0:01

Again, if you're not keeping human in the loop, God

0:03

knows what can happen with your

0:05

algorithm. Human antelope? Human

0:07

in the loop. Human in the loop. In the

0:10

loop. I thought you were saying antelope. In

0:12

the loop. I was like, okay, I don't

0:14

understand this part. Okay, humans

0:17

in the loop.

0:19

Gotcha. It's

0:23

actually really hard to say because artificial

0:26

intelligence and neural networks are

0:28

quite often considered black boxes. You

0:31

can't really explain what's

0:33

happening, why they made this decision.

0:38

Because it's really good just to meet motivational

0:41

people. Sometimes you get such a great

0:43

energy boost just talking with someone

0:45

who's passionate about something. It doesn't

0:47

even have to be your field.

0:50

My name is Anastasia Georgievska. I'm

0:52

the CEO and co-founder of my

0:55

startup, which is called Hout.ai. Hout

0:57

is skin in German and hence

1:00

the name. My company is developing

1:03

software as a service for skincare

1:05

field. It's image-based system.

1:07

We think of it as a beauty intelligence system

1:09

that ultimately helps consumers

1:12

all around the world shop better and

1:14

eliminate unnecessary guesswork and anxiety

1:17

when it comes to picking up the right skincare and beauty

1:19

products. A little bit about myself. I'm

1:21

based in Tallinn in Estonia. I'm now 29

1:23

years old. I founded

1:25

this startup when I was 24 as

1:28

a fresh graduate. And yeah,

1:30

I'm super excited about artificial

1:32

intelligence, about the beauty field. I think

1:34

there's a lot to enhance and

1:37

a lot to change in this field. And

1:39

we are working every day to make it better and

1:41

more consumer-friendly.

1:43

And so I guess the website is Hout.ai,

1:47

if anyone wanted to check it out. Yes, that's

1:49

correct. Okay. Well, can you tell

1:51

us a little bit more about this? How you're using AI for

1:54

your company and the skincare? Sure.

1:56

I would say that my company is not only using artificial

1:59

intelligence. artificial intelligence as the only

2:01

tool in our toolbox.

2:04

Generally, the main capability of

2:06

our software is computer vision algorithms.

2:09

And computer vision doesn't necessarily

2:11

mean that algorithm has to be artificial

2:14

intelligence, which usually entails

2:16

using of some of the algorithms like

2:18

neural networks. We also use a lot of classical

2:21

computer vision models to identify

2:23

skin features. So right now we can track

2:26

around 150 facial

2:28

biomarkers to help personalize purchase

2:31

offerings. So the software by itself

2:33

can help

2:34

a consumer capture a picture

2:36

using just a simple smartphone camera.

2:39

You don't need any advanced high-end

2:41

medical devices to do that.

2:43

After the picture is captured, we're sending it to

2:45

our secure cloud where we run our computer

2:48

vision models in parallel and can

2:50

deliver results pretty quick in about

2:52

two, three seconds. And after the results

2:54

are complete, which means that we have built

2:57

your unique skin profile based on your facial

2:59

features, we will be matching skin

3:01

care products with your skin profile.

3:04

And how many people work in your company? And

3:07

can you give us a little bit of an idea as far as

3:09

revenue or amount of customers that you have?

3:11

So our company is a startup stage. At the

3:13

moment, we have 29 persons

3:16

in our team.

3:17

We work with multiple companies everywhere

3:20

around the world. We have around 90 clients,

3:23

which includes skin care manufacturers,

3:25

retailers like Ulta Beauty, and

3:27

also a number of smaller companies like

3:30

Longevity Clinics or Skin Clinics. Pretty

3:32

much businesses that produce services

3:35

or goods that have a factor in skin are

3:37

among our clients.

3:39

And I guess you gave us the number of clients, but

3:41

as far as where they're located, I know you said kind

3:43

of all around the world and you said you're in Estonia.

3:45

So I'm curious where most of those clients are.

3:47

They are US based or can you give us a

3:50

better idea of how you even pitch these clients,

3:52

what you're saying to them? Yeah,

3:53

US market is one of their... It's

3:55

not one of the biggest. It's actually the biggest market

3:58

for skin care products. So of course...

3:59

We're targeting US clients and US

4:02

is a big geography for us. Also Europe

4:04

and Asia Pacific regions, not

4:07

only limited to this, three locations,

4:09

we have clients in Australia, in Colombia,

4:12

in South Africa. So that's why I'm saying

4:14

everywhere around the world. When

4:15

we're pitching to them, I would say that

4:18

pitch is universal. Of course, for a different

4:20

alteration, depending, there

4:22

is a big difference on how we pitch in Asian

4:25

region and in the US. Use

4:27

cases sometimes are also different. But generally,

4:29

I think what makes our

4:31

software easy to understand is that people

4:34

can have very different opinions about beauty,

4:36

a beauty-beauty standard. But what

4:39

is universal is, and

4:41

when I'm saying universal, most population

4:44

will agree that having healthy skin, free

4:46

of imperfections, free of diseases, is

4:49

considered beautiful or at least

4:51

attractive, appealing.

4:52

And the fact that our software helps accurately

4:55

identify what are the skin concerns and

4:57

how to address them makes our pitch easier

4:59

because again, it's something universal, it's

5:02

something that every one of us would like to have.

5:04

And we are pitching to our clients that

5:07

if they start working with us, they

5:09

can personalize offering to

5:11

their clients much better, which obviously

5:14

increases the satisfaction of

5:17

your purchases. It helps you improve

5:19

some short-term metrics like I want

5:21

to sell more, right? But more and

5:24

more players in the beauty field are

5:26

saying that there is a better alternative to

5:28

just transactional relationships when

5:31

a customer comes to your platform and

5:33

you would never see them again, right? It's kind

5:35

of one-time purchase.

5:37

Brands, especially direct-to-consumer brands, whose

5:39

main touch point is online want to

5:41

build relationships with clients.

5:44

We want to build this trust. We want to

5:46

offer education. And this

5:48

is exactly what our software is doing. So it

5:50

helps explain what's wrong with your skin

5:53

and it also explains what products

5:55

it should be using and why.

5:57

And so are you more of like a B2B play

5:59

where you're a help?

5:59

I don't know if you can even release

6:02

some of the clients' names that you kind of give them

6:04

this AI. They can put it on their app

6:06

or whatever. And then if it's a certain brand,

6:09

then these people are like, hey, you should use

6:12

this brand to clear your acne or something

6:14

like that.

6:15

Exactly. So our software is primarily

6:17

B2B. We do, however, have plans

6:19

about launching our own B2C

6:22

app at some time point. I'm not sure this

6:24

is confirmed at the moment. To name

6:26

you some clients, our clients are Ulta

6:29

Beauty. I think if you are listening

6:31

to this podcast from the US, you are pretty familiar

6:33

with this beauty retailer.

6:36

Among manufacturers, one of our clients is Beyersdorf,

6:38

which is the owner of Nivea.

6:40

Yeah, so we help companies

6:42

target the right products to their customers.

6:44

If we're talking about retailer and manufacturers,

6:47

use cases will be slightly different because manufacturers

6:50

have usually particular brands, so

6:53

they would be matching products from

6:55

this brand's assortment to the skin concerns.

6:57

With a retailer, it can be more

7:00

sophisticated because retailers obviously have

7:02

bigger number of brands. Ulta

7:04

has, I think, thousands of brands

7:06

that they are offering. But

7:09

then the idea is that how you can offer

7:11

the right brand and the right products at

7:13

the same time, that also matches the budget, that

7:15

also matches the values of the consumer.

7:18

So do I want to show from the local

7:20

producers? Or maybe I want

7:22

to shop for K-pop skincare? Or

7:25

maybe I want to, I don't

7:27

know, only purchase cruelty-free products? So

7:30

that adds additional layers of complexity and

7:32

at the same time, it offers additional,

7:35

more deep personalization of your purchases.

7:38

That

7:38

makes sense. But how do they decide

7:40

it or you decide it? I guess that's what I'm trying

7:42

to figure out as well, as we kind of dive more

7:44

deep into this AI that you have.

7:46

So we let algorithms decide. We

7:49

use not only computer vision models

7:51

to recognize face features, but we

7:53

also use recommendation engines,

7:56

which utilize both decision

7:58

trees and some of the requirements.

7:59

neural networks, I will explain

8:02

what it basically means. So usually, neural

8:05

network is trained once,

8:07

like once per iteration, so you have

8:10

your training dataset, then

8:12

you have a set of answers

8:14

that you want the system to learn

8:17

how to reproduce. Let's say you have a

8:19

picture and you have an expert grading, maybe

8:21

a dermatologist, who would classify

8:24

what type of acne this is, what

8:26

type of wrinkles these are. So your

8:28

idea is to build a network that

8:31

will reproduce answers

8:33

as if it was a dermatologist. So you kind

8:36

of want to mimic a professional grading.

8:38

But this is not the only type

8:40

of

8:41

artificial intelligence algorithms. So the example

8:43

I just explained is called supervised learning,

8:46

because obviously, there's a supervisor, in this case,

8:48

it's dermatologist. Some networks

8:51

are trained and they function differently. So

8:54

the goal of this networks is not to only

8:56

learn from an expert, but also

8:58

learn from the consequences. So these

9:00

networks are called recurrent. So their

9:03

idea is that you will utilize

9:05

outcomes as input

9:08

data for improvement of their

9:10

accuracy of this algorithm. And we're

9:12

using these networks also to see what products

9:15

were performing better. So whenever

9:17

clients are using our system, they can

9:19

upload their inventory, we will be

9:21

looking at the ingredients, we will

9:23

be looking at our knowledge

9:26

of how different ingredients and combinations

9:28

perform on different persons to then

9:31

match with products. So decisions

9:33

are carried out by the algorithm. But we

9:35

also understand that brands might want

9:37

to shape the recommendations

9:40

as well. So this is where we're also offering

9:42

additional AI based tools to

9:46

make recommendations personalized and

9:48

aligned with what brands is trying

9:50

to achieve. Hope I didn't confuse you too much.

9:53

No,

9:53

that makes sense. I'm just trying to take it in and trying

9:55

to make sure everyone else does too. When you're talking

9:57

about the reoccurring network versus something learning.

9:59

What was the first one that you stated? Yes,

10:02

so it's just different approaches. So it's supervised

10:04

learning. Supervised learning. Okay. Yes.

10:07

Gotcha. That makes sense. I mean, anyone who's

10:09

context-closed, you're like, okay, they're learning from

10:11

someone who's supervising them, right? In this

10:13

case, a dermatologist versus their recurrent

10:16

network. Is that what you said?

10:17

Yes. So if we would compare, you know,

10:20

let's come up as an example, let's say treatment

10:23

of acne. So if we were to

10:25

only use a supervised learning,

10:28

we would want it to detect

10:30

different skin eruptions and maybe

10:33

classify them. So is it like blackhead

10:35

or is it papule or maybe it's post-acne

10:38

inflammation?

10:38

So the network can detect these regions and

10:41

classify them.

10:42

Then what if I want to solve

10:45

a different task? So I want to,

10:47

I know what acne a person has based on

10:49

their skin profile. And I want to

10:52

see how different treatments would

10:54

improve the condition of skin or maybe they

10:57

will not improve. Maybe even skin will get worse,

10:59

right? But I want to extract this learning.

11:02

So what product helped

11:04

to this consumer

11:05

or like didn't help, as I mentioned.

11:08

So if I'm also able

11:11

to fit

11:12

the outcome to the neural network,

11:14

maybe I can remeasure skin in

11:17

two weeks after I started using the product.

11:19

So then the neural network can also learn

11:22

what is the probability that this product

11:25

is suitable for other consumers

11:28

similar to consumer in my

11:30

training set. So in that sense,

11:32

you not only just say what's

11:35

the skin condition, you also try to

11:37

build correlations or

11:39

if you are very lucky causality because

11:42

correlations don't always mean

11:44

that,

11:44

some products will be helpful

11:47

for a particular group of users

11:49

or like consumers with a higher probability

11:51

than other products. So you're

11:53

saying

11:53

based on your acting discussion

11:56

and kind of having those different things, you're saying the

11:59

AI. you're doing the recurrent network,

12:01

is it automatically going to take into someone's age?

12:03

They're like, okay, this person's 80 versus person 16.

12:05

I'm going

12:07

to recommend this because I know they're 16

12:09

versus the 80-year-old might be dealing with wrinkles

12:12

instead. And so it's automatically

12:14

taking that into account as well and learning

12:16

from that.

12:17

It's actually really hard to say

12:20

because you maybe have heard that

12:22

artificial intelligence and neural networks

12:25

are quite often considered black boxes.

12:27

You

12:27

can't really explain in

12:30

most times what's happening,

12:32

why they made this decision. It's one

12:35

of the problems of general

12:37

AI and special AI. So general

12:39

AI, it's kind of a field overall

12:41

right and then special AI is application

12:44

of AI in the particular field.

12:46

But again, you can't always understand why

12:49

it's making the decisions. There are different ways

12:51

how you can interpret using heat

12:53

maps. For example, in computer vision,

12:55

you can try to build attention

12:57

maps. Attention map stands

12:59

for kind of heat maps that

13:02

try to describe what

13:04

pixels in the picture of an algorithm

13:06

considered to be most predictive. Again,

13:09

it's quite often speculative. So

13:11

you can then try to

13:13

kind of run additional tests and understand what

13:15

features made an impact. Making

13:17

algorithms more interpretable, you

13:20

actually always need to have a person in the loop

13:22

to understand if results make

13:25

sense. Depending on their field,

13:28

subject matter experts can tell if results

13:31

make sense, if they're aligned with the general knowledge.

13:33

It's actually one of the reasons why

13:35

here in Europe we have AI

13:38

act that will be adapted, I think,

13:40

in two years from now, that describes

13:43

the procedures of building

13:45

non-harmful AI, safe

13:47

AI. And one of the requirements

13:49

is at every stage of

13:52

development or using of artificial

13:54

intelligence algorithms, you will always have

13:57

a person in the loop, the one who is supervising

13:59

happening

14:01

and also checks that this

14:03

algorithm does make sense.

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15:53

I'm here with John Austinson. How are you doing

15:55

today, John? Hey, Austin. Doing great.

15:57

Thanks for having me. Absolutely. podcast

16:00

and I interviewed John on episode 250

16:03

of this very podcast. So you can hear more about John's

16:05

story and how he grew Fran Bridge consulting

16:07

right here. But in the meantime, would you mind reminding

16:10

our listeners what you do and what you could

16:12

potentially help them with? Yeah. You know, we work

16:14

with entrepreneurs and investors across the country,

16:16

helping them get into business ownership through franchising.

16:19

And when I say franchising, you likely think fast

16:21

food. And yet there's so many other industries out there

16:24

from home and property services to health

16:26

and wellness, from kids, pets, the aging

16:28

population, oil changes, all

16:29

of these understandable cash flowing businesses

16:32

that oftentimes are recession resistant. And 90%

16:35

of our clients end up purchasing an opportunity they

16:37

never thought about. We work with the largest brokerage

16:39

in the country, over 600 different franchise companies.

16:42

Having been a franchisor and a franchisee myself,

16:44

I'm very picky about which ones that we show to our clients.

16:47

Only the best of the best. The great thing, Austin

16:49

is it's entirely free to work with us. We're

16:51

funded by the companies very much like an executive

16:53

search type model. So our clients never pay us in nickel

16:56

and we do more deals for our clients than anybody

16:58

else in the country.

16:59

And what does a typical client look like for you?

17:01

Two thirds of our clients would be looking to keep

17:04

their day job. They're looking to get into business ownership,

17:06

maybe as a side hustle, or maybe they're already a business

17:08

owner and they can't get their full attention. We work with doctors,

17:11

lawyers, existing business owners, corporate

17:13

executives, really a wide array of

17:15

backgrounds all around the country.

17:17

As far as anyone who might be interested in your

17:19

service, is there a best way for them to reach you?

17:22

Yeah. Come out to our website, franbridgeconsulting.com.

17:24

That's F-R-A-N-Bridge-Consulting.com.

17:27

For all of your listeners, Austin, we'll also

17:29

send them a copy of our new book, either audio

17:31

or PDF version, or they can purchase it on Amazon.

17:34

But I would love to share that. Our book is called Non-Food

17:36

Franchise and we've gotten great feedback

17:38

since its release. If you're interested in taking

17:40

a next step, let my assistant Ashley know

17:43

and she'll schedule a call and we'll

17:45

discuss your situation and what could be a good fit.

17:47

Yeah. And I know you've already scheduled a few call with our

17:49

listeners. Could you just tell them what that typically

17:51

is like, like how long and if it's free for

17:53

them to do? Yeah, we've had a great response from your

17:55

listeners entirely free because

17:58

of the caliber of folks that we work with.

17:59

cut to the chase. We usually spend 20 to 30

18:02

minutes on that first call. And then as the next

18:04

step, that following week will come to them with

18:06

opportunities, usually around 10 or so in

18:08

their market. They're available to check all the

18:10

boxes. And we talk them through those and

18:13

then make introductions to the ones that seem

18:15

most intriguing to them.

18:16

Well, that sounds awesome. And again, if someone was interested

18:18

in scheduling a call, where's the best place for them to

18:21

go ahead and sign up? Yeah, come out to our website, franbridgeconsulting.com,

18:24

F-R-A-N bridgeconsulting.com, and

18:27

we would love to engage.

18:30

I mean, that makes perfect sense, it seems like

18:32

to me, especially mathematical

18:34

people. You're like, I need to know why it chose

18:36

whatever it chose, but you're saying that's not possible.

18:39

I mean, it's not always possible. Or

18:41

sometimes you can only understand half

18:43

of facts.

18:44

Primarily why you cannot

18:47

always understand how AI

18:49

is making its decisions is because

18:52

usually, like let's compare some previous

18:54

methods that existed before artificial intelligence.

18:57

And let's talk about image recognition.

19:00

So before artificial intelligence, you

19:02

usually would be crafting features

19:05

yourself, features that you want to

19:07

identify from the picture. And you say,

19:10

I

19:10

introduced this feature, this is how I describe

19:12

it, and this is how I want

19:15

to measure that.

19:16

And in that sense, you kind of give instructions

19:18

to algorithms which you implement in

19:20

a code,

19:21

and it works, hopefully, according

19:23

to your protocol, right? If you didn't make

19:25

any mistakes or it's just possible to

19:27

launch this algorithm. Then if I

19:29

think about artificial intelligence, quite

19:32

often they say,

19:33

okay, we should admit the fact that as

19:35

humans, our perception is also limited,

19:38

right? So what if we tell

19:40

a smart system that

19:42

is designed in a similar

19:44

way as our brains, and

19:47

we tell the system that the system

19:49

is not limited to choosing the

19:51

features, to choosing how it

19:54

will be solving the task. Let's kind of

19:56

give it full freedom.

19:58

So if you do that... algorithm

20:00

starts to learn dependencies in

20:03

the data to run its predictions.

20:05

And the fact why it's sometimes

20:08

so hard to discover what

20:11

features were used or like how

20:13

AI made this decision is because

20:15

we don't always understand the complex

20:18

dependencies in the data. This is why

20:20

they use this algorithms because we want to consolidate

20:23

knowledge

20:24

from the data because we cannot do it

20:26

ourselves because you need a lot more

20:28

computational resources.

20:30

And humans are very smart and this is why

20:32

we have this gut feeling, but just machines

20:34

can do it in a more structured way. So because

20:37

these dependencies are complex, again,

20:39

quite often it's hard to understand

20:42

why algorithm made one decision over another.

20:44

But maybe I'd like to talk more about how

20:47

we ensure that our AI makes

20:49

sense.

20:50

We're using the principles of human-in-the-loop.

20:53

So whenever we are training algorithms, it's supervised

20:55

by dermatologist or skin experts, and

20:57

then we're releasing our models. We're

21:00

testing them on retrospective data. We're

21:02

testing them on the real data coming

21:04

from consumers, and then we're ensuring that it works

21:06

correctly.

21:07

Again, if you're not keeping human-in-the-loop, God

21:09

knows what can happen with your algorithm.

21:12

Human-antelope? Human-in-the-loop.

21:14

Human-in-the-loop. In the loop. Oh, I thought you

21:16

were saying antelope. I

21:19

was like, okay, I don't understand this

21:21

part. Okay. Humans-in-the-loop. Gotcha.

21:24

Human-in-the-loop. Yes. Okay. So all

21:26

your AI has that right now, you're saying, right? Yes.

21:28

You're not doing the recurrent network. Where

21:30

do recurrent networks as well? Don't get me

21:33

wrong. We're using different techniques, and I

21:35

would say we're much more

21:37

existing and constantly developing, whereas

21:40

in your field of transformers, there

21:42

are generative adversarial networks.

21:45

I would say that one need to understand,

21:48

and again, I'm not a super-technical person,

21:50

write my backgrounds in biophysics, not in

21:52

software engineering. It's very important

21:54

to understand maths behind

21:57

AI, how it works, so that you

21:59

know what a limit is.

21:59

limitations, so you know what are the traps

22:02

and where you have to be extra conscious.

22:04

And then you just have a very rich toolbox

22:07

and you can choose the sharpest tool depending

22:09

on what task you need to solve.

22:11

But even if we went back to the, remember the two

22:13

things that you talked about, the supervised learning network versus

22:16

the recurrent network, you could run

22:18

AI on both of those and then kind

22:20

of decide, right? Someone can, I mean, I guess it is

22:22

supervised at the end. If you just look what the recurrent

22:25

network would have picked versus the supervised learning

22:27

network would have picked as far as like skin condition

22:29

and everything.

22:30

So technically recurrent network also

22:32

supervised because you have kind of a target.

22:35

It's just different approaches with recurrent networks

22:37

can also learn from experience. And

22:40

then, for example, a neural network,

22:42

which is trained for segmentation task to

22:45

segment objects in the picture, wouldn't

22:48

have kind of learning because just know the fact there

22:50

is a hyper-pigmentation spot and the

22:52

network detected that. But if you, for

22:55

example, apply some cream and then

22:57

you want to say, look, this is what happened after

23:00

a month of using this cream. This is how

23:02

my skin metrics looked before

23:04

a month ago. This is how they look right now.

23:07

So if you use recurrent networks, you can utilize

23:09

this experience as a learning to

23:12

then be able to predict

23:14

better recommendations in the future

23:17

using that learning from experience.

23:19

But what would you classify that as? Would

23:21

you still classify that as a recurrent network?

23:24

So recurrent network, it's kind of the network

23:27

architecture. It's an approach,

23:29

but you can use. You use primarily with networks

23:32

for learning from experience. I

23:34

guess we can dive more into this AI if that's

23:36

okay. I was just far just understanding the basics because

23:39

that was my thing when you said you didn't know why the

23:41

AI kind of chose it. That kind of boggled my mind

23:43

because the whole idea with even blockchain,

23:46

right? Like I'm not in the crypto or blockchain or anything

23:48

like that. But the whole idea with the blockchain is that you

23:50

can see every transaction that was made

23:52

and is tied to this basically for life,

23:54

right? So we know why this happened or

23:57

whatever occurred with this transaction from

23:59

my understanding. Again, I don't mess around with blockchain

24:01

or anything like that. But then with this recurrent

24:04

network, you didn't necessarily know, which is disturbing,

24:06

it seems like.

24:07

Yes, that's correct. You don't necessarily know how

24:09

it's making decisions. And it's

24:12

again, one of the big, I would say, community

24:14

concerns when it comes to artificial intelligence,

24:17

but it's not always interpretable.

24:20

And I would say, as far as I know,

24:22

it's never 100% interpretable.

24:25

So this, of course, raises concerns, right? It

24:27

offers lots of benefits,

24:29

but

24:30

we always need to make a step

24:32

back, maybe turn down our excitement

24:35

and think, you know, what's really happening?

24:37

So what are the benefits and what are the negatives?

24:39

So the benefits are the

24:42

fact that you can learn from massive

24:44

amounts of data. So you can, again,

24:47

uncover some dependencies that you wouldn't

24:49

do if you were not using this

24:51

big data approach, then a lot

24:54

of tasks can be automated. For

24:57

some specific fields, you can

24:59

achieve quite high accuracy very

25:02

fast. For example, in computer

25:04

vision, in natural language processing,

25:07

you can take some of the models open source

25:09

available on GitHub, you know, with their free

25:11

license, and you can train it on your

25:14

data and you can get more

25:16

than 80% accuracy in a

25:18

matter of like week just playing around.

25:20

So if you maybe spend additional month,

25:22

you can achieve accuracy for

25:25

specific tasks in a month.

25:27

Then what happens after that is achieving 96%

25:30

takes more time, but then going

25:32

from 96% to 99%

25:35

will be super, super hard,

25:37

right? So I would say that automation of some

25:39

stereo tasks can be done easily.

25:41

Then if you have been watching what's happening

25:43

in generative AI, now,

25:46

Dali, a project by opening AI can generate

25:48

images just from the textual input, which

25:51

is considered to be a

25:52

big breakthrough in the creativity

25:55

because we can generate some concepts

25:57

or some ideas because it's considered

25:59

to be.

25:59

very helpful for art

26:02

people, designers or artists

26:05

or

26:06

even social media managers. So you

26:08

can go to Dali and generate a picture

26:10

which will be unique and you can use it with a

26:13

copyright on your social platforms. So

26:15

then it opens up a lot of time

26:18

for being creative and then there's a tool

26:20

that can carry out the boring work,

26:22

if you will, or the hard work of actually

26:25

producing an art object.

26:27

Then OpenAI, Chagigipi

26:29

also generate a lot of interest around

26:31

that with being able to reproduce

26:34

the human conversation. So that

26:36

even save up time, you can use it for maybe

26:39

write some email with you didn't find

26:41

enough time to write in the whole week and

26:43

then within five minutes you can get your

26:46

email follow up.

26:47

So that frees time for

26:50

being creative, for carrying out

26:52

the tasks that artificial intelligence

26:54

cannot do, which requires human

26:57

involvement

26:58

and a lot of just daily things. We don't think

27:00

about that, but a lot of things are

27:02

AI based like our internet

27:05

searches, selection of the music,

27:07

maybe things we don't interact daily,

27:09

but in some warehouses you're

27:12

using KI for logistics, for

27:14

supply chain, for managing

27:16

the warehouses.

27:18

It's becoming more and more part

27:20

of our daily life. So these are obviously the benefits,

27:22

some tasks which took more time

27:25

before you can now do faster.

27:27

The disadvantages, I would say

27:29

that the only concern is for me as a

27:31

person who's working in the AI field is the

27:33

fact that you don't always

27:35

understand 100% why it made its

27:38

decisions. So this is why you need to ensure

27:40

you have the right system to monitor biases,

27:43

to detect any anomalies.

27:45

So you kind of have to be conscious about how you're

27:48

using that.

27:48

Other disadvantages that AI still

27:51

requires a lot of computational resources,

27:53

a lot of electricity, a lot of power,

27:55

a lot of GPUs to be trained,

27:57

which

27:58

I would say people

27:59

can argue. but that's not necessarily

28:01

really environmentally friendly because, you

28:03

know, electricity also means CO2

28:06

emission, right? You know, that's just what

28:08

comes up from the top of my head.

28:10

You're doing a good job. I mean, one thing I was writing

28:13

down, I was trying to figure out, do you consider Google

28:15

Maps AI? What do you mean Google

28:17

Maps AI? Yeah, yeah. Well, say if I want

28:19

to drive somewhere eight hours away

28:21

and I put it into my phone

28:23

and I put the address, is that considered artificial

28:26

intelligence or no?

28:27

So it's not necessarily what I was talking

28:29

is like Google Maps AI, but rather

28:32

when you're browsing the net, you have a very specific

28:34

query, you want to find

28:36

the hits and then what AI does, it filters

28:39

out the hits that you unlikely

28:42

need to make sure

28:44

that your search results are relevant

28:47

to what you were trying to achieve. So a

28:49

lot of these algorithms are also artificial intelligence

28:51

based.

28:52

Well, then I think Google Maps would have to be too. Just

28:54

thinking that way. Or I'm sure you've used

28:57

Apple Maps or whatever you use to getting where because it's

28:59

just taking how many people are put in that address,

29:01

right? Maybe at first when I don't even know

29:03

how long has AI been around?

29:05

So I would say that if you look

29:07

back into the history, AI as a

29:09

concept and neural networks has been around since

29:12

around 1980s, it was

29:14

already there.

29:15

So there is a very good book by Exeo

29:17

of Google China, which is called AI

29:19

Superpowers.

29:21

And this book,

29:22

the author talks about how AI

29:24

emerged and what enabled

29:26

it big breakthroughs.

29:28

When you said that Google search, so we're actually

29:30

all using AI, it's just the different versions

29:32

of it, kind of is what you were saying, like the

29:35

Superpowers or Nucleus Recurrent.

29:36

Yeah, I would say there is AI

29:38

that everyone thinks of AI when

29:40

we're talking right now, right? So

29:43

some modern techniques. And as I mentioned

29:45

this book, it's actually rolled by Kai-Fully, which

29:47

is a very famous thing tank in

29:50

AI space. And what

29:51

is it called again? Just so everyone can get that. So

29:53

the book is called AI Superpowers

29:55

and the author is Kai-Fully. So

29:58

there, let's say modern AI, you know.

29:59

true branding

30:01

happened in around 2015

30:04

or 2016 because GPUs became more available.

30:07

Also, a lot of developments from NVDA

30:09

came around and it became accessible

30:11

not only by super big corporations,

30:13

but for example, back in 2016,

30:16

we as a startup could use

30:18

artificial intelligence and GPUs to

30:20

train our first models. So I would say that

30:23

around 2016 was a time

30:25

when high-scale adoption

30:27

of AI began.

30:29

And that's when I started hearing, and again, just

30:31

so everyone knows that the author's name is K-A-I-F-U.

30:35

That's the first name and last name is L-E-E.

30:38

And we'll have it in the show notes if anyone's interested

30:40

in this book. But I think that's when I started hearing

30:42

about, well, I know it's funny that they're talking about AI

30:44

superpowers in China with this book, but that's

30:47

really when they started doing facial recognition. I feel

30:49

like I started hearing about it a few years ago, so obviously

30:51

they started doing it years before that. So it's

30:54

funny that you're saying AI 2015, 2016, when

30:56

it really started taking off, if you will.

30:58

Yeah, they obviously started doing

31:01

that earlier, maybe as R&D, a proof of concept.

31:03

From my experience, it really takes

31:06

time between R&D and leveraging

31:09

something to a real system. There's

31:11

a very big difference between just lab

31:14

prototype.

31:15

There's that joke around developers, which

31:17

is like, works on my machine, right? So how

31:19

to make sure that something you have developed

31:21

for R&D works in real life.

31:23

You're sometimes surprised. You think something will work

31:25

perfectly and then it fails. And this is same

31:28

as with AI, right? It's not super

31:30

resilient when it comes to change

31:33

in the input data. So sometimes real

31:35

life welcomes you as kind of, you understand

31:37

it's a failure, right? You have to start from scratch. It's

31:39

not working.

31:40

Thank you for giving me the history on it, at least

31:42

as simply as possibly as you could. But I was curious, going

31:45

back to your company, do you mind if we jump back to that?

31:47

Absolutely. I like talking about AI, and

31:49

I really like my startup. So I like talking about

31:51

that as well. So how do you actually make

31:54

money with these searches

31:56

that you're doing, or if you're making these app

31:58

for these brands or whatever? I don't know if they're

31:59

different types of revenue? Or can you tell us

32:02

how you figure out how to charge people for that

32:04

and actually make money doing this? Of

32:06

course, it's not something that we have developed from

32:08

the very beginning. It was a journey as

32:11

all the startups. Right now we're using

32:13

subscription model. So it's SaaS software, you can

32:16

subscribe to the software. We're charging

32:18

you for usage depending on how many

32:20

pictures you're processing.

32:22

At the beginning, I would say we

32:24

started as two scientists. So myself, a microfounder,

32:27

we have scientific background, myself

32:29

and biophysics and bioinformatics and microfounders

32:32

background is in theoretical physics.

32:34

Before we even started to sell something,

32:36

we wanted to build something that people want.

32:39

So that was the first thing. We launched two

32:41

beauty apps back in 2016

32:43

when we just wanted to understand if

32:45

someone will be interested in analyzing

32:48

their skin. With the launch of these apps,

32:50

we got first users, they were

32:52

really excited, they were waiting for the result

32:54

and we saw yes, there is something in that. It looks

32:56

like consumers want to understand what's happening with their

32:59

skin and they want to also

33:01

find a secret source how to make

33:03

their skin better. This also was a time when

33:05

we were approached by our first

33:08

partners in their beauty field skincare

33:10

companies

33:11

who wanted to bridge the gap

33:13

between marketing and R&D

33:14

because quite often marketing builds

33:17

up its messages based on

33:19

what R&D is able to achieve. So

33:22

whether these are better formulation or

33:24

maybe better packaging or recorded

33:26

results for particular demographics.

33:29

So all of that is used by marketing to

33:31

kind of address right consumers and

33:34

market their products to the right groups.

33:36

We realized that there is scarcity

33:40

of selfie based, like something simple

33:42

because skin is analyzed in very different

33:44

ways. Quite often it requires a lot of expensive

33:47

equipment, different type of microscopy

33:49

methods that we can cost up to a million.

33:52

You can't put it into every point of

33:54

sales, you can't leverage to every

33:56

consumer so you need something which is more affordable

33:59

and this is more of a business.

33:59

So I would say it was quite

34:02

right time for us in 2016 because AI

34:04

started to become more and more popular, but also

34:06

consumer devices as smart homes

34:09

became more affordable. So I like

34:11

to think about that right now when

34:13

our brands or like our clients want

34:15

to start using our system, they sometimes

34:18

don't understand that the most important hardware

34:20

investment has already been made

34:22

by their clients and it's in their pocket. And

34:24

this is where smartphone.

34:26

So we thought, yes, we need to go into

34:29

smartphone software.

34:31

We, of course, like developed multiple methods

34:33

for this different derma scans and we still wanted

34:35

to utilize our previous R&D.

34:38

And one of our biggest breakthroughs technically

34:40

was again to translate

34:43

clinical methods into the fall. It took

34:45

us a long time, around two and a half years,

34:47

but we finally got there. So we developed software

34:49

for selfie. We thought, OK,

34:52

you can buy that now. Let's go to the market

34:54

and let's offer it to the brands. Let's offer it to potential customers. So

34:58

it turned out that it's quite hard

35:00

to sell something

35:01

that you cannot test easily, but

35:03

you cannot start using easily because it takes

35:05

like especially bigger companies more time

35:08

just to get on track for

35:10

them to experiment. It requires, I

35:12

don't know, six people at least to

35:14

coordinate, especially if it's a big company. So

35:17

you really need to have a solution which is

35:19

easy to use, fast to use, and

35:22

something that they can play around.

35:23

So this is when we started to go into API business. So it says

35:25

business. We put our algorithms

35:27

into the cloud and every client could send

35:30

requests to our cloud, send their data and

35:32

get it back annotated with a skin profile

35:35

or maybe user clustering.

35:38

So something that's accessible everywhere

35:40

around the world. So we started to sell

35:42

the software to bigger corporations, more

35:45

kind of enterprise type of deals. But

35:48

then we saw that there is a big opportunity on

35:50

the market because beauty market is huge. Beauty

35:52

personal care, I think, is

35:54

now estimated around 580 billion US dollars.

35:58

It's growing quite fast. There

36:00

is additional market for

36:02

AI and beauty, which is

36:04

valid around $4 billion. So

36:08

the market is huge and there's a lot of players there.

36:10

But

36:11

you can't sell to enterprise

36:13

in the same way

36:14

as you would be selling to SMEs. So

36:17

we tried selling solution as API

36:19

to SMEs. It turned out they

36:21

can't use API.

36:22

Very few SMEs have technical

36:25

resources to take your

36:27

API, which still requires coding

36:30

converse side, developing backend, developing

36:32

frontend, connecting it

36:34

with your cloud. For most

36:37

SMEs, this is not achievable.

36:39

So then we thought, okay, it looks like

36:41

there is an opportunity, but our product does not fit

36:43

for this big opportunity

36:45

of clients. And we started to think how we can

36:48

do it better. And this is when we came up with

36:50

an idea of WordPress for

36:52

skincare. So it's a widget that

36:54

you can put on your website, whether it is Shopify

36:57

or WooCommerce or BigCommerce, and

36:59

start recommending products from this widget, which

37:01

you can customize using online portal.

37:04

You don't need to make any developments. It's

37:06

kind of zero code, zero

37:08

development product, but you can get all

37:10

of the benefits of our artificial intelligence system.

37:13

So this is when we came to the idea that

37:16

technology is very important. If

37:18

you have bad technology, no one will buy your solution.

37:20

But it's not the only thing which

37:23

your clients will base their decision on. You

37:25

need to have a technology that you can sell.

37:28

And you also need to have a technology that someone can

37:30

buy. It means that they actually can use it.

37:33

And you doing the ease of use thing where

37:35

you're saying you made a WordPress widget or whatever,

37:38

versus the AI. And when you're saying SME,

37:40

you're just talking about small medium enterprise in case

37:43

anyone didn't know. So that's most small

37:45

businesses. It makes sense they don't have the people

37:47

in the background to connect to your AI, but by

37:50

you making a widget or something simpler for them

37:52

that they can just put on their WordPress site, which I feel

37:54

like most small businesses use now.

37:56

That made it much more open, I guess, to you getting more clients,

37:59

huh?

37:59

It's actually enabled us quite good growth

38:02

last year. So we tripled our number of

38:04

clients during 2022.

38:06

Yeah, that makes total sense to me. Cause like beforehand,

38:08

when you're talking about, even I understand being

38:11

able to hook up to an API, but it's not

38:13

easy. Like if you use, for instance,

38:15

I use Cloudflare on my website,

38:17

or if you use Zapier or something,

38:20

it's like, if they're connecting it, it's because usually it has

38:22

an open API and then you have to get a code from there,

38:24

put it in there, make sure they connect and then

38:26

things can change on those platforms. So I

38:29

definitely see how that made it much easier for

38:31

people like me to be like, okay, I can use this if

38:33

it's an app versus me trying to get

38:35

someone to code it up through Upwork

38:36

and connect with your features and see

38:38

what I can do. Just to make it as basic as we

38:40

could. And it seems like that was the big play that helped

38:43

you out a lot.

38:44

Precisely. And also we need to think of support,

38:46

right? As you mentioned, APIs tend

38:48

to change from time to time because, you know, some

38:51

libraries of,

38:52

for example, Python or like Java languages

38:55

quite often used for backend development or like

38:57

for frontend development, different languages.

39:00

The code evolves. It's getting updated.

39:03

So if you didn't update

39:05

your code, it's likely that your API

39:07

can also break. And small companies

39:10

don't have those resources that maybe

39:12

can pay someone for one time set

39:14

up integration fee. But it's quite

39:16

hard for them to invest in

39:19

the support as well. So this is where I say

39:21

we suggest to use our widget for

39:23

your commerce. And we make

39:26

sure that we are responsible for supporting

39:28

that for you. And you just continue

39:31

using that and building better relationships

39:33

with your clients. Yeah.

39:34

I do want to come back to that. Let's come

39:36

back to how you decided to do that and

39:39

came up with that idea. But you kind of

39:41

briefly talked about it as far as your age

39:43

today, your 30, correct? Or 29?

39:45

Yes, almost 30, 29. Yeah.

39:48

So when you came out of college, you

39:50

were 23 when you were thinking about this, right? This

39:52

was your first thing that you started doing

39:54

right out of college. You didn't have another job or

39:56

anything like that. You just started working

39:58

with AI right away.

40:00

I actually did have a job as bioinformatician. I

40:02

was working as research associate

40:05

in a company but was performing drug

40:07

discovery using all sort of visual

40:09

intelligence, but my work was primarily

40:11

focused on analysis of different

40:13

biological samples data. So not

40:16

exactly what I'm doing right now. I got

40:18

this job as a part of, I think, some internship

40:21

from university and I didn't have any savings.

40:23

I didn't have,

40:24

honestly, any kind of plan for

40:26

my life. I was scientist by

40:29

training, so I was thinking maybe I should

40:31

be pursuing an academic career as

40:34

most of my friends at university

40:36

did, get a PhD and then after

40:38

postdoc position and continue being a scientist.

40:41

The problem that I had in

40:43

my university years is I honestly

40:45

didn't understand, you know, what the opportunities existed

40:48

except for

40:49

scientific career.

40:51

The education I had was great, it was fundamental,

40:54

but with nothing about entrepreneurship,

40:56

right? You knew nothing about how you

40:59

can utilize your skills. The best

41:01

opportunity that you could think outside

41:03

of academic career would be working for pharma,

41:06

big pharma because, you know, skills in bioinformatics

41:08

are kind of a good match for them, but that's

41:11

it. But at one of the competitions

41:13

for bioinformatics where I also met my co-founder,

41:16

Konstantin, I also met Alex

41:18

Zabrunkov, who is the CEO

41:20

of Insilica Medicine, which is a unicorn

41:22

drug discovery company,

41:24

who became my mentor.

41:25

So he did a lot for me, I

41:27

think he taught me everything about business. He

41:30

always believed in myself, he

41:32

was kind of promoting me

41:34

as a female founder and just telling me, you

41:36

know, what it takes to kind of

41:38

run a business and I should explore the idea

41:41

of building this skincare startup,

41:43

right? Then after met Alex, I also started

41:45

to get into that startup

41:48

life, right? You start to meet founders, you

41:50

discuss peer to peer, what's happening, how

41:52

you are trying to troubleshoot and

41:55

make sure, you know, your startup can

41:57

continue existing, right? Because you don't have any

41:59

money.

41:59

At the beginning, you don't have any investment,

42:02

you don't know if your idea will work or not, if someone

42:04

will buy that, how your life will go, or maybe

42:07

you should stop doing that and get back to this big pharma.

42:09

So I would say what was quite messy, but life,

42:12

especially of a startup life, it's kind of a marathon,

42:15

it's not a sprint. So you kind of just continue

42:17

doing everyday, evolving

42:20

your product, evolving your technology. And

42:22

eventually, if technology is good, you

42:25

start to understand how it can be applied, what

42:27

could be the use cases, you expand your network,

42:29

start to

42:29

meet not only kind of foreign people, but you start

42:32

to build business people. And if

42:34

you happen to find good ones, they will help

42:36

you find this direction. So that's why I

42:38

think it's so important if you are

42:41

coming from scientific backgrounds and

42:43

don't necessarily have training in business. It's

42:46

very important to find your team,

42:48

it's important to build a network, just finding

42:51

people with different opinions, with different skill sets,

42:53

discussing your ideas, getting

42:56

feedback, ideally finding a mentor.

42:58

If you can find a mentor who

42:59

is a stupid star in your field, that's amazing.

43:02

That's not always happening, right? Start

43:04

finding experts, start finding

43:07

people who have good reputation,

43:09

and you can figure out different ways

43:11

how to partner with them. But it's very important for

43:13

scientists not to be inside of their bubble,

43:15

right? It's very important to understand that technology

43:18

is great, but it's not the

43:20

only component of a startup, right? There

43:23

should be more to that, like

43:25

product strategy. And if

43:27

you don't feel like you can do it by yourself,

43:30

you should be looking for people who

43:32

can join your team and help you bring

43:35

your startup to where you see the

43:38

right fit for that and what's your vision.

43:40

But how about for your particular startup?

43:43

I mean, did you stop doing your job

43:45

that you said you had right out of college and then go

43:47

all in on this? Or was it kind of part-time as far

43:49

as trying to figure out your timeline of how you

43:51

grew your company to where it is

43:54

today?

43:54

So at the beginning, I had like my day job,

43:57

which was an office job, and then at night I would

43:59

be...

44:00

running my startup or on the weekends. So I kind

44:02

of first years, I think I had two jobs

44:04

because already graduated. So I had

44:06

to earn some money as well. The startup didn't

44:08

earn any money the first years. So then

44:11

we started to get first clients. It

44:13

became obvious, you know, but you have to choose. And

44:15

the choice was obvious for me, right?

44:17

I was super excited about what I'm doing. I

44:20

couldn't imagine my life in a different way.

44:22

And this is why I didn't hesitate to just

44:24

dedicate 100% of my time to my startup.

44:27

But when you decided to go 100% in,

44:29

were you making money on it? Yes, I

44:31

was making money, not a lot, but you

44:33

know, it was enough to cover my base

44:36

cost of living, which again, was not very high because

44:38

I was just a student.

44:39

So I was making money, not a lot, but

44:41

it was good enough for me to continue working

44:44

on buildings, the company. Well,

44:45

can you take us over the journey of the

44:48

first couple of years? I mean, cause I know you said today

44:50

you have 28 employees, but it's just curious of

44:52

the growth of your company. What other highlights

44:54

you've had over the last six or seven

44:56

years as far as building this thing?

44:58

Sure. So at first, where's

45:00

my son, when I make a founder working on

45:02

the startup, we started to get some

45:05

contracts from clients and the complexity

45:07

of his contracts.

45:09

It was getting higher. So we understood

45:11

we cannot do it ourselves. So my co-founder

45:13

said, I know a guy, I studied

45:15

with him also for critical physics and

45:18

he's great, but he works in a bank and

45:21

he's data science leader, right?

45:23

And we kind of have very little money.

45:25

I don't understand what we can offer to him, but

45:27

you know, we have very cool projects, have really cool

45:29

technology. We have interesting use case. So

45:32

he said, you know, I will probably go and have some

45:34

beers with him, you know, and just maybe ask

45:36

if he can maybe help us part-time. Maybe

45:39

we can have a small hackathon to make

45:41

a boost to our technology. So my co-founder

45:44

went to have some beers with that guy. And that

45:46

was one of our first team plays because he

45:48

was also working at his full-time job

45:51

for some time and then eventually in the year he joined

45:53

us. So it became free of us. We started

45:55

to look for people in our network, people

45:58

we studied this also like. by

46:00

physicists joined us, also some people

46:02

from physical faculty. Then we went

46:04

on hackathons to look for smart

46:07

people and motivate it because you have to be smart

46:09

and motivated to kind of dedicate your

46:11

weekends for hackathon. Hackathon

46:14

for those who have never visited

46:16

one is a programming competition. So it's

46:18

coming from towards hack and

46:21

marathon. So it's hackathon. So we

46:23

went to this programming competitions

46:25

to look for people. We were kind of pitching them what

46:27

we're doing. Everyone got excited.

46:30

What was AI? It's medical.

46:33

It's like health. It's wellness.

46:35

It has social impact. So

46:37

to some extent, when you are

46:39

looking for people, when you're trying to hire,

46:42

you are selling them the

46:44

idea of your company, especially when

46:47

you cannot offer them strong

46:49

incentives like high salary or fancy

46:52

laptops or a fancy office,

46:56

you have to be patient as a founder. At

46:58

least one of you has to be. And this

47:00

is how our first of our hiring happened. Maybe

47:02

until we were 10 people, when we were 10 people,

47:05

we already started to make some money,

47:07

but we could pay salaries and then we

47:09

could even find recruiters or

47:12

afford a recruiter who would be looking for

47:14

more team members to us. But

47:16

again, we still were a small startup. So

47:19

until now, we still

47:21

take a lot of efforts to hire people.

47:24

It's important to hire

47:26

people who

47:27

have the same attitude as you

47:29

are, who are passionate about what you're doing,

47:32

especially when you're a small company.

47:34

I don't mean like people have to be super

47:36

excited in a way that some people

47:38

just do their job right. Everyone has different motivation.

47:40

Sometimes it's what you're doing. Sometimes

47:43

it's the impact. Sometimes it's money.

47:45

But my personal thoughts is

47:47

if you're building a startup and money is the only

47:50

motivation, it's not good. I

47:52

don't know how it functions in bigger corporations.

47:55

I think when you have an already very

47:57

well-established managerial structure,

48:00

managers, KPIs, systematic

48:03

performance reviews, then maybe you

48:05

can have more people who are just motivated

48:07

by money.

48:08

But I think in a startup, it's not a way to

48:10

go.

48:11

It doesn't mean that you have to kind of, on the interview,

48:13

a person will only tell you how great

48:15

you are, how technology is great, but you need to

48:17

understand what is in it for them,

48:20

right? So why does he want to join your

48:22

company? That has to be authentic. You

48:24

don't have to hire people just because they told

48:26

you too many compliments as a founder.

48:28

Or you, especially when you're saying these hackathons.

48:31

So anyone who's listening now, let's say you're

48:33

not even in the technology game, like you're going

48:35

to build a technology type business. Well, you got

48:37

to think where your clients are going to be, right, and how

48:39

you make money from them as far as the

48:41

client base. But you also have to, if you want to

48:43

hire, you're like, okay, where would people be

48:45

that I want to hire that are specialized in

48:48

this? So maybe different types of

48:50

meetups, depending on what your specialty is, but

48:52

you'd want to go to those type of events to

48:54

try to find people, at least the first couple

48:56

of people, seems like to make sure that, yeah,

48:58

maybe you need a program or someone to help you even develop

49:01

an app. And you're like, okay, let me try one of these hackathons.

49:03

Maybe that's somewhere where I need to go because I know nothing about

49:05

it. So that was obviously a clever idea and

49:07

I think pretty smart. So kudos to

49:10

figuring that out.

49:11

Yes, everyone should now look up

49:13

some hackathon and sign up for that because it's really

49:15

good just to meet motivational people. Sometimes

49:17

you get such a great energy boost,

49:20

just talking with someone who's passionate

49:22

about something. It doesn't even has to be your

49:24

field. So that's really inspirational. I

49:27

think it's a really good place to kind of occasionally

49:29

visit.

49:30

Makes total sense because even when you're hiring, when

49:32

you're talking about hiring, I guess in general, some

49:34

people are like, you can tell it in their voice, are super

49:37

excited when they get up to work on something. But

49:39

maybe they're not as good proficiently

49:41

as someone who might be a little bit calmer.

49:44

Sometimes when you hire both those people, they kind of bring

49:46

out the best in everybody where you have this guy who has

49:48

good energy, makes everyone more positive versus

49:50

if you have hired someone who is negative and

49:53

might be good at their job, no one wants that

49:56

in their company because it's going to bring down everybody else. So

49:58

maybe they're personally really good at their job. but

50:00

the negative energy they're bringing to everybody else is bringing it

50:02

down. It makes it not worth it. So again, yeah,

50:05

I think that's a great idea. Trying to find a tech person,

50:07

look at packathons in your area, but there's different

50:09

ways you could brainstorm your next business idea

50:11

and think about, okay, where would be a perfect co-founder

50:13

be? Where would I find that person? Am I going to find

50:16

that person on the basketball court down

50:18

the street? More than likely not. But what happens

50:20

if I go to some type of event that's

50:22

in the industry that I want to be in? Hey, you

50:25

have a much higher percentage of trying to find someone

50:27

who will work with you and hopefully be an employee or

50:29

maybe a co-founder

50:29

or something of that nature.

50:31

I totally agree. And I even

50:33

want to add to what you said about how to

50:35

keep the moral in the team. Some

50:37

people are negative, some people are just

50:39

very excited about things and just

50:42

very temperate is different. But generally,

50:44

I think that skills are important, of course,

50:46

especially if you are a tech startup, especially

50:49

if you are building deep tech

50:51

product. You have to understand how

50:53

it works, you have to hire professionals.

50:56

But

50:57

I would say that you still can

50:59

teach skills if some of them are missing, but

51:02

it's very hard to teach someone an

51:04

attitude. I think it's impossible.

51:06

So you kind of have embraced people who are who they

51:09

are. If you think it will work

51:11

out,

51:12

yes, you need to hire this person and

51:14

move forward. If you are saying over time,

51:16

it's not working out anymore,

51:19

I think it's a good idea to just kind of be frank

51:21

about that, talk with that person, maybe

51:24

just should be parting your ways. Because if

51:26

they are negative, probably there's some reason behind

51:29

that. Maybe we don't believe in a product. Maybe

51:31

we don't like some things about the company

51:33

and they're like a way to improve this. But

51:36

attitude is very important. It's

51:38

as important as skills. I think quite

51:41

often, it's even more

51:42

important than skills. No, I agree.

51:44

Because even what I kind of said earlier,

51:46

I'm like, if you have a person who has positive

51:48

energy and they're uplifting all these other people, just

51:51

think about the percentage better that you're doing all over

51:53

in your company versus again, if you had a negative person

51:55

bringing everyone down, maybe 20%, 30%. So I think that makes

51:57

a huge difference.

51:59

difference. And so after you made

52:02

your first couple of hires, can you walk us through

52:04

maybe the next big step in

52:06

your journey of creating this company?

52:08

Yeah. So right now as a company,

52:10

we think that we have identified where

52:13

segment, we were doing things maybe

52:16

a little bit better than everyone else. So

52:18

this is where we kind of want to grow.

52:19

Yeah. But real quick before when

52:22

you're talking about doing AI, even in the beginning,

52:24

did you decide that it was going to be the beauty field right

52:26

away? Did you just look at stats and decide

52:28

you're going to get in that field? Like how did you decide that?

52:31

Initially, we wanted to go into health tech in

52:33

a medical software, but medical software

52:36

usually has very small data sets.

52:38

And it's kind of crucial part for AI

52:40

to be able to train well is to

52:42

have large data sets.

52:44

In 2016, structured

52:46

repositories of data or

52:48

data lakes for clinics

52:51

didn't exist. There was no idea

52:53

of how this medical data will be regulated.

52:55

Everyone was scared. Everyone was kind of frozen

52:58

and hesitant to act.

53:01

I think that this medical revolution is

53:03

only happening like really in

53:05

the last couple of years when also

53:08

medical regulation changed, for example,

53:10

in Europe and became more straightforward.

53:13

Still, it's kind of not perfect, but it's, you know,

53:15

there's immense work done there. Also,

53:17

it was FDA similar approach for

53:20

how we should be treating AI software. But

53:22

let's think of like back in 2016, there

53:25

was no clear legal framework and

53:27

we just thought we're like two

53:29

person company with no experience

53:32

in the medical field. So we

53:34

wanted to kind of experiment and like kind of move faster.

53:37

So this is why we started to look at

53:39

fields that are not necessarily medical,

53:42

but they're also wellness. So this is when

53:44

we started to think about skin. This is when

53:46

we started to think about beauty. And

53:49

one of the decisions was, you know, let's try something

53:51

in beauty because it looks like there is less

53:53

regulation there and it's also

53:56

a challenging task and it's a big market. So this

53:58

were some of the rationals behind the

53:59

choice to go into the beauty space.

54:02

Got to ask. Cause yeah, that way you were thinking,

54:04

you're like, it's going to be much easier to get a lot more data

54:06

to actually use in our AI versus if you're

54:09

only making sales on so many medical devices

54:11

or something like that, it's almost impossible to even

54:13

know how much data, but you know, the data is like so low

54:16

amount that AI is not really going to help as much.

54:18

Yeah. So quite often you will be only

54:21

developing a model that works on

54:23

a very limited business case.

54:25

So AI has an attribute

54:27

which is called generalization capability.

54:30

It means how well your

54:33

model or like algorithm can generalize.

54:36

So if you have particular data input

54:38

and then you change it slightly, will the system

54:40

now think it's completely different thing or

54:43

will it think, okay, that's something similar

54:45

to what I did before. So the problem is

54:47

when you have a very small dataset, your

54:50

generalization capability is off. So

54:53

you can slightly iterate an image and

54:55

then for an algorithm that will be completely

54:57

different thing, but the algorithm never encountered

55:00

before. And quite often we'll just produce

55:02

results that don't make any sense. It's kind of rubbish.

55:05

So after you made this move into

55:08

getting into skincare, did you specifically

55:10

decide skincare or I guess if you can tell us a little

55:12

bit more detail, how this has panned out.

55:15

The main idea for us was what's an

55:17

easy way to capture data.

55:19

So we thought of scan because

55:21

you can photograph your scan with a selfie

55:24

phone. So I think it was one of the main

55:26

rationals for us. It's quite hard to

55:29

make a photo of any other parts of your body,

55:31

right? If you want to go, I don't know, ear

55:33

throat, or I don't know, even if you want

55:36

to track a patient, take, you know, their

55:38

picture of the top of your head. You can try

55:40

that. It's very hard. We will also try it as

55:42

a part of one of our software offering for

55:44

here. So yeah, that happened

55:46

quite naturally. Everyone wants

55:48

to have healthy skin, very smart phone. You can make

55:51

pictures of your skin. Let's try going

55:53

into skin wellness. So that

55:55

was 2018, right? Because that's when you said you

55:57

kind of started that AI. Yes. Right.

56:00

Okay. And then can you take us over

56:02

those couple of years? Cause I know we kind of jumped

56:04

to, I think 2022, you said you three X

56:08

your company by doing the WordPress plugin, but could

56:10

you fill us in between 2018 and 2022, the lessons you learned

56:12

that hopefully can help

56:15

anyone listening right now?

56:17

Yes, sure. So 2018, we

56:19

just started the company. We incorporated,

56:21

but we already had some R&D we

56:24

did before

56:25

and it was time to sell.

56:27

We had to figure out how to sell

56:29

these to someone.

56:30

We had multiple conversations with beauty

56:33

brands, with companies. They honestly were not

56:35

buying it the first year.

56:36

No one could understand, okay, that's AI.

56:38

It's super hype. It's super attractive

56:41

to be associated with AI. What's

56:43

the actual use case? Why should we buy

56:45

in case software? How I will be buying a

56:47

software?

56:48

Do I buy it and I send you pictures

56:50

to send me back the report or how does it

56:52

work? What if I want to process 1000 pictures? How

56:55

fast can you send me results? What if I want to do

56:57

it in a retail kiosk? Do I have to wait

56:59

an hour for you to send me results? I

57:02

had conversations with people in their

57:04

field who back in 2014 were applying

57:07

virtual makeup in the pictures

57:09

and you would be coming into a store

57:12

making a picture, it would be sent overseas,

57:15

I don't know, to maybe a completely different location

57:17

and there an artist would be applying

57:19

some virtual makeup on you, like real

57:22

makeup, just painting that and sending back to

57:24

their grocery store where you maybe made this

57:26

picture, I don't know, like Walmart, and

57:28

then you will be coming back and seeing

57:30

your results, right? So there

57:32

was no clear understanding what would

57:34

be the use case around that. So we spent

57:36

a lot of time just, you know, talking to people, trying

57:39

to understand what we would buy, trying to understand

57:41

what's their problem. In 2019,

57:45

2020, we started to get first idea, you know, but it

57:47

has to be something that you can use

57:49

easily. So this is when this idea of API

57:52

business came to us. We

57:54

spent quite a lot of time on engineering

57:57

sites, right? Because you have this amazing

57:59

AI model. It runs in

58:01

the cloud, it can generate meaningful

58:04

good results, which are comparable

58:06

in accuracy with an expert grading. But

58:09

what if someone sends

58:12

us 1000 pictures in one minute? For

58:14

the test of reasons, maybe it's like very high

58:16

demand for this API or maybe

58:19

there is like some fraud or attack, like maybe

58:21

someone's trying to corrupt your system, what you're gonna

58:23

do? So then realize that if

58:25

we want to work with corporations, they

58:28

value reliability, they value

58:30

well engineered things that don't break. So

58:33

again, in 2020, we were really investing

58:35

in our infrastructure, thinking through

58:37

the use cases, how we can make it secure, how we can

58:39

make it fast. So after that, AP

58:41

business was more or less established. But

58:44

another question came, like how we can sell more now, right?

58:47

So there is only 200 big brands,

58:50

big companies on the market. So how

58:52

we can sell above this to 200 brands and companies,

58:56

like the largest ones. So this is when we

58:58

started to think what could be the delivery system.

59:00

The idea of e-commerce widget came to

59:02

us really spontaneous. I don't even

59:05

remember how. I think we just had some

59:07

daily stand up with everyone

59:09

just talking about what's we gonna do next

59:12

for our software and then the idea came

59:14

like, but can I buy it without API?

59:16

How can I do that? And I think

59:19

with everyone started to reflect on that idea and

59:21

the idea of widget came by because

59:24

you want to embed it into website, make

59:26

it seamless. We also knew that we are developing

59:29

software for beauty. We need to kind of

59:31

make a step back and think, what's the difference

59:33

between beauty and no manufacturing? Or

59:35

I don't know, maybe machines or manufacturing cars.

59:38

Or like, what's the difference between beauty and pharma, right? Or

59:41

like maybe medical dermatology. So beauty

59:44

is immersive. It's a way of self-expression.

59:47

And this is some of their points

59:50

we wanted to make address

59:53

in our software.

59:54

Beauty is about expressiveness

59:57

if beauty is to be immersive. Software

59:59

with

59:59

we will deliver to customers who are

1:00:02

businesses also should allow them to

1:00:04

express themselves, meaning that they

1:00:06

should be able to kind of build branding

1:00:08

on our platform or customize

1:00:11

the language or customize the looks.

1:00:13

They want it to be seamless because experience

1:00:16

is important. Sometimes you go to some

1:00:18

brick and mortar stores, you know, just to maybe

1:00:20

take the bottle, feel how heavy

1:00:22

it is, feel like what's the texture,

1:00:25

maybe smell the scent of a cream. So

1:00:27

how we can translate this immersiveness,

1:00:29

how we can build trust, right?

1:00:32

Because if I want to say, you know, it's a better way to shop,

1:00:34

like, why do you think so? Why

1:00:36

would you be saying that using Hout AI

1:00:39

software is better alternative than, you

1:00:41

know, maybe using a quiz or, you

1:00:43

know, maybe better alternative to just randomly

1:00:45

choosing products? So this is when we started to think,

1:00:47

okay, software should develop a

1:00:49

comprehensive report, which is not too

1:00:52

advanced in a way that it's advanced,

1:00:54

but it's not kind of overwhelming. So

1:00:57

this again, our ideas kind of run product should

1:00:59

be trustable. It should be easy to

1:01:01

understand, like, what predictions

1:01:03

were made, why products were recommended. It

1:01:05

should be nicely visually

1:01:07

designed. We should have an option to customize

1:01:10

that. So you start to think, you know, what's what

1:01:12

are the kind of special traits

1:01:14

of this field? You start

1:01:17

to think more about what's important for

1:01:19

my clients. You can ask them. Sometimes

1:01:21

they tell you straightforward what they want. They quite

1:01:23

often don't tell you what they want. You

1:01:25

need to observe. You need to

1:01:27

listen. And all of these conversations,

1:01:30

everything, all of your experience, even like unsuccessful,

1:01:33

will form your vision. This is, I think, what

1:01:35

we did with our unsuccessful attempts. We

1:01:37

solved things that were not successful. But

1:01:39

then we also talked with our clients. We grow a network.

1:01:42

And this is how we came up with the idea of this constructor,

1:01:45

e-commerce widget, but we're selling right now very

1:01:47

effectively. And yeah, that's how

1:01:49

our journey was.

1:01:50

Being gradually making connections,

1:01:53

exploring what's happening, exploring

1:01:55

the trends, and consolidating this knowledge

1:01:58

into our product.

1:01:59

What does your revenue look like today?

1:02:02

That's information is confidential, so I will not

1:02:04

be disclosing exact number, but it's still

1:02:06

under 10 million in annual recurring

1:02:08

revenue. But we're hoping in

1:02:10

a couple of years, maybe in three years, to

1:02:13

surpass that threshold.

1:02:14

And how many clients? Because I know you said you looked

1:02:17

at the top 200, that's when you had the AI,

1:02:19

right? I was curious, after you looked at that,

1:02:22

I imagine you reached out to all of them.

1:02:23

Yes, unfortunately, not everyone

1:02:25

bought them.

1:02:26

Well, you don't necessarily have to do it with

1:02:28

everybody, right? But yeah, I was curious, how many of those

1:02:30

big clients you landed first before you decided you

1:02:32

had to kind of do a widget in order to reach

1:02:34

different demographics

1:02:35

of companies? So I think we

1:02:38

had around 30 clients, maybe

1:02:40

like 25, 30 clients, when we started

1:02:42

to think of how we can sell more.

1:02:44

I mean, it's obviously not a very big number, but

1:02:46

you need to think back, we were a startup, we

1:02:49

are a startup now. Right. And yeah, zero

1:02:51

in the beginning. So I don't think it's small

1:02:53

or anything. Yeah. And especially if you're selling to big

1:02:56

companies, that's much more difficult.

1:02:58

Well, it's to some extent more

1:03:00

difficult. And I think to some extent, it

1:03:03

has some benefits. Because honestly,

1:03:05

big companies, they will take a good

1:03:07

technology in shape of a product

1:03:09

that's undercooked,

1:03:11

because they have their technical capacities

1:03:13

to finish a product, polish that, maybe

1:03:16

change some user interface, or maybe

1:03:18

build additional business logic. They do have the resources,

1:03:21

they have designers, they have product

1:03:23

people to help them shape

1:03:25

your technology into the product and

1:03:27

consumer facing experience they want. In

1:03:29

that sense, yes, they can do that. Small companies

1:03:32

will not do that, right? They need to have something finished

1:03:34

ready, but they can just take and use.

1:03:36

I'm here with Megan Bennett. How's it going, Megan?

1:03:39

It is going great. How's my favorite

1:03:42

podcast host and the most handsome young man?

1:03:44

I'm doing fantastic. Thanks for stating

1:03:47

the obvious, Megan. But we're here to talk about you

1:03:49

and your company light years ahead.

1:03:51

I interviewed Megan on episode 177 of this very podcast. And

1:03:56

she helped all of our Patreon members on

1:03:58

group call three. So you can hear more. about Megan

1:04:00

and how she helped our Patreon members there as

1:04:02

well. So would you mind telling us what you

1:04:04

do and how you could help our listeners, Megan?

1:04:07

Yes. So my agency

1:04:09

is Light Years Ahead and we're boutique,

1:04:11

but we're a national PR firm. We're

1:04:14

women owned and we focus on emerging

1:04:16

brands, experts and services in the consumer

1:04:18

lifestyle space. We're based throughout the

1:04:21

US. We're in New York, Kansas City,

1:04:23

LA and Dallas. And we really specialize

1:04:25

in maximizing media exposure

1:04:27

for brands and experts, which can then create

1:04:30

more sales and brand awareness and

1:04:32

influence buying decisions. Our clients

1:04:34

range everything from small startups looking to

1:04:36

make a name for themselves to large brands

1:04:38

that are trying to become relevant again. My agency

1:04:41

Light Years Ahead, we target the very top editors,

1:04:43

writers and producers across all different

1:04:45

media outlets. And we've been doing this for over 20

1:04:48

years, which has earned us a very strong

1:04:50

reputation with the top media, with

1:04:52

outlets like Buzzfeed, Today Show,

1:04:54

Good Morning America, Refinery 29, PopSugar,

1:04:57

Forbes, and many more. We can

1:04:59

help you grow your brand into a household

1:05:02

name.

1:05:03

That sounds awesome. So if someone

1:05:05

might be interested in your service, what's the

1:05:07

best way for them to reach you? The

1:05:09

best way to reach out is to email me at megan,

1:05:11

M-E-G-A-N, at lightyearsahead.com.

1:05:14

That's megan at lightyearsahead.com. Or

1:05:16

you can check out our services and capabilities

1:05:19

at lightyearsahead.com, our website.

1:05:21

And I know you've helped a few of our past guests

1:05:23

as well with their PR and they do sing your

1:05:25

praises. So hopefully it can help some of our

1:05:27

listeners as well.

1:05:28

Absolutely. And we love working with

1:05:31

your listeners and entrepreneurs who are really

1:05:33

passionate about what they're doing. And

1:05:35

this is what we want to offer your listeners. The

1:05:37

first five listeners that schedule

1:05:39

a call with us to develop a PR

1:05:41

campaign will receive

1:05:43

$500 off their first month of services

1:05:45

with us. It's a great deal. Awesome. And

1:05:48

one more time, what's the best place for them to reach you to

1:05:50

pick you up on that offer?

1:05:52

You can reach me at megan at lightyearsahead.com

1:05:55

or check out our website at lightyearsahead.com

1:05:58

or you can go to our Instagram page.

1:05:59

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1:07:54

Well, I guess it's understanding, knowing who's

1:07:57

gonna be your clients first. You knew it had to be bigger

1:07:59

companies. because you need the data, right?

1:08:02

But

1:08:02

I'm sure you're probably underselling yourself right now

1:08:04

as far as how difficult it is to get your

1:08:06

foot in the door in the beginning. And then after you get in,

1:08:09

it's probably easier and it makes sense. And

1:08:11

like you said, they have these tech teams or whatever, but right

1:08:13

when you started, they didn't know who you were. I

1:08:15

guess you're in Estonia, right?

1:08:17

So it's like, who's this girl in Estonia emailing

1:08:20

me about AI, right?

1:08:22

Yes. Whenever we were connecting

1:08:24

with clients back then, first time we would say, you know,

1:08:26

Estonia, it's the company where Skype came from.

1:08:29

And everyone would say, oh, Skype.

1:08:30

Is that actually true or no? Yes, it's

1:08:33

true. So Skype is an Estonian company and

1:08:35

we still have a lot of Skype

1:08:37

first team players who are now also building where

1:08:40

maybe like second, third startup.

1:08:42

Skype co-founder is also like

1:08:44

the founder of Atomica VC firm,

1:08:46

which is based in UK. But I know

1:08:48

that all of their Skype X founders

1:08:51

are promoting entrepreneurship and

1:08:53

startup ecosystem. This is why Estonian startup

1:08:55

ecosystem is so powerful and so friendly.

1:08:58

Yes, we have amazing companies here. Do you know

1:09:00

CRM software, Pipedrive?

1:09:02

Yes. It's also Estonian company. Okay,

1:09:04

yeah, I use it. Both systems, which

1:09:07

like kind of Uber competitor is also Estonian company

1:09:09

initially. Transfer wise, initiative Estonian

1:09:11

companies. We have a lot of amazing companies that came from

1:09:13

Estonia.

1:09:14

Right, and specifically your own talent

1:09:16

Estonia, right? Which is the capital? Yes,

1:09:18

that's correct. Is that where all the startup people

1:09:21

are? Cause I'm just curious of case anyone who's listening,

1:09:23

maybe they're in Estonia, didn't even know that

1:09:25

or whatever, but I'm just curious. After you

1:09:27

listed off all these companies or most of the people

1:09:29

just in the capital there.

1:09:30

Well, we have also like many

1:09:32

people are based in here in the capital city

1:09:34

in Tallinn. We also have Tarteau, which is

1:09:36

very famous for its university.

1:09:39

It's amazing natural sciences university

1:09:41

and also like has offers degree in software

1:09:44

and maths.

1:09:45

We also have a big bio bank in

1:09:47

Tarteau, which is very famous in the

1:09:49

world, especially for longevity

1:09:51

and bioinformatics companies because it has an amazing

1:09:53

deposit of data.

1:09:55

And Tarteau university offers also like

1:09:57

different types of partnerships. But yeah, getting

1:09:59

back to the... that thing, but we didn't know who we are.

1:10:02

Again, people sell to people. So

1:10:05

you try to explain what the

1:10:07

technology does. You collect

1:10:10

proofs because you also, we had

1:10:12

research papers where we explained what we're doing

1:10:14

and how. So you have to build this trust.

1:10:16

You have to, it was not an easy thing,

1:10:19

right? And this is why you build relationships

1:10:21

with corporations for a long time because

1:10:23

they kind of evaluate. Is there any

1:10:25

reputational risks for me working with this company? Is

1:10:28

it worth my time or like

1:10:30

my team time on working with this company? So

1:10:33

I would say you have to be specific

1:10:35

about what you're offering. You have to

1:10:37

be knowledgeable of your product. You have to be knowledgeable

1:10:39

of your technology. But yeah,

1:10:41

as soon as you land a couple of big clients,

1:10:44

you start to have a reputational, they work with some

1:10:46

other big companies. Maybe I should kind of

1:10:48

be less skeptical about having a call with a super

1:10:50

small startup from Estonia, not even

1:10:53

all people know where Estonia is.

1:10:54

Right. I guess if anyone's wondering, kind

1:10:56

of by Sweden and Finland, they can think like that

1:10:59

or right by Russia. Yeah. It's a smaller

1:11:01

country, but maybe not a lot of people have heard about,

1:11:03

but back to even landing that first client,

1:11:06

right? Did you just keep emailing them out of

1:11:08

these top 200 companies that you're talking about and

1:11:10

maybe it took a year or two for you just giving

1:11:12

them updates every once in a while? I'm just curious if anyone's

1:11:15

listening now and they, they're

1:11:16

trying to sell to a big company, what

1:11:18

it took for you and what thoughts you might have

1:11:20

for them.

1:11:21

Yeah, I would say that email marketing

1:11:23

is still a thing. Social media marketing

1:11:25

when you're reaching out to people on LinkedIn is

1:11:28

also important, telling, like sharing

1:11:30

more about what you are doing, whether it is LinkedIn

1:11:33

blog or company blog, just, you know, whenever people

1:11:35

who are interested, because if people are interested,

1:11:38

if they're looking for something similar

1:11:40

to what you're doing, it's more

1:11:42

likely that they will notice

1:11:45

you. It's more likely that they will be

1:11:47

open to have a conversation with you.

1:11:48

So that's why email marketing is important. But

1:11:51

let's say you send, you know, maybe 10 emails

1:11:53

and a person already starts to think that you're

1:11:55

like stocking them, right? They finally

1:11:57

give you a chance and they think, okay, let's maybe connect.

1:11:59

a call, but they for sure will look

1:12:02

you up on our internet, right?

1:12:04

They type in your name and your company.

1:12:06

If your website is kind of doesn't even

1:12:08

explain what you're doing or you know, at least kind

1:12:10

of high level, if they don't see any

1:12:12

kind of social media where you maybe explain

1:12:15

more about your product or technology, however you're doing

1:12:17

that, they feel very skeptical,

1:12:19

right? So, I think like having a good website,

1:12:22

it doesn't have to be kind of a masterpiece

1:12:24

of arts, right?

1:12:25

But at least explaining your product,

1:12:28

explaining your technology, what it does,

1:12:30

you know, how it works is super helpful.

1:12:32

We were sending our clients, we were building

1:12:34

up with credibility over time, we realized that

1:12:37

we will publish our research work,

1:12:39

right? You quite often don't publish

1:12:41

your research work, but we thought

1:12:43

we want to explain how technology works. People

1:12:46

are still not understanding fully AI, that's normal,

1:12:48

you know, you can't be kind of expert in everything that's

1:12:50

happening. If you would expect with everyone that

1:12:52

understands AI or whatever

1:12:55

specific field you're working, it's like, do

1:12:57

you understand, you know, some of the very advanced chemistry,

1:12:59

but maybe it was developed in the last two, three

1:13:01

years, right? Likely not. So,

1:13:04

you always have to think if you're

1:13:06

in the shoes of another person, maybe a client with whom

1:13:08

you try to land, until people can

1:13:11

understand, you know, at least how it works,

1:13:13

high level, what you're trying to do, they will not

1:13:15

be convinced, right? So, we were continuing

1:13:18

also like sending emails or offering

1:13:20

to have an update and maybe sending a quick

1:13:22

blurb of what we have done

1:13:24

and how we have done that.

1:13:26

Even if I don't

1:13:27

respond to you, you know, maybe you send

1:13:29

them another email in a year, I think, okay, I

1:13:31

maybe remember these guys, it looks like they made

1:13:33

some progress. They already made it

1:13:35

through the year, which is great for a startup, some startups

1:13:38

die, you know, in less than a year, like in less than two

1:13:40

years. So, you need to kind of think

1:13:42

of how you can build credibility and

1:13:44

you build this credibility for explaining how your product works,

1:13:47

demonstrating that idea is legit,

1:13:49

that technology is legit or like product

1:13:51

is helping, you know, maybe collecting some metrics

1:13:53

and reaching out to your clients from time to time. Of course,

1:13:56

not abusing them,

1:13:57

but building your social media, building your

1:13:59

website is an important component of marketing.

1:14:03

I think for many people who can do marketing

1:14:05

in a more creative way, I'd like to meet them, but

1:14:07

this is what we were doing back then.

1:14:09

That's smart because at first you're probably just sending

1:14:11

the emails without like a great website

1:14:13

or anything like that. And

1:14:14

again, if anyone wants to check yours out

1:14:16

is haut.ai. But

1:14:19

it's very simple. It's like basically a one pager where

1:14:21

you have all the information there. And let's

1:14:23

say you didn't have this beforehand and

1:14:25

you didn't have on LinkedIn activities

1:14:28

where you're writing or doing interviews or

1:14:30

something like that. Like you said, you have to put yourself

1:14:33

in their shoes as a client. And like kind we

1:14:35

said earlier, finding your first employee or

1:14:37

whatever you got to think of where would those people

1:14:39

be? So especially today, people, if

1:14:41

they're not sure and eventually they want to get on a call with

1:14:43

you, they're going to do some Googling. So just Google

1:14:45

yourself or Google your company, see what's

1:14:48

coming up. If nothing's coming up, then you're thinking,

1:14:50

okay, maybe I need to improve

1:14:52

my SEO or at least get your social

1:14:54

media kind of thing started. It's not like something you have

1:14:56

to update all the time. But I think the more touches where

1:14:58

they can see that you're a legit company

1:15:00

or feel more confident, they might

1:15:03

not even tell you that's what they finally took them

1:15:05

to reply to the email. But in their

1:15:07

head, that's probably what it did before

1:15:09

if you didn't have all those social accounts or proof

1:15:11

of, okay, this person knows what they're talking about. You

1:15:14

don't always know why they got back to you, but guaranteed

1:15:16

that's one of the reasons.

1:15:18

Can't agree more. You need to have your social

1:15:21

presence. So eventually, like

1:15:23

you said, you did the WordPress plugin, which kind of

1:15:25

really catapulted things. And then I guess

1:15:27

what's gone on over the past year since

1:15:30

kind of done

1:15:30

that. So we started to think we

1:15:32

made some, you know, product changes to make

1:15:34

our product easy to use.

1:15:37

As we were growing, a number of clients realized

1:15:39

that some of their use cases, some

1:15:41

of the new use cases were developed by our

1:15:43

clients. You know, we were complaining that some

1:15:46

things are kind of buggy

1:15:47

or, you know, it's not very convenient to use some

1:15:49

particular features. It turned out that

1:15:52

customers have developed new features,

1:15:54

new kind of use cases, without us

1:15:56

understanding that.

1:15:58

So making sure, you know, that we...

1:15:59

now kind of streamline our user experience,

1:16:02

but this product is easy to use and it's evolving.

1:16:04

It's something that we're working on at the moment.

1:16:07

Then we also like adding additional

1:16:10

capabilities to help our

1:16:12

clients extract more value from our

1:16:14

software, including

1:16:16

automated inside generation or

1:16:19

suggestions for their product inventory. So

1:16:21

whatever makes their life easier. In fact,

1:16:23

what we're doing right now, we are continuing

1:16:25

building up the value on the initial

1:16:28

offering of computer vision-based

1:16:30

skin analysis. So we're evolving

1:16:33

constantly the feature set or

1:16:35

what you can do with our software. That's

1:16:38

what is keeping us busy. So we

1:16:40

want to understand how we can serve better to our clients.

1:16:43

We do it through collecting their feedback. We do

1:16:45

it through analyzing the product

1:16:47

data and also constantly keeping

1:16:49

an eye on what's happening in the industry, what

1:16:52

consumers want, conducting interviews

1:16:54

with real people, especially consumers

1:16:57

who are not tech-savvy. It's so

1:17:00

seductive to build a

1:17:02

bias unconsciously in your product, especially

1:17:04

if you kind of have a lot of technical team

1:17:07

members. I will give you an example. We

1:17:09

recently conducted a research

1:17:11

with users. So we recruited some users

1:17:14

through special websites where you can test

1:17:16

your products. And again, if you think that you need

1:17:19

to test something, there is so much

1:17:21

software. You can do it. You don't have to have a big

1:17:23

budget to start testing if your product

1:17:25

makes sense. For example, in this particular case,

1:17:27

I was using usability hub.

1:17:29

And it may be costing me $100 to run this test.

1:17:33

But getting back to the experiment,

1:17:35

we recruited some respondents and we asked

1:17:37

them what we want to have in the software. So

1:17:41

they told that they want to have skin analysis.

1:17:43

They want to have recommendations. And that

1:17:45

they also want to have an

1:17:47

educational component. So these three things.

1:17:50

So then we also invited our friends to participate

1:17:52

and give their feedback.

1:17:53

And results were different. So they

1:17:55

said that we want face scan, that we want to have recommendations,

1:17:58

and then that we also want to.

1:17:59

track skin dynamics, right?

1:18:02

So then we started to analyze this data

1:18:04

and understand, you know, why we have different

1:18:07

results from our network and like from our

1:18:09

initial users who were like randomly recruited.

1:18:12

So the thing is that tracking over time is technically

1:18:15

very advanced.

1:18:16

So

1:18:17

our assumption is that because we recruited from, you know,

1:18:19

our kind of network and a lot of developers or

1:18:21

like AI people, computer vision engineers,

1:18:24

we just think that we started to prioritize this feature

1:18:26

because it's technically very advanced

1:18:28

technique. You know, it takes a lot to

1:18:30

do that. Making sure the team

1:18:32

is growing, as a product is evolving, making

1:18:35

sure you know that use cases and

1:18:37

usage is relevant to the consumer,

1:18:40

not only kind of to the development team, but

1:18:42

I think that's very important. And this is kind of what we're

1:18:44

now reiterating in our product. And

1:18:47

this is how we want to grow our customer

1:18:49

base through serving better to their needs rather

1:18:51

than only doing something which is very technically

1:18:54

advanced just for the sake of that.

1:18:56

As you're in it right now, you know, I think that we

1:18:58

all learn in business, we try to reiterate or try

1:19:00

to make things better as we learn and get more information.

1:19:03

And obviously you're kind of doing that with your company

1:19:05

and it

1:19:06

seems like that's what your company is all about. So

1:19:08

just want to say thank you for coming on the show and spending

1:19:11

the time of helping me understand AI

1:19:13

a little bit and

1:19:14

telling us a little bit about your company and how everything's

1:19:17

going. I guess kind of in closing, I don't know if you

1:19:19

have any last

1:19:20

words of wisdom for all the entrepreneurs

1:19:22

out there or you can help anybody else,

1:19:24

what's kind of the best way for them to reach you?

1:19:27

So first of all, I want to say Austin, thank

1:19:29

you so much for inviting me. I was really excited

1:19:31

to share our journey and I

1:19:33

really hope that someone will listen to that

1:19:35

and think, okay, maybe you have this crazy idea, I never

1:19:38

thought it can come to life, but you

1:19:40

should try that. The worst thing that will happen,

1:19:43

it will not fly and maybe you can try some other

1:19:45

things. It's a good thing you can find. I think

1:19:47

that you will be passionate and will motivate you

1:19:49

and like improve a lot of your areas of your life. So

1:19:52

believe in yourself, writing is the right

1:19:54

people, it's very important and I think

1:19:56

it can lead to great success if you

1:19:59

find this special...

1:19:59

and find the right people to join your

1:20:02

team.

1:20:02

And if you'd like to get in touch with

1:20:04

me, just connect with me on LinkedIn. I'm really

1:20:06

happy to help whatever I can

1:20:08

do.

1:20:09

So anyone who's like listening now, as

1:20:11

far as maybe they could be a client

1:20:13

for yours, are they only kind of skin companies?

1:20:16

Who should reach out if you can actually see

1:20:18

if they can implement your AI here? Who are

1:20:20

the best people to reach out to you on your website?

1:20:23

I would say that we're a beauty intelligence company.

1:20:25

So we're not only limited to skin, we

1:20:27

have software for hair analysis, for

1:20:29

body analysis. We now entered oral

1:20:32

care and animal erosion. So

1:20:34

if you are in the beauty field, in aesthetics,

1:20:37

in longevity, and skin science, we

1:20:39

will highly appreciate if you reach out.

1:20:42

We'd like to connect with you, we'd like to talk with you

1:20:44

what's happening in the field and how we can help you.

1:20:46

Should they just reach out on hot.ai?

1:20:50

Yes, we have a contact form and it's the best way

1:20:52

to reach out to us. All right, well, thank you again

1:20:54

for coming on and sharing your story. Thank you

1:20:56

so much, Austin, for having me.

1:20:58

On the line, on the line. But

1:21:02

it's bad when you do it to your wife, though, because

1:21:04

then you have to crash on the couch. See,

1:21:07

I have to sleep on the couch every night too, man. See, we're

1:21:09

the same. Was

1:21:11

that helpful at all, Gary? Say no. Worst

1:21:15

experience of my life, one star review.

1:21:18

Yeah, thank you. I'm used to this. Wish I could

1:21:20

leave no stars. Oh yeah, hell yeah. No,

1:21:22

thanks guys. It was a really great experience.

1:21:26

Feel like there's a lot to reflect on. So

1:21:28

yeah, thank you.

1:21:30

And I can connect you with somebody too. Okay.

1:21:33

I have connections on that so I can help you get it.

1:21:35

Custom made, dirt cheap. I'll

1:21:37

share that with you. Look at that Patreon membership

1:21:40

already paying off.

1:21:42

No, look at that.

1:21:45

Thanks for coming, member. Oh, well,

1:21:47

I gotta thank my business partner. She's

1:21:49

signing me up because I've been talking about you. Well,

1:21:51

awesome business partner. I'm gonna have to use that as a plug

1:21:53

to tell people to do the same thing. Yeah,

1:21:55

yeah, it's really cool. But anyway, yeah, thanks for

1:21:58

setting this up. Get kind of the...

1:21:59

VIP treatment, if you like. Well,

1:22:03

I thought it was a lot more intimate than I thought it was going to be.

1:22:05

Like anyone who's thinking about doing it, you'll

1:22:07

be able to get involved, ask a question,

1:22:10

you know, which I don't have a lot of experience

1:22:12

with other group calls, but I would assume

1:22:14

that there's kind of a hierarchy to it. But this one,

1:22:17

if you're in there, you're going to get your shot to ask

1:22:19

an expert a question. So I

1:22:21

tried to compare my group calls. I started joining

1:22:24

random entrepreneur groups and just joining

1:22:26

their group calls and try to see what they're like. Dude,

1:22:29

the one you were on

1:22:29

and all of them have kind of gone that way. They're

1:22:32

all 10 X better than any other group I've been

1:22:34

in because become

1:22:36

a member to find out. So

1:22:39

with Patreon, I heard it many times

1:22:42

because you have that many episodes of signup,

1:22:44

so that's always in the back of mind, but then I checked

1:22:47

it out a few times and I was like, do I

1:22:49

really want to do this? So I'll push it off a

1:22:51

little bit. And then you posted your

1:22:54

goal achievement of 69

1:22:56

Patreon members. And I was like, you know what, what

1:22:58

better time than now? Originally,

1:23:00

I was going to go for the lower one than $9 a month. But

1:23:03

one, I want to have the conversation with you. But

1:23:06

two, I always find that anytime I cheap

1:23:08

out, I always find that I want to

1:23:10

return it and upgrade to what I really,

1:23:12

really wanted. So that's

1:23:14

why I'm paying the higher one, if

1:23:17

that makes sense. But it was just constantly

1:23:19

pushing it off, pushing it off. And then I

1:23:22

would just say, fuck it. I already listened to

1:23:24

all of them, so why not?