience
to a very rich, engaging experience, which we believe and
have shown results in better conversion, which has a material impact.
Speaker 3 (07:30):
Change in the conversion.
Speaker 4 (07:31):
Well, it's only been going, you know, a short period
of time, but we announced only a few weeks ago
that we've seen twenty five percent uplifting conversion, which is massive.
Speaker 1 (07:40):
Massive.
Speaker 2 (07:41):
What's to stop a company like chat g ept from
just selling directly to target Ordagostino and saying, hey, this
is the this is the this is what's going to
power it. Yeah, come to us because we have this
data set.
Speaker 4 (07:53):
So they're a generalist, right, So the issue with that
is that they already think they know everything, and that
can eminates the model. Okay, so if you ingest a
whole load of cosmetics and fragrances, they're going to have
names like savage and beast, and their descriptions are going
to be things like BlackBerry notes with sandalwood and barbecue.
(08:13):
Right now, a GENAI model like open AI doesn't understand
that well and will hallucinate thinking you're talking about.
Speaker 1 (08:19):
A beast in the wood. We don't.
Speaker 4 (08:21):
We have a clean slate. We're ingesting fragrances. We tell
the model it's just fragrances you're ingesting, and then it
knows how to sell that. So it's a different approach.
And it's why Microsoft and Google partner with us because
they recognize, both of them with significant investments in AI,
recognize that we have really, you know, focused on this
vertical to make this the most appropriate solution.
Speaker 3 (08:42):
Well, it sounds like each one because you're dealing with
a particular retailer. It's their kind of.
Speaker 4 (08:47):
Closed data sets in some respect, it is, right, yeah.
Speaker 3 (08:49):
And so you just kind of whittle it down. All right,
I pull up the FA function. I know you're a
young company, you just started, So what are your expectations
in terms of revenue growth? Like, where do you see
this company?
Speaker 1 (09:01):
We're getting it out of the park straight away. Well,
we started at the.
Speaker 3 (09:05):
Beginning last I mean, it's easy around next year.
Speaker 4 (09:07):
We had nothing beginning of this year. You know, we
announced couple of weeks ago, forty one million consumers have
our technology on their phones. I mean, it's like almost
instant we're going to exit this year with one hundred
million dollars of arr and we are. And we signed
a deal only two or three weeks ago in partnership
with Google, which was a ten million dollar annual deal
with Liverpool in Mexico, a.
Speaker 1 (09:26):
Very large department.
Speaker 3 (09:27):
When is that partnership with Google? Because that's fascinating, right,
they're doing their own thing. They're worried about potentially being
replaced in terms of a search engine, which is why
they're doing Gemini. But they're watching very carefully right in
terms of how people are now communicating online to find
a soft.
Speaker 4 (09:42):
And Google both wanted to do an exclusive deal with me.
I rejected both of those advances and we ended up
doing a non exclusive deal with both. It demonstrates our
position in the market and the valuable asset that we
have in this vertical Google.
Speaker 3 (09:55):
So what's the deal with them? Tell me how that
was Google.
Speaker 4 (09:58):
Google are introducing our technology to their retail customers as
a solution which ties in those customers to Google Cloud
and to us, So they see us as a sticky
enhancement to Google Cloud. Microsoft are doing exactly the same
thing and see us as a sticky enhancement to AZOR.
Both Azor and Google Cloud are competitive cloud services that
(10:19):
are really it's a you know, you can move from
onto the other very easily, but with our technology sort
of embedded in those customer accounts. They see us as
you know, retaining those customers when they come up for renewal,
and as a result, they're pushing us out to ninety
percent of the retailers on the planet because those two
companies basically deal with pretty much everybody.
Speaker 2 (10:37):
You've got a few different products in the suite of
software as a service products. How do you know that
consumers are okay with this at this point?
Speaker 4 (10:46):
Well, I think it's Look, I'm an innovator. All the
products and services I've created in my career, which have
all gone on to become market leaders, have always been very,
very innovative. The first company I set up in ninet
eighty four was a pioneer and online information. In fact,
Mike Bloomberg licensed my content newspapers for the Terminals back
in eighty six. So we were pioneering stuff back then.
We're pioneering stuff in the late nineties, and we're doing
(11:08):
it again now. I started this in twenty sixteen. We're
nine years in and now we're productizing it and going
to market. We're way ahead of anybody else in the space,
I believe, and I think it's being shown by the
takeup we're seeing in the last few months that we're.
Speaker 1 (11:20):
On to a winner.
Speaker 4 (11:21):
This is a this is a step up for commerce,
and right now I believe the environments and the interfaces
that we're using in commerce are so out of date,
they're forty years old, that they're due a complete refresh.
Speaker 3 (11:34):
Just really quickly. Thirty seconds. I haven't heard Amazon. Are
you talking with Amazon?
Speaker 4 (11:38):
No? Obviously Amazon is the big competitor to every retailer
on the planet.
Speaker 1 (11:41):
They do a great job.
Speaker 4 (11:42):
We're not going to try and sell to them, and
we're certainly not going to be partnering with them. You know,
our customers are everybody else fascinating.
Speaker 3 (11:50):
Come back in six months, twelve months, that's coursings are going.
I really enjoyed it. Daniel Wagner, he's the chairman CEO
of the publicly traded enterprise software as a service company.
We're talking about Reserve AI joining us right here in
our Bloomberg Interactive Broker studio of Taniel thank you so much,
really appreciate it,
Hosts And Creators
Tim Stenovec
Carol Massar
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