TrulySignificant.com welcomes back tech futurist Shane Tepper of Revelation. Shane provides the inside story on the gaps within companies including go to market intelligence systems.
Hear about solutions that Revelation solves and how to drive visability, broader pipeline of leads all in an accurate forecasting methodology.
How do you plan to optimize AI and human creativity? Listen carefully to Shane Tepper and redefine what it means to be a creative.
Become a supporter of this podcast: https://www.spreaker.com/podcast/success-made-to-last-legends--4302039/support.
Hear about solutions that Revelation solves and how to drive visability, broader pipeline of leads all in an accurate forecasting methodology.
How do you plan to optimize AI and human creativity? Listen carefully to Shane Tepper and redefine what it means to be a creative.
Become a supporter of this podcast: https://www.spreaker.com/podcast/success-made-to-last-legends--4302039/support.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:09):
Hey, welcome back to truly significant dot com Presense. I'm
Rick Tokeinny. We have a wonderful returning guest, Shane Tepper,
who caught our attention on his previous show about AI
and the vision and direction that he's going, and he
is here today to debut his latest new company, Revelation.
(00:33):
And Shane is if you didn't hear those shows, he's
a digital marketing strategist. Shane, great to have you on
to talk about Revelation and answer all these other deep
philosophical questions I have for you.
Speaker 2 (00:47):
It is great to be back on Rick. I'm eager
to provide my thoughts.
Speaker 1 (00:52):
All right, mister Tepper, tell us about Revelations for starters
and what has happened since the last time you and
I spoke?
Speaker 2 (01:00):
Certainly? Yeah, So, you know, a few months ago I
sort of became aware that AI native platforms Perplexity, Chat, GPT, Claude,
et cetera were becoming a starting point for buyers doing
brand research, you know, whereas in Google, you know, you
(01:21):
type in a handful of keywords and then you get
a bunch of links surface and you have to go
through these links and read a bunch of you know,
mostly irrelevant content. And if you're a buyer, particularly if
you're a B to B buyer, you kind of have
to assemble all of these comparative lists yourself. It's a
lot of work, it's very inefficient, whereas if you're starting
(01:43):
on an AI native platform, you can have a very long,
highly contextualized prompt. You know, I'm a CMO. I manage
a team of twenty five and we need a CRM,
a client relations management tool that integrates with our existing
tech dec and immediately it pulls in information from relevant websites.
The API of the LLLN does the searching for you.
(02:07):
It finds the relevant content and that builds a table
right there in front of you in real time. Super helpful,
super efficient. And you know, brands were approaching me as
a content consultant wanting to know how they could show
up better and more accurately in these AI native platforms.
(02:28):
So I linked up with an engineer, very talented engineer,
and we have built a system, a soft piece of
software that allows us to track how brands are showing
up in AI native platforms and then give them sort
of like a roadmap or a playbook to create content
(02:51):
and distribute that content so that they can improve their
visibility within these platforms.
Speaker 1 (02:58):
Yeah, and that is so timely. And there's there's no
question that there's brands that are gonna be left in
the dust unless they step up and follow your roadmap.
So inside Revelation, what's going to be different from Revelation
(03:18):
versus Retina Media.
Speaker 2 (03:21):
Yeah, so so Retina Media, you know, you're sort of
you're hiring me. You're you're hiring my expertise, and you
know my experience, and I you know, I'm difficult to scale.
I'm difficult to scale.
Speaker 3 (03:33):
Uh.
Speaker 2 (03:33):
And I am not as scientific or as data informed.
I'm very data informed and scientific, but not as data
informed or scientific as a as a piece of software
that can apply you know, data science in a scalable,
consistent way. So you know, I had thought about I
can see it's building a company like this several months ago,
(03:55):
kind of when of the first started really focusing in
on generative engine optimization, answer engine optimization, LLM optimization, whatever
you want to call it. But you know, I don't
have I certainly don't have the technical wherewithal to build
something like this. I can describe the system to an
engineer who can then take that idea and certainly improve
upon it. And that's exactly what we did. But I
(04:20):
think what distinguishes us from existing solutions is a the
quality of the prompts that we're able to generate that
are representative of high intent buyer questions that would be
asked of an LLLM. We have a very sophisticated way
of creating these synthetic sets of prompts of buyer prompts.
(04:41):
And then that last piece, the actionability piece, is very
is crucial as well, right because if a brand isn't
showing up well or accurately in an LLLM, you want
to know how to improve it. And Rick, we're also
working on a way to tie that back to that
increased visibility, back to pipeline and revenue as well, because
that is what you know, that's what your c suite
(05:02):
cares about, is it's you know, you want to be
more visible. Of course, that makes sense. More visibility intuitively
translates to two more leads, more pipeline, and ultimately more deals. One.
But if you can if you can do that not
only directionally, but in a pretty in a pretty accurate
way that very closely approximates how much pipeline and revenue,
(05:25):
you know, dollar wise, you're you're creating because of this
increased visibility. That is something that is super super valuable
to any company.
Speaker 1 (05:34):
It really is. It's I think it's gonna separate the
wheat from the chef, so to speak, because now it's
going to come down to, well, you have AI, but
what are you going to do with this tool? And
one of the things that we learned from the last
time we talked with you is where's this division about
human creativity in the age of AI. So here's my question.
(05:58):
When you look back on your journey since you took
this career path, what has surprised you most about the
human creativity and the age of AI.
Speaker 2 (06:10):
Yeah, and I think I think it's the adaptability of
human creativity and how much AI is sort of redefining
what it means to be a creative. You know, in
the past, originality as we conceived of it, that was
something that came entirely from the human mind. It was
(06:31):
something you know, from from the conception of an idea,
the molding of the idea, and you know, eventually whatever
the execution of the idea looked like. Now that process
at pretty much every stage is being mediated or assisted
augmented by by AI, by AI tools. And as far
(06:53):
as the implication that has for creativity, it's sort of
it's kind of ambiguous what it means to be creative. Now,
if I have some ideas, uh, and I have a conversation,
you know, I have some thoughts on how I'd like
to solve a problem. Let's say, and I have a
conversation with CHATCHYBT about different approaches to solving this problem,
(07:14):
and CHATCHYBT recommends an approach that I hadn't really considered before.
It that that recommendation came through our interaction. I it
was my you know, my prompting, my thinking about the
problem and a potential solution. How I articulated that to
the model that allowed us to eventually arrive at that
(07:39):
proposed solution. But is that representative of original thinking? It's unclear.
Speaker 1 (07:47):
Uh.
Speaker 2 (07:48):
You have this sort of collaborator there that's not another person.
It is equivalent to the smartest person you know, friend
you have who has extensive knowledge on literally every subject
across the spectrum of human knowledge. And it's it's sort
of like a sounding board, but it's also introducing all
(08:09):
of these elements from its training data, and if it's
if it's doing live retrieval from the Internet, it's introducing
up to date data as well. So it's it's it
looks quite a bit different than you know, you're then
the creative process five ten years ago even, and a
lot of people, a lot of pure creatives are are
(08:31):
uncomfortable with this, uh, and not without reason. Beck. I
don't think.
Speaker 1 (08:36):
It's because it's a threat. It's yeah, it's a threat
to Shane, you know it. It's a threat to what
they think is there. They're so in their talent as
a storyteller.
Speaker 2 (08:50):
That's that's absolutely correct. I think it's both that and
the fact that these models were trained on original, pure,
purely original human creative output, and that that the original
artists are not getting compensated or credited for that. I
think that's that's that's aspect number one. And then aspect
number two is exactly what you just said, the threat
(09:12):
that it poses UH to to create it. And it
really comes down to, I think what you value about
art and creativity as a consumer. You know, can a
can an ai generate a beautiful image of something in
any any style that you'd like, Yes, it can. But
(09:34):
I think for me, at least, what makes art, What
makes art interesting to me, whether it's a film or
you know, a painting, a novel, a poem, is is
the context in which it was created, the time period,
the circumstances, you know, all that, all of that context
(09:55):
informs the artist's vision and is it's a product of
that particular or place and time and that particular individual's
you know, mindset having having been a product of that
particular place in time. And that's what makes art interesting
to me. And if it's being put out by uh,
(10:16):
a machine that doesn't have any of that context, it's
it feels a bit different. It feels a bit different.
I think an identical work, that is that is put
out by a human and a machine, the machine lacks
that lived human experience that informs the creative work. So
(10:38):
I don't think it's just about quality, right. I don't
think it's just about quality, because I think we've seen,
you know, at this point, without question, AI can put
out high quality video, high quality text, high quality imagery.
But is it's it's as you said, it's like kind
of soulless, it's not informed by the lived human experience,
and I think that makes it I think that makes
(10:59):
it less valuable as a pure creative endeavor. But if
we're talking about applied creativity in a business context, right,
you know, an ad that you create, an a b
test across social or search or whatever, any sort of
you know, a video that pitches your your your new product,
or announces you know, a features update of your product.
(11:22):
That is sort of where it becomes a little more ambiguous.
I guess it's because it's the melding of the creative
with the business objective. And I think I think I
have less of a negative reaction to that, or certainly
less of a negative reaction to that. It feels viscerally
(11:43):
less harmful to to the very I guess the very
notion of creativity if it's if it's applied in the
business context, because you're, you know, ultimately you're trying to
generate leads, close deals, generate more awareness of your brand.
So no one expects that to be a purely creative function, right,
It's creativity in the service of a business function. But yeah,
(12:07):
you know, I don't want to go to a gallery
and see a bunch of hanging images that were generated
by by by AI. I think I think, you know,
personally for me and a lot of creatives would disagree.
I think AI can be sort of like a tool
or an intermediary in the creative process. But you know,
(12:28):
to what extent can you do that without it affecting
the authenticity of the output? Is it's really I'm not sure.
Speaker 1 (12:37):
I'm not sure, Rick, Yeah, I really appreciate your transparency
and obvious honesty on that. There's a I want I
want you to take a bit of a teacher that
I didn't put this in today's questions. But just because
we've done about forty one hundred interviews and ask let's
(13:00):
approximately forty thousand questions interview questions over the last sixteen years,
that doesn't necessarily mean that all of that information and
all those interviews and voices are going to land in AI.
Is it?
Speaker 2 (13:13):
When you say land in AI, what do you mean specifically.
Speaker 1 (13:17):
Like today's interview, yesterday, the last time that I interviewed,
or the first interviews I did in two thousand and
nine with some great, notable people. Just because I did
it doesn't mean that that content rests in AI.
Speaker 2 (13:33):
Yeah, No, not necessarily, you know, The processes by which
these various platforms you know, train their train their models
is pretty opaque at this point. You know, we're not
exactly sure how they how they you know, crawl the internet,
pull in information. Uh, They're they're trained, you know, they
(13:54):
have a at least in the case of of a
of an l M like Claude or Chatchy BT, they're
trained on an existing set of data. GPT stands for
Generative pre Trained Transformers. So you train the model on
a bunch of data, and then it also does what's
called RAG, which is retrieval augmented generation. That's when it
(14:14):
crawls the live Internet and pulls in information in real time.
I think, I'm not sure, but I think the I
think all of the chat GPT subscriptions at this point
do RAG, but at least early on the unpaid the
unpaid subscriptions would only access the the data that that uh,
(14:36):
that the model was was trained on. I wouldn't go,
uh exp you know, crawl the open the open internet, uh,
the live Internet, and pull in pull in real time data.
But so that's the foundation of how those models work.
There's a there's a base of training data, you know,
billions of data points you.
Speaker 4 (14:55):
Know, uh, you know histories, UH, you know novels, you know,
business strategies, any sort of content that you can think of,
uh is ingested into this model and it learns to
make connections between between different different domains and different ideas uh,
(15:15):
and then it's augmented.
Speaker 2 (15:17):
By that live web retrieval piece. So yeah, So that
that's sort of a roundabout, a roundabout way of saying, yeah,
we're not really sure how you know. These companies understandably
don't disclose a lot about how their models are trained.
But yeah, no, I would say that it's not it's
not necessarily guaranteed that you know, interviews that you that
(15:38):
you've you did fifteen years ago would be part of
the training data for these for these models.
Speaker 1 (15:46):
On on an ethical matter, I did send you over
a question that said, how do you safeguard against AI
bias or manipulation and creative campaigns? What say you about the.
Speaker 2 (16:01):
Yeah, it's really difficult because the bias can enter at
so many different points. It can enter via the data
that the data that the models are trained on. It
can enter via the way the models are designed themselves. Right,
engineers have biases, whether whether you know, conscious or or subconscious,
(16:24):
and how they think, how they believe, not necessarily about
subjects or themes, but about how they best think. They
can design software that can parse different subjects and themes,
So that's another source. And then we you know, the humans,
the prompters, the users we also are giving, you know,
can be biased in the way that we phrase a
(16:44):
question or frame frame an issue, and all of these things.
You know, bias can seep in from all of these
different areas, these three primary different areas, primary different areas,
and it's just you know, there's we can't really control
the training data. We can't really control how the models
synthesize answers. The one thing we can control is our
(17:06):
like our ability to think critically and be discerning consumers
of information, not taking, not necessarily taking anything at face value.
I always, Zach ask a lot of follow up questions,
you know, follow up prompts. I want to drill down
into answers. I want data, you know, I want data
to to support the the the ideas that are being
(17:29):
surfaced to me. I don't want, you know, I don't
I want to to get as close to the source
of the knowledge as possible, uh, because you know, I
don't want to I don't want to be repeating or
parrotying something that's that's not correct. You know that hurts
my credibility, right, sou And you know, I think we
(17:50):
should all we should all care about being credible, reliable people.
So I think we should you know, demand the same
in in uh, you know, from from the content that
we consume, not taking any creating value, understanding the origins
of the bias, and then you know, behaviorally making changes
in the way that we interact with this technology.
Speaker 1 (18:12):
Radoh. Okay, Shane, We're going to cut to commercial, and
I want you to take the first half of the
commercial and describe in Layman's terms, what Revolute Revelation will
do to benefit companies and or consumers and what is
(18:35):
going to be your unique selling proposition.
Speaker 2 (18:38):
How do we benefit How do we benefit business? It's
a it's a B two B focused you know, a venture.
So we're benefiting businesses. I guess indirectly, we're benefiting uh,
the end users of these business of these uh, you know,
the the products, the software products of the businesses, because
we you know, what we do helps these users find
these products more easily these products that are solving business
(19:03):
issues for these users. That's that answer as far as
our unique value proposition goes. So I described earlier in
the show what I am referring to as our wedge product,
right the LLLM discoverability piece where we generate user queries
highly representative of what your best highest intent buyers are
(19:24):
typing into LLLMS. And then we you know, we get
a bunch of response data that we slice and dice,
we pars this information. We can learn you know, how
frequently you're being surfaced compared to your competitors. If you're
only showing up in five percent of responses and your
competitors are showing up in twenty percent of responses, that's
(19:45):
an issue. Are you being Are your offerings being represented
accurately in these in these responses sentiment analysis to the
extent that you're being represented in these answers? Are people
saying good things about your product or bad things about
your product? Are they just acknowledging that you are a product?
We also critically get citation sources, So what sources are
(20:07):
these models considering high authority, high reliability sources because that
gives us clues about where we can place content and
create content and the type of content that we can
create that should presumably increase your visibility inside of these models,
and then based on all of that data, we can
put together an evidence based plan of action for you
(20:32):
create these pieces of content. You know, this comparative table,
this listicle, this FAQ chart talking about these issues, and
beyond that, just beyond just the content that is visible
to the user, we can also give you recommendations and
how to optimize the content technically. You know what sorts
of meta descriptions to use, what sort of schema to include,
(20:54):
and then all of this together you implement this, You
implement this plan, and then you go back to the
beginning and you run the same set of queries and
you can measure the lift that the creation and deployment
of this of this content created. And we can also
I mentioned the revenue piece as well. We are working
(21:14):
on an attribution model as well that will allow us
to translate the lift in visibility to web traffic, which
then we can then extrapolate an increase. We can look
downstream at your CRM and connect that to pipeline and
ultimately deals closed one. So you know, we will be
(21:38):
able to say with a reasonable amount of certainty that
you're you know, you went from five percent to twenty
five percent. That twenty percent lift invisibility translated to you know,
six hundred thousand dollars in pipeline and one hundred and
twenty five thousand dollars worth of deals closed one additional
(21:59):
deals closed one. So that's that's the wedge product. That's
what we enter market with. The vision is much larger
than that. The vision is to create an end to
end what we're referring to as a go to market
intelligence system that goes beyond uh, you know, using looking
(22:19):
at at LLLM visibility and tying that's revenue. What we
would what we are doing is if you consider an organization,
you have many different business functions within that organization. You
have sales, you have marketing, you have customer support, you
have product and each of these departments, each of these
(22:40):
functions uses their own technology, uses their own software. Right,
so you know, you have a user that is in
marketing that is also maybe present in the sales pipeline,
but there's like they're disconnected. You don't really know, you
know what that what this buyer in one aspect of
(23:03):
your in one one silo, one function of your business,
how it relates to that's what what is that same
user in a different uh area of of your business?
Uh they use because these different systems use different ways
of tracking the same the same user. So what we
(23:23):
are building is a an agentic engine, an AI engine
that is capable of ingesting all of this data from
across your different functions. Uh. You know, from sales you're
getting c r M data, your leads, how far along
the uh you know, how how deep into the sales
funnel they are, the marketing and the marketing function you're understanding.
(23:45):
Uh you know your your your AD spend and how
different creative is performing across social search? Uh you know
from the customer service UH silo, your your understanding, you
know what sorts of problems are customers are your users
surfacing when it comes to your product, what sort of
features there they like to use? Uh? And then and
(24:08):
then from the product silo, you understand how people are
actually using your product. You know what features are breaking,
what features users wish were would be further developed or incorporated.
So we kind of take all this data from all
these disparate systems we go pass it through a normalization
layer so that they become mutually able to be understood
(24:31):
by a single system, whereas previously they were, you know,
only able to be understood within their distinct functional specific
functional specific systems. We ingest all of that and then
we you know, in addition to that original ll M
visibility piece, and we use that to help businesses use
that to inform their their go to their go to
(24:53):
market strategy UH, which marketing activities to focus on UH
and and you know, where to allocate marketing spend. And
based on these internal signals, we will also supplement that.
And this is you know, this is a process that
is going to take twelve to eighteen months. We estimate
we are also going to pull in data externally, so
(25:14):
so industry trends data, competitive data, any sort of market
analysis data. We pull all of these signals in internal
and external and we create a unified market intelligence system.
Speaker 1 (25:33):
So it's it's remarkable what you just said. We're going
to cut to commercial that I'm gonna ask you my
follow up question. So standby, we're with Shane Temper today
here on truly significant dot com presense.
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Speaker 1 (27:32):
We're back with Shane Tepper. I mean, I don't cuss,
but damn, that's exactly what the market needs, is this
master of an ecosystem, a go to market intelligence system
that embraces all of those disparate parts. And how are
(27:55):
you going to use AI to build it?
Speaker 2 (27:59):
Yeah? So you know, we're talking about billions of data
points here, right, making sense making sense of all of
that data. This is something that you know, I guess
conceivably could be done by a team of data scientists
and marketing experts. It is much more cost effective to
(28:20):
use an AI engine to make sense of all of
this data. It can do it in a much more
efficient way. It can do it in a much more
consistent way. Uh. You know, AI gets plugged into the
system at many different points, going back to our wedge product,
going back to that LLL in visibility tool. We use
AI to to generate the queries. For instance, right, we
(28:43):
use AI to go out and gather information about your brand,
about your competitors brand, and about about Uh. We use
it when we use that data to create ideal customer profiles,
and we use that to understand your offerings. We use
that to understand your positioning. We use that to understand
(29:05):
your value prop and your differentiating factors. All of this
gets pulled in, you know, the data gets pulled in,
and then essentially the AI does what AI does best.
It synthesizes information and makes sense of a vast volume
of data in a way that would take humans much
much longer to do.
Speaker 1 (29:28):
Exactly. But you leave me with the question of you
and your partners at Revelation have come up or you
will debut a go to market intelligence system that will
use AI as a tool. But could AI ever have
thought of this ingesting of the kind of data that's
(29:54):
desired by brands to make sure that their dollar that
they've invested is and to have the highest return.
Speaker 2 (30:02):
I think this product was conceived out of hundreds of
conversations with AI. So it goes back to that that
question that you asked earlier about creativity and AI and
AI's role in creativity. Of course, it was our It
was our curiosity and our questioning and our thinking around
(30:26):
how we could conceivably build a model like this, build
a system like this, an end to end, go to
market intelligence system, and AI helped us, you know, kind
of kind of home in on specific things to focus
on specific pro helped us do research on you know,
this was obviously supplemented with many many conversations with cmos
(30:49):
and CROs and actual actual human beings. Rick right. Still
very important to talk to to talk to your customers,
but the the the approach was was very much mediated
by AI in conversations with AII. So you know, AI,
I can't just I can't just go to I mean
you could. You could go to AI and type and
(31:09):
give me a business idea. AI, It's gonna be a
pretty shitty one, right, but if you think about you
have to think about the problem as a human and
you have to understand what problems other humans are having
before you can even start to really create something that
that provides value. So A I absolutely played. Uh you know,
(31:30):
from from the from the ideation of the idea to
the to the development of the product itself, huge huge role.
I mean, uh, this this, Uh you necessarily have to
use AI in the product because we are solving the
problem of AI visibility. We need to know how AI
is surfacing brands right in response to buyer queries. That's
(31:50):
the whole you know, that was the reason for for
you know that we that we started on this on
this endeavor in the first place, is because we were
curious about how AI was affecting the buyer journey and
we wanted to help brands be more visible to buyers
along this journey. So yeah, I mean it's really is
uh you know AI. AI played a crucial role in
(32:13):
in every aspect of this of this business that we're
that we're building.
Speaker 1 (32:17):
No surprise, when did you say it's going to debut?
Speaker 2 (32:23):
So we have we have an early version of this
product in market already. We're working with some clients and
some agency partners. Uh, you know, in the course of
of developing this this business and this concept. You know,
I come from a from a creative agency background. I
used to be a copywriter, which is which is absolutely
something that you know, we're we're in we're an endangered
(32:45):
species because of because of AI. But no, I talked
to to some agency leaders. They had they they uh
informed us that they had a lot of their their clients,
a lot of their their clients were coming to them
and being like, hey, you know, we need to be
visible inside of these AI systems. We know that people
are now uh, these high intent buyers are now going
(33:07):
to chat ept and and Claude and Gemini to to
to learn about brands and compare brands and create shortlists,
and we need to be more visible in there. They
didn't have that capability in house, so they came to
us and they're like, we want to be able to
offer this service to our clients, and uh, you know
where we're in position to do that. So mm so
(33:31):
that's what we have today. We have a h you know,
an m VP, a minimum viable product version of this
wedge offering, this ll M visibility offering. It's not fully
productized yet.
Speaker 1 (33:42):
Uh.
Speaker 2 (33:43):
You know, we we generate the reports ourselves. There's no
sort of uh you know, dash in dashboard where a
user could log in and see all the see all
the u you know, the the metrics and the recommendations themselves.
We're actually we're we're raising money right now. We're in
the we're in the the of a pre a fundraise
(34:03):
to to be able to you know, the hire the
talent that we need to to build out this this
product version, this sassified version, if you will, of our offering,
of this initial offering.
Speaker 1 (34:16):
And your client could be the big ad agencies like Omnicom,
who haven't thought of this, and or they should just
pick it off the shelf if it's working as beautiful
as you stated.
Speaker 2 (34:30):
Yeah, yeah, I think I think agencies are thinking about this.
They just don't really have a you know, know how
to approach it. Yet. They don't know how to approach it.
They don't have the internal tools necessarily to be able
to offer this service. So yeah, I think our clients, uh,
you know, and this is you know, this is subject
to evolve over time, but agency partners I think could
(34:51):
be could be big for us. And then of course
we want to go directly to to enterprises uh you know,
mid mid market software and enterprise software companies as well,
and do implementations directly with them as well.
Speaker 1 (35:05):
Of course. Yeah, I think that that's the sweet spot
for you. I find our conversations absolutely fascinating, and I
when i'm you know, we build these questions out for you,
and then I go, well, we're going to take another
detour because there's so many things that I think about
while you're talking about this. One is that you've made
(35:27):
a career change based on this right time, right place,
and you and I talked about that last time, and
based on what you just said over the last fifteen
twenty minutes, I wonder what's the good career counseling for
kids coming out of high school much less college, on
how they can be above the ecosystem and not not
(35:50):
be a worker bee and be wiped out in the
next six months years.
Speaker 2 (35:57):
What's your view? I think, Yeah, you know, AI is
going to leave virtually no industry untouched by its impact,
and I don't think the concern is as much about replacement,
although that will you know, AI is certainly going to
eliminate some eliminate jobs that you know don't require critical
(36:20):
thinking execution mostly execution based jobs, right, but strategic strategic
thinking is still extremely valuable. So I think the question
the question is more, you know, how how they can
use AI to make existing business functions more efficient and
(36:41):
how to how they can use AI to to add
value to to business operations. I think that's the question,
you know, businesses. I read recently Mi T did this
this really interesting study over the summer, and you concluded
that ninety five percent of business AI implementations fail fail
(37:07):
to show ROI, which is fascinating to me because this
it's so obvious how transformative this technology is. And what
that tells me is that businesses are doing AI rob
they're implementing it incorrectly. You know, they're seeing all this
buzz about AI. They're they're they're doing sort of this,
(37:30):
they're taking sort of like a scattershot approach. They're trying
to implement it in so many places, so rapidly across
their organizations, Whereas what they should do is they should
do you know, a small pilot or set of pilot
programs with AI focus on a specific function. They need
to understand what metrics, what KPIs. They're trying to influence
(37:54):
using you know, by incorporating AI into their workflows so
they can get an understanding of how to best implement
and then they can they can move on and scale
that to other functions. And I think that is the
deliberate intentional implementation of AI. I think is somewhere is
what is causing these implementations to fail. Not that not
(38:15):
that the technology itself isn't isn't useful and transformative. So
to circle back to your question about you know, young
people entering the workforce, I think they I think the
mindset they need to adopt is how can this technology
Because businesses are still having trouble figuring it out. How
(38:37):
can we use this technology to to improve existing business functions?
And if you go into an interview for a role
and you you talk about you know, I've I've looked
at all these AI tools. I've seen that you know,
companies A, B, and C have seen direct are ALI
through the implementation of these tools because they improve the
(38:58):
workflow and in X y Z ways. That is something
that will impress impress an employer. I think because you're
not just You're not just saying we need to incorporate AI,
You're doing it. It shows that you've been thinking about
exactly how to incorporate the tools, which tools, and you're
thinking in an outcomes based way, like what are you
(39:19):
trying to do with the AI? It's not about getting AI,
you know, you know people are like, get me AI,
get me AI. We need to incorporate AI. But the
question that needs to be asked first is what are
we trying to accomplish with the AI? What results do
we want to see?
Speaker 1 (39:37):
Yes, sir okay, and I'm convinced that you are in
that pioneering position to help companies to discover how to
use AI. Properly. So how do people contact you and
would you like to say a little bit about your
rates or do you want to just have them contact you?
Speaker 2 (39:57):
Yeah, have them contact me. You know, most of our
most of our pricing is done on a like a
project by project basis. So yeah, that's something that's you know,
particular to the client their needs. But yeah, absolutely would
love to talk to anyone about about their AI needs,
particularly when it comes to a discoverability UH and business
impact perspective. Uh. My email address is Shane at Revelation
(40:21):
h Q dot com R E V E L A
T I O N h Q dot com. You can
also visit us at revelation hq dot com. UH, learn
about learn about what we do and how we're uh,
you know, transforming I would say, uh market intelligence and
brand discoverability in the AI era, you know. And I
(40:43):
love I love talking about uh a I generally and
and how it's impacting businesses. Uh, you know, just something
I've become very passionate about, uh, you know, and and
really since this technology has emerged over the past two
or three.
Speaker 1 (40:58):
Years, it's it is evident. Do you have a few
more minutes to answer a couple of questions without Okay,
So I want I want to take one particular company,
and I know last time you said, well, just have
them call me and we'll set up an agreement. But
I think if you, if we go through this one example,
(41:19):
it'll help our listening audience understand the journey that they'll
the customer journey that they'll have with you.
Speaker 2 (41:26):
Okay, absolutely, No, I think that's a great idea. It
becomes much more tangible if we can talk about an
actual use case versus you know, I guess hypothetically how
all this works. So yeah, let's do it. Let's have
out erik.
Speaker 1 (41:37):
Okay, So here's the situation. The problem is one of
of corporate culture. It's about increasing the number of current
employees in their engagement in volunteering because the company says
(41:58):
that they want to have more people volunteer and they
but they don't want to force it down the employee's throat,
and they want people to be involved in volunteer because
they've learned over the course of ten years that their
retention of their employees is greater when those people are
(42:20):
actually involved in activities after work with other employees that
kind of that glue them all together. Okay, so the
question is how would your company help them accelerate.
Speaker 2 (42:37):
I would say that's that's outside of the of the
real of the of the problems that we solve at
at Revelation HQ. But you know, we're more in the
brand discoverability and uh and and market intelligence business. But
I mean this is this is a very interesting management
question that you're asking. How can you incentivize employees too,
(43:00):
you know, get involved in community service? Uh? Uh you
know you're seeing this data that that is indicating that
would you say it helps with retention? It probably makes
for more satisfied employees. You know, it's a it's a
sense of you know, you're kind of you're kind of
melding like the business culture with with the you know,
(43:20):
the the do gooder culture and and uh you know
you're having an impact not just on the business, but
on the community as well. That all makes sense. How
do you incentivize how do you incentivize your uh, you know,
your employees to participate in programs like this? You know,
beyond just it'll make you feel good, you're gonna have
(43:41):
an impact on the community. I think you need to
understand what motivates people first. And foremost, and you know,
people want to feel good, they want to feel like
they're having an impact. How how can you accomplish this.
It's a good question, it's a good question. You could
(44:05):
build and and I think I've read about this. I
think there are companies, there are tech companies that are
building systems that that encourage, you know, that with the
purpose of encouraging employees to participate in these sort of
you know, after hours, after work hours, community oriented activities.
I'm not sure what the mechanisms are that they use
(44:28):
to to to induce more participation in those activities. You know,
it could be you know, you you you hate to
talk about introducing any sort of financial incentive when it
comes to when it comes to you know, stuff like
this community oriented work. But you know, I don't know
(44:53):
if it's like if it's like some sort of a
reward system where you know, the more the more hours
of community service you log, there's you know, there's there
has to be I think, you know, if people aren't
going to do it out of the goodness of their heart,
I think there has to be some sort of incentivization
element factor there where it's you know, or a competit
(45:16):
you know, it's like a competitive or gamification piece maybe, right,
you know, showing where where you know, how many hours
have you done versus you know, sally down and accounting, right, Like,
there's that having that competitive element I think could be interesting.
But I would have to look into I would have
to look into you know, it's a business psychology question, right,
(45:38):
It's like an organizational psychology question, I think, and it
gets into you know, what motivates people to do different things?
Speaker 1 (45:45):
Is Yeah, yes, it's very it's very admirable for you
to set all that. But sometimes people you've got to
recognize that if it's not a part of your business,
then you go, I don't know, but why don't you
do more research about it? Because it is like a
it's a very fuzzy situation it is.
Speaker 2 (46:06):
Yeah, yeah, no, it's no, it's an interesting it's an
interesting thing to think about. Yeah. Absolutely, Well obviously, well
outside of what we do, but no, Rick, like I
want to you know, if you if there are questions
that that fall outside of you know, what I do
and what we do, it's still you know, I'm a
I'm a problem solver at the end of the day,
whether that's you know, usability within AI platforms or or elsewhere.
(46:30):
And you know, I like, I like, I like talking
about these things and trying to figure them out.
Speaker 1 (46:35):
Uh.
Speaker 2 (46:35):
You know, if if if I were on my own,
if you had given me this assignment and we're like
Shane helped me design a system that helps incentivize employees
to do more community service, I would have absolutely gone
to to you know, my my AI tools and and
and work something out.
Speaker 1 (46:53):
Of course, of course. Okay, final question for you is this,
And I wrote this and it was I really actually
wrote this in conjunction with AI. And the question was
about in ten years, how do you want Revelation to
be remembered ten years? How about ten months for ten weeks,
(47:13):
so let's drop the number of the time element. How
do you want Revelation to be remembered in the story
of AI and creative evolution.
Speaker 2 (47:23):
Yeah, I would love for Revelation in its fully expansive
final state. I don't know if we can call it
a final state, but this in its fully realized go
to market intelligence system state, I would like it to
be remembered as the platform that transformed AI native go
(47:44):
to market intelligence, the first platform that unified all these
disparate signals both internally and externally, took advantage of really
all the data available that could inform a company's go
to market strategy marketing uh and how that ties back
to revenue and pipeline. The first company to unify all
(48:08):
of those different things, uh and and present a coherent
picture of how marketing efforts impact the bottom line at
your company, setting a new standard for how for how
data is used to inform marketing and then tying it
all back to pipeline and revenue. That's that's what that
(48:28):
blaze the trail there you.
Speaker 1 (48:30):
Go, That is that is bulls eye from our perspective,
remembering him as the platform, the company that pioneered, the
platform that unified all the disparate signals. Man, that's perfect. Hey, Shane,
thank you so much for being on today. Give out
your information on your company one more time.
Speaker 2 (48:48):
Certainly you can find us where revelation and you can
find us at revelation HQ dot com. UH And if
you like to get in touch with me directly, that
is Shane s h A n E at Revelation h
Q dot com. That's r e V E l A
t i O N h Q dot com.
Speaker 1 (49:07):
Thank you Rick, Thanks Shane Tepper, and hang on a
second after the show. Good one other questions ask you
and we appreciate Shane being on our show today and
again go to Revelation hq dot com to find out
more information. Folks, we hope that you have a great
week and as usual, we wish you success on your
(49:27):
way to significance.
Speaker 2 (49:29):
How good week.
Hey, welcome back to truly significant dot com Presense. I'm
Rick Tokeinny. We have a wonderful returning guest, Shane Tepper,
who caught our attention on his previous show about AI
and the vision and direction that he's going, and he
is here today to debut his latest new company, Revelation.
(00:33):
And Shane is if you didn't hear those shows, he's
a digital marketing strategist. Shane, great to have you on
to talk about Revelation and answer all these other deep
philosophical questions I have for you.
Speaker 2 (00:47):
It is great to be back on Rick. I'm eager
to provide my thoughts.
Speaker 1 (00:52):
All right, mister Tepper, tell us about Revelations for starters
and what has happened since the last time you and
I spoke?
Speaker 2 (01:00):
Certainly? Yeah, So, you know, a few months ago I
sort of became aware that AI native platforms Perplexity, Chat, GPT, Claude,
et cetera were becoming a starting point for buyers doing
brand research, you know, whereas in Google, you know, you
(01:21):
type in a handful of keywords and then you get
a bunch of links surface and you have to go
through these links and read a bunch of you know,
mostly irrelevant content. And if you're a buyer, particularly if
you're a B to B buyer, you kind of have
to assemble all of these comparative lists yourself. It's a
lot of work, it's very inefficient, whereas if you're starting
(01:43):
on an AI native platform, you can have a very long,
highly contextualized prompt. You know, I'm a CMO. I manage
a team of twenty five and we need a CRM,
a client relations management tool that integrates with our existing
tech dec and immediately it pulls in information from relevant websites.
The API of the LLLN does the searching for you.
(02:07):
It finds the relevant content and that builds a table
right there in front of you in real time. Super helpful,
super efficient. And you know, brands were approaching me as
a content consultant wanting to know how they could show
up better and more accurately in these AI native platforms.
(02:28):
So I linked up with an engineer, very talented engineer,
and we have built a system, a soft piece of
software that allows us to track how brands are showing
up in AI native platforms and then give them sort
of like a roadmap or a playbook to create content
(02:51):
and distribute that content so that they can improve their
visibility within these platforms.
Speaker 1 (02:58):
Yeah, and that is so timely. And there's there's no
question that there's brands that are gonna be left in
the dust unless they step up and follow your roadmap.
So inside Revelation, what's going to be different from Revelation
(03:18):
versus Retina Media.
Speaker 2 (03:21):
Yeah, so so Retina Media, you know, you're sort of
you're hiring me. You're you're hiring my expertise, and you
know my experience, and I you know, I'm difficult to scale.
I'm difficult to scale.
Speaker 3 (03:33):
Uh.
Speaker 2 (03:33):
And I am not as scientific or as data informed.
I'm very data informed and scientific, but not as data
informed or scientific as a as a piece of software
that can apply you know, data science in a scalable,
consistent way. So you know, I had thought about I
can see it's building a company like this several months ago,
(03:55):
kind of when of the first started really focusing in
on generative engine optimization, answer engine optimization, LLM optimization, whatever
you want to call it. But you know, I don't
have I certainly don't have the technical wherewithal to build
something like this. I can describe the system to an
engineer who can then take that idea and certainly improve
upon it. And that's exactly what we did. But I
(04:20):
think what distinguishes us from existing solutions is a the
quality of the prompts that we're able to generate that
are representative of high intent buyer questions that would be
asked of an LLLM. We have a very sophisticated way
of creating these synthetic sets of prompts of buyer prompts.
(04:41):
And then that last piece, the actionability piece, is very
is crucial as well, right because if a brand isn't
showing up well or accurately in an LLLM, you want
to know how to improve it. And Rick, we're also
working on a way to tie that back to that
increased visibility, back to pipeline and revenue as well, because
that is what you know, that's what your c suite
(05:02):
cares about, is it's you know, you want to be
more visible. Of course, that makes sense. More visibility intuitively
translates to two more leads, more pipeline, and ultimately more deals. One.
But if you can if you can do that not
only directionally, but in a pretty in a pretty accurate
way that very closely approximates how much pipeline and revenue,
(05:25):
you know, dollar wise, you're you're creating because of this
increased visibility. That is something that is super super valuable
to any company.
Speaker 1 (05:34):
It really is. It's I think it's gonna separate the
wheat from the chef, so to speak, because now it's
going to come down to, well, you have AI, but
what are you going to do with this tool? And
one of the things that we learned from the last
time we talked with you is where's this division about
human creativity in the age of AI. So here's my question.
(05:58):
When you look back on your journey since you took
this career path, what has surprised you most about the
human creativity and the age of AI.
Speaker 2 (06:10):
Yeah, and I think I think it's the adaptability of
human creativity and how much AI is sort of redefining
what it means to be a creative. You know, in
the past, originality as we conceived of it, that was
something that came entirely from the human mind. It was
(06:31):
something you know, from from the conception of an idea,
the molding of the idea, and you know, eventually whatever
the execution of the idea looked like. Now that process
at pretty much every stage is being mediated or assisted
augmented by by AI, by AI tools. And as far
(06:53):
as the implication that has for creativity, it's sort of
it's kind of ambiguous what it means to be creative. Now,
if I have some ideas, uh, and I have a conversation,
you know, I have some thoughts on how I'd like
to solve a problem. Let's say, and I have a
conversation with CHATCHYBT about different approaches to solving this problem,
(07:14):
and CHATCHYBT recommends an approach that I hadn't really considered before.
It that that recommendation came through our interaction. I it
was my you know, my prompting, my thinking about the
problem and a potential solution. How I articulated that to
the model that allowed us to eventually arrive at that
(07:39):
proposed solution. But is that representative of original thinking? It's unclear.
Speaker 1 (07:47):
Uh.
Speaker 2 (07:48):
You have this sort of collaborator there that's not another person.
It is equivalent to the smartest person you know, friend
you have who has extensive knowledge on literally every subject
across the spectrum of human knowledge. And it's it's sort
of like a sounding board, but it's also introducing all
(08:09):
of these elements from its training data, and if it's
if it's doing live retrieval from the Internet, it's introducing
up to date data as well. So it's it's it
looks quite a bit different than you know, you're then
the creative process five ten years ago even, and a
lot of people, a lot of pure creatives are are
(08:31):
uncomfortable with this, uh, and not without reason. Beck. I
don't think.
Speaker 1 (08:36):
It's because it's a threat. It's yeah, it's a threat
to Shane, you know it. It's a threat to what
they think is there. They're so in their talent as
a storyteller.
Speaker 2 (08:50):
That's that's absolutely correct. I think it's both that and
the fact that these models were trained on original, pure,
purely original human creative output, and that that the original
artists are not getting compensated or credited for that. I
think that's that's that's aspect number one. And then aspect
number two is exactly what you just said, the threat
(09:12):
that it poses UH to to create it. And it
really comes down to, I think what you value about
art and creativity as a consumer. You know, can a
can an ai generate a beautiful image of something in
any any style that you'd like, Yes, it can. But
(09:34):
I think for me, at least, what makes art, What
makes art interesting to me, whether it's a film or
you know, a painting, a novel, a poem, is is
the context in which it was created, the time period,
the circumstances, you know, all that, all of that context
(09:55):
informs the artist's vision and is it's a product of
that particular or place and time and that particular individual's
you know, mindset having having been a product of that
particular place in time. And that's what makes art interesting
to me. And if it's being put out by uh,
(10:16):
a machine that doesn't have any of that context, it's
it feels a bit different. It feels a bit different.
I think an identical work, that is that is put
out by a human and a machine, the machine lacks
that lived human experience that informs the creative work. So
(10:38):
I don't think it's just about quality, right. I don't
think it's just about quality, because I think we've seen,
you know, at this point, without question, AI can put
out high quality video, high quality text, high quality imagery.
But is it's it's as you said, it's like kind
of soulless, it's not informed by the lived human experience,
and I think that makes it I think that makes
(10:59):
it less valuable as a pure creative endeavor. But if
we're talking about applied creativity in a business context, right,
you know, an ad that you create, an a b
test across social or search or whatever, any sort of
you know, a video that pitches your your your new product,
or announces you know, a features update of your product.
(11:22):
That is sort of where it becomes a little more ambiguous.
I guess it's because it's the melding of the creative
with the business objective. And I think I think I
have less of a negative reaction to that, or certainly
less of a negative reaction to that. It feels viscerally
(11:43):
less harmful to to the very I guess the very
notion of creativity if it's if it's applied in the
business context, because you're, you know, ultimately you're trying to
generate leads, close deals, generate more awareness of your brand.
So no one expects that to be a purely creative function, right,
It's creativity in the service of a business function. But yeah,
(12:07):
you know, I don't want to go to a gallery
and see a bunch of hanging images that were generated
by by by AI. I think I think, you know,
personally for me and a lot of creatives would disagree.
I think AI can be sort of like a tool
or an intermediary in the creative process. But you know,
(12:28):
to what extent can you do that without it affecting
the authenticity of the output? Is it's really I'm not sure.
Speaker 1 (12:37):
I'm not sure, Rick, Yeah, I really appreciate your transparency
and obvious honesty on that. There's a I want I
want you to take a bit of a teacher that
I didn't put this in today's questions. But just because
we've done about forty one hundred interviews and ask let's
(13:00):
approximately forty thousand questions interview questions over the last sixteen years,
that doesn't necessarily mean that all of that information and
all those interviews and voices are going to land in AI.
Is it?
Speaker 2 (13:13):
When you say land in AI, what do you mean specifically.
Speaker 1 (13:17):
Like today's interview, yesterday, the last time that I interviewed,
or the first interviews I did in two thousand and
nine with some great, notable people. Just because I did
it doesn't mean that that content rests in AI.
Speaker 2 (13:33):
Yeah, No, not necessarily, you know, The processes by which
these various platforms you know, train their train their models
is pretty opaque at this point. You know, we're not
exactly sure how they how they you know, crawl the internet,
pull in information. Uh, They're they're trained, you know, they
(13:54):
have a at least in the case of of a
of an l M like Claude or Chatchy BT, they're
trained on an existing set of data. GPT stands for
Generative pre Trained Transformers. So you train the model on
a bunch of data, and then it also does what's
called RAG, which is retrieval augmented generation. That's when it
(14:14):
crawls the live Internet and pulls in information in real time.
I think, I'm not sure, but I think the I
think all of the chat GPT subscriptions at this point
do RAG, but at least early on the unpaid the
unpaid subscriptions would only access the the data that that uh,
(14:36):
that the model was was trained on. I wouldn't go,
uh exp you know, crawl the open the open internet, uh,
the live Internet, and pull in pull in real time data.
But so that's the foundation of how those models work.
There's a there's a base of training data, you know,
billions of data points you.
Speaker 4 (14:55):
Know, uh, you know histories, UH, you know novels, you know,
business strategies, any sort of content that you can think of,
uh is ingested into this model and it learns to
make connections between between different different domains and different ideas uh,
(15:15):
and then it's augmented.
Speaker 2 (15:17):
By that live web retrieval piece. So yeah, So that
that's sort of a roundabout, a roundabout way of saying, yeah,
we're not really sure how you know. These companies understandably
don't disclose a lot about how their models are trained.
But yeah, no, I would say that it's not it's
not necessarily guaranteed that you know, interviews that you that
(15:38):
you've you did fifteen years ago would be part of
the training data for these for these models.
Speaker 1 (15:46):
On on an ethical matter, I did send you over
a question that said, how do you safeguard against AI
bias or manipulation and creative campaigns? What say you about the.
Speaker 2 (16:01):
Yeah, it's really difficult because the bias can enter at
so many different points. It can enter via the data
that the data that the models are trained on. It
can enter via the way the models are designed themselves. Right,
engineers have biases, whether whether you know, conscious or or subconscious,
(16:24):
and how they think, how they believe, not necessarily about
subjects or themes, but about how they best think. They
can design software that can parse different subjects and themes,
So that's another source. And then we you know, the humans,
the prompters, the users we also are giving, you know,
can be biased in the way that we phrase a
(16:44):
question or frame frame an issue, and all of these things.
You know, bias can seep in from all of these
different areas, these three primary different areas, primary different areas,
and it's just you know, there's we can't really control
the training data. We can't really control how the models
synthesize answers. The one thing we can control is our
(17:06):
like our ability to think critically and be discerning consumers
of information, not taking, not necessarily taking anything at face value.
I always, Zach ask a lot of follow up questions,
you know, follow up prompts. I want to drill down
into answers. I want data, you know, I want data
to to support the the the ideas that are being
(17:29):
surfaced to me. I don't want, you know, I don't
I want to to get as close to the source
of the knowledge as possible, uh, because you know, I
don't want to I don't want to be repeating or
parrotying something that's that's not correct. You know that hurts
my credibility, right, sou And you know, I think we
(17:50):
should all we should all care about being credible, reliable people.
So I think we should you know, demand the same
in in uh, you know, from from the content that
we consume, not taking any creating value, understanding the origins
of the bias, and then you know, behaviorally making changes
in the way that we interact with this technology.
Speaker 1 (18:12):
Radoh. Okay, Shane, We're going to cut to commercial, and
I want you to take the first half of the
commercial and describe in Layman's terms, what Revolute Revelation will
do to benefit companies and or consumers and what is
(18:35):
going to be your unique selling proposition.
Speaker 2 (18:38):
How do we benefit How do we benefit business? It's
a it's a B two B focused you know, a venture.
So we're benefiting businesses. I guess indirectly, we're benefiting uh,
the end users of these business of these uh, you know,
the the products, the software products of the businesses, because
we you know, what we do helps these users find
these products more easily these products that are solving business
(19:03):
issues for these users. That's that answer as far as
our unique value proposition goes. So I described earlier in
the show what I am referring to as our wedge product,
right the LLLM discoverability piece where we generate user queries
highly representative of what your best highest intent buyers are
(19:24):
typing into LLLMS. And then we you know, we get
a bunch of response data that we slice and dice,
we pars this information. We can learn you know, how
frequently you're being surfaced compared to your competitors. If you're
only showing up in five percent of responses and your
competitors are showing up in twenty percent of responses, that's
(19:45):
an issue. Are you being Are your offerings being represented
accurately in these in these responses sentiment analysis to the
extent that you're being represented in these answers? Are people
saying good things about your product or bad things about
your product? Are they just acknowledging that you are a product?
We also critically get citation sources, So what sources are
(20:07):
these models considering high authority, high reliability sources because that
gives us clues about where we can place content and
create content and the type of content that we can
create that should presumably increase your visibility inside of these models,
and then based on all of that data, we can
put together an evidence based plan of action for you
(20:32):
create these pieces of content. You know, this comparative table,
this listicle, this FAQ chart talking about these issues, and
beyond that, just beyond just the content that is visible
to the user, we can also give you recommendations and
how to optimize the content technically. You know what sorts
of meta descriptions to use, what sort of schema to include,
(20:54):
and then all of this together you implement this, You
implement this plan, and then you go back to the
beginning and you run the same set of queries and
you can measure the lift that the creation and deployment
of this of this content created. And we can also
I mentioned the revenue piece as well. We are working
(21:14):
on an attribution model as well that will allow us
to translate the lift in visibility to web traffic, which
then we can then extrapolate an increase. We can look
downstream at your CRM and connect that to pipeline and
ultimately deals closed one. So you know, we will be
(21:38):
able to say with a reasonable amount of certainty that
you're you know, you went from five percent to twenty
five percent. That twenty percent lift invisibility translated to you know,
six hundred thousand dollars in pipeline and one hundred and
twenty five thousand dollars worth of deals closed one additional
(21:59):
deals closed one. So that's that's the wedge product. That's
what we enter market with. The vision is much larger
than that. The vision is to create an end to
end what we're referring to as a go to market
intelligence system that goes beyond uh, you know, using looking
(22:19):
at at LLLM visibility and tying that's revenue. What we
would what we are doing is if you consider an organization,
you have many different business functions within that organization. You
have sales, you have marketing, you have customer support, you
have product and each of these departments, each of these
(22:40):
functions uses their own technology, uses their own software. Right,
so you know, you have a user that is in
marketing that is also maybe present in the sales pipeline,
but there's like they're disconnected. You don't really know, you
know what that what this buyer in one aspect of
(23:03):
your in one one silo, one function of your business,
how it relates to that's what what is that same
user in a different uh area of of your business?
Uh they use because these different systems use different ways
of tracking the same the same user. So what we
(23:23):
are building is a an agentic engine, an AI engine
that is capable of ingesting all of this data from
across your different functions. Uh. You know, from sales you're
getting c r M data, your leads, how far along
the uh you know, how how deep into the sales
funnel they are, the marketing and the marketing function you're understanding.
(23:45):
Uh you know your your your AD spend and how
different creative is performing across social search? Uh you know
from the customer service UH silo, your your understanding, you
know what sorts of problems are customers are your users
surfacing when it comes to your product, what sort of
features there they like to use? Uh? And then and
(24:08):
then from the product silo, you understand how people are
actually using your product. You know what features are breaking,
what features users wish were would be further developed or incorporated.
So we kind of take all this data from all
these disparate systems we go pass it through a normalization
layer so that they become mutually able to be understood
(24:31):
by a single system, whereas previously they were, you know,
only able to be understood within their distinct functional specific
functional specific systems. We ingest all of that and then
we you know, in addition to that original ll M
visibility piece, and we use that to help businesses use
that to inform their their go to their go to
(24:53):
market strategy UH, which marketing activities to focus on UH
and and you know, where to allocate marketing spend. And
based on these internal signals, we will also supplement that.
And this is you know, this is a process that
is going to take twelve to eighteen months. We estimate
we are also going to pull in data externally, so
(25:14):
so industry trends data, competitive data, any sort of market
analysis data. We pull all of these signals in internal
and external and we create a unified market intelligence system.
Speaker 1 (25:33):
So it's it's remarkable what you just said. We're going
to cut to commercial that I'm gonna ask you my
follow up question. So standby, we're with Shane Temper today
here on truly significant dot com presense.
Speaker 5 (25:51):
This is Marcus Aurelius reappearing to proclaim that truly significant
conversations with big hearted people is a rare piece of literature.
This book reminds me of one of my more stirring quotes,
waste no more time arguing what a good man should.
Speaker 2 (26:10):
Be be one.
Speaker 5 (26:12):
If you're stepping into your next life chapter of your
career and questioning what lies beyond success, this book is
for you. Dive into forty soul stirring stories from luminaries
like doctor Jane Goodall, ed Asner, and Emily Chang, stories
(26:33):
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(26:54):
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Speaker 2 (27:00):
Maybe your epitaph will.
Speaker 3 (27:02):
Read she gave outrageously extended grace unceasingly and lived to
help others so that death found her empty.
Speaker 5 (27:14):
Visit truly significant dot com and celebrate the most truly
significant people in your life with the truly Significant community.
How bold of you to make your next chapter matter
and be truly significant.
Speaker 1 (27:32):
We're back with Shane Tepper. I mean, I don't cuss,
but damn, that's exactly what the market needs, is this
master of an ecosystem, a go to market intelligence system
that embraces all of those disparate parts. And how are
(27:55):
you going to use AI to build it?
Speaker 2 (27:59):
Yeah? So you know, we're talking about billions of data
points here, right, making sense making sense of all of
that data. This is something that you know, I guess
conceivably could be done by a team of data scientists
and marketing experts. It is much more cost effective to
(28:20):
use an AI engine to make sense of all of
this data. It can do it in a much more
efficient way. It can do it in a much more
consistent way. Uh. You know, AI gets plugged into the
system at many different points, going back to our wedge product,
going back to that LLL in visibility tool. We use
AI to to generate the queries. For instance, right, we
(28:43):
use AI to go out and gather information about your brand,
about your competitors brand, and about about Uh. We use
it when we use that data to create ideal customer profiles,
and we use that to understand your offerings. We use
that to understand your positioning. We use that to understand
(29:05):
your value prop and your differentiating factors. All of this
gets pulled in, you know, the data gets pulled in,
and then essentially the AI does what AI does best.
It synthesizes information and makes sense of a vast volume
of data in a way that would take humans much
much longer to do.
Speaker 1 (29:28):
Exactly. But you leave me with the question of you
and your partners at Revelation have come up or you
will debut a go to market intelligence system that will
use AI as a tool. But could AI ever have
thought of this ingesting of the kind of data that's
(29:54):
desired by brands to make sure that their dollar that
they've invested is and to have the highest return.
Speaker 2 (30:02):
I think this product was conceived out of hundreds of
conversations with AI. So it goes back to that that
question that you asked earlier about creativity and AI and
AI's role in creativity. Of course, it was our It
was our curiosity and our questioning and our thinking around
(30:26):
how we could conceivably build a model like this, build
a system like this, an end to end, go to
market intelligence system, and AI helped us, you know, kind
of kind of home in on specific things to focus
on specific pro helped us do research on you know,
this was obviously supplemented with many many conversations with cmos
(30:49):
and CROs and actual actual human beings. Rick right. Still
very important to talk to to talk to your customers,
but the the the approach was was very much mediated
by AI in conversations with AII. So you know, AI,
I can't just I can't just go to I mean
you could. You could go to AI and type and
(31:09):
give me a business idea. AI, It's gonna be a
pretty shitty one, right, but if you think about you
have to think about the problem as a human and
you have to understand what problems other humans are having
before you can even start to really create something that
that provides value. So A I absolutely played. Uh you know,
(31:30):
from from the from the ideation of the idea to
the to the development of the product itself, huge huge role.
I mean, uh, this this, Uh you necessarily have to
use AI in the product because we are solving the
problem of AI visibility. We need to know how AI
is surfacing brands right in response to buyer queries. That's
(31:50):
the whole you know, that was the reason for for
you know that we that we started on this on
this endeavor in the first place, is because we were
curious about how AI was affecting the buyer journey and
we wanted to help brands be more visible to buyers
along this journey. So yeah, I mean it's really is
uh you know AI. AI played a crucial role in
(32:13):
in every aspect of this of this business that we're
that we're building.
Speaker 1 (32:17):
No surprise, when did you say it's going to debut?
Speaker 2 (32:23):
So we have we have an early version of this
product in market already. We're working with some clients and
some agency partners. Uh, you know, in the course of
of developing this this business and this concept. You know,
I come from a from a creative agency background. I
used to be a copywriter, which is which is absolutely
something that you know, we're we're in we're an endangered
(32:45):
species because of because of AI. But no, I talked
to to some agency leaders. They had they they uh
informed us that they had a lot of their their clients,
a lot of their their clients were coming to them
and being like, hey, you know, we need to be
visible inside of these AI systems. We know that people
are now uh, these high intent buyers are now going
(33:07):
to chat ept and and Claude and Gemini to to
to learn about brands and compare brands and create shortlists,
and we need to be more visible in there. They
didn't have that capability in house, so they came to
us and they're like, we want to be able to
offer this service to our clients, and uh, you know
where we're in position to do that. So mm so
(33:31):
that's what we have today. We have a h you know,
an m VP, a minimum viable product version of this
wedge offering, this ll M visibility offering. It's not fully
productized yet.
Speaker 1 (33:42):
Uh.
Speaker 2 (33:43):
You know, we we generate the reports ourselves. There's no
sort of uh you know, dash in dashboard where a
user could log in and see all the see all
the u you know, the the metrics and the recommendations themselves.
We're actually we're we're raising money right now. We're in
the we're in the the of a pre a fundraise
(34:03):
to to be able to you know, the hire the
talent that we need to to build out this this
product version, this sassified version, if you will, of our offering,
of this initial offering.
Speaker 1 (34:16):
And your client could be the big ad agencies like Omnicom,
who haven't thought of this, and or they should just
pick it off the shelf if it's working as beautiful
as you stated.
Speaker 2 (34:30):
Yeah, yeah, I think I think agencies are thinking about this.
They just don't really have a you know, know how
to approach it. Yet. They don't know how to approach it.
They don't have the internal tools necessarily to be able
to offer this service. So yeah, I think our clients, uh,
you know, and this is you know, this is subject
to evolve over time, but agency partners I think could
(34:51):
be could be big for us. And then of course
we want to go directly to to enterprises uh you know,
mid mid market software and enterprise software companies as well,
and do implementations directly with them as well.
Speaker 1 (35:05):
Of course. Yeah, I think that that's the sweet spot
for you. I find our conversations absolutely fascinating, and I
when i'm you know, we build these questions out for you,
and then I go, well, we're going to take another
detour because there's so many things that I think about
while you're talking about this. One is that you've made
(35:27):
a career change based on this right time, right place,
and you and I talked about that last time, and
based on what you just said over the last fifteen
twenty minutes, I wonder what's the good career counseling for
kids coming out of high school much less college, on
how they can be above the ecosystem and not not
(35:50):
be a worker bee and be wiped out in the
next six months years.
Speaker 2 (35:57):
What's your view? I think, Yeah, you know, AI is
going to leave virtually no industry untouched by its impact,
and I don't think the concern is as much about replacement,
although that will you know, AI is certainly going to
eliminate some eliminate jobs that you know don't require critical
(36:20):
thinking execution mostly execution based jobs, right, but strategic strategic
thinking is still extremely valuable. So I think the question
the question is more, you know, how how they can
use AI to make existing business functions more efficient and
(36:41):
how to how they can use AI to to add
value to to business operations. I think that's the question,
you know, businesses. I read recently Mi T did this
this really interesting study over the summer, and you concluded
that ninety five percent of business AI implementations fail fail
(37:07):
to show ROI, which is fascinating to me because this
it's so obvious how transformative this technology is. And what
that tells me is that businesses are doing AI rob
they're implementing it incorrectly. You know, they're seeing all this
buzz about AI. They're they're they're doing sort of this,
(37:30):
they're taking sort of like a scattershot approach. They're trying
to implement it in so many places, so rapidly across
their organizations, Whereas what they should do is they should
do you know, a small pilot or set of pilot
programs with AI focus on a specific function. They need
to understand what metrics, what KPIs. They're trying to influence
(37:54):
using you know, by incorporating AI into their workflows so
they can get an understanding of how to best implement
and then they can they can move on and scale
that to other functions. And I think that is the
deliberate intentional implementation of AI. I think is somewhere is
what is causing these implementations to fail. Not that not
(38:15):
that the technology itself isn't isn't useful and transformative. So
to circle back to your question about you know, young
people entering the workforce, I think they I think the
mindset they need to adopt is how can this technology
Because businesses are still having trouble figuring it out. How
(38:37):
can we use this technology to to improve existing business functions?
And if you go into an interview for a role
and you you talk about you know, I've I've looked
at all these AI tools. I've seen that you know,
companies A, B, and C have seen direct are ALI
through the implementation of these tools because they improve the
(38:58):
workflow and in X y Z ways. That is something
that will impress impress an employer. I think because you're
not just You're not just saying we need to incorporate AI,
You're doing it. It shows that you've been thinking about
exactly how to incorporate the tools, which tools, and you're
thinking in an outcomes based way, like what are you
(39:19):
trying to do with the AI? It's not about getting AI,
you know, you know people are like, get me AI,
get me AI. We need to incorporate AI. But the
question that needs to be asked first is what are
we trying to accomplish with the AI? What results do
we want to see?
Speaker 1 (39:37):
Yes, sir okay, and I'm convinced that you are in
that pioneering position to help companies to discover how to
use AI. Properly. So how do people contact you and
would you like to say a little bit about your
rates or do you want to just have them contact you?
Speaker 2 (39:57):
Yeah, have them contact me. You know, most of our
most of our pricing is done on a like a
project by project basis. So yeah, that's something that's you know,
particular to the client their needs. But yeah, absolutely would
love to talk to anyone about about their AI needs,
particularly when it comes to a discoverability UH and business
impact perspective. Uh. My email address is Shane at Revelation
(40:21):
h Q dot com R E V E L A
T I O N h Q dot com. You can
also visit us at revelation hq dot com. UH, learn
about learn about what we do and how we're uh,
you know, transforming I would say, uh market intelligence and
brand discoverability in the AI era, you know. And I
(40:43):
love I love talking about uh a I generally and
and how it's impacting businesses. Uh, you know, just something
I've become very passionate about, uh, you know, and and
really since this technology has emerged over the past two
or three.
Speaker 1 (40:58):
Years, it's it is evident. Do you have a few
more minutes to answer a couple of questions without Okay,
So I want I want to take one particular company,
and I know last time you said, well, just have
them call me and we'll set up an agreement. But
I think if you, if we go through this one example,
(41:19):
it'll help our listening audience understand the journey that they'll
the customer journey that they'll have with you.
Speaker 2 (41:26):
Okay, absolutely, No, I think that's a great idea. It
becomes much more tangible if we can talk about an
actual use case versus you know, I guess hypothetically how
all this works. So yeah, let's do it. Let's have
out erik.
Speaker 1 (41:37):
Okay, So here's the situation. The problem is one of
of corporate culture. It's about increasing the number of current
employees in their engagement in volunteering because the company says
(41:58):
that they want to have more people volunteer and they
but they don't want to force it down the employee's throat,
and they want people to be involved in volunteer because
they've learned over the course of ten years that their
retention of their employees is greater when those people are
(42:20):
actually involved in activities after work with other employees that
kind of that glue them all together. Okay, so the
question is how would your company help them accelerate.
Speaker 2 (42:37):
I would say that's that's outside of the of the
real of the of the problems that we solve at
at Revelation HQ. But you know, we're more in the
brand discoverability and uh and and market intelligence business. But
I mean this is this is a very interesting management
question that you're asking. How can you incentivize employees too,
(43:00):
you know, get involved in community service? Uh? Uh you
know you're seeing this data that that is indicating that
would you say it helps with retention? It probably makes
for more satisfied employees. You know, it's a it's a
sense of you know, you're kind of you're kind of
melding like the business culture with with the you know,
(43:20):
the the do gooder culture and and uh you know
you're having an impact not just on the business, but
on the community as well. That all makes sense. How
do you incentivize how do you incentivize your uh, you know,
your employees to participate in programs like this? You know,
beyond just it'll make you feel good, you're gonna have
(43:41):
an impact on the community. I think you need to
understand what motivates people first. And foremost, and you know,
people want to feel good, they want to feel like
they're having an impact. How how can you accomplish this.
It's a good question, it's a good question. You could
(44:05):
build and and I think I've read about this. I
think there are companies, there are tech companies that are
building systems that that encourage, you know, that with the
purpose of encouraging employees to participate in these sort of
you know, after hours, after work hours, community oriented activities.
I'm not sure what the mechanisms are that they use
(44:28):
to to to induce more participation in those activities. You know,
it could be you know, you you you hate to
talk about introducing any sort of financial incentive when it
comes to when it comes to you know, stuff like
this community oriented work. But you know, I don't know
(44:53):
if it's like if it's like some sort of a
reward system where you know, the more the more hours
of community service you log, there's you know, there's there
has to be I think, you know, if people aren't
going to do it out of the goodness of their heart,
I think there has to be some sort of incentivization
element factor there where it's you know, or a competit
(45:16):
you know, it's like a competitive or gamification piece maybe, right,
you know, showing where where you know, how many hours
have you done versus you know, sally down and accounting, right, Like,
there's that having that competitive element I think could be interesting.
But I would have to look into I would have
to look into you know, it's a business psychology question, right,
(45:38):
It's like an organizational psychology question, I think, and it
gets into you know, what motivates people to do different things?
Speaker 1 (45:45):
Is Yeah, yes, it's very it's very admirable for you
to set all that. But sometimes people you've got to
recognize that if it's not a part of your business,
then you go, I don't know, but why don't you
do more research about it? Because it is like a
it's a very fuzzy situation it is.
Speaker 2 (46:06):
Yeah, yeah, no, it's no, it's an interesting it's an
interesting thing to think about. Yeah. Absolutely, Well obviously, well
outside of what we do, but no, Rick, like I
want to you know, if you if there are questions
that that fall outside of you know, what I do
and what we do, it's still you know, I'm a
I'm a problem solver at the end of the day,
whether that's you know, usability within AI platforms or or elsewhere.
(46:30):
And you know, I like, I like, I like talking
about these things and trying to figure them out.
Speaker 1 (46:35):
Uh.
Speaker 2 (46:35):
You know, if if if I were on my own,
if you had given me this assignment and we're like
Shane helped me design a system that helps incentivize employees
to do more community service, I would have absolutely gone
to to you know, my my AI tools and and
and work something out.
Speaker 1 (46:53):
Of course, of course. Okay, final question for you is this,
And I wrote this and it was I really actually
wrote this in conjunction with AI. And the question was
about in ten years, how do you want Revelation to
be remembered ten years? How about ten months for ten weeks,
(47:13):
so let's drop the number of the time element. How
do you want Revelation to be remembered in the story
of AI and creative evolution.
Speaker 2 (47:23):
Yeah, I would love for Revelation in its fully expansive
final state. I don't know if we can call it
a final state, but this in its fully realized go
to market intelligence system state, I would like it to
be remembered as the platform that transformed AI native go
(47:44):
to market intelligence, the first platform that unified all these
disparate signals both internally and externally, took advantage of really
all the data available that could inform a company's go
to market strategy marketing uh and how that ties back
to revenue and pipeline. The first company to unify all
(48:08):
of those different things, uh and and present a coherent
picture of how marketing efforts impact the bottom line at
your company, setting a new standard for how for how
data is used to inform marketing and then tying it
all back to pipeline and revenue. That's that's what that
(48:28):
blaze the trail there you.
Speaker 1 (48:30):
Go, That is that is bulls eye from our perspective,
remembering him as the platform, the company that pioneered, the
platform that unified all the disparate signals. Man, that's perfect. Hey, Shane,
thank you so much for being on today. Give out
your information on your company one more time.
Speaker 2 (48:48):
Certainly you can find us where revelation and you can
find us at revelation HQ dot com. UH And if
you like to get in touch with me directly, that
is Shane s h A n E at Revelation h
Q dot com. That's r e V E l A
t i O N h Q dot com.
Speaker 1 (49:07):
Thank you Rick, Thanks Shane Tepper, and hang on a
second after the show. Good one other questions ask you
and we appreciate Shane being on our show today and
again go to Revelation hq dot com to find out
more information. Folks, we hope that you have a great
week and as usual, we wish you success on your
(49:27):
way to significance.
Speaker 2 (49:29):
How good week.
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