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Interview: Afzal Hussain Mohammed Nakheeb
Episode 93 • 17th March 2026 • AgTech Digest • AgTech Media Group
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In this bonus episode of AgTech Digest, Sepehr Achard sits down with Afzal Hussain Mohammed Nakheeb , the visionary founder and head of strategy at T57. Together, they embark on a thoughtful journey through the complex world of food security, agtech innovation, and the hidden challenges faced by farmers around the globe.

Discover why, despite producing more food than required, hundreds of millions still go hungry—and how T57 is tackling critical issues like food waste, market access for small producers, and the disruptive role of technology. Afzal Hussain shares eye-opening anecdotes from international projects, dives deep into the reality of trade finance complexities, and reveals how AI can empower everyone from the smallest farmer to the largest food manufacturer.

Curious about how new models and digital platforms can dismantle traditional middlemen, or what the future of AI-driven agriculture might hold by 2035? This episode is packed with real-world examples, bold ideas, and a compelling vision for a smarter, more equitable food ecosystem. Don’t miss the chance to hear firsthand how innovation is reshaping the global food landscape, one problem at a time.

https://www.linkedin.com/in/afzalhussain/

http://t57.ai/

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Transcripts

Sepehr Achard:

All right.

2

:

Thank you Zel, uh, for this interview.

3

:

To kick off conversation, can you

present yourself, your background

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and how you got involved with T 57?

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Afzal Hussain Mohammed Nakheeb:

Thank you for the time you took,

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and, uh, very good morning.

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It's a long story.

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Okay.

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I'll try to, make it short as, possible.

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my name is Abdal Hussein.

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I'm a founder, chairman, and

head of strategy of T 57.

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T 57 is a first platform, uh, which

is a, um, very unique platform

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where, uh, agriculture trade

and food security is addressed.

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how we get into this was, I'm

into social, economic development.

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Last, uh, more than a decade, I

did some of the projects in Saudi

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Arabia, where with a little bit

effort, we were able to achieve more

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than 50,000 SMEs and jobs globally.

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After that experience, it motivated

us that we can do something good.

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So we were in this journey, that is a

time we met, um, the chairman of, IOFS,

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Islamic Organization of Food Security.

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They were going through

some kind of challenges.

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They had hired four consulting firms,

consortium of consulting firms, one

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from Europe, one from, Far East, one

from Dubai and some other countries.

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they were trying to set

up funds and those things.

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They spent two years develop a

strategy and present it to their

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board, and it was rejected.

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And that is a time the chairman had

viewed our, uh, what we were driving that

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time, social dev, economic development.

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he says, can you guys help us?

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our team is based in 30 different

countries, different expertise

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from, from economy experts to,

agriculture trade, you name it.

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Okay.

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Finance, banking experts.

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So we, we took it as a challenge.

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We worked with this organization

for around a year and a half.

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Okay.

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Now it's much more than that.

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So when, when we saw the.

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Uh, challenges.

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Okay.

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That organization was more of a

knowledge based organization and

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not a impact based organization.

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And we are coming from a

background of impact, creating

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jobs, SMEs, and uh, so on.

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When we looked at, initially when we met

them, we read their 10 year strategy.

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Uh, honestly, I could

not understand anything.

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I went to my colleagues,

my friends, I said, can you

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read it and explain it to me?

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Because I could not understand, because

what I was looking is organization

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who has a, a mandate for 57 countries

should be talking about some impact.

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There was no impact anywhere.

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So my colleagues came back to me and said.

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a knowledge based organization.

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It's not an impact based organization.

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So I went back to, the DG director

general, uh, lan by and, uh, the

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chairman of the organization, uh,

who's a Qatari national Mass Maori.

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I said, uh, your excellencies.

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Can we, uh, rework on the

strategy, for your organization?

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So the DG said, are you joking?

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You know, we are an international

organization to change any strategy.

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We need to have a resolution passed, okay?

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In a, we need to bring

these countries, okay?

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In a general assembly,

there'll be voting happen.

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We need to present to them.

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They have to approve.

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I says.

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I don't know what it takes,

but this is not where we, we

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are reaching, it's like a boat.

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You have holes, you're trying

to cover each hole again.

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You cover one, you will

open some something else.

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You're not addressing anything.

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See, we did, we worked with them for

almost one and a half, two years.

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It was pro bono.

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We didn't charge them a dollar because

we want to be honest and vocal about it.

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We, we, we never took money.

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Okay.

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Because we want to tell what is the truth.

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So in that meeting, I challenge

both of them that where

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you are heading is nowhere.

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You're not reaching anywhere.

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So chairman said, do one thing.

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You guys develop a

strategy, come back to us.

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We'll see.

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So we developed a

complete ecosystem, okay?

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And present, presented to them

when they saw the presentation.

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Okay?

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The whole ecosystem,

they said it's too big.

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It's beyond our capacity.

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Then I explained them.

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If you look closely, these

are like a Lego blocks.

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You do something here, you do

something in Pakistan, something

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in Africa, something in Malaysia.

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Still, it'll be effective,

but you need to do something.

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It was designed in a way that

small efforts from years,

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small efforts from there.

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It had a major impact,

but it was all impact.

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How to create jobs, how to eliminate

hunger, how to you know, support

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your SMEs, how to, work on a

complete, connected ecosystem

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globally for food security.

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So after a few meetings,

they agreed, okay, in, uh,

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September or October of 2023.

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There was a ninth OIC

ministerial conference.

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We presented the, strategy and,

all the countries accepted.

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And today, they're implementing this

in across OIC countries, that strategy.

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Okay.

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But when we were.

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Looking at the strategy and you know,

the team was, with us who are there

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are all very successful individuals

from across different, uh, sectors

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and industries and continents.

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We said that apart from governments,

what can we do ourself, okay.

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Where we can bring, because what we

know is we can really bring a change.

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Okay.

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We should work.

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We can, we cannot give a strategy and,

uh, relay that government will do the

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job, or organizations will do the job.

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What can we do ourself?

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So then we deep try to

understand those challenges.

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There were more than

a thousand challenges.

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these, uh, developing

countries are facing, okay?

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One of them, it could be, food wastage

around 33% of the food post harvest.

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it doesn't reach the plate.

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It's around trillion dollars

globally in OIC countries.

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It's around $350 billion yearly.

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We try to understand deep

what are the problems inside.

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It was basics, simple challenges Each

challenge we'll be discussing, uh, some

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of them, later on, on the discussion.

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we wanted to deep down what are the major

three problems so that we address ourself.

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we realized that there

are three major problems.

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One of them is access to market.

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The second is access to technology.

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The third is access to finance.

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Then we said that, how can we address

this three with our own capacity?

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We said that let's build a platform.

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Okay.

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That was the name we started with T 57.

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So that was the birth of T 57 started

how we could use, A technology

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to address these challenges.

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T 57.

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we try to address, your access to

market so that any buyer sitting from

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anywhere in the world in a smallest

village, could buy from anywhere across

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the globe, could be from the farmer or

manufactured or processed, anything.

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It's, we are very closely focused on food.

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We are not looking any other categories

because food requires lot of work

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required because what we understand

last three years working in this

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industry is there is lot of challenges.

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The challenges are massive.

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I'll give you some of

highlights to understand.

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See today, okay, the world needs

around 8 billion tons of food.

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We produce 10 billion tons.

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That's a good news, right, that

it's not the challenge that

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we don't know how to produce.

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We are producing, but still 733

million people sleep hungry every day.

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22 of OIC countries are in acute hunger.

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If I tell you the OIC numbers, okay.

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In OIC, okay?

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we produce, contribution to

the global food production.

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We produce 33% of the global food.

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The population is only 22%.

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Still we are excess, right?

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Still 22 of our, uh, OIC

countries are in acute hunger.

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Imagine it doesn't match up.

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Okay.

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The, the numbers doesn't match up.

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Okay.

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It's not the problem of we

don't know how to, produce.

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It's not the problem that there

is no technology in this industry.

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It's not the problem of, you know, we

don't know how to move the products.

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it's there, but the

challenge is we are in silos.

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Each industry work independently.

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They don't communicate.

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This is one of the oldest industries

since the humanity begins food.

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Okay.

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But still there are a

lot of inefficiencies.

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So this is what we are trying

to address, uh, with T 57.

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Sepehr Achard: Wow.

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Fascinating.

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And two questions.

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First one on some of the challenges

that you've talked about.

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having had conversations with

a lot of startups and also a

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lot of established companies.

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A lot of them mentioned also a

lack of infrastructure in some of

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these countries, to install some

of these more precise equipments

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so that they can track and monitor.

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my question on that is

what do you think of that?

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Is there any truth to it or is

there more of a question of adapting

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the technology to the market?

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And my second question is more

regarding this food waste aspect, a

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lot of it is also related to some of

the natural, fats about these produce.

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Some of them just perish very quickly.

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Uh, so how do you at T

57 handle these products?

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It's a two question.

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Afzal Hussain Mohammed Nakheeb: the

first question was, uh, about, lack of

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infrastructure, uh, what we believe is,

uh, much more than lack of infrastructure

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is there is a kind of mafia.

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In between the middlemen

who controls it, okay?

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It's not about the lack

of infrastructure today.

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agriculture is one industry,

food is one industry.

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Even governments invest.

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There's lot of international

organization invest.

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when you see, globally, it's

a $9 trillion, uh, business.

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Okay?

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It's a $9 trillion.

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Uh, if you see, uh, G 20 countries,

okay, invest around 7% of the budget.

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for food security, One of the highest

today in last decade, money, which

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is invest is on food security.

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Uh, more than infrastructure is the

system, which is imagine yourself

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as a farmer, you produce something.

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When you produce something, you

want to take it to the market, okay?

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Market is built in a way that

any newcomer will not survive.

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Otherwise that's a reason.

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If you see most of, uh, major producing

countries, let it be in Asia or Africa

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or any other countries, or even in,

um, central Asia, Asian countries.

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The big players will not have problems.

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The big, uh, corporates, they

know how to, they somewhere

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control the entire industry.

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They will not waste it.

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They will produce it, process it.

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They, they make their own brands

and sell it, but others, the smaller

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players, when they produce something,

when they want to go to the market

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and sell their product, okay?

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It's not about the

infrastructure, the market.

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Doesn't accept it.

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It's, it's, it's a kind of, monopoly.

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They have, they will

not allow any new entry.

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So that's the reason in many, in our case

studies when we went to, many countries

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try to understand, okay, when, the

most farmers today, when they produce

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something, they're selling the middleman,

who comes buy it for very cheap.

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Sepehr Achard: Yeah.

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Afzal Hussain Mohammed Nakheeb: And

he takes to the market and he, he is

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already making two, 300% there and

selling it to them in the market.

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That is how the system works

if you are part of the system.

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It works beautifully.

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Okay.

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This is one when, if you see, if you

go back around 30 years ago or 20

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years ago, B2C was the same challenges.

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Okay.

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online, uh, when, uh, Amazon came

in, enter in many different markets.

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Okay.

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I even remember, uh, when Amazon

entered the market in India.

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There, there was a big,

protest against it.

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They said, they will take our jobs,

they will do this, this, this.

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But today the same old, mom and

pop stores are benefiting from,

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uh, the quick commerce and retail.

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They're, they're profits

have maybe tripled it, okay.

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Earlier because of the infrastructure.

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But when you talk about in

food B2B, there's no strong

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platform who focuses on the food.

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Okay?

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Even there are.

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Hundreds of platforms.

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We did deep down studied

150 different platforms.

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Let it be Amazon business, Alibaba

Trade Key, India Mar Top five and 150.

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The challenge is everyone

is doing everything.

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There is no focus, very focus

on food to improviser in food.

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The access to the market is

one of the major challenge.

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We could address this.

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It's not the infrastructure.

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It's like, I always give this example,

it's like, you have a large piece of land.

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You have five children grown up and

there is a, a river flowing next

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to your home and you have a seed.

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You know how to grow,

but still you're hungry.

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This is the state in most of the

countries we are in, we know how to grow.

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Okay, we have done this from

generations, but still we are hungry.

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So this could be addressed through,

that's the reason when T 57 was launched.

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How can we address these kind

of challenges using technology?

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And another major challenge

is the use of uh, right data.

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Okay?

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Today, most of the people who are

in the food industry, majority

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of them are less educated.

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This is one of the challenge, whatever.

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today the research happening,

advanced research happening in many

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universities, research centers,

it is not going to the end user.

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Okay?

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They are not acute or they don't

have tools that they could use.

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This, the data coming from satellite

for weathers are used by large corpor.

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The smaller ones cannot do this.

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The ai, also are implemented

in the large corporations.

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This is where you need a

platform where they can use it.

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A farmer, imagine, okay, I'll

give you another example.

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Imagine a farmer producing something.

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Let it be strawberries

or tomatoes or something.

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Okay?

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In his farm there is some

insect came or something came.

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Where does he has to go

to get an expert advice?

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Do you think if he goes to his,

uh, minister of Agriculture or any

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government bodies, they will help him.

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They'll welcome him with a open arm,

or there is anywhere there are experts

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and he can afford to bring them to, uh,

address those simple issues, basic issues.

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You'll be surprised

that, there's no answers.

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So where does he end up?

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He go to a store who's selling pesticides.

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And do you think agronomist sitting there

or a scientist sitting there to advise

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you or an expert sitting there to say

that this disease, this insect, this

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problem you need to address with this?

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There is a salesman sitting there

imagine, for a one hectare, um, um.

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Uh, land.

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You need to use five liters of,

uh, you need to dilute with,

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uh, in the water and spread it.

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That guy will sell you 500

liters because he's a salesman.

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He wants to make more profit.

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That's the reason.

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One of the countries we

were, we did this experiment.

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We took samples, 60 different samples

from that country, send it to four

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different countries to understand, uh,

what is happening with, uh, pesticides.

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Sepehr Achard: Yeah.

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Afzal Hussain Mohammed Nakheeb: Okay.

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One of the reason why we did study was in

that city, it was the highest number of CA

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cancer patients in in the country itself.

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When we took the agriculture sample, sent

it to four different countries, okay?

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Surprisingly result came was high

pesticide residues in basic commodities.

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It's not that farmers wants to kill the

humans, it's about the knowledge gap.

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Yeah, they don't know how to address this.

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I'll give you one of the examples.

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In one of the projects I was working

on, we had an expert for, medicine

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aromatic plants, uh, from Egypt.

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this expert, when he came, he met the

farmers and he was telling that, okay,

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if you have this problem, take a.

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Uh, leaves.

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Crush it in the water, throw

it and your insects will go.

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The, the, the farmers were saying,

these trees we have from decades,

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we don't know how to use it.

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So basic information, how we can

give it to those farmers, okay.

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It start from the farm, okay?

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In our platform, we try to

address from the farm to the folk,

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everything which comes there.

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Let it be logistics, let it be farming,

let it be finance, Including finance

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is one of the major challenges today.

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When you talk about agri banks

are scared to fund you, that's

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a reason you need a platform.

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Somewhere they could even do a,

crop collateral, uh, financing.

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Okay.

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There is no platform

today wants to do this.

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That's a reason.

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This was a gap.

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We came into it

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Sepehr Achard: very interesting

and yeah, I actually agree.

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And yesterday in our panel you

also cited that example that

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was quite starking about that.

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Conversation you had in

South Africa with Yeah.

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Researchers and only 1% or less than 1% of

farmers in Cape Town read their research.

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yeah, and it's something that

we're seeing across the board.

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And that's also something that's

quite interesting is we see the

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same challenges everywhere in

the world, not just in Africa or

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Asia, but in Europe it's the same.

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And North America it's the same.

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and so.

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My next question is in terms of your

presence right now and the types of

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projects that you are doing right now.

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So where are the markets where you're

most active, uh, at the moment, and

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what type of crops are they doing?

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What are some of the trends that you're

seeing in this, in, uh, in this area?

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Afzal Hussain Mohammed Nakheeb:

See, today we are focusing, Uh,

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it says, uh, global SaaS model.

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Anybody could join our

platform from across the world.

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Okay.

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There's no restriction, but

initial focus, uh, where we have

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our teams, uh, for onboarding

and supporting is five countries,

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which is UA is our headquarters.

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Then we have Saudi Arabia,

we have Malaysia, we have

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Turkey, and we have India.

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One of the reason we choose these

five countries is we wanted to test

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in a very different geographies.

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Okay.

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With different challenges and

see how the people will adopt.

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What are the challenges?

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Because, uh, when we

talk about food, okay.

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Even on a AI level, okay, uh,

we cannot have a large, models.

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Okay.

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Every region has its own challenges.

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Every, as I mentioned in my

talk yesterday also, that uh,

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weather conditions are different.

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Soils are different, foods are different.

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consumer behaviors are

completely different.

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Food tastes are different.

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Okay?

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Problems are different.

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Okay?

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How do we address it?

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That's the reason we wanted to understand.

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We took different regions.

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To understand their challenges and

try to address those challenges.

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At the moment, we're not

focusing specific crops.

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Okay.

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But if, imagine if we are

focusing in Malaysia, okay?

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There are certain, like for example, oil

is, palm oil and those things is one and.

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fruits are fruits.

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Those are there in militia

when we talk about India, our

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different commodities altogether.

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Okay.

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Saudi Arabia, uh, is into dates.

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Okay.

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And UA is more processed,

packed, food there.

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when we talk about Turkey,

it's uh, it's a hub for Central

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Asia wheat and other products.

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:

And we are not only focusing

on crops, it's a platform where

396

:

it's a, uh, entire food is there.

397

:

F process packed.

398

:

Okay.

399

:

Brands, um, all of them, we cover it.

400

:

So across, regions have different, we

are focusing on different, commodities.

401

:

we partnered with, in some countries,

even agri universities in research

402

:

centers also to, Use their data and, uh,

we partnered them that how we could help

403

:

from their research and make it minimize

it so that the end user could use.

404

:

I remember in one of my, keynote,

uh, in Saudi Arabia for, uh,

405

:

14th, trade finance, summit.

406

:

I got a very interesting question.

407

:

the audience, they asked me that, you

mentioned that how today you, uh, as I

408

:

mentioned today, uh, that, very less,

uh, educated people are handling food.

409

:

They asked me, how did, how

do you address this in T 57?

410

:

My answer was very simple.

411

:

I.

412

:

simple is straight financing.

413

:

If you today, ask any layman what is

straight financing, can he address?

414

:

They said, no, it's quite complicated.

415

:

Finance and trade finance.

416

:

I said, do one thing.

417

:

Open your charge, GPT or any LLM

ask this question that, okay, I'm

418

:

a six years old or a 10-year-old.

419

:

Explain me what is state finance

will charge GPT able to answer.

420

:

They said yes.

421

:

I said we are doing the same thing.

422

:

We take the data from research centers,

agri universities with the governments,

423

:

with large organizations, and we

buy the data and we train our L lms.

424

:

We have our own, LLM.

425

:

where.

426

:

Our subscribers could access

to that focus on only food.

427

:

And when we talk about food,

it'll start from the farm.

428

:

A farmer can take a picture of his

crop and post it, and he might get an

429

:

answer to it, that there is a challenge.

430

:

At the same time, he

can even get an expert.

431

:

could be sitting from other part of,

in his own town or other part of the

432

:

world, they can come into a, a video

call and say that it's a simple thing.

433

:

Why don't you start

out with this chemical?

434

:

This is, how you can have alternative.

435

:

Don't use pesticide.

436

:

Use this plant, a leave to

be crushed in the water, as I

437

:

mentioned, name or something.

438

:

It'll be addressed through simple issues.

439

:

Okay, so we use data, we train it.

440

:

Once a farmer imagine comes in,

in our platform we have rigorous

441

:

KYB and KYC On onboarding, I

say it's like a club membership.

442

:

It's not a platform that

anybody could, subscribe.

443

:

There is a proper KYB when a farmer comes.

444

:

Okay.

445

:

When he enter that, uh, he is, um,

he's agronomist, he's a scientist.

446

:

The kind of language the, uh, AI will

speak to him is a different than a guy

447

:

who has not even, passed his fifth grade.

448

:

Okay.

449

:

It'll be, completely tailored to them how

their, their understanding, so, the AI

450

:

will interact with them on their capacity.

451

:

Sepehr Achard: Very interesting.

452

:

Now, coming back on trade financing, a

topic that we've covered a lot, especially

453

:

in America and Europe, and I'm curious

to hear your opinion on that, is the

454

:

lack of diversity in financing products

available both for companies and farmers?

455

:

Usually it's always either VCs, private

equity banks, whereas on other markets.

456

:

You have more crowdfunding, you have

more derivative derivative products,

457

:

you have more collateral financing,

and these things are available.

458

:

Do you think that that's

also something that's needed?

459

:

Afzal Hussain Mohammed Nakheeb: It is.

460

:

See, um, when we did, uh, deep

understanding about trade, finance and

461

:

food, okay, it's, uh, quite challenging.

462

:

Okay.

463

:

trade financing requires

a complete reform.

464

:

And, uh, especially, um, global south.

465

:

Okay.

466

:

The challenges start with, the banks

not having clear, data understanding.

467

:

and, uh, it's a complete

document heavy, okay?

468

:

Some of our research open eyes, like

in many cases, and most of the cases,

469

:

food reaches before the documents.

470

:

Okay.

471

:

Document is so heavy in this world

where, blockchain and AI is advancing.

472

:

Still.

473

:

Our banks are working

into heavy documentation.

474

:

one of the challenge we understand

was in when, uh, blockchain started

475

:

in 2020, every bank invested billions

of dollars by:

476

:

even right off their, investments

on blockchain and, uh, technologies,

477

:

because it was not helping them.

478

:

When we understand what are the

challenges were, uh, earlier

479

:

they were having, Physical silos.

480

:

What they had created

was, was digital silos.

481

:

Okay.

482

:

They were, there was no platform that

they could understand and interact

483

:

to each other in the industry.

484

:

They were, they were

talking to themselves.

485

:

That's the reason blockchain

filled in the bank.

486

:

when we sit with the bank.

487

:

Okay, today there are, uh, tens of

global banks are partnered with T 57.

488

:

What we are offering to banks

today is imagine there is a trade

489

:

financing opportunity, that there

is a, x guy, once $10 million, okay?

490

:

The Bank King process today takes

weeks or months to get it approved.

491

:

Imagine if this is a complete

blockchain and bank has a clear

492

:

visibility from where he's buying.

493

:

To whom he's selling.

494

:

What is a margin?

495

:

Okay?

496

:

What are the risks?

497

:

And, imagine a platform could

address those kind of risk.

498

:

Imagine, they doesn't need

to give entire their amount.

499

:

Okay?

500

:

One shot, they could release one

amount as per a smart contract.

501

:

They could see that.

502

:

Now we need to release.

503

:

20%.

504

:

Okay.

505

:

As an advance, they could give and

the bank can see the clear clarity

506

:

that what is happening in the

complete transaction, if there is

507

:

anybody did anything wrong, okay?

508

:

That, insurance company could go, penalize

the party who, who, who defaults it.

509

:

In today's, uh, food

industry, there's no clarity.

510

:

that's the reason why food is wasted.

511

:

It's not only because of infrastructure,

because, there is no platform,

512

:

which gives you a clarity from,

starting point till the end point.

513

:

Sepehr Achard: Yeah.

514

:

Interesting.

515

:

And I agree.

516

:

I mean, some of the conversations

I had as well with banks is that

517

:

more than the clarity is that

today the market is so volatile.

518

:

Both in terms of commodity pricing

and also the fact that historically,

519

:

especially, I don't know if it's the

case in Africa or in Asia, the markets

520

:

that you cover, but in America and

Europe, historically it has always been

521

:

the banks had guarantees on the land.

522

:

But today the problem is that there's not

enough people buying the lands because

523

:

no one wants to be a farmer anymore.

524

:

And so then they're left with land,

which is cool, which was historically

525

:

seen as a safeguard for everything real

estate, but now no one's buying it.

526

:

Uh, so is this something that you

see as well happening in Africa or in

527

:

Asia, in the markets that you cover?

528

:

Afzal Hussain Mohammed Nakheeb: See

one of the major challenges, okay.

529

:

Today, it's a very important question.

530

:

Very interesting question.

531

:

today, because of the way

food is moved today, okay?

532

:

Food's being, capitalized, okay?

533

:

farmers are not making money.

534

:

That's the reason.

535

:

If you see in India, there

are 12,000 farmers of suicide

536

:

living last year, for example.

537

:

Okay?

538

:

This is happening across, imagine a

farmer in a country who has a land,

539

:

And he sell his lands and go work as a

labor in a, in a, a metropolitan city.

540

:

It's a disaster.

541

:

Okay?

542

:

Why he does this?

543

:

Because there is, they, they, they're

not able to sell their product.

544

:

If they sell their product, they're

not getting the right price.

545

:

So this is happening, this is

not, uh, in Asia or Africa.

546

:

It's a global phenomenon.

547

:

only who are benefiting

is the large corporations.

548

:

are happy.

549

:

They know how to control, they

know how to, market themself.

550

:

Okay.

551

:

But smaller, farmers has these challenges.

552

:

Not only farmers, even smaller

manufacturers, smaller brands

553

:

who are coming in the products.

554

:

They have amazing product, right pricing,

right quality, right price, uh, taste, but

555

:

still, they're not able to access this.

556

:

This is the middleman.

557

:

This is where T 57 comes in and give

the direct access to the buyers.

558

:

The seller, there's no middleman,

In this case, what happens?

559

:

They'll, they'll, they get today,

whatever, they're making money, okay?

560

:

They can multiply, few x.

561

:

Because the direct they're selling

to let it be to a supermarkets or

562

:

to the restaurants or anybody else

directly, they're buying from that.

563

:

They don't need a middlemen.

564

:

See, today there is no, challenge

with, logistic companies.

565

:

For example, every country, most of

the countries have amazing logistic

566

:

system inside the country itself.

567

:

If I give you an example, like

a, a country like Saudi Arabia.

568

:

Okay.

569

:

Saudi Arabia is a large country.

570

:

when we see, the brands who

manufacture let it be dates, brands,

571

:

or something, they have a good product.

572

:

As I said, their price, their

packaging, their, the quality,

573

:

their taste, everything is nice.

574

:

But still, when they try to enter the

market, those few people who control.

575

:

It's not that easy to enter the market.

576

:

But if you see, in other case, there

are platforms like in India called oan.

577

:

they call commerce.

578

:

Okay.

579

:

In first year, they had

around 11 million subscribers.

580

:

Okay.

581

:

That imagine, uh, a farmer

could sell directly to retailer.

582

:

Uh, maybe a manufacturer could sell Okay.

583

:

Directly to retailer.

584

:

You don't need a middleman today.

585

:

There are logistic companies, even there

are financing companies in this region.

586

:

I'm sure you are aware about Tamara.

587

:

Well, we could work with them to

give B2B finances similar to this.

588

:

Okay.

589

:

That if their banking rating is good,

these retailers, they could get an,

590

:

a quick loan or in, in some of the

banks, we are working, we, we are even

591

:

discussing about pre-approved loan

because we are, we are working on,

592

:

that's a reason we do, Rigorous KYB where

banks are involved with us to do that.

593

:

know your bank, know your

business and know your customer.

594

:

Who is the, founder or partners?

595

:

We do their KYC also, once it is done

so there is pro approved loans to

596

:

given to them, even up to their, it

depends on their, historical finances.

597

:

So this could be addressed.

598

:

In a smarter way.

599

:

That's the reason platform like T

57 was critical, important, okay.

600

:

One is on a global level trade between

the countries, even in your own country.

601

:

Okay?

602

:

When, imagine when a retailer or any B2B

businesses wants to buy something that

603

:

can guide buy from the factory directly.

604

:

Okay.

605

:

There's no middlemen.

606

:

Sepehr Achard: Yeah.

607

:

Afzal Hussain Mohammed Nakheeb: Okay.

608

:

using Right, logistics, payment

systems, or even credit system,

609

:

it'll solve the problem.

610

:

A retailer is happy because

he's buying cheaper.

611

:

Okay?

612

:

Manufacturer is happy because

he's getting higher margin.

613

:

He doesn't need to give

10 people distribution.

614

:

There is a people middleman in into it.

615

:

farmer is happy because he can sell

directly to those retailers, and people

616

:

are looking for good quality food.

617

:

It's not that, there's no demand.

618

:

There's a high demand

population is uh, rising.

619

:

Okay.

620

:

As I mentioned in my talk yesterday.

621

:

By 2050, the population is expected

to grow by 10 billion people.

622

:

The food increase required is

around, 60 to 70% from what we

623

:

are producing today with current

infrastructure, it'll be a disaster.

624

:

So we need to think smart.

625

:

We need lot of platforms like T

57 to come in to address these

626

:

issues so that we work smartly.

627

:

To to solve each problem using,

ai, blockchain and advance, uh,

628

:

machine learning, uh, technologies.

629

:

Sepehr Achard: Yeah, I agree.

630

:

And now moving on to the AI part,

I mean, a topic that we've covered

631

:

extensively in our panel yesterday.

632

:

as a news publisher, I can guarantee

you two thirds of the articles I

633

:

publish has an AI component to it.

634

:

even when we cover some of the

financial statements published by

635

:

larger companies like the John Deeres

and Syngentas, 99% of it has an ai.

636

:

Okay.

637

:

They mentioned ai.

638

:

it has become a lot of a buzzword,

uh, with a lot of companies just

639

:

releasing a modified chat, GPT at best.

640

:

How do you communicate that value

and how do you think, and how do

641

:

you at T 57 implement these ai?

642

:

I mean, we know that you covered,

like, for instance, customizing to

643

:

the farmer's needs, and really have

a dedicated AI to particular farmer,

644

:

which is why you have that KYB.

645

:

But what other things you do at T 57

that guarantees that the AI brings

646

:

a lot of value to the farmers?

647

:

I've had conversations with

farmers telling me like, yeah,

648

:

it doesn't bring me anything.

649

:

Ai, it's just a fancy thing.

650

:

And that's it at best.

651

:

so how do you do that?

652

:

Afzal Hussain Mohammed Nakheeb:

See this topic?

653

:

I can speak for next two hours.

654

:

Okay.

655

:

Uh, it's a, it's a, it's

a very important topic.

656

:

Okay.

657

:

See, AI It can solve lot of, uh,

the challenges we mentioned today.

658

:

Okay.

659

:

Uh, let it be starting from the farming.

660

:

imagine today we are working from last

year's data, not tomorrow's production.

661

:

Okay.

662

:

Ai, what it could do,

minimum changes, okay?

663

:

Is tomorrow's prediction.

664

:

See today, the data which we have.

665

:

we can use that data and

predict something for tomorrow.

666

:

Imagine a farmer wants

to produce something.

667

:

let's start from the farm,

then we'll go till finance.

668

:

Uh.

669

:

A farmer if they want to produce,

does they know what will be

670

:

the demand after four months?

671

:

When the four months, six months,

when the crop coming out, what will be

672

:

their price when the four, after four,

six months, the crop will come out?

673

:

Will they get a fair price to them?

674

:

Okay.

675

:

What will be their ROI as a farmer?

676

:

Okay.

677

:

Do you think any platform today or

any AI today, or any governments

678

:

today, or any bodies addressing this.

679

:

No, this is where T 57 is beginning.

680

:

is developing our modules to

address these kind of challenges.

681

:

And when we talk about farmers,

uh, you are right when pharma says

682

:

we are not getting any benefited.

683

:

One of the reason is

because AI is expensive.

684

:

what I just said, to develop this, you

need maybe 200 to $500,000 of investment,

685

:

which farmer will not able to do it,

or he doesn't have a set knowledge

686

:

to do these kind of, he has access

to these data when T 57 coming in.

687

:

Okay.

688

:

We want to bring that data, as I

mentioned from research centers, let

689

:

it be from the governments, let it

be from, many different, aspects.

690

:

When we develop these data and give access

to these farmers in their own language,

691

:

okay, it's, they, they don't need to

learn English to speak to the platform.

692

:

They they could use their voice

in any language they want.

693

:

The platform will translate

and they will answer to them.

694

:

It could be as simple from a WhatsApp.

695

:

They could answer it, link

to the platform, they could

696

:

get an answer to them.

697

:

We want to make it.

698

:

Easier.

699

:

we are not saying that we

need to train the entire food

700

:

industry, uh, how to use ai.

701

:

No AI should, we need to use as an

enabler, uh, and become easier so

702

:

that each, actor in the industry, uh.

703

:

should understand and use AI to help them.

704

:

So we could address this from,

predictive analysis for tomorrow.

705

:

It could be, uh, a government normally

change policies like after imagine an

706

:

winter, uh, because of shortages of, uh,

certain, uh, commodities, they will open

707

:

their, Imports from, uh, other countries,

it might bring products cheaper and

708

:

you know, they might not get the price.

709

:

Okay, it might be rain.

710

:

today we have access to even satellites

of data, which could be very useful to

711

:

the farmer, which farmer cannot access it.

712

:

We could access to that data and give

the access to the farmer so that he

713

:

could understand that why he's growing

certain things and how many people in his.

714

:

Town, his city, his country

growing certain things.

715

:

Imagine we have 10,000 farmers.

716

:

Everyone is growing tomatoes.

717

:

What happens end of the day?

718

:

That's the reason we see sometime.

719

:

Tomato is half a dollar.

720

:

Sometime it's $10.

721

:

Okay.

722

:

So there is no demand supply.

723

:

There is no tool that a farmer or

anybody can go and access to it

724

:

and say that what is the demand?

725

:

Where is the gap?

726

:

Where should I work?

727

:

How we can become a bit smarter?

728

:

To ensure that we have a decent ROI.

729

:

So this is one from a very

small module I'm talking about.

730

:

I'll give you another example.

731

:

Ai, how it could reform.

732

:

we acquired one of the companies,

uh, trading companies to understand,

733

:

when we were doing T 57, we wanted to

understand, we acquired few con companies.

734

:

One of them is a company

based out of India.

735

:

They're doing trade between,

uh, different continents.

736

:

one of the transaction I want to mention

and to give you, put a light to it.

737

:

What are the challenges?

738

:

We imported six containers of

Apple from South Africa to India.

739

:

We use one of the prominent,

global platform B2B platform Okay.

740

:

To do transaction through them.

741

:

Okay.

742

:

The company who supplied

Apple is one of the well-known

743

:

companies who supplied the Apple.

744

:

When it reached the, uh,

the consignment to India.

745

:

30% of the apples were open.

746

:

When we asked basic

questions, what went wrong?

747

:

Nobody had an answer.

748

:

We had some vague answer saying

that this year, temperature due

749

:

to global warming, raised 2%.

750

:

Maybe that is a reason.

751

:

Okay.

752

:

Nobody knows where is the challenges.

753

:

See food is very critical.

754

:

When we handle food,

there is a science to it.

755

:

If I give you another example from Banana,

for example, from from Philippines,

756

:

it, it shipped out of Philippines.

757

:

Imagine it comes to Europe

or us to, GCC or India or.

758

:

Okay.

759

:

There is a proper science.

760

:

It has to certain ripe condition.

761

:

They have to harvest it.

762

:

It'll take two weeks, one

week, three weeks, okay?

763

:

When it reach the market, then

supermarket still, it is in a green form.

764

:

When it reach the shelf, it's

already become yellowish.

765

:

When it reach your home, it is ripe.

766

:

So it, there is lot of

science behind it today.

767

:

People do it with their

experiences today, AI could.

768

:

In the case of Apple, when we lost

this 30%, we developed a module, okay.

769

:

Today in South Africa, we could go use

your smartphone or AI enabled cameras.

770

:

Uh, it could be, professional camera or

even your smartphone, take a picture.

771

:

AI will analyze and tell

you that it is too, right?

772

:

Okay.

773

:

It'll not be reaching, India if needs

two weeks of, transport time, you

774

:

better ship it to other country, okay.

775

:

the trade, or let it be farmer or

middle, the, logistic companies.

776

:

Nobody's working with a bad intention.

777

:

They want to serve.

778

:

Okay.

779

:

Even farmer, he doesn't

want to lose his customer.

780

:

Right?

781

:

He wants him to come back.

782

:

Their intentions are nice, but there

is no tools in their hand that they

783

:

could, solve their challenges today.

784

:

We could address this in ai, take a simple

picture and say that this is true, right?

785

:

Okay.

786

:

Or imagine there is something happened in,

Philippines, there was a excessive rain.

787

:

The truck could not reach.

788

:

Last two, it was delayed for

two days, they could not load.

789

:

Okay.

790

:

The AI will say that

it's difficult to reach.

791

:

It'll be all right.

792

:

Better you ship it somewhere else.

793

:

Sepehr Achard: Yeah.

794

:

Afzal Hussain Mohammed Nakheeb: Okay.

795

:

You have the demand in your

local market or regional market.

796

:

You do other harvest and too, rather

than you get into lawsuits today.

797

:

What happening is, most of the cases

are once it reach the destination.

798

:

This trying to address what is

a problem not before starter.

799

:

So if we only address this, maybe five

to 10% of the food loss we could address

800

:

through this particular AI itself.

801

:

Okay.

802

:

The models there at the

same time, even buyers.

803

:

Imagine when they're buying it.

804

:

Okay?

805

:

I want to import oranges from Turkey.

806

:

Okay.

807

:

A buyer, can he use productive analysis,

deep understanding of the markets to

808

:

see where the market is going Next,

two weeks, will the price stay the

809

:

same price or it'll crash, or, my

market is going up, I should, when

810

:

should I buy after two weeks or today?

811

:

Okay, so these kind of lot of, with

the data, it'll be trained for sure.

812

:

Today it'll not be ready a

hundred percent, but it'll

813

:

get trained step by step.

814

:

But this will help everyone

in the, uh, food chain.

815

:

See, lot of times what happens,

importer invested money.

816

:

Okay.

817

:

Sometime millions of dollars,

they import the product.

818

:

It takes two weeks to

reach their one set reach.

819

:

Mar market collapse, they lost

30%, 20%, and sometimes they

820

:

will not even sell the product.

821

:

So these kind of, how can we

train our modules, To address

822

:

these kind of challenges.

823

:

And another challenge is.

824

:

There are a lot of beautiful, studies,

lot of agriculture, universities.

825

:

One in in India care lab.

826

:

Okay.

827

:

We were working with, very closely

with one of the universities

828

:

and there are some cooperatives.

829

:

We are working from different countries.

830

:

They have developed amazing,

food processing unit.

831

:

It could be even in 200 square meters.

832

:

You can set up foot processing

unit when you know that there is.

833

:

This product will not reach, you could

process it and you could, store it.

834

:

It could be deep, frozen, dry, or it could

be different ways to, preserve your food.

835

:

I'll give you one of the examples

is I was discussing with one of the

836

:

cooperatives s during COVID, okay.

837

:

They were producing bananas.

838

:

Uh, suddenly COVID came.

839

:

They were not able to sell bananas.

840

:

That cooperative was quite smart.

841

:

So they, they invested and

bought some, deep, freezing.

842

:

Uh, they take the hu humidity

out of, uh, the fruit they gave

843

:

to, uh, many different villages.

844

:

They keep gave this machine, they said

you cut the banana and start putting in

845

:

this machine so it'll deep freeze it.

846

:

Take the moisture and you can store it.

847

:

For many days earlier, the price

they were selling, imagine it was a.

848

:

20 cent, per kilo.

849

:

Yeah.

850

:

Okay.

851

:

They started selling after drying $2.

852

:

Okay.

853

:

So that today it become a new industry

that they don't want to sell raw banana.

854

:

They're, freezing it and they're selling.

855

:

So the lot could, we have lot of data

across, we could analyze and bring

856

:

the solutions, to preserve the food.

857

:

Sepehr Achard: Yeah, absolutely.

858

:

And that is also something that we see

across our data is that there's more

859

:

and more farmers that are actually

offering these deri derived products,

860

:

such as like a tomato farmer would

also offer the tomato paste and

861

:

so on to recycle that food waste.

862

:

So it's absolutely

something that we're seeing.

863

:

but my question is, so we focus a lot on

food waste at a farm level, but there's

864

:

also food waste at the people's level.

865

:

Like, I can guarantee you that example

of the banana now that comes to my,

866

:

uh, to my fridge in a week's time,

it's probably black and, uh, probably

867

:

gonna go to, to the other trash.

868

:

So is there a part of education that also

needs to be given to people in general

869

:

and like how to, Adapt your consumer

behavior and not buying too much or

870

:

knowing how to store these products.

871

:

So is there maybe an AI that

you guys are working on to, uh,

872

:

teach people how to do that?

873

:

Afzal Hussain Mohammed Nakheeb:

It's a good question.

874

:

Interesting question.

875

:

Okay.

876

:

See, when we talk about B2C.

877

:

Okay.

878

:

There's a lot of ai, there are a

lot of retailers, amazing companies.

879

:

Amazing work is happening.

880

:

And even, when you talk about Amazon or

others, Even AI are already there, okay.

881

:

They could even help you to understand,

okay, how much you should buy,

882

:

what is missing in your fridge?

883

:

Okay.

884

:

There is lot of modules already developed

and it's coming to the market slowly.

885

:

At the moment, we are not focusing B2C.

886

:

Mm-hmm.

887

:

Because B2C, there are a lot of players.

888

:

Okay.

889

:

They're doing amazing job.

890

:

Okay.

891

:

They're doing lot of

efforts happening there.

892

:

The challenge we are addressing for

the 33% of the food, which is wasted

893

:

before coming to the plate, after coming

to the plate, or the post harvest.

894

:

lot of B2C brands are

working on it for sure.

895

:

Education is required.

896

:

today, AI could help there.

897

:

AI modules could help you buying

also that you are buying too

898

:

much or you are buying less.

899

:

What is missing there?

900

:

that is required, but we are not

focusing at the moment because

901

:

for us, we need to address, uh.

902

:

On other side of, uh, larger challenges.

903

:

how to protect what is being wasted

because, if we only, as I mentioned, we

904

:

were working with thousands of problems.

905

:

Sepehr Achard: Yeah.

906

:

Afzal Hussain Mohammed Nakheeb:

If we address one problem, In

907

:

Shaah, nobody will sleep Hungary.

908

:

There are other 999 problems

we are addressing even them.

909

:

mention another problem.

910

:

What we are, addressing is, in OIC

countries, you'll be surprised to

911

:

know that 75% of the farmers are

using hand tools in their agriculture.

912

:

When the world moved to ai, when

the world moved to drones, when

913

:

the world using, satellite to

understand, and blockchain on the food.

914

:

75% of the farmers are using hand

tools They don't have a tractor.

915

:

Maybe it was invented a 100 150 years ago.

916

:

and still we are producing 33%.

917

:

Imagine if we address this problem, okay?

918

:

How much of impact we can bring.

919

:

So what we did was in our platform is we

introduce, uh, Uber kind of model where.

920

:

The people who has 25%, who has those

drones, who has those harvesters,

921

:

who has those machines, could

rent their, tools in our platform

922

:

with the smaller fees so that.

923

:

We can solve these challenges and even

we invite, SMEs and investors, okay.

924

:

Our startups to come in this industry,

invest on drone company and you could

925

:

rent out and make money out of it.

926

:

Okay?

927

:

we are discussing with lot of

agri universities where, when, um.

928

:

Our, uh, highly educated,

children coming out of it.

929

:

We want them to have startups.

930

:

We guide them where to invest, how

to invest, where is the market, how

931

:

much you could make money out of

so that we encourage people to come

932

:

invest in these industries and create

a model where this could be shared.

933

:

And solve the actual problem and

platforms gives this access that they

934

:

could share their technology tools.

935

:

How did we understand this was,

you know, while developing T 57?

936

:

Or I could say, well building

a strategy for, uh, IOFS.

937

:

We studied 350 different case studies

globally across the continent.

938

:

One thing we understood is.

939

:

two case studies were very important.

940

:

One of is Brazil, second is Netherland.

941

:

Both of them are few decades

ago, dependent on food import.

942

:

Today they're feeding the world.

943

:

You know, what is the reason?

944

:

Technology.

945

:

Technology, technology, right?

946

:

Investment, techno technology.

947

:

Not only government is

investing on technology.

948

:

Private sector is investing on technology.

949

:

Netherland or.

950

:

So today they're, they're supplying Brazil

supplies everything to the world today.

951

:

So if we go that direction, we

can solve lot of challenges.

952

:

See, it's about, again, the

challenges we have, it cannot

953

:

be addressed by one industry.

954

:

It could not be addressed

only by agriculture.

955

:

It has to be addressed by agriculture,

logistic, trade finance, governments.

956

:

So it's a integration problem.

957

:

Not one industry challenge.

958

:

So the challenge, this is what T

57 is addressing, is to bring all

959

:

these stakeholders on one table

and trying to find the solution.

960

:

Sepehr Achard: And so now to wrap

up this conversation, I have one

961

:

last question, and it's a question

I ask everyone my interview.

962

:

It's a crystal ball question,

but the year is:

963

:

Where is T 57 at?

964

:

And where is the industry at?

965

:

Afzal Hussain Mohammed Nakheeb:

Uh, lovely question.

966

:

Okay.

967

:

by 2035, what we envision is, okay, at

least from the food waste from 33%, okay.

968

:

We want to reduce 50% of the food waste,

and we ensure nobody sleeps hungry.

969

:

Okay.

970

:

We, this is our intention, okay.

971

:

With all good faith, okay.

972

:

And all bringing all the, uh, platforms.

973

:

And by year 2035, it'll be a

global platform across, Okay.

974

:

It'll be, uh, used by across,

food ecosystem where we'll have

975

:

governments involved in it.

976

:

Okay.

977

:

Different ministries involved in

it, let it be trade agriculture.

978

:

Okay.

979

:

SME development authorities

or industry development

980

:

industry, ministries part of it.

981

:

We develop a model where.

982

:

We have a data from the government, from

the universities, the retailers, from the

983

:

manufacturers, build localize, AI models

And solve most of the challenges in food.

984

:

I know it's very big promise.

985

:

Okay?

986

:

Very challenging.

987

:

But that is what, Give us motivation

to wake up next day to do something.

988

:

So it is doable today using uh,

right tools, right modules, and

989

:

even if you see the way AI is being.

990

:

training itself improvising the AI

for itself,:

991

:

Okay.

992

:

Uh, AI will be, quite advanced and

especially our focus is on food

993

:

where we can take the AI so that

a farmer could easily before in

994

:

by 2035, before putting his seeds.

995

:

Okay.

996

:

He can have it.

997

:

A complete dashboard in his simple phone.

998

:

Who, who could see if I do this, if I put

a seed of tomato, what should be my ROI?

999

:

And who is my customer?

:

00:49:48,761 --> 00:49:49,151

Okay.

:

00:49:49,151 --> 00:49:53,051

We can even have a platform

where before he put a seed, he

:

00:49:53,051 --> 00:49:55,181

will have buyer ready to commit.

:

00:49:55,491 --> 00:49:56,151

Something.

:

00:49:56,181 --> 00:49:56,661

Okay.

:

00:49:56,901 --> 00:49:59,181

And finance should not be a challenge.

:

00:49:59,241 --> 00:49:59,691

Okay?

:

00:49:59,721 --> 00:50:02,986

Banks, will be ready to finance the crop.

:

00:50:03,061 --> 00:50:08,528

Banks will mitigate those risk, it'll

be, uh, let it be trade financing.

:

00:50:08,872 --> 00:50:10,071

it'll be much easier.

:

00:50:10,071 --> 00:50:10,131

Yeah.

:

00:50:10,281 --> 00:50:13,431

At the same time, we want to

work with governments how to do,

:

00:50:13,690 --> 00:50:17,780

pre-approved, on the ports when the

product arrive, customs and those

:

00:50:17,780 --> 00:50:20,270

things, how we could do pre clearance.

:

00:50:20,450 --> 00:50:20,750

Okay.

:

00:50:20,750 --> 00:50:24,530

Digital clear clearance today

already 10, 11 countries, including

:

00:50:24,530 --> 00:50:27,845

Abu Dhabi, and many other countries

are working on e clearance.

:

00:50:28,265 --> 00:50:28,685

Okay.

:

00:50:28,715 --> 00:50:31,385

So this has to be a norm globally that.

:

00:50:31,710 --> 00:50:34,590

It has to be, uh, pre-clearance documents.

:

00:50:34,650 --> 00:50:39,090

Should be, imagine a farmer could

upload his document and Port

:

00:50:39,090 --> 00:50:40,560

could see that in a platform.

:

00:50:40,860 --> 00:50:44,760

We integrate those government platforms

so that it should not become a

:

00:50:44,760 --> 00:50:46,380

paper, should not become a problem.

:

00:50:46,470 --> 00:50:46,860

Okay.

:

00:50:46,860 --> 00:50:50,040

They should approve it

before the product comes in.

:

00:50:50,580 --> 00:50:51,060

For sure.

:

00:50:51,065 --> 00:50:56,941

There'll be physical, clearance required,

but it has to be not weeks, not months.

:

00:50:57,001 --> 00:51:02,628

It has to be, few hours that it comes,

even the, technology will improvise.

:

00:51:02,658 --> 00:51:03,078

Okay.

:

00:51:03,078 --> 00:51:06,738

When the product comes, they can

scan it through better scanners

:

00:51:07,038 --> 00:51:11,898

and, uh, doesn't require to take

days to, uh, get it out of the port.

:

00:51:12,178 --> 00:51:13,198

has to be simple.

:

00:51:13,438 --> 00:51:19,498

So today B2B trade is, if not

months, it's weeks of process

:

00:51:19,498 --> 00:51:21,328

to do close one transaction.

:

00:51:21,718 --> 00:51:25,083

What T 57 is envisioning

is few clicks survey.

:

00:51:25,673 --> 00:51:25,733

Wow.

:

00:51:26,093 --> 00:51:31,703

This is what we see by:

completely to make, uh, like

:

00:51:31,703 --> 00:51:34,643

today how B2C we are in a meeting.

:

00:51:34,643 --> 00:51:40,433

Imagine if I need to order any food

or any perfume or any fashion from end

:

00:51:40,433 --> 00:51:43,043

consumer during I'm talking to you.

:

00:51:43,043 --> 00:51:45,653

I can go select location

it delivers to you.

:

00:51:45,658 --> 00:51:46,733

There is no how.

:

00:51:46,733 --> 00:51:49,433

We have learned this in B2C.

:

00:51:49,763 --> 00:51:52,433

We want to change this into B2B.

:

00:51:52,433 --> 00:51:55,103

This is our vision 40 57.

:

00:51:55,643 --> 00:51:56,393

Sepehr Achard: Fascinating.

:

00:51:56,783 --> 00:51:59,363

And for folks that want to find

more information about your

:

00:51:59,363 --> 00:52:02,243

company or get in contact with

your team, how can they do that?

:

00:52:02,903 --> 00:52:04,373

Afzal Hussain Mohammed Nakheeb:

T 50 seven.ai,

:

00:52:04,403 --> 00:52:07,139

shortest domain, across the platforms.

:

00:52:07,529 --> 00:52:12,806

Let it be LinkedIn, TikTok, Instagram,

including Telegram, WhatsApp, anywhere.

:

00:52:13,946 --> 00:52:19,106

Just remember T five seven do AI

as simple as it is and you could

:

00:52:19,106 --> 00:52:22,886

even fi find my number Founder's

mobile number on the website.

:

00:52:22,886 --> 00:52:23,906

You could reach me directly.

:

00:52:24,506 --> 00:52:25,016

Sepehr Achard: Perfect.

:

00:52:25,136 --> 00:52:27,176

Well, thank you very

much Al for your time.

:

00:52:27,536 --> 00:52:28,706

It was a pleasure talking to you.

:

00:52:29,066 --> 00:52:29,546

Afzal Hussain Mohammed Nakheeb: Thank you.

:

00:52:29,546 --> 00:52:32,306

It, uh, it's a really

good, uh, to talk to you.

:

00:52:32,581 --> 00:52:33,026

Sepehr Achard: Thank you.

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