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How did our Bootstrapped Startup Land Million-Dollar Deals with S&P, Bloomberg?
Episode 5121st December 2023 • B2B SaaS Podcast • Upendra Varma
00:00:00 00:24:16

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Shownotes

Alexandre Abu-Jamra, Co-Founder & CMO of Klooks, talks about their remarkable journey from a bootstrapped startup to landing million-dollar deals with industry giants like S&P and Bloomberg. Specializing in extracting financial data from PDFs and images, Klooks has become a trailblazer in transforming this data into actionable, verified insights for diverse analyses.

Here are the talking points,

🚀 The 0 to 1 Journey:

Alexandre unfolds the narrative of Klooks' inception, highlighting the challenges and triumphs encountered during the transition from a bootstrapped startup to a key player in the financial data landscape.

🤝 Landing Million-Dollar Deals:

Learn the secrets behind Klooks' success in securing substantial deals with major players like S&P and Bloomberg. Alexandre discusses the strategies, negotiations, and pivotal moments that led to these significant partnerships.

💡 Customer Acquisition Strategies:

Gain insights into Klooks' customer acquisition playbook. From outbound strategies involving cold calls and LinkedIn outreach to inbound strategies like SEO and newsletters, Alexandre shares the diverse approaches that fueled their growth.

🔍 Data Extraction Expertise:

Explore how Klooks became an expert in data extraction, especially from complex formats like PDFs and images. Alexandre sheds light on the unique challenges they faced and the innovative solutions that set them apart.

🌐 Vision and Future Growth:

Discover Klooks' vision for the future. Alexandre outlines their plans for leveraging data mining, integrating AI like ChatGPT, and creating automated solutions for various financial analyses, setting the stage for scalable and intelligent growth.

🔥 Insights and Lessons Learned:

Listen to the valuable insights and lessons learned throughout Klooks' journey. Alexandre shares key takeaways for startups navigating the complex intersection of finance and technology.

You can also watch this episode on youtube here.

Transcripts

Speaker:

AlexandreAbu-Jam: When we were finishing

our negotiation, they called us to Sao

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Paulo to meet in person and this stuff.

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And when we got to the meeting, that

was like the CEO of the company.

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That was, um, was one of the hairs of,

uh, McGraw Hill, which is the owner of.

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S& P which owns Capital IQ.

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So I was like, well, yeah,

that's, that's really big

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Upendra Varma: Hello everyone.

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Welcome to the B2B SaaS podcast.

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I'm your host Upendra Verma and

today we have Alexandre with us.

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Alexandre is the co founder and

CMO of a company called Klux.

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Hey Alexandre, welcome to the show.

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AlexandreAbu-Jam: Hello, Pendra.

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Thank you.

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Thanks for inviting me

to talk to your audience.

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It's a great pleasure.

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Upendra Varma: Alright Alexandre,

let's try to understand, right, what

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you do, right, what Klux does and like

why customers are paying you money.

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What's, what's the product all about?

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AlexandreAbu-Jam: Great.

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Uh, well, we are specialized

in a very specific problem.

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Um, long story short, we are very good

on, uh, taking financial data out of

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PDFs and images and turning it into

explorable data that you can use in

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different analysis, like credit analysis,

investment analysis, uh, statistic models.

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Uh, so basically, uh,

what we do is we take.

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Data from balance sheets and income

statements out of PDFs and images and

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enter that into verified, uh, high quality

data, uh, to, to be used in analysis.

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Upendra Varma: Got it.

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And so what exactly do

you sell to your customer?

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Is it, is it the software that

you sell or is it the process

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data that you typically sell?

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Talk about the product there.

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AlexandreAbu-Jam: Great question.

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There are two things we

sell to our customer.

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Um, we combine that

skill with data mining.

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Uh, so we mine financial statements that

are public in the web, and then we extract

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the data out of the PDFs and make the

data, um, all beautiful and verified.

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And, and sell that to,

to distributors abroad.

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So, uh, Bloomberg gets our data, Moody's,

S& P, and these guys, they, they sell

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it in, in what we call our wholesale.

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Uh, so that's like a data

as a service model, a desk.

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Uh, but we also sell, uh, the, the.

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Financial spreading service.

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So, well, a bank needs to spread a lot of

financial statements every day and they

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can send it to us and we give it back to

them, spread it in the, um, In the model

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they use in the standard they use with

their file with the they use a json or

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txt or if they use an excel, we customize

everything, um, uh, for the banks to in

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order to use that data in their analysis

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Upendra Varma: Got it.

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And, and, uh, talk about the

second part of it, right?

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So is it more like a service

thing or, or do you sort of sell

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it, uh, sell the software here?

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Exactly.

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AlexandreAbu-Jam: is more like a

service thing, uh, for one one specific

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reason, um, there is no way to Spread

financial data nowadays with current

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technology with the quality that banks

need in credit analysis So you can

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do it automatically with an ocr or

something like that, but it's not good

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It doesn't get as good as they need.

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Uh, so what we do we do use the

ocr we we OCR, you know, Optical

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Character Recognition is the technology

used to, to read images with text.

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So we use the OCR, we extract the

data out of the PDFs and images, uh,

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uh, but we, we have a verification

process, a quality assurance process

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done by, by trained accountants to make

sure that everything is in order and

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everything is, is in the, the correct,

uh, places in, in our, uh, deliverables.

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Upendra Varma: Got it.

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Essentially you use the software that

you created for your own purposes.

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You use it internally to give banks

the data that they really want.

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Something

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AlexandreAbu-Jam: Yeah, we

can, we combine different OCRs.

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Uh, to extract the data out of

PDFs and that it gets into, uh,

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into, into our software and, uh,

our quality assurance software.

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That's a proprietary software

that we've done from, from zero.

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Um, and we apply that to that data with a

human labor to make sure that everything

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is, uh, all right and send that to banks.

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And that's, that's more or less a

three hour time, uh, turnaround time.

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Upendra Varma: Got it.

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And let's move on to

your customers, right?

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So how many customers are you serving

today across both of these, you

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know, financial models that you have?

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AlexandreAbu-Jam: Looking for

this service of, of financial

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Upendra Varma: both of them.

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And you can just give me a split, right?

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So where, how many customers

you have got for both of them?

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So we

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AlexandreAbu-Jam: uh, right, we have, we

have three products, um, on, on, on the

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financial spreading service might be more

or less 15 banks, Brazilian banks that

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are using it, uh, we nowadays are, are

focused in, in Brazil because it's our

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country, but the technology is usable,

uh, in, for any language basically,

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and, and we do have, uh, uh, banks that

are in Brazil, but they, they send us,

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um, um, um, Files in other languages

and from their other, uh, uh, branches.

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Uh, so that's more or

less 15 banks in Brazil.

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And well, when we talk about our deaths,

uh, of our data as a service where

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we, we, we send financial data in our

wholesale, uh, might be six or seven.

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Uh, but we have assess as well.

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It's a, it's a platform that

you can access financial data,

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Brazilian companies, and that's

more or less 50, uh, clients.

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Upendra Varma: 50, is it?

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So let's just give me an

approximate sense, right?

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So I definitely want to deep dive

into SAS because that's what my

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audience are looking forward to here.

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But just in terms of approximate revenue

that you're doing across these three,

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you know, models that you have, right?

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So just give me an

approximate split, right?

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So where are you in terms of, you know,

how, how well is your DAS working?

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How well is your SAS working?

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And you know, how your

services model working?

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Just give

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me an

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AlexandreAbu-Jam: like, uh,

like a revenue breakdown.

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Upendra Varma: yes, yes.

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AlexandreAbu-Jam: All right.

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Uh, might be 50 percent DAS, uh, 40

percent our, uh, financial spreading

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service and 10 percent our SAS.

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Upendra Varma: No, it's

just getting started with.

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AlexandreAbu-Jam: Yeah, our,

our assess is much more clients,

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but the ticket is much lower.

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Upendra Varma: Let's let's

stick our conversation to just

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your SaaS product for now.

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Right.

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So, just talk about the origin story here.

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Right.

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So how did you sort of start your SaaS?

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Like, and just give a sense of like

how you got those first few customers.

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AlexandreAbu-Jam: Great.

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Um, our, our, uh, vision to start

was to, to be assessed, right?

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So, uh, when we started, uh, we,

what we thought of doing was to

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be like a Brazilian capital YQ,

but that was the vision, right?

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Uh, capital YQ at that time didn't have,

uh, the data from Brazilian companies.

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Uh, it started having later on because

we started selling to them on our debt

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service, but when we started, they

didn't have, and then we thought, well,

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let's Grab the data from Brazilian

companies and sell that as a SaaS and

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then we will, uh, get rich doing that.

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Uh, but it didn't work out in the start.

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Um, we, we built the, the, the, the

software and all the bots to get the data.

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And we've put that in a, into, um,

a platform, but it lacked features.

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So we just.

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We're just like exploring

financial statements.

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Uh, and we didn't have the, the financial

spreading, uh, tuned at that time.

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Uh, we thought we would have it easily,

but well, then we invested a lot.

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We tried to, to use, to make like an

automatic financial spreading and then

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did a workout and, and, and, you know,

uh, so we had only PDFs in a few.

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Accounts that people could explore

and we started to try to sell that

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we sold like four or five licenses.

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We could keep doing that but uh, it

would take too long to make that work

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and so we we just pivoted to selling it

to Uh to other financial intelligence

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platforms in abroad that didn't have data

from brazilian companies at the time.

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So um We started as a sass and then we

pivoted to be a desk and and then later on

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Upendra Varma: so just

help me complete the story.

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Right.

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So in, in, in DAS deals that

you typically do, right.

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So like, how much do these

external platforms pay you?

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So what sort of a deal says,

are we talking about the six,

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seven, you know, big platforms,

like companies that use you.

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AlexandreAbu-Jam: Um, in our DAS

service, basically we, we have,

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uh, more than a hundred bots mining

financial statements of Brazilian

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companies in many different sources.

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Brazil has a lot of different sources.

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It's different from other countries

that have like centralized financial

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statements from private companies.

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Uh, the private companies in

Brazil, they just publish their

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financial statements like everywhere.

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So there, there must have, you must have

like Specialized bots to make the work.

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So we did that, uh, and we are

like a data hub for, for, uh, this

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financial intelligence platform

like Capital IQ or Abura Von Dyke.

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Um, Bloomberg gets our data as well.

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Uh, TTR, LexisNexis, we

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Upendra Varma: So, like, my question

is, like, just give me an approximate

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sense of, like, how much Bloomberg pays

you to sort of get this data from you.

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AlexandreAbu-Jam: um, I don't

have that right now, but

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Upendra Varma: just looking

for an approximate range.

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Is it a 10, 000 deal?

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Is it a 100, 000 deal per year?

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Like, what are we looking at?

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AlexandreAbu-Jam: Might be

like 20 per company per year.

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Uh, so let's say I find 25, uh,

uh, uh, thousand companies a year.

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That's more or less my, my volume.

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Then you can do the math.

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Upendra Varma: okay.

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That's a lot of money.

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Alright.

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Uh, yeah.

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Keep on going.

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So, you've started, you pivoted to

DAS and then what happened after that?

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AlexandreAbu-Jam: Uh, uh, so we, we, we

turned into like a desk company because

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it would be easier to fund and to grow.

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I mean, we could go to venture capitals or

to investors and grab data and sell them

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our, our assess, um, a model, uh, but.

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But we didn't want to raise capital.

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We wanted to do it on our own.

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We wanted to, to bootstrap.

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So, um, the, the, the way to

finance it more easily would be

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to, to make the data service.

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Cause that was like a low hanging fruit.

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They needed the data.

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Uh, there wasn't much

clients, you know, like.

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Not like a thousand, uh, uh, financial

intelligence platforms around.

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Uh, but, but, uh, it was low hanging.

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It, it, there wasn't, it wasn't very

scalable, but it was low hanging.

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So that's why we, we directed our efforts.

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Um, and, and it worked very well.

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They, they really wanted and needed that,

and it was, Effectively a low, a low

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hanging, uh, opportunity, but it wasn't

that easy to get to these guys at that

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point because we were very, very small,

you know, like we're starting and trying

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to build something that didn't exist,

but there's an interesting story there.

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Our, our first, first desk client was

Capital IQ and, and that was, that was

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really fun to, uh, to, um, to explore.

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Upendra Varma: how, how did you

sort of convince them to sort

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of start using your platform?

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And you, you guys like were

no one for them, right?

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So you're just starting out.

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So how did that work out

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AlexandreAbu-Jam: Yeah, it

started with a cold call.

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So I called them on their, on their,

uh, San Paolo office and I was like,

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Hey, can I talk to your director?

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Uh, they had like a director that,

that would take care of everything.

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It wasn't, wasn't like a

specialized director of data here.

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It was like more of a, of

a commercial office and.

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And so I, I got to their director and

said, Hey, well, um, I'm from Klooks.

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Um, and we are with this product.

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We have a lot of financial data

of private Brazilian companies.

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I've noticed that in your

platform, you don't have that.

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And then he was, he was like,

wow, really you have that?

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I can't believe it.

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I really want it.

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But, uh, you know, um, right now it's not

the good momentum to explore that because.

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Capital ICO at this point is investing

in Asian data and I wouldn't be

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able to sell that internally.

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So, well, I'll keep that in mind.

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I'll get back to you if that

makes sense to us later on.

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And, well, I hang the phone and

I just thought, well, right, I'm

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never gonna hear from him again.

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But after six months, he surprisingly

called me and I was like, uh, Hey, hello.

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Oh, here's Pedro from Capra Waikou.

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Do you remember me?

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And I was like, yeah, sure.

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Well, it's great to hear from you.

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And then he said that at that point

was a good time to start exploring

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Brazilian data because they were in

the Latin American cycle of investing.

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And then, and then after that things,

uh, um, just, um, ran smoothly, but.

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When we were finishing our negotiation,

they called us to Sao Paulo to

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meet in person and this stuff.

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And when we got to the meeting, that

was like the CEO of the company.

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That was, um, was one of the hairs of,

uh, McGraw Hill, which is the owner of.

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S& P which owns Capital IQ.

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So I was like, well, yeah,

that's, that's really important.

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That's, that's really big.

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All right, cool.

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Um, and, and that was it.

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Then I, we got Capital IQ.

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And then after that, um, it was easier

to, to approach other, other clients.

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Upendra Varma: and all of this, you,

you were doing bootstrap, right?

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So you were just doing it on your own

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AlexandreAbu-Jam: yeah, that

was, that was bootstrapped.

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Upendra Varma: then go back,

like, go, like come forward.

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Right.

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So I'm assuming you're doing

a couple million or more than

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that in Dash revenue today.

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Right.

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So when did you decide to move to SaaS?

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Like, and what, what was the journey like?

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So when exactly did you decide,

okay, enough is enough, let's

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move on to SaaS and let's get it.

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So talk about that story.

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AlexandreAbu-Jam: Uh, when we

decided to move to SaaS, you mean?

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Yeah, SaaS was our, was our first

product, but then we pivoted

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and then we got back to it.

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Actually, we never gave up of it, because

we, maybe we should have, because it, it

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kind of, uh, uh, uh, make, makes things

late for us to, to keep investing in that.

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But we, we understand that when we, we

find our scalability on SaaS, that's...

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Where we are going to grow really, um,

really big, you know, that, that's,

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that's the best way of scalability

once you have, uh, a good product.

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And we think that we are close to that.

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Not sure how close, but

right now we've, uh,

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Upendra Varma: you've got

50, 50 odd customers on your

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SaaS platform today, right?

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So, so how much do they pay

you on an average today?

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It's like, how big of a deals

are we talking about here?

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AlexandreAbu-Jam: Uh, it's, um,

2, 000 more or less in, in, uh,

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in dollars, 400 more or less.

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That's monthly.

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That's monthly

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Upendra Varma: it.

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And just like, so where are you

getting all of these customers from?

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Like, so I'm assuming they're not

from your DAS platforms, right?

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So there's got to be, these

are new customers, right?

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Have different set of requirements.

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So where are you finding them?

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How are they discovering you?

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AlexandreAbu-Jam: Yeah.

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Perfect.

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We have, um, we have two channels.

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Uh, we just do the basic, we are, we

are trying to do the basic well done.

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Uh, we do inbound and we do outbound.

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Our outbound front, we have, uh,

an STR, a sales representative,

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uh, trying to, to hit the leads.

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So, uh, he, he, once the lead is, is

hit, he can send it to our closer, which.

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Usually closes the deal and well, the,

uh, our SDR, he just, um, prospect

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a lot of in LinkedIn, like searching

for companies in our persona and,

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you know, he did the basics, like

on LinkedIn, he, he gets connections

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and, and, uh, gets in contact with

people, but he also reheats, um, like.

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Older leads that came to us, but

didn't close at some point in

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history and our inbound sales.

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Well, that's, uh, that's pretty

much, uh, SEO and our newsletter.

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Actually, our newsletter

is our main inbound, um,

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Upendra Varma: So how big of a

newsletter are we talking about?

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How many subscribers do you have on there?

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AlexandreAbu-Jam: Um, we have like

six or 7, 000 subscribers, but

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like, um, that usually open our, our

newsletters that we are sending them

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like regularly, it might be 3000.

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Upendra Varma: So like, when did

you start building this newsletter?

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Like, was it a personal thing for you

let's talk about that newsletter journey.

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AlexandreAbu-Jam: Yeah.

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We.

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In the start, we did a

lot of outbound, right?

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So, um, I was like sending emails

and getting contacts on LinkedIn and

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well, made a lot of outbound sales.

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Um, it was quite new for people.

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So it was interesting and they

would be interested in giving

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me their contacts so I could get

in contact later or, and so on.

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Um, once we had like.

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500 people, um, uh, listed and already

in our outbound sales that went

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in, in our outbound sales process.

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Um, then we started doing the newsletter

and, and we, we would feed our, our

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newsletter, um, Uh, list, uh, with

our outbound sales, uh, effort.

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So I would, I would make a

prospection in outbound sales.

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I would get the contact of someone.

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Uh, I would ask him, Oh, can I, can I

put you on my, um, on my newsletter?

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Mailing list?

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He would say yes.

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And then I would put him in

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Upendra Varma: so, so, you would

reach out to somebody who's

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like, who you never know, right?

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It's a cold lead and you would strike

a conversation with them and if they're

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not ready, you would ask them to sort

of, can I put them in my newsletter

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and then that's how you ended up

building the newsletter, is it?

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AlexandreAbu-Jam: That's right.

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That's right.

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Exactly.

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Upendra Varma: Okay,

that's, that's wonderful.

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And like, you mentioned 400

is what they're paying on,

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paying you on an average.

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Is it like per month or per year?

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AlexandreAbu-Jam: It that's a monthly fee.

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Uh, and the contract is yearly.

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Upendra Varma: Got it.

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:

Right.

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:

So like, just, uh, talk about the,

you know, bottom of the funnel, right?

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So when somebody discovers you, right,

so how do you convince them so how do

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you convert them into a paying customer?

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How,

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AlexandreAbu-Jam: Perfect.

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Um, usually, you know, uh, we, we know

the personas that convert, so we have

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some, some sort of people that, that

we know, well, this guy is, is hot.

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Well, this guy might not be that hot.

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So we would put much more

effort on the hot ones, right?

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That we know that is in our persona.

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Once we get the guy in our persona, we

need to validate if he's, he's in good

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timing to signing, if he has the budget.

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And if the guy I'm talking to is the

guy that decides, so I'm talking to

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an analyst that won't have the right

level of decision making to sign

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:

something, I would try to escalate to his

manager before getting to our closure.

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So that's, that's the job of our SCR.

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Hits the lead, he gets in contact

with the, with the manager,

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uh, which decides, he validates

that the guys have a, a budget.

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And then, uh, he sends it to our closer.

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And in this process, of course,

we, we try to, uh, to show them

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all the value of our solution.

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Um, so we, we work with data, right?

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So it's, it, it's not that

difficult to show the value of it.

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Uh, the difficult part is, um, Is to

prove them that they will use that data.

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Um, regularly.

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It's not only, it's not only going to be

curiosity, you know, like it's science

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to, because it's curious of something

and then it uses three or two, three

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times and, and he gives up, uh, using it.

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And then that's, that's a

big problem for our CS team.

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Then our, our, our customer success needs

to reheat this guy and find out how we

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can use the, the platform regularly.

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And, um, so there is this job of, of.

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Convincing them that they're

going to be regularly.

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:

Upendra Varma: take to sort of close this?

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Like how long is your sales

cycle today on an average?

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AlexandreAbu-Jam: Oh, I

don't have this number.

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It varies a lot.

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Sometimes some guys come and say, well,

I want to sign and okay, let's sign.

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Upendra Varma: what's a typical number?

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is it is, is it months, weeks?

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You know, is it quarters?

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Like, how does that look like typically?

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AlexandreAbu-Jam: the regular, the regular

cycle might be, um, One month and a half

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or something like that from getting to

the lead, finding the decision making

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guy, uh, validating budget and showing

all the organization that you're going

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to take advantage of the platform that's

that might take one month and a half

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more or less, but right now with, um,

we are doing a lot of things integrated

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with chat GPT and, um, and, and some,

um, present automatic presentations.

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They use chat GPT on their back end.

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Um, And that's helping us to convince them

much more on the value of the platform

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and how they could use that regularly.

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Upendra Varma: Got it.

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:

Like, just help, help me understand

it, how you're growing, right?

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:

So exactly 12 months before

today, how many customers you

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:

had on your SaaS platform?

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:

AlexandreAbu-Jam: We had

our platform in our SAS.

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It was.

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Upendra Varma: Yeah.

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You almost doubled something like that.

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You almost doubled something like

that over the past 12 months.

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:

Got it.

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And then can you just

help me quantify, right?

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:

So in terms of your outbound versus

inbound, where are most of these

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people discovering, is it just 50

50 or is there any split there?

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AlexandreAbu-Jam: I think that

would be 60 percent outbound, 40

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percent inbound, but the inbound

lead, he comes, um, more prepared.

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He's already, he's already hitting.

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So, um, he already received

a lot of those letters.

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He already went into our blog and read

a lot of stuff and he, he already.

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He knows what he wants.

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So it's a, it's a shorter

cycle to close it.

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Um, the outbound might take longer

and a lot of times it just goes

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to our mailing list and, and keeps

receiving our, our communications.

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Upendra Varma: Got it.

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:

So I was asking about your churn, right?

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So how, how, how have you been able

to sort of keep your customers?

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Right.

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So basically like you had 25 odd

customers, like 12 months before.

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:

Right.

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:

So how many of them still

use your platform today?

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:

AlexandreAbu-Jam: Yeah, we had a churn

problem in, uh, it was one year ago,

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we had a lot of churns happening.

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And then when we didn't have a CS.

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Um, as he has, uh, the,

uh, area structured.

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So we started to, to

structure our CS process.

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Um, and well, it took a little time

to understand what was the problem.

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What was, why would the guy's

churning and how to mitigate that?

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We, we got a lot better,

but still we have some churn

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:

Upendra Varma: Can you quantify that?

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:

Like, like, how does that look like?

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It's like you had a, you had around

30 odd customers 12 months before.

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:

Right.

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:

So how many of them still, you

know, use your platform today?

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:

Among that 30 odd cohort.

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:

AlexandreAbu-Jam: From those

30, I've, uh, five or six churns

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and we've put other 30 on.

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:

So it might be like 50 somewhere now.

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:

Um,

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:

Upendra Varma: Yeah, Alexander.

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:

So what's with one last question, right?

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:

So what's the vision here?

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:

So where do you see your company going

going in the next two to three years?

448

:

Like what's gonna happen now?

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:

AlexandreAbu-Jam: uh, we are doing a

lot of effort on data mining right now.

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:

We basically our, our tech team is.

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Fully dedicated on data mining because

we have a lot of financial statements

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:

lost in the web from private companies

and, um, we're not getting it yet because

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:

they are in really hard to find sources,

you know, and for in this in this

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environment of financial data in Brazil.

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We are definitely the most

advanced company on that.

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We're most of our effort is on that.

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:

Uh, and even though there's a lot of

financial systems we're not getting, so

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we are putting a lot of effort on that.

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So that's that's going to be

our our our midterm vision.

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:

For the next 12 months, that might

be what we're going to do, but after

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:

having a lot of data, it might be wise

to extract intelligence out of it, you

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:

know, so, um, the, the integrations

with chat GPT and verifications of what

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chat GPT does, because he makes mistakes

and all this, um, this cycle is going

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:

to be our effort after that, probably.

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:

Uh, you know, extracting different

sorts of analysis, uh, making automatic

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:

credit analysis, using it and using

credit models, um, creating different

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:

analysis for, for investment banks

and SWOT analysis and benchmarking

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:

analysis and all this stuff.

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Uh, all this automatically, we

understand is going to create a lot

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:

of value and we, it's going to, um,

make us able to, to reach new clients.

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:

Upendra Varma: Thanks, Alexandra.

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Thanks for taking the time to talk to me.

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:

Hope you scale your company

to much much greater heights.

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:

AlexandreAbu-Jam: Thank you, Pedro.

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:

Thanks for the opportunity.

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:

It was great talking to you.

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