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.

AlexandreAbu-Jam: When we were finishing

our negotiation, they called us to Sao

2

Paulo to meet in person and this stuff.

3

And when we got to the meeting, that

was like the CEO of the company.

4

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

uh, McGraw Hill, which is the owner of.

5

S& P which owns Capital IQ.

6

So I was like, well, yeah,

that's, that's really big

7

Upendra Varma: Hello everyone.

8

Welcome to the B2B SaaS podcast.

9

I'm your host Upendra Verma and

today we have Alexandre with us.

10

Alexandre is the co founder and

CMO of a company called Klux.

11

Hey Alexandre, welcome to the show.

12

AlexandreAbu-Jam: Hello, Pendra.

13

Thank you.

14

Thanks for inviting me

to talk to your audience.

15

It's a great pleasure.

16

Upendra Varma: Alright Alexandre,

let's try to understand, right, what

17

you do, right, what Klux does and like

why customers are paying you money.

18

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

19

AlexandreAbu-Jam: Great.

20

Uh, well, we are specialized

in a very specific problem.

21

Um, long story short, we are very good

on, uh, taking financial data out of

22

PDFs and images and turning it into

explorable data that you can use in

23

different analysis, like credit analysis,

investment analysis, uh, statistic models.

24

Uh, so basically, uh,

what we do is we take.

25

Data from balance sheets and income

statements out of PDFs and images and

26

enter that into verified, uh, high quality

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

27

Upendra Varma: Got it.

28

And so what exactly do

you sell to your customer?

29

Is it, is it the software that

you sell or is it the process

30

data that you typically sell?

31

Talk about the product there.

32

AlexandreAbu-Jam: Great question.

33

There are two things we

sell to our customer.

34

Um, we combine that

skill with data mining.

35

Uh, so we mine financial statements that

are public in the web, and then we extract

36

the data out of the PDFs and make the

data, um, all beautiful and verified.

37

And, and sell that to,

to distributors abroad.

38

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

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

39

it in, in what we call our wholesale.

40

Uh, so that's like a data

as a service model, a desk.

41

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

42

Financial spreading service.

43

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

financial statements every day and they

44

can send it to us and we give it back to

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

45

they use in the standard they use with

their file with the they use a json or

46

txt or if they use an excel, we customize

everything, um, uh, for the banks to in

47

order to use that data in their analysis

48

Upendra Varma: Got it.

49

And, and, uh, talk about the

second part of it, right?

50

So is it more like a service

thing or, or do you sort of sell

51

it, uh, sell the software here?

52

Exactly.

53

AlexandreAbu-Jam: is more like a

service thing, uh, for one one specific

54

reason, um, there is no way to Spread

financial data nowadays with current

55

technology with the quality that banks

need in credit analysis So you can

56

do it automatically with an ocr or

something like that, but it's not good

57

It doesn't get as good as they need.

58

Uh, so what we do we do use the

ocr we we OCR, you know, Optical

59

Character Recognition is the technology

used to, to read images with text.

60

So we use the OCR, we extract the

data out of the PDFs and images, uh,

61

uh, but we, we have a verification

process, a quality assurance process

62

done by, by trained accountants to make

sure that everything is in order and

63

everything is, is in the, the correct,

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

64

Upendra Varma: Got it.

65

Essentially you use the software that

you created for your own purposes.

66

You use it internally to give banks

the data that they really want.

67

Something

68

AlexandreAbu-Jam: Yeah, we

can, we combine different OCRs.

69

Uh, to extract the data out of

PDFs and that it gets into, uh,

70

into, into our software and, uh,

our quality assurance software.

71

That's a proprietary software

that we've done from, from zero.

72

Um, and we apply that to that data with a

human labor to make sure that everything

73

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

74

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

three hour time, uh, turnaround time.

75

Upendra Varma: Got it.

76

And let's move on to

your customers, right?

77

So how many customers are you serving

today across both of these, you

78

know, financial models that you have?

79

AlexandreAbu-Jam: Looking for

this service of, of financial

80

Upendra Varma: both of them.

81

And you can just give me a split, right?

82

So where, how many customers

you have got for both of them?

83

So we

84

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

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

85

financial spreading service might be more

or less 15 banks, Brazilian banks that

86

are using it, uh, we nowadays are, are

focused in, in Brazil because it's our

87

country, but the technology is usable,

uh, in, for any language basically,

88

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

are in Brazil, but they, they send us,

89

um, um, um, Files in other languages

and from their other, uh, uh, branches.

90

Uh, so that's more or

less 15 banks in Brazil.

91

And well, when we talk about our deaths,

uh, of our data as a service where

92

we, we, we send financial data in our

wholesale, uh, might be six or seven.

93

Uh, but we have assess as well.

94

It's a, it's a platform that

you can access financial data,

95

Brazilian companies, and that's

more or less 50, uh, clients.

96

Upendra Varma: 50, is it?

97

So let's just give me an

approximate sense, right?

98

So I definitely want to deep dive

into SAS because that's what my

99

audience are looking forward to here.

100

But just in terms of approximate revenue

that you're doing across these three,

101

you know, models that you have, right?

102

So just give me an

approximate split, right?

103

So where are you in terms of, you know,

how, how well is your DAS working?

104

How well is your SAS working?

105

And you know, how your

services model working?

106

Just give

107

me an

108

AlexandreAbu-Jam: like, uh,

like a revenue breakdown.

109

Upendra Varma: yes, yes.

110

AlexandreAbu-Jam: All right.

111

Uh, might be 50 percent DAS, uh, 40

percent our, uh, financial spreading

112

service and 10 percent our SAS.

113

Upendra Varma: No, it's

just getting started with.

114

AlexandreAbu-Jam: Yeah, our,

our assess is much more clients,

115

but the ticket is much lower.

116

Upendra Varma: Let's let's

stick our conversation to just

117

your SaaS product for now.

118

Right.

119

So, just talk about the origin story here.

120

Right.

121

So how did you sort of start your SaaS?

122

Like, and just give a sense of like

how you got those first few customers.

123

AlexandreAbu-Jam: Great.

124

Um, our, our, uh, vision to start

was to, to be assessed, right?

125

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

what we thought of doing was to

126

be like a Brazilian capital YQ,

but that was the vision, right?

127

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

uh, the data from Brazilian companies.

128

Uh, it started having later on because

we started selling to them on our debt

129

service, but when we started, they

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

130

let's Grab the data from Brazilian

companies and sell that as a SaaS and

131

then we will, uh, get rich doing that.

132

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

133

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

software and all the bots to get the data.

134

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

a platform, but it lacked features.

135

So we just.

136

We're just like exploring

financial statements.

137

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

spreading, uh, tuned at that time.

138

Uh, we thought we would have it easily,

but well, then we invested a lot.

139

We tried to, to use, to make like an

automatic financial spreading and then

140

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

uh, so we had only PDFs in a few.

141

Accounts that people could explore

and we started to try to sell that

142

we sold like four or five licenses.

143

We could keep doing that but uh, it

would take too long to make that work

144

and so we we just pivoted to selling it

to Uh to other financial intelligence

145

platforms in abroad that didn't have data

from brazilian companies at the time.

146

So um We started as a sass and then we

pivoted to be a desk and and then later on

147

Upendra Varma: so just

help me complete the story.

148

Right.

149

So in, in, in DAS deals that

you typically do, right.

150

So like, how much do these

external platforms pay you?

151

So what sort of a deal says,

are we talking about the six,

152

seven, you know, big platforms,

like companies that use you.

153

AlexandreAbu-Jam: Um, in our DAS

service, basically we, we have,

154

uh, more than a hundred bots mining

financial statements of Brazilian

155

companies in many different sources.

156

Brazil has a lot of different sources.

157

It's different from other countries

that have like centralized financial

158

statements from private companies.

159

Uh, the private companies in

Brazil, they just publish their

160

financial statements like everywhere.

161

So there, there must have, you must have

like Specialized bots to make the work.

162

So we did that, uh, and we are

like a data hub for, for, uh, this

163

financial intelligence platform

like Capital IQ or Abura Von Dyke.

164

Um, Bloomberg gets our data as well.

165

Uh, TTR, LexisNexis, we

166

Upendra Varma: So, like, my question

is, like, just give me an approximate

167

sense of, like, how much Bloomberg pays

you to sort of get this data from you.

168

AlexandreAbu-Jam: um, I don't

have that right now, but

169

Upendra Varma: just looking

for an approximate range.

170

Is it a 10, 000 deal?

171

Is it a 100, 000 deal per year?

172

Like, what are we looking at?

173

AlexandreAbu-Jam: Might be

like 20 per company per year.

174

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

uh, uh, thousand companies a year.

175

That's more or less my, my volume.

176

Then you can do the math.

177

Upendra Varma: okay.

178

That's a lot of money.

179

Alright.

180

Uh, yeah.

181

Keep on going.

182

So, you've started, you pivoted to

DAS and then what happened after that?

183

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

turned into like a desk company because

184

it would be easier to fund and to grow.

185

I mean, we could go to venture capitals or

to investors and grab data and sell them

186

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

187

But we didn't want to raise capital.

188

We wanted to do it on our own.

189

We wanted to, to bootstrap.

190

So, um, the, the, the way to

finance it more easily would be

191

to, to make the data service.

192

Cause that was like a low hanging fruit.

193

They needed the data.

194

Uh, there wasn't much

clients, you know, like.

195

Not like a thousand, uh, uh, financial

intelligence platforms around.

196

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

197

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

scalable, but it was low hanging.

198

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

199

Um, and, and it worked very well.

200

They, they really wanted and needed that,

and it was, Effectively a low, a low

201

hanging, uh, opportunity, but it wasn't

that easy to get to these guys at that

202

point because we were very, very small,

you know, like we're starting and trying

203

to build something that didn't exist,

but there's an interesting story there.

204

Our, our first, first desk client was

Capital IQ and, and that was, that was

205

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

206

Upendra Varma: how, how did you

sort of convince them to sort

207

of start using your platform?

208

And you, you guys like were

no one for them, right?

209

So you're just starting out.

210

So how did that work out

211

AlexandreAbu-Jam: Yeah, it

started with a cold call.

212

So I called them on their, on their,

uh, San Paolo office and I was like,

213

Hey, can I talk to your director?

214

Uh, they had like a director that,

that would take care of everything.

215

It wasn't, wasn't like a

specialized director of data here.

216

It was like more of a, of

a commercial office and.

217

And so I, I got to their director and

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

218

Um, and we are with this product.

219

We have a lot of financial data

of private Brazilian companies.

220

I've noticed that in your

platform, you don't have that.

221

And then he was, he was like,

wow, really you have that?

222

I can't believe it.

223

I really want it.

224

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

the good momentum to explore that because.

225

Capital ICO at this point is investing

in Asian data and I wouldn't be

226

able to sell that internally.

227

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

228

I'll get back to you if that

makes sense to us later on.

229

And, well, I hang the phone and

I just thought, well, right, I'm

230

never gonna hear from him again.

231

But after six months, he surprisingly

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

232

Oh, here's Pedro from Capra Waikou.

233

Do you remember me?

234

And I was like, yeah, sure.

235

Well, it's great to hear from you.

236

And then he said that at that point

was a good time to start exploring

237

Brazilian data because they were in

the Latin American cycle of investing.

238

And then, and then after that things,

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

239

When we were finishing our negotiation,

they called us to Sao Paulo to

240

meet in person and this stuff.

241

And when we got to the meeting, that

was like the CEO of the company.

242

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

uh, McGraw Hill, which is the owner of.

243

S& P which owns Capital IQ.

244

So I was like, well, yeah,

that's, that's really important.

245

That's, that's really big.

246

All right, cool.

247

Um, and, and that was it.

248

Then I, we got Capital IQ.

249

And then after that, um, it was easier

to, to approach other, other clients.

250

Upendra Varma: and all of this, you,

you were doing bootstrap, right?

251

So you were just doing it on your own

252

AlexandreAbu-Jam: yeah, that

was, that was bootstrapped.

253

Upendra Varma: then go back,

like, go, like come forward.

254

Right.

255

So I'm assuming you're doing

a couple million or more than

256

that in Dash revenue today.

257

Right.

258

So when did you decide to move to SaaS?

259

Like, and what, what was the journey like?

260

So when exactly did you decide,

okay, enough is enough, let's

261

move on to SaaS and let's get it.

262

So talk about that story.

263

AlexandreAbu-Jam: Uh, when we

decided to move to SaaS, you mean?

264

Yeah, SaaS was our, was our first

product, but then we pivoted

265

and then we got back to it.

266

Actually, we never gave up of it, because

we, maybe we should have, because it, it

267

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

late for us to, to keep investing in that.

268

But we, we understand that when we, we

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

269

Where we are going to grow really, um,

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

270

that's the best way of scalability

once you have, uh, a good product.

271

And we think that we are close to that.

272

Not sure how close, but

right now we've, uh,

273

Upendra Varma: you've got

50, 50 odd customers on your

274

SaaS platform today, right?

275

So, so how much do they pay

you on an average today?

276

It's like, how big of a deals

are we talking about here?

277

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

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

278

in dollars, 400 more or less.

279

That's monthly.

280

That's monthly

281

Upendra Varma: it.

282

And just like, so where are you

getting all of these customers from?

283

Like, so I'm assuming they're not

from your DAS platforms, right?

284

So there's got to be, these

are new customers, right?

285

Have different set of requirements.

286

So where are you finding them?

287

How are they discovering you?

288

AlexandreAbu-Jam: Yeah.

289

Perfect.

290

We have, um, we have two channels.

291

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

are trying to do the basic well done.

292

Uh, we do inbound and we do outbound.

293

Our outbound front, we have, uh,

an STR, a sales representative,

294

uh, trying to, to hit the leads.

295

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

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

296

Usually closes the deal and well, the,

uh, our SDR, he just, um, prospect

297

a lot of in LinkedIn, like searching

for companies in our persona and,

298

you know, he did the basics, like

on LinkedIn, he, he gets connections

299

and, and, uh, gets in contact with

people, but he also reheats, um, like.

300

Older leads that came to us, but

didn't close at some point in

301

history and our inbound sales.

302

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

much, uh, SEO and our newsletter.

303

Actually, our newsletter

is our main inbound, um,

304

Upendra Varma: So how big of a

newsletter are we talking about?

305

How many subscribers do you have on there?

306

AlexandreAbu-Jam: Um, we have like

six or 7, 000 subscribers, but

307

like, um, that usually open our, our

newsletters that we are sending them

308

like regularly, it might be 3000.

309

Upendra Varma: So like, when did

you start building this newsletter?

310

Like, was it a personal thing for you

let's talk about that newsletter journey.

311

AlexandreAbu-Jam: Yeah.

312

We.

313

In the start, we did a

lot of outbound, right?

314

So, um, I was like sending emails

and getting contacts on LinkedIn and

315

well, made a lot of outbound sales.

316

Um, it was quite new for people.

317

So it was interesting and they

would be interested in giving

318

me their contacts so I could get

in contact later or, and so on.

319

Um, once we had like.

320

500 people, um, uh, listed and already

in our outbound sales that went

321

in, in our outbound sales process.

322

Um, then we started doing the newsletter

and, and we, we would feed our, our

323

newsletter, um, Uh, list, uh, with

our outbound sales, uh, effort.

324

So I would, I would make a

prospection in outbound sales.

325

I would get the contact of someone.

326

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

put you on my, um, on my newsletter?

327

Mailing list?

328

He would say yes.

329

And then I would put him in

330

Upendra Varma: so, so, you would

reach out to somebody who's

331

like, who you never know, right?

332

It's a cold lead and you would strike

a conversation with them and if they're

333

not ready, you would ask them to sort

of, can I put them in my newsletter

334

and then that's how you ended up

building the newsletter, is it?

335

AlexandreAbu-Jam: That's right.

336

That's right.

337

Exactly.

338

Upendra Varma: Okay,

that's, that's wonderful.

339

And like, you mentioned 400

is what they're paying on,

340

paying you on an average.

341

Is it like per month or per year?

342

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

343

Uh, and the contract is yearly.

344

Upendra Varma: Got it.

345

Right.

346

So like, just, uh, talk about the,

you know, bottom of the funnel, right?

347

So when somebody discovers you, right,

so how do you convince them so how do

348

you convert them into a paying customer?

349

How,

350

AlexandreAbu-Jam: Perfect.

351

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

the personas that convert, so we have

352

some, some sort of people that, that

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

353

Well, this guy might not be that hot.

354

So we would put much more

effort on the hot ones, right?

355

That we know that is in our persona.

356

Once we get the guy in our persona, we

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

357

timing to signing, if he has the budget.

358

And if the guy I'm talking to is the

guy that decides, so I'm talking to

359

an analyst that won't have the right

level of decision making to sign

360

something, I would try to escalate to his

manager before getting to our closure.

361

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

362

Hits the lead, he gets in contact

with the, with the manager,

363

uh, which decides, he validates

that the guys have a, a budget.

364

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

365

And in this process, of course,

we, we try to, uh, to show them

366

all the value of our solution.

367

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

368

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

difficult to show the value of it.

369

Uh, the difficult part is, um, Is to

prove them that they will use that data.

370

Um, regularly.

371

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

curiosity, you know, like it's science

372

to, because it's curious of something

and then it uses three or two, three

373

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

374

And then that's, that's a

big problem for our CS team.

375

Then our, our, our customer success needs

to reheat this guy and find out how we

376

can use the, the platform regularly.

377

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

378

Convincing them that they're

going to be regularly.

379

Upendra Varma: take to sort of close this?

380

Like how long is your sales

cycle today on an average?

381

AlexandreAbu-Jam: Oh, I

don't have this number.

382

It varies a lot.

383

Sometimes some guys come and say, well,

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

384

Upendra Varma: what's a typical number?

385

is it is, is it months, weeks?

386

You know, is it quarters?

387

Like, how does that look like typically?

388

AlexandreAbu-Jam: the regular, the regular

cycle might be, um, One month and a half

389

or something like that from getting to

the lead, finding the decision making

390

guy, uh, validating budget and showing

all the organization that you're going

391

to take advantage of the platform that's

that might take one month and a half

392

more or less, but right now with, um,

we are doing a lot of things integrated

393

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

um, present automatic presentations.

394

They use chat GPT on their back end.

395

Um, And that's helping us to convince them

much more on the value of the platform

396

and how they could use that regularly.

397

Upendra Varma: Got it.

398

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

400

had on your SaaS platform?

401

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.

420

So I was asking about your churn, right?

421

So how, how, how have you been able

to sort of keep your customers?

422

Right.

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So basically like you had 25 odd

customers, like 12 months before.

424

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?

435

Like, like, how does that look like?

436

It's like you had a, you had around

30 odd customers 12 months before.

437

Right.

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So how many of them still, you

know, use your platform today?

439

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.

460

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.

475

Thanks for the opportunity.

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It was great talking to you.