How to Build & Sell AI Automations: Ultimate Beginner’s Guide

In a world being transformed by AI, one

skill stands above all others. AI

automation. Master this and you won't

just survive the AI revolution, you'll

thrive in it. I'm living proof of this.

Just 2 years ago, I taught myself how to

build no code AI automations without any

prior experience. And since then, I've

built multiple AI businesses, generated

millions of dollars in revenue, and

grown this channel to over 500,000

subscribers, and built AI systems for

some of the biggest brands in the world.

It's pretty safe to say that learning

how to build AI automations has

completely changed my life. So, in this

full course, I'll teach you everything

that I've learned about building AI

automations and making money with them,

even if you don't know how to code. And

as they say, AI will not replace you,

but the person using AI will. So, my

hope is that with this video, you too

can learn this incredibly powerful skill

to build the life of your dreams before

it's too late. And as you can tell by

the length of this video, I'm not going

to be holding anything back. So, I've

split it into three different chapters.

Firstly, we'll build your foundational

understanding of AI automation, covering

what it actually is, the different types

of AI automations, how they work under

the hood, and the key concepts you need

to know before we start building.

There's no technical background required

to understand any of what I'm going to

teach you there. Secondly, we'll dive

deep into building out actual AI

automations, taking you over my shoulder

every step of the way as we build some

of the most in- demand AI automation use

cases in the market today. This includes

building things like cuttingedge voice

agents, too. And in the third and final

chapter, I'll be giving you my proven

blueprint for monetizing your AI

automation skills while this technology

explodes. I'll share the exact

strategies that I've used to generate

millions of dollars with the skill set.

So, if you're new to the channel and

don't know who I am, let me quickly

share why I am qualified to teach you

about AI automations in the first place.

So, my name is Liam Mley and just 2

years ago, I started learning AI with no

prior experience in the field. Teaching

myself how to build AI automations and

chatbots through my own self-study,

which I documented here on this YouTube

channel from day one. This led me to

starting Morningside AI, my AI

automation agency, where we build AI

systems and agents for businesses from

basic customer support systems when we

started to now full AI SAS platforms for

some of the biggest brands in the world.

And I also have my own AI SAS called

Agentive, which has over 70,000 users on

it. At Morningside AI, we've worked with

publicly traded companies and even an

MBA team recently. And I also run the

world's largest AI automation and

business community with over 180,000

members on school. So through this

community and my YouTube channel, I've

taught hundreds of thousands of people

from all backgrounds how to build and

make money from AI automation. And

everything I'm about to teach you today

is exactly what helped me to achieve all

of this. So let's dive in. So there's a

lot to cover here. I don't want you to

give up halfway. So let's quickly get

clear on why learning AI automation is

one of the most valuable skills anyone

can have over the coming decade. Whether

you're a student, an employee, or an

entrepreneur. Here's some quick truths

about AI and jobs. McKenzie predicts

that AI and automation can replace up to

50% of current work activities by 2030.

And the World Economic Forum states that

41% of companies plan to reduce staff

due to AI. Now, this is a lot of doom

and gloom and many are naturally worried

about their career in the future when

they hear the stuff, but it's not

actually all bad if you know where to

look. So, on the flip side of this same

data, these same reports reveal an

enormous opportunity for those willing

to seize it. The board economic forum's

future of job report states that 50% of

employees plan to reorient their

business in response to artificial

intelligence and 66% of employees plan

to hire talent with specific AI skills

such as AI workflow automation. So on

one hand we have the expectation of

massive layoffs and automation of work

over the next 5 to 10 years. But on the

other we have the majority of employers

searching for people who have AI skills

or really just some form of basic AI

literacy. Why is this? Well, it's

because AI literate individuals who can

identify opportunities for automation

and automate them themselves can have 5

to 10x the output of someone who doesn't

know this and can't automate their own

work. And I promise you that brushing up

on your AI and actually becoming AI

literate so that you can be on the

winning side of this next 5 to 10 years

is so much easier than you think. I

mean, it's it's literally as easy as

watching this entire video in order to

build your AI skills base. If you don't

believe me when I say that a little bit

of self-study like this video goes a

long way, here is an excellent clip from

the All-In podcast from one of the most

respected investors and technologists in

the world, Naval Ravakant, alongside a

whole bunch of other biners. Again, I

would say the easiest way to see that AI

is not taking jobs or creating

opportunities is go brush up on your AI,

learn a little bit, watch a few videos,

use the AI, tinker with it, and then go

reapply for that job that rejected you

and watch how they pull you in. This

video is exactly what Naval is talking

about. This is why I create these. So

whether you're a student wanting to

stand out in a competitive job market or

an employee aiming to become

irreplaceable at work or an entrepreneur

like me looking to scale your business

with cuttingedge tools and automations,

I have made this video for you. Now

close out of all your other tabs, get a

notebook and a pen and a beverage of

your choice and make a commitment right

now to yourself to finish this training

and to ensure that you're going to be

empowered by AI and not replaced by it.

That is all I want out of this video for

you all. So if you've done all that,

let's get stuck into it.

All right. So, step one in building AI

automations is knowing what an

automation actually is. And the term

gets thrown around a lot these days.

First thing we need to realize is that

the AI part of the term is is relatively

new. Automation itself has been around

for a long time. So, let's start there

to make this super easy to grasp. In

simple terms, an automation is a system

that does a task for you without you

having to lift a finger. It's kind of

like setting up a little robot to do

boring, repetitive stuff automatically,

so you don't have to waste your time on

it. These are what we'll call old school

automation. The kind that existed way

before chat GBT came along. They were

often built on platforms like Zappia and

lots of small to medium-sized businesses

used them for the past 5 10 years. And

they did basic things like automatically

saving info, for example, when someone

filled out a form on a website. You

could make an automation that would take

their name and their email and then just

pop it into a spreadsheet. So just a

little automation automating that boring

stuff. Or for example, when an email

came in, it would send a quick alert to

a chat app like Slack. So it's kind of

like having a little helper who's

following a super simple checklist. If

this happens, then do that. No thinking,

just just doing, right? And the benefit

of this is huge. It freed humans from

doing super basic and boring work. And

for business owners, it meant not having

to pay more people just to handle these

tiny and annoying tasks. It's kind of

like having a tireless assistant who

never complains about doing the same

boring thing over and over and over

again. So for decades, all was well in

the automation space. And these old

school automation saved time and money,

and everyone was super happy. That was

until the release of Chat GBT in late

2022. It blew the entire field wide

open, turning automation from some niche

trick used by some savvy companies into

the biggest thing since the internet.

Generative AI models like chat GBT added

to automation was like putting a V12

onto a bicycle for this automation

space. They made it possible to do more

than just simple tasks. This was because

these powerful AI models could handle

much trickier stuff that used to require

a human brain. Instead of just updating

a spreadsheet row automatically, these

automation platforms can now use the

power of Chat GBT to do things that only

people could have done before. So

platforms like make.com, which you'll be

learning more about later in this video,

enable us to automate things like write

a whole post for LinkedIn sounding just

like you. Pulling out names, places, and

phone numbers from giant documents in

seconds. Reading and figuring out if an

incoming email is someone asking for a

refund or just wondering where their

order is. Shrinking huge piles of info

into short and easy to read reports,

spotting things in the picture like

identifying a product in a photo, or

even creating brand new images and

videos from just a few words. So what

chat GPT and the explosion of other

amazing generative AI tools gave us was

basically human intelligence on demand.

These AI models are kind of like having

a super smart friend who can do almost

anything that you ask them as long as

you tell them clearly what you want. And

using automation platforms, we can

easily set up these super smart friends

into our systems and use these kinds of

models in thousands of different ways.

All you have to do is pick the right AI

tool for the job, give it a clear

instruction, call a prompt, and watch

the magic happen before our eyes. And

that is how the AI automation industry

was born. So, with that little history

lesson out of the way, let's get back to

our original question of what is an AI

automation. Well, it turns out that this

field is so new that there isn't even an

official definition for what an AI

automation is. So, here's mine. Just

keeping it nice and simple. An AI

automation is a system that uses AI to

automatically do complex tasks that

would normally require a human. So, the

big difference between these old school

automations that we've just talked about

and today's AI automation is the kinds

of tasks that they can handle. Thanks to

these recent advances in AI technology,

we've gone from just moving data around

and putting stuff in spreadsheets to

being able to solve problems that need

thinking and creativity and

decision-making. It's kind of like

upgrading from a basic toy robot that

only moves forward to a high-tech robot

that can solve puzzles and move around

the world. So, when you learn AI

automation, you are basically learning

how to build digital workers that can do

very powerful things for you without

ever having to lift a finger. This is

why so many people are racing to pick up

the skill right now before it's too late

to stay ahead. It's like the ultimate

cheat code because you can build them to

do exactly what you need, tailored to

any kind of job or workflow. For

example, for students, it's kind of like

having a helper that can organize your

study notes automatically. Or for

employees, your AI automations can be

like a buddy that handles the boring

paperwork so you can shine on bigger

projects. And for entrepreneurs, it's

like having a team member who runs your

business tasks while you sleep. And the

great thing is that they all cost way

less than hiring extra people. And they

don't need breaks or vacation days, and

they don't grumble about doing the

boring stuff. So, I'm sure you can see

why businesses and students and anyone

looking to save time and money are so

excited about this technology and why

anyone who knows how to build these

systems is instantly 10x more valuable.

Just imagine automating something simple

in your life or work like sorting emails

or scheduling tasks and how much time

you'd be able to save to focus on what

really matters in your life.

Now, before we dive deeper, it's

important to understand that AI

automation is a super broad term these

days, covering wide ranges of different

systems and applications that can be

built with AI. This is largely due to

the rapid advances in areas like AI

agents and AI tools. So, over the past 2

years, as I built my own AI agency,

Morningside AI, and helped thousands

through my communities to do the same,

I've had to create a clear system for

making sense of the chaos that is the AI

automation landscape. And I really want

to share that with you today because

it's been super helpful for me and many

of my students to be able to figure this

space out and at least have some mental

buckets that you can put things in.

Here's the three different categories

that you need to keep in mind. And

please stick with me. This will all make

sense in a second. So firstly, we have

conversational AI. These are systems

that chat with people handing back and

forth conversations. It's kind of like

having a friendly robot that talks to

customers for you. These kinds of chat

bots can be found on things like

websites and answer questions or they

can be voice agents that pick up phone

calls. These used to need real people to

talk, but now AI can automate these

kinds of conversations. The second

category is AI tools, and these are

systems that use AI to do a specific job

when a person asks them to, and it's

mostly to help workers get more done.

For example, I can make a custom AI tool

that takes a link to a cool blog post

that I found, grabs the info from the

web page, does extra searches on the

topic, and then uses something like

chatbt to write a new beta version for

my own blog. And third and final is AI

workflow automations. These are systems

that do a whole series of tasks by

themselves, starting when something

happens, like a trigger or on a set

schedule like once a day. They use AI to

make decisions that used to need a human

brain. It's kind of like having a smart

robot manager that runs the whole

process for you. For example, an

automation can call customers of an

online store 14 days after they buy

something using an AI voice agent to ask

for feedback and review all without you

having to do a thing. So, you set it up

on that trigger of 14 days after

purchase, then execute this workflow.

Now, those three categories might be a

little bit confusing if you are

completely new to the space, but don't

worry. In the building section of this

video, we're going to be creating three

automations which integrate each of

these different types, and you get to

see them in action, which will make it

super clear. So, AI automation is

essentially an umbrella term under which

all of the exciting stuff in the AI

space is happening right now. However,

when people talk about AI automations,

they are typically referring to the last

type that I mentioned. So, AI workflow

automation, and these are what we're

going to be building later in the video.

And an automation refers to one chain of

steps that uses AI in various ways to do

certain tasks. And if I'm honest, this

last category of AI workflow automation

is really the most powerful because it

can incorporate all elements of AI

automation like agents, conversational

AI, and tools in order to build

end-to-end processes that are much more

valuable than if they were just alone.

So, what I'm saying is that what you're

about to learn is the key skill that

lets you do pretty much anything in the

AI space these days. It is the

foundation for building systems that

save time, make money, or just make life

easier, whether you're at school or work

or running your own show. And after

years in the game, I can tell you that

it's been one of the most valuable

skills that I have ever picked up.

So, now that you understand what AI

automations are, let's take a little

peek under the hood and see how they

actually work. So, don't worry if this

sounds tricky. I've been breaking down

this kind of complex AI stuff for years

now. So, I'm going to make this super

easy to understand for you. So, you can

think of an AI automation like a facto's

assembly line, right? There's different

stations and they're all working

together to build something awesome from

start to finish. It's kind of like

having a a team of little robots, each

with a special job passing the project

along until it's done. So, let's go

through the five key parts to make this

magic happen. Firstly, we have the

trigger. This is the very first step of

an automation. You can think of it as

the facto's start button or the whistle

that says, "Let's go." It's what kicks

everything into gear. It could be

something like a new email popping into

your inbox, a form being filled out on a

website, or even a specific time of day.

This is more of a a schedule. Secondly,

we have a filter. So, not everything

that starts in the automation should

keep going through it. So, a filter

essentially checks if what came in is

the right stuff to work on. It's like

how a factory worker does some kind of

quality control and checks that if the

materials that they've received are good

enough to use in the final product. If

they are not then they get tossed out

and if they are then they move forward

to the next part of the sequence. You

can think of it kind of like a bounce at

a club where only the important stuff

and the good things that you want inside

the club or in your automation are

allowed through. Thirdly, we have

actions. So this is where the real work

gets done. Actions are the steps your

automation takes like the different

stations in a factory where each one

does a specific job. So for example,

your automation might send an email,

update a list or create some kind of

report. Often there are going to be

several actions one after another just

like a product moving down the line

getting built bit by bit in order to

achieve one of these outcomes. Next we

have the intelligence layer. So this is

where the AI magic shines. This part is

like having a super smart robot on the

assembly line that can think, analyze,

and make decisions on the spot. And you

can tell it how to think using

prompting. The AI inside your automation

can look at each task, figure out what's

needed and adapt based on the context

you provided, like deciding how urgent

something is, writing a custom message,

or pulling out key info from a big mess

of data. These intelligent steps can go

way beyond just following some kind of

preset rules that we saw with old school

automations. Next, we have the format.

So, just like items in production line

may need some sanding or trimming before

another piece can be added on top, the

data in our automations often need some

kind of adjustments along the way. And

this is where we use a formatter to

prepare things for the next step. And

finally, we have the output. This is

your finished product, just how Carac

Factory packages up the final item and

ships it out. Your automation delivers

the completed work at the end of the

sequence. This could be a message sent

to your team, an updated file in your

system, or a finished document that's

ready to go. At the end of the line,

it's where everything comes together.

It's like after making pizza and putting

all the toppings on and prepping the

dough, you're finally getting out of the

oven. It's hot. It's ready to eat and

ready to go. So, let me show you how all

of these parts work together with a real

example that anyone in a job can relate

to. Say you're an employee who wants an

automation to handle incoming customer

emails while at work. So the trigger is

going to be when a new email lands in

your company's support inbox. The filter

is going to check if it's something

important like if the email mentions

urgent or problem then it will pass it

along to the next step. If not, it will

end the sequence there. Thirdly, we have

the intelligence layer which is going to

use AI to read the email, figure out

what it's about and then helps to draft

a helpful response. And in this case,

the intelligence layer also acts as a

formatter which is essentially packaging

the response and making sure it's in the

format we want. Then the actions in the

automation can actually send that reply

and alert the boss on Slack if needed.

The output logs everything neatly in

your system and marks it as handled. So

that's just a simple example, but these

same building blocks are used to

automate much bigger things too, which

is why AI automations are such a game

changer these days. They combine these

parts to create truly smart systems that

can save time and boost efficiency,

whether you are you're juggling tasks at

work or crushing it in school or running

your own business. So, now that you've

got a handle on what AI automations are

and how they're built, let's take a

quick look at the tools that make all of

this possible.

Now, don't worry if this sounds a little

bit too techy. Soon, these tools are

going to literally be second nature for

you, and it's going to feel so easy to

do. Creating automation starts with

picking a main automation platform.

These platforms are typically called

workflow builders, and they are the

command center of your automation

factory. They give you a blank canvas to

design your automation on using the

building blocks we talked about. Some of

which are going to be powered by AI like

chatb. Popular workflow builders include

make.com which we're going to be using

later in this video in the tutorial

section. We have Zapia which is great

for quick setups and we have NAM which

is perfect if you want a bit more

control. They are the brain of your

operation and they're really just

controlling how everything fits

together. So workflow builders don't

work alone. They are essentially a place

to hook up all sorts of other tools to

get the job done. So these are the

categories of tools which you can

connect to your workflow builders.

Firstly, we have databases and

spreadsheets. So for storing information

and data, you can use things like Air

Table or Google Sheets. You can think of

them as basically the filing cabinets

where you keep all your data neat and

tidy so that you can save new things to

the database or you can pull it into

your automations as needed. Secondly, we

have communication tools for sending

messages. Things like Slack or Google.

They're like kind of walkie-talkies that

can pass information around

automatically for you. Then we have AI

models. And these can add that smart

human level thinking like open AI's chat

GPT which you can think of kind of like

a genius buddy who solves problems for

you when you give him the instructions

that he needs. Then we have scheduling

tools which can handle time and meetings

things like calendarly or Google

calendar and these are essentially like

your personal planner to keep things

running on schedule. Then we have forms

and intake too. So this can collect

information from people where you have

things like type form or tally. These

are essentially input forms and triggers

for your automations when someone fills

them out. So as your automation goes

through it's like calling up all your

friends on a group project. Each one is

bringing their own special knowledge or

capabilities to the table in order to

help you to get to the end of that

sequence and finish off the automation

to create the final product. By building

out a workflow, you are basically the

boss at the factory. Once you know what

each tool can do, you can mix and match

them to work together smoothly along

this assembly line. It's kind of like

building with Lego blocks. You can just

like snap the right pieces together to

make something awesome. You get to

decide which tools you connect in what

order and how AI can make the whole

system smarter as well. Which AI models

do you use? Gemini, do you use OpenAI?

to use complexity for searching. There's

so many different decisions you have to

make. Uh but it's really cool to be able

to flex your kind of creativity in order

to solve these problems. It's really

like a a new age form of problem

solving, which is why I love it so much.

I say, I know I need to take this and

get to this. How can I use AI to do

that? And really forces you to explore

what's out there in terms of AI tools

these days. So, what kinds of workflows

can we build? So for students, for

example, you could build a study

material organizer that automatically

summarizes lecture recordings for you,

creates flashcards from notes, and

schedules review sessions based on your

exam dates. An employee could improve

their workflow with a meeting assistant

that records and transcribes their

meetings, and then generates action item

summaries off that and updates project

management tools. Entrepreneurs could

design a lead qualification system,

which automatically qualifies leads,

calls them with an AI voice agent, and

then sends them a custom proposal to

automate that whole process. And that is

exactly what you'll learn how to build

by following along with me in this next

section. So, I'm going to show you how

to build out a lead qualification

automation step by step, starting simple

and getting more complex as we go. We're

going to be focusing on this

business-based workflow because these

kinds of business systems offer the best

opportunity for monetizing your new

skills, which I'll go into depth on at

the very end. So, stick with me and

you're going to be learning how you can

start to make money immediately with

these kinds of skills.

So, before we get into that, let's do a

quick summary of this section. So

firstly, an AI automation is a system

that uses AI to automatically perform

complex tasks that used to require

humans. Secondly, we build them inside

of workflow builders that incorporate

integrations with tons and tons of other

tools. And every automation has six key

components. Firstly, a trigger, what

starts the workflow, a filter,

conditions that need to be met, the

intelligence layer, which processes info

and makes decisions with AI, the

actions, which are the tasks that

actually get performed, formatterers,

which clean things up, and the output.

the final result or deliverable. So, if

you're feeling unclear about anything

we've covered so far, feel free to go

back and rewatch some of those sections

and be ready to join us in the next step

when we begin building. So, it's really,

really important that you do understand

everything that we've gone over there

because you're not going to have a solid

foundation to build your technical

skills on top of, which we're doing in

the next section. So, please, please,

please, I've taken a lot of time to put

all of this information very, very

gradually together. So, you must

understand everything that I've gone

over just here before we go into the

next phase. If that's all good, then

let's take a look at what we're going to

build together.

Okay, so now that we have the

foundational knowledge built that you

need, we can now get into the second

chapter of this video where we're going

to be building three AI automations from

scratch. We're going to be starting off

with something super beginner friendly

to get you started and then working our

way up to a much more advanced and

valuable one by the end. Now, very

important note is that each of these

automations build on each other. So, you

have to be able to do the first one in

order to be able to make the second and

so on. So, you you cannot skip ahead.

I've planned this out very carefully to

gradually layer on your skills. So, it's

all very intentional. So, please just

trust the process. So, over the next

chapter, you'll learn almost all of the

key skills you need to start building

your own AI automations from scratch and

be able to tap into this enormous

opportunity that is AI automation. The

system that we're going to be building

gradually over the next three sections

is an AI lead qualification and proposal

generation system for a business. In the

first tutorial, we'll be setting up the

base using AI to automatically qualify

leads after they submit a form on their

website. In the second tutorial, we'll

improve that qualification ability by

implementing an AI voice agent that can

call the lead for more information. And

finally, in the third tutorial, we'll

implement an automated proposal

generator that can instantly create

proposals for qualified leads using the

information collected on the phone call

and in the lead form. The whole point of

this is that as soon as someone's

interested as contacting the business,

they can kick things off with them

immediately by getting a proposal in

their hand. So this process of

qualification is a crucial part of

running any business at scale because at

the end of the day, not all people who

come to a business are going to be a

good fit for their services. So for an

example, someone may run an accountancy

business, but they only choose to work

with doctors. They are specialized in

helping doctors with their finances. But

if a builder fills out their website

form, then that lead would not be

qualified, right? Taking a call with

them would be a waste of time because

they are not a doctor. Therefore, to

stop wasting time, almost all businesses

need some kind of qualification system.

And for qualified leads, most businesses

need to make some kind of proposal for

them in order to kind of look at it and

and see what they're proposing and then

agree to that proposal. Since most

proposals don't lead to a deal, maybe

20% if you're lucky, this is a huge

waste of time and resources for the

company. So, long story short, what

we're about to build will solve a number

of key problems for basically any

business, making it extremely valuable

for you to be learning how to do it. And

in fact, this is something you'll be

able to go and sell directly to

businesses when you're done. Here's what

this process looks like without AI

automation first. So, a human sales rep

must constantly check for new form

submissions from the website, review

each lead's details, and evaluate if

they're worth pursuing, research the

company to understand how to help them,

make phone calls, and deliver competent

pitches, and then manually create custom

proposals. That is hours of repetitive

work per week. And it could lead to

potential leads slipping through the

cracks. If maybe they get the

qualification wrong, they don't

understand what the business is doing,

they don't properly research it, or

they're just too slow at getting back.

They may have to wait a whole day for

the lead to hear anything back. So

decreasing the time it's going to take

for a qualified lead to hear back from

them. It's going to drastically increase

their conversions. With AI automation,

we can transform this tedious process

into something much more efficient,

which is exactly what we're going to be

building together. So here's how it

works. It's going to start as usual with

a lead filling out the form on the

website. Then the system is going to

automatically qualify them using AI.

Then it's going to further research the

company using AI also. Then it's going

to send an automated phone call to pitch

your offer using an outbound AI voice

agent. The system then saves the call

outcome, summarizes the conversation,

and generates a personalized proposal

for them, all without a human having to

lift a single finger and in a super

scalable way. This is the power of AI

automation. We're taking tasks that used

to demand hours of manual work and

turning them into workflows that run all

by themselves. and we're going to build

it all using several of the most popular

tools from the automation ecosystem. So,

if you're as excited to learn this as

I'm to teach you, then let's get

started. And everything that you need to

follow along with it, including all

resources templates prompts etc. is

going to be available for free on my

school community. You can find it in the

first link in the description. You'll

need to request to join. It will take 1

to 2 minutes to be accepted. Once you're

in, you can just search for the title of

this video, and then you'll be able to

find all the resources attached to it.

So, that is how you can get all the

resources to follow along. Let's get

stuck into it.

All righty, guys. Just to clarify things

before we jump in, it's very important

that you understand what we're doing in

this first build, but also in the second

and the third and how it all fits

together and you get kind of an idea of

where we're going. Understanding this

technology and then being thrown in a

whole bunch of random things uh like Air

Table and and Slack and make and all of

these different terms can be a bit

confusing. So, I just want very quickly

to give you a bit of an orientation

before we jump into things of what we're

building and why and how it works

because I've touched on it before, but I

don't think I did a good enough job. So,

I'm filming this after just to really

make sure you guys are fully clear on

what we're building here. So, and why

it's valuable really as well. So um as

we touched on this is going to be an AI

qualification system um for inbound

leads to a business. So easiest way to

explain this is if we had a a website

and say we had this book a free

consultation form and we might be

running ads to it. We might just have

that on a website and people just

discover it by searching the web or or

we're showing up in Google results. But

at some point, a business like the

example I gave before, if you're an

accountant and you only work with uh

builders or plumbers or whatever the the

example I gave, some people are going to

fill out this form who are not

necessarily the right person for you.

Like the my accountancy firm that only

works with plumbers, if a surgeon comes

in or if a a pilot comes in and fills

this thing out and wants to get accounty

services, we would ask them questions

around, okay, well, what's your name?

What what do you do? Uh what sort of

industry do you work in? And we'll

collect this information here. And as I

mentioned before, not all of the people

who fill out your forms, particular if

you're running ads or or doing content

and it's driving traffic to this landing

page as a business, not all of them are

going to be people that you actually

want to get on calls with because if you

get on calls with them and they're not

your ICP or the person that you could

potentially work with, then it's going

to be a complete waste of time. So, a

qualification is a very essential part

in in pretty much any business. And what

you're going to build is a very very

powerful system starting off with

something basic like this. And then

we're going to progress it to something

a bit more advanced like this. And then

finally, something extremely valuable

and powerful that you guys are all going

to be able to build along and ultimately

sell this to someone if you wanted to

afterwards. So that's going to be

covered in the monetization section. But

just to clarify sort of what we're going

to be doing here in this first build, uh

you'll start off with building a form on

a telly. So we're essentially

replicating this kind of website form,

but we're just going to build it in

tally in this case. You can embed these

into different websites, etc. But just

think of this as a form that people will

get sent to. So maybe they clicked on an

ad and then they get sent to this. How

can we help? We're going to build out

this form and then when people click

submit on this form, it's going to send

them into this basically a fancy

spreadsheet. And in this case, we're

using Air Table. Air Table is like a

really awesome platform which you're

going to get to to use a lot within this

build. Uh but Air Table, you can think

of it like a fancy spreadsheet and we're

going to be able to map uh each of these

answers to a row in this database here.

So every time someone fills this form

out that we create, it's going to load

it and add it as a row into this

database. And the cool thing there is

that air tableable allows us to within

make.com our workflow builder it allows

us to set up events that when things

happen in air table like okay let's

watch for new records that arrive and

that's what this here is doing. So this

trigger of our automation is going to be

watching for new records that arrive in

this and then it's going to take

anything that arrives in here say this

first row and then it's going to put it

through this automation here. And so

this is going to be a very basic one

where we've taken actually the AI

component out of this. Before Air Table

really got advanced with the AI

features. What you're doing here is take

the information from the Air Table.

You'd use some chat GPT step here and

then you'd be able to do things

afterwards. But in this case, I'm making

it extra simple for you guys and that

we're actually just using Air Table's

built-in AI features. So down here, you

can see in the tutorial that you're

about to go into, we will use Air Table

to create an AI column here that's going

to take in information like uh like here

where they're talking about their their

company, their budget, and about their

needs. We can write a prompt in here and

it's going to automatically fill out

this row. So as soon as a new person

arrives in this spreadsheet, this field

that we're creating here is going to

automatically qualify them and use AI

and this prompt that we write here to

analyze the information that they gave

us and then output if they are qualified

or if they're not. So we're doing the

qualification with AI built into Air

Table which is going to save us a lot of

time and messing around with jumping

back and forth. So So that is basically

the start of build one. We will do

immediate qualification with AI within

Air Table and then we're going to if

they are qualified we're going to send

an email to them and because we're

already doing the qualification here in

Air Table it makes it a lot easier for

us over at make.com because for this

particular node as you'll see in a

second we're going to set it up so that

it's only actually going to look for uh

the rows or records in this table that

have been AI qualified as qualified. So

if this prompt here that we've written

analyzes all the information in the row

and then outputs qualified then and only

then will it pass it into here. And this

is when we're going to be sending an

email to the prospect and saying, "Hey,

we're interested in in hopping on a

call. Here's a link to book in a call

with us." But at the same time, it'll

also send a message to our sales team in

Slack and say, "Hey, look, we've just

had a new qualified lead." So, this is

very much a a minimized version of what

you could do uh maybe a year ago. But

because of the Air Tables AI features,

uh we can do a lot of the heavy lifting

here and writing a prompt that's going

to analyze what the person has filled

out here, aka are you a are you a

builder or are you a pilot? And if they

were a builder, in the case of the

example I just gave, it's we're going to

be using a different example in the

build. Um, but it would come out as

saying qualified here. And because it

says qualified, then it would

automatically trigger uh the rest of

this automation, which is sending an

email to them and taking the next step

and saying, "Yes, we are interested in

talking to you further, making sure that

we're not talking to anyone that we

shouldn't be talking to and at the same

time letting the sales team know." So,

that is build one. Um, that is how it

all fits together. And I'm going to be

doing these little updates just before

each build so that you guys are 100%

clear on what we're going to be doing.

To get started, we need a way to gather

our leads information. While there are

several great options for this build,

we'll be using Tally, an easy to use

form builder. Just sign up for free or

log into your account.

Then we'll click on new form and give

our form a title. Anyone using the form

will see this title. So, we want it to

make sense for the user. So, in our

case, we can name it, how can we help? I

want to build it from scratch. So, I'll

hit enter. The way we create forms in

tally is with building blocks. So, if we

click the plus button, we can select the

type of block we need from this list of

input options. We'll start with a short

answer input for the user to write in

their first name. And we'll do the same

for their last name. Notice how we're

adding a label above each input, which

tells the user what to write in each of

these. Then, we'll select the email

block for the user's email. Add in a

phone number block.

Then, another short answer input for the

name of their company. Since their

budget is a number, we'll add a number

block to grab that. And finally, we'll

add a long answer input where leads can

describe their specific needs. Now that

our form inputs are ready, we can

customize the form's appearance to match

our branding. We can adjust elements

like the background color, text color,

button color,

and the accent color. Feel free to

choose whichever colors you'd like. Now,

we can go ahead and publish the form.

When it's ready, we'll see a sharable

link.

Let's copy this and actually visit the

published form. It's looking great and

ready to use. In the real world, you

could either use this as a landing page

or embed it into a website. Whenever a

new lead fills out this form, we need to

be storing that response somewhere. For

our build, we'll store our form response

inside Air Table. You can think of Air

Table kind of like Google Sheets on

steroids. It's your database that not

only stores information, but can process

information and if you push it far

enough, can even be used to build out

more complex apps. So sign up for free

or sign in if you already have an

account. On the home screen, you can see

that we could use AI to build things or

start with a template like one for a

marketing campaign or a project tracker.

But for our build, we'll start by

creating a base from scratch. Base is

just Air Table's name for a database.

Again, it's really similar to a Google

sheet or Excel sheet, but can be layered

with complexity. We'll name it lead

base. We'll reference this name later

when setting up the connections between

our tally form and air table and later

between make and air table. For every

field in our form, we need a matching

field in air table. So, we'll add fields

for our leads. First name,

last name,

email,

phone,

company,

budget will reference this to determine

a lead's qualification. notes, details

they submit about their needs created on

when the lead was added to this base.

Importantly, we need to know if our lead

is qualified or not. Ultimately, lead

qualification can be more nuanced, but

for our needs, we'll qualify leads whose

budget is 10,000 plus. By using Air

Table's AI assistant, we can prompt it

to create a custom field for us by

asking it to create an AI field called

qualification that sets the lead as

qualified or not qualified based on

whether the budget field is greater than

or equal to $10,000. Now, Air Table will

use the power of an LLM open AI at the

time of this recording to help with

creating this custom field. After

processing, the AI fields modal will

appear. Since we want leads to be

autoqualified, we'll enable the

automatic generation option.

If you encounter errors about missing or

invalid fields while saving your AI

prompt, it's typically because the

system can't clearly identify which

fields you're referencing. Double check

that all fields are valid and properly

linked to the correct data in your base.

We want our database not just to

autoqualify leads intelligently, but

also to generate a descriptive message

about each lead that we can share with

our sales team. So, we'll use the AI

assistant again to create a field that

generates this message based on the

lead's information.

After double-checking that this new

prompt looks good, we can save it and

move on to the final field. We'll add a

date field called contacted on that will

store the date when we contacted the

lead through our automated email. In the

make workflow, we'll add the date to the

field from make at the very end of our

workflow. Finally, we'll clean up this

base by renaming the sheet to lead

contacts and set up each record or row

to be called a lead. Now that we have

all the necessary Air Table fields that

match our form fields, we can connect

Tally to Air Table via Tally's Air Table

integration.

Just name the connection, select the

database and table you're syncing to,

and map each of the tally fields to

their respective data fields in Air

Table.

After saving this connection, we'll see

a confirmation that the integration was

successful. Now, let's test the

connection by filling out the form with

some dummy data,

making sure to set the budget to $10,000

or higher.

When we submit, we should see the new

lead record populate in our Air Table

database. And voila, it's showing up

perfectly. Since the budget is over

$10,000, it's automatically marked as

qualified. And we've got an AI generated

message ready to send to our sales team

via make. With our lead form synced up

to our database, we're ready to start

building out the automation workflow.

This is what we're about to build out.

We'll have a module that watches our Air

Table database for new leads. And when

it finds one, it sends them an email via

the Gmail module, then updates that same

leads air table row with the time we

contacted them. It also formats and

sends a message into Slack channel. If

you're not familiar with Slack, it's a

professional group chat with channels

dedicated to specific topics like

marketing. With this plan in mind, let's

head over to make.com.

Remember, this is our workflow builder,

the assembly line where we'll be

constructing our automation. You can get

started for free or login if you already

have an account. Once inside, you'll see

your dashboard, which displays info

about your workflows, which are called

scenarios in Make. You'll see how busy

they've been and how much data they've

used. This helps you monitor your make

plan usage, which is particularly

important if you're on a free plan with

usage limits. You can also see if any of

your scenarios require attention due to

things like bugs and errors. The

scenario tab is where you'll spend most

of your time, and you can organize your

scenarios into folders like you see

here. Before we start creating our own,

I want to bring your attention to the

fact that you have a bunch of templates

to get started from and adapt to your

own use cases. And over time, you'll be

managing the connections here in this

tab, which shows you which external apps

like OpenAI and Google you have

connected to from within make. With that

brief tour out of the way, let's get

into it and create a new scenario. We'll

start by setting up the trigger. After

selecting the Air Table module, we'll

configure it to watch records.

Since make doesn't yet know which

records to watch, we'll need to

establish a connection between Make and

our Air Table account. We'll do so with

a token because as you see in this

warning keys are deprecated. So we just

need to create a token and paste it

here. To get that token go to your Air

Table account settings,

navigate to the developer hub

and create a new token named something

descriptive like make token.

When configuring the scope, set it to

allow both reading and writing records

with full database access for read and

write operations.

I'm granting access to all current and

future bases, though you may want to

restrict this depending on your specific

needs. Now, click create token and

immediately copy and store it somewhere

secure. You'll never be able to see the

complete token again. Once we paste the

token back into make, our connection to

air table should be successfully synced

up. Now we just select the bas and table

we want make to be watching

and set the trigger field to created on.

The trigger field tells make which field

to monitor for changes in order to

determine when a record is new. By

setting it to created on make will only

pull records whose timestamp is later

than the last time it checked for new

records. That way it only acts on newly

added leads and doesn't rerun on old

ones. As for the label field, we'll set

it to the company. This field is just

for display purposes inside makes

scenario logs. It helps you identify the

records that are being pulled in, but it

doesn't change how the automation works.

The formula field lets you write an air

table style formula that filters records

before they're passed into your

automation. Make only processes records

when this formula evaluates to true. So,

if a lead's qualification field is equal

to qualified, they'll be pushed through

to the next step of the automation. If

they're not qualified, they don't pass

this step. If you ever need help writing

these filters, you can ask an LLM like

chat GPT or even have a chat with Make's

built-in AI. Once we hit save, we'll

choose to start from now on. That way,

make only looks at new leads created

after this point and skips anything

already sitting in Air Table. If we hit

the run button, it should successfully

find the newly created qualified lead

and display that records details. Once

you run this scenario, any qualified

leads that were found will not be found

again on rerun due to this created

onfield. You'll have to add a new lead

each time you want to test this

scenario. Again, the automation is

filtering for only new leads. Since we

want to send qualified leads an email to

schedule a call and notify our sales

team about this lead via Slack, we'll

need to set up two actions. This is

where a router comes in. It allows our

scenario to branch into these two

separate tasks simultaneously. Without a

router, setting up tasks sequentially

means if the email step fails, the Slack

step won't run at all. We want to avoid

these waterfall effects where one

failure blocks other actions. Using a

router prevents this issue while making

our scenario easier to troubleshoot and

expand over time. On the first route,

we'll add the Gmail module and select

the send an email action, which requires

us to set up a connection to our Gmail.

If you're using a non-personal email

like mike@edge.ai,

this setup is very simple. You just log

into your Gmail account here. But if you

need to use a personal Gmail, one that

actually ends in gmail.com, the process

is more involved. So, I'm going to ask

you to be patient because there are

quite a few steps involved to connect a

personal Gmail account here. If you're

just watching and not building along

yet, feel free to jump past this

section. Same goes for those of you who

are using a non-aggmail.com email. But

if you do need to connect your personal

Gmail account, here's what that looks

like. As you can see, if we pop open

these advanced settings, this requires

two key pieces of information. a client

ID and a client secret. These act as the

key that allows make to unlock access to

your Gmail account. Make provides

instructions for how to generate these,

which you can find by clicking on this

guide link. At the time of this

recording, it links to the help center.

From here, click into apps

documentation, then click on

communication and scroll down to Gmail.

If you're wondering why make didn't just

link us to this page, so am I. From

here, we'll click on create a custom

oorthth client. And now we need to log

into the Google Cloud Platform. Once you

log into Google Cloud with the Google

account you want to send emails from,

you'll click select project at the top

left.

From there, click new project, then give

the project a name like make and hit

create.

Once it's created, a notification will

appear in the top right corner. Click

select project to open your new project.

Now that we're inside, let's enable the

Gmail API. Go to APIs and services

and click enable APIs and services. In

the search bar, type Gmail and select

the Gmail API from the results.

Then click enable.

Next, we need to tell Google who is

requesting access and what kind of data

is being requested. So, we'll click on

Oorth consent screen and hit get

started. To fill out the app

information, we need to give the app its

name. So to keep things consistent,

let's call it make like we did for the

project name earlier. We also need to

tell it which email we want to give

these new abilities to. Now we select

external for the audience type and next.

Under contact information,

enter an email for Google to notify you

about this project. Hit next. Then agree

to the terms, continue, and hit create.

We now need to give make permission to

interact with Gmail on your behalf. So

head into the data access tab and click

add or remove scopes. We're adding Gmail

API scopes. So, search for Gmail and

then select the following scopes. Both

scopes to read, compose, and send

emails. Manage drafts and send view your

emails and settings. View your metadata.

Add emails to your mailbox. Send email

on your behalf. See, and edit labels.

With all those labels selected, hit

update. Then, scroll down and make sure

to save these scopes. Next, let's

configure the branding settings. Scroll

down to authorize domains and add both

make.com and integromat.com which is the

old name make used to be called. Then

save. After that head to the audience

section and click add users

where you'll add your Gmail address and

hit save.

Now it's finally time to create the

actual credentials that make will use.

Go to the client section and click

create client.

For the application type, choose web

application and name it make.

Scroll down to authorize redirect URIs.

Click add URIs and paste the exact

redirect URI provided by Make. You can

find this in their Gmail integration

tutorial page that I showed you earlier.

Click create and you'll be given your

special client ID and client secret.

You will copy both of these and paste

them into the field in makes Gmail

module.

Now we can click sign in with Google.

This sign-in window will appear. Select

your Gmail account. Grant the requested

permissions and hit continue. Once

that's done, your Gmail account should

be successfully connected to make. I

know that was a lot and I wish it was

simpler too. But the good news is we can

now start sending emails from that Gmail

account. So, let's now create the email

template. We'll set up a subject line

using the lead's name from their Air

Table record and whatever engaging text

you want to add in here. For the content

of the email itself, we can address them

by name and say, "We received the

request about their company and would

love to discuss their goals on a call

they can book via our scheduling link.

For this, we will be using a handy tool

called Kalanley. If you're not familiar,

Calendarly is a scheduling tool that

lets people book meetings with you. You

connect it to your calendar, set your

availability, add conferencing tools

like Zoom or Google Meet, then create

event types like an introductory call

and share a link for people to book

these meetings with you. So just set up

a new account if you don't have one or

log in and we'll create a new event type

and call it intro call. This event will

have a 30-inut duration and the location

will be Google Meet. Keep in mind you

may have to connect a conferencing tool

if you're using something like Zoom. Now

we can set our availability for this

intro call event. I already have mine

set up, but to do that, you just head to

the availability tab and go to calendar

settings. Here you can see I already

have a couple calendars synced up, but

to add a new one, click connect to

calendar account and select your

provider, such as Google. Then just

choose the account you want to sync and

log in. Again, I've already set this up,

so I'll close out of this and head back

to the event. For each event, we can set

the availability for that event type. I

may want to change the time I'm free on

Friday, either a specific day or every

Friday. When I apply these changes, that

will update the availability for just

this event type and does not apply to

other events I may have set up in

Calendarly. Down in more options, I can

do things like add an event description

where I can tell the person booking that

I'm excited for our chat. Once we hit

save changes, the event is ready to

share. So, let's copy this link and head

back into make and paste it into the

body of the email we're sending to our

lead. Finally, we of course need to add

the recipient of this email. So, we'll

fill that in with our lead's email

address. With that set up, we can move

on to our other route below. Ultimately,

we're going to be posting a message to a

Slack channel about our new lead. But if

you don't use Slack and don't want to go

through the steps of connecting another

external tool, you could sub this out

for another Gmail module where we send

an email to our sales team letting them

know that we have a new qualified lead

and give them the lead's name and other

relevant info.

Then set the recipient email to

whichever test email here. But since I

want to show you how to use a bunch of

popular tools, let's look at setting

this up to post to Slack. We'll choose

the Slack app and select the create a

message action. If you already have a

Slack organization, you can simply

connect make to that module. Now,

if you don't, let's quickly walk through

creating a new one. You'll head to and

sign in with a new account, then click

create a workspace. Give it a name.

Here, I'm just adding the name of our

mock agency that we're qualifying leads

for. Then just run through these steps

to confirm your name. Skip adding new

members for now and start with the free

version. Now we're in our new Slack

organization. As you can see, there are

channels here for different topics and

direct messages down here. We'll add a

new channel called marketing because

this is where we'll be posting our Slack

message into for the entire sales team

of our imaginary agency to see. With

these ready to go, we can head back to

make and connect the Slack module to our

Slack or by allowing access.

We'll tell the module where to post the

message by selecting from a list of our

public channels, specifying the

marketing channel we just created. Now,

what should it post to this channel? It

should post the AI message from our Air

Table lead. So, we'll add that in here.

If we run the scenario, it will work,

but the message will look messy because

it's technically a collection, which

just means it includes extra stuff that

we don't want or need to display here.

We can solve this by inserting a

formatter module just before we post to

Slack. We'll select a text passer, which

will do a pattern match using a regular

expression to pull just the clean

message text out of it. While this might

look complicated, think of it like a

smart highlighter that scans the text

and grabs only the part we care about,

the messages value itself and not the

messy metadata. If you ever need to use

a regular expression or rejects in the

future, just ask an LLM to draft one for

your use case. Now, we tell our text

passer which text to pass, the AI

message. With that formatter taken care

of, we can head back into the Slack app

and tell it to create its message based

off that freshly passed text instead of

the original messy version.

As a final step, we want to circle back

to Air Table and update our lead record

with information about when we auto

contacted them. After the Gmail module,

we'll add an Air Table module and use

the update a record action. We'll

configure it to return to the same lead

base and contacts table targeting the ID

of the record that just pushed through

this scenario.

We'll set the contacted onfield to the

current date and time by using the now

expression which simply tells make to

insert the exact moment the automation

runs. If we run the scenario and head

back into air table, we'll see the date

was successfully added to the contacted

on field giving our sales team context

about when the lead was auto that email.

Our scenario is all set up and working

great, but up until now, we've only been

running it manually by clicking the run

once button by toggling on the schedule.

We can configure our scenario to run on

autopilot at regular intervals such as

every 15 minutes or on a specific day of

the week or month. You can even set a

custom schedule using advanced

scheduling using time ranges with start

and end dates if you only want it to run

during a certain window. Once you're

confident it's working, just flip that

schedule toggle on and your automation

will run in the background while you

focus on more important things. Of

course, you'll want to frequently save

the scenario. And if you ever need to

revert to a previous version, you can

revert to that version from here.

Scenario inputs are useful for more

advanced use cases, like when one

scenario's output becomes another

scenario's input, but that's beyond the

scope of this basic build. You've also

got your scenario settings, a place for

notes, an auto aligner if your workspace

gets a little messy, and even a little

animation that explains the workflow.

Finally, there's a quick reference to

every app and module used in your

scenario, helpful for getting a bird's

eye view of your automation. And so,

that's build one explained, just a very

basic AI qualification based out of Air

Table. But to give you an idea of where

we're going with this, in the next

build, build two, we're going to be

adding in a voice agent here. You may

have heard about voice agents before,

but they're are a really a really

exciting uh area of the AI space right

now. You have two main types. You have

inbound and outbound. And uh inbound is

when you can set up a phone number and

people can call. When people call that

number, then they get to talk to the AI

directly. Outbound voice agents is what

we're going to be building here where we

can initiate a call using our automation

here uh to send a call out to someone

and say my phone starts ringing and I

can pick it up and I'm talking to the AI

that we've created here. So this is

really really cool stuff and super

powerful. But the reason we're doing

this is because the as you will have

seen on the form that we set up, we have

quite a limited amount of information

that we're collecting from the business

and this is for good reason. You don't

want to put too much information here uh

or it will decrease the number of people

who fill it out. So sort of having a

lean form and then if we initially

qualify them here in the second build

we're going to expand it so that we can

actually once they are qualified as

we've explained here the AI and air

table is going to determine

preliminarily if they are qualified then

we will use our voice agent to actually

call the person and ask for more

information and we'll walk through a

script basically and ask them hey tell

me a bit about your business and your

needs what are you hoping to get out of

us da da da da basically collecting a

bunch more information that can then be

used for even greater and more accurate

qualification. So here we have our Vappy

voice agent which is going to call them

and then we're going to get the data of

that call, the transcription of that

call and Vap is actually going to be

able to analyze it for us to determine

if it was a successful qualification or

not. And then we have to build the

automation to handle a few different

cases because of course not everyone's

going to pick up the phone. So here we

create a route that handles if they

answered the phone and then based off

the information were they interested or

were they not interested and if they

didn't answer we have some other things

that we can do to handle it here and

update the air table and things like

that. So, it's essentially build two is

adding on top of what we've already

built on build one uh where we have just

a basic qualification and sort of send

them an email to book in a call or to

let the sales team know. Here, we're

trying to do even deeper qualification

and making sure that our sales team

really isn't getting on any calls they

shouldn't be by sending out a voice

agent to collect more information for us

to do an AI pre-qualification for this

business's lead. So, let's take this

automation to the next level by adding

AI voice calls to qualify leads even

more effectively. We've already built a

strong foundation, a system that

identifies qualified leads, follows up

via email, and notifies your team. This

alone puts you ahead of the curve. But

what if we could make the system even

more responsive, more conversational,

more human even? That's where an AI

voice agent comes in. By weaving it into

our workflow, we can automatically call

leads, gauge their interest through real

conversation, and tailor our follow-up

based on their responses, all without

lifting a finger. This is where

automation becomes more than a task

runner. It becomes a teammate. It's the

closest thing we have to a human

conversation at scale. And when it comes

to leads, timing and tone matter.

Research shows that responding to leads

immediately increases success rates by 7

to 9x. In this next section, we'll

integrate VP, a popular AI voice agent

service, into our workflow. First, we'll

set up our voice agent. Then, we'll

configure make to have it call our

leads. Finally, we'll enhance its

effectiveness by feeding it custom

research about each lead, enabling more

targeted pitches. So, what exactly is an

AI powered voice agent? You can think of

them like a version of Siri or Alexa,

but one that's specifically designed to

have natural phone conversations with

people. Depending on your needs, it can

either make calls for you or answer

calls. These agents can do amazing

things like follow up with potential

customers, schedule appointments, answer

common questions, collect feedback, or

conduct surveys. There are several

platforms that offer AI voice agents

like 11 Labs, great for highly realistic

voices, and Vappy, known for being fast

and affordable for our automation. We'll

be using Vapy because it's beginner

friendly, cost-ffective, and integrates

well with other tools. If you haven't

already created an account, go ahead and

do that first.

Once inside, we'll go to the assistance

tab and click create assistant. This

starts the process of configuring our

assistant. You can start from a template

like a lead qualification specialist,

but we'll create our own from scratch

and name it Ben.

When choosing which provider and model

to use, you'll want to balance ability

with speed and cost. So, keep the price

in mind and the time it takes to

actually respond, which we call latency,

which you can see next to the model

options. For now, we'll go with the

cheapest and fastest option, which is

GPT4.1 Mini at the time of this

recording. The most crucial element is

the prompt, which defines your agents

behavior and objectives during the call.

First, we specify the opening greeting

message that the agent will use when

calling someone. We'll open up the call

asking if it's a good time to talk about

the lead's business needs.

The actual prompt is much more detailed

and I'll paste in one I wrote earlier.

You can find this prompt and everything

else you'll need to follow along in the

first link in the video description.

Let's take a look at what it includes.

As you can see, we start off by defining

its identity and role. A voice assistant

representing Edge AI and AI automation

agency.

then specify its goal to pitch our

services and determine whether the lead

would like to receive a proposal. We

then set its tone and behavior. A

knowledgeable rep who is curious but not

pushy and who uses casual natural

language with words like uh and mhm.

This section helps humanize your

assistant. Finally, we outline the

structure of the call that it should

replicate. The conversation flow starts

with introducing our AI services, then

moves to understanding the prospect's

needs through targeted questions. After

acknowledging their responses, we pivot

to offering a proposal. If pricing comes

up, we defer to the proposal for

details. The call ends either by

confirming they'll send the proposal

over email or with a plight goodbye if

they decline. It's best to outline the

steps as clear bullet points like this,

but keep them focused. Too many steps

can overwhelm the AI assistant. In our

prompt, we included context about the

company the assistant is representing.

For our needs, this is sufficient. But

if our call was more involved, like a

customer support agent who is receiving

hundreds of nuanced calls, we could add

files into its knowledge base that it

can reference, like all of our policies

and procedures. But keep in mind, this

will increase the response latency since

the assistant will need to make round

trips to these files as it generates its

responses. So, if you can get away

without adding supplemental files, your

agent will be more performant. Our

assistant is almost ready to start

making calls on our behalf. But before

we publish it, let's add some extra

configuration. As we scroll down, we'll

see that we can tweak the transcriber

settings, which we'll leave alone for

now. We can also select different voices

depending on the personality we want our

agent to have. For our agent, we'll go

with Vap's Elliot voice, but feel free

to try out different options to feel out

which one is best for your use case. You

can also add background noise to the

call. Adding ambient sound makes

conversations feel more natural and

helps mask any brief delays that occur

while the agent generates responses

instead of awkward silence. Callers will

hear realistic background sounds. Next,

you'll see the tools section, which

unlocks powerful ways for your agent to

take action during a call, like sending

data to your CRM, triggering external

workflows, or running custom logic. This

is where Vappy really shines for more

advanced automations. However, since

it's beyond the scope of this tutorial,

we'll leave it untouched for now. What

we will be using is the call summary.

Vari automatically transcribes the call

and the prompt inside here tells it how

to summarize the call based on that

transcript. If we open it up, we'll see

there's already a built-in prompt that

tells the assistant how to create its

call summary. While this default prompt

works decently, we can make it even more

valuable for our make scenario by

customizing it to capture additional

details. Here, I'm pasting in a new

prompt telling it to summarize the call

in a couple sentences with details that

will help out with creating a proposal

later on. Since we'll add this call

summary to our lead's air table record,

capturing detailed insights here is

valuable. We also want to tell Vappy how

to judge if a call was successful or

not. There's already a decent default

prompt here, but we'll customize it for

our needs by telling it to judge success

solely on whether the lead wants to

receive a proposal. We'll evaluate

success with a pass fail, which simply

gives us a true or false value that we

can use in our automation workflow.

While there are additional advanced

options here, the final step we're

concerned about is the voicemail

detection. Let's make sure we toggle

that on with VP as the provider. This is

helpful for our make scenario since we

need to accurately track whether the

call was actually answered or if it just

went to voicemail. We're now ready to

hit publish and test our newly created

voice assistant. Hey, it's Ben from

Edji. Is now a good time to talk about

your business needs? Yes, it is. Great.

I'm following up on your submission

because we build custom AI agents and

automations for companies like yours.

anything from customerf facing bots to

internal workflow optimization. I'd love

to understand a bit more about what led

you uh to reach out. What sort of

processes or tasks are you looking to

automate? I'm looking to implement

custom AI agents to facilitate learning

on an e-learning platform. That sounds

really interesting. Um so you're looking

um to enhance uh the learning experience

on your e-learning platform with AI

agents. Are there any specific

challenges or bottlenecks you're facing

right now that you hope these agents

could help with? Uh, yeah, just

streamlining the learning of the content

library on my platform.

Got it. So, you're looking to streamline

how users access and learn from your

content library. That makes a lot of

sense. Thanks for sharing that. Would it

be helpful if I sent a quick proposal

your way showing how we could support

your goals? Yeah, that'd be great.

Perfect. I'll send that to the email we

have on file. Thanks again for your time

and I look forward to helping you with

your project. Great. Thanks. Bye. You're

welcome. Have a great day. Bye. Great.

Our assistant is working as intended.

It's now the voice of our lead outreach

standing by, ready to make calls on our

behalf. Before integrating this

assistant inside of Make, let's walk

through the new path a lead will take

through our extended scenario. We left

off here, but we're going to extend this

for the trigger. Nothing changes here. A

new Air Table record marked qualified

still kicks things off. Then our voice

agent will call the lead using the

number from their record. We'll add a

short pause to wait for the conversation

to finish before we analyze what

happened. We grab the call record from

VAP. Then we check if the call was

answered. Answered. Great. We log the

call summary and mark the lead as

interested or not in Air Table. No

answer. No problem. We fall back to

emailing them a link to schedule a call

and alert our sales team. Since we're

going to be logging call summaries and

interest level about our leads, we'll

just need to head to our lead base in

Air Table and add those. We'll add a

long text field called summary,

a checkbox for interested, and while

we're at it, let's add a new date for

when a proposal was sent on since in the

final phase of this buildout, we'll be

generating and sending proposals to

these interested leads. Back over in

make, we're ready to extend the lead

qualifier scenario that we built

earlier. To keep things clean, let's

just clone what we built and extend from

there. Name it lead qualifier plus voice

agent. And now we're ready to continue

building.

The beginning step remain the same where

we watch for new leads.

Then we'll add the VP module to create

an outbound phone call.

We will need to set up the connection to

our VP account,

which means we'll head back into that

dashboard, click on API keys, then copy

your private key. If you don't see one

in there, just go ahead and add a new

one. Give it a relevant name. Decide if

you want to restrict it to only work on

certain sites. Or if you only want it to

work with specific assistant, then

create the private token. You'll see it

pop up here. Then just copy it. But make

sure to save this somewhere safe since

you won't be able to view it again.

Pasting the key into the VP module

should set up our connection. Then we

can configure our call. We'll just fill

in these fields, giving it the

assistance ID, which you copy from the

top of the Vappy Assistant page. We

provide the assistant with the lead's

phone number pulled from their air table

record. But what number is our assistant

calling from? If we head back into Vappy

and click on the phone numbers tab, we

can create a new free phone number with

Vappy, specifying the area code to call

from. Note that at the time of this

recording, only US area codes are

supported by Vappy. Since we're building

this assistant for learning purposes, we

can simply use a US area code like 223.

However, if you plan to deploy an

assistant for production use and you or

your client are located outside the US,

you'll need to import a number from

something like Twilio or Vonnage. Okay.

Once that number is created, we can copy

its ID, not the number itself, and paste

it into the makevarpy module. And with

that, our voice agent is set up and

ready to make calls triggered by our

make scenario. Once the call is sent, we

need to wait a bit before we check what

happened. Calls take time to connect,

ring, and potentially have a

conversation. If we try to fetch the

result too quickly, we could just get an

empty response or an error. So, let's

add a short pause. Insert a tools sleep

module. Set the delay to 300 seconds.

This little breather, 5 minutes exactly,

gives Vappy time to do its thing before

we peek in to see how it went. To ensure

things are working so far, let's add a

new lead to our Air Table base. We could

add them via the form, but it's quicker

just to duplicate an existing lead in

order to create a new one. Now, we'll

head back and make and run the scenario

to test that it's all working.

Hey, it's Ben from Edgeai. Is now a good

time to talk about your business? Yeah,

it is.

Great. I'm following up on your

submission about exploring AI solutions.

We build custom AI agents and

automations for companies like yours.

Anything from customerf facing bots to

internal workflow optimization. I'd love

to understand a bit more about what led

you to reach out. What sort of processes

or tasks are you looking to automate?

Yeah. So, I'm just interested in um

seeing how we can build out some maybe

AI powered workflows to help the

operations in my company.

That sounds interesting. AI powered

workflows can really streamline

operations. Are there any specific

bottlenecks or manual tasks you're

hoping to reduce? Hoping to help my uh

learning management system run more

smoothly.

Got it. Optimizing your learning

management system can definitely enhance

efficiency and user experience. Uh,

thanks for sharing that. Um, would it be

helpful if I sent a quick proposal your

way showing how we could support your

goals? Yeah, definitely.

Perfect. I'll send that to the email we

have on file. Thanks again and I look

forward to your feedback. Thank you.

You're welcome. Have a great day. Now

comes the part where the automation

becomes observant. We're not just

automating calls. We're building a

system that pays attention to what

happened. Did someone pick up? Did the

assistant get the job done? Was the lead

interested? To answer those questions,

we need to ask VBY for the results of a

specific call. And to do that, we'll use

an HTTP module to make a request for

that call record. To better understand

this module's function, let's explore

what an HTTP request is and how it fits

into the broader workings of the

internet and how APIs fit into all of

this. I know it sounds complicated, but

let's break it down with a simple

analogy. Remember earlier we learned how

an automation workflow is like a facto's

assembly line where each module is like

a machine performing a step in the

process? Well, factories don't operate

in isolation. Sometimes your factory

needs supplies, information, or services

from outside its own walls. A real

factory might call a supplier to order

more raw materials. Ask a logistics

company where a shipment is. Verify

parts. Meet quality standards with lab

testing. request a maintenance crew to

check the temperature of a remote

machine. Simply put, a factory

coordinates with external partners to

handle tasks outside its own expertise.

Just like real world suppliers don't

take factory orders shouted over a

fence, external services need a

structured way to receive requests.

That's what an API or application

programming interface is. In our factory

analogy, the API is like the official

order form that your external partners

use to process requests, which you would

fill out to order some special machine

parts to use within your factory. Online

services like Vappy, Air Table, or Slack

all offer APIs that follow the same

principle. They give outside entities

like make.com a structured way to send

and receive information. APIs give us a

clear, consistent way to ask for

something and get a predictable,

reliable response. There are several

types of HTTP requests that serve

different purposes. So, you could be

saying, "Get me this thing. Post or add

this new thing. Put this info where it

belongs. Replace the whole thing. Patch

just this one part of the info, but

don't replace the whole thing. Delete

this thing." In our case, since we're

about to be making an HTTP get request

inside of Make, we're essentially

saying, "Get me this call summary.

Here's what I want, and here's who I am.

I've got the proper permission to access

what I'm requesting." While platforms

like make.com and n10 provide a visual

interface for building workflows, under

the hood, they're actually making API

calls to connect with these external

tools. So even though you're working

with visual blocks, these modules use

the same underlying language that

developers use to connect services

across the web. Yes, this is no code

development, but that doesn't mean code

isn't running. It's just happening

behind the scenes. That was quite the

detour, but an important one because it

gives you a firm grasp on how things

work on a deeper level. Now that you

understand what an HTTP request is and

how it fits into the big picture, let's

set this module up to go get the record

for the call that was just made. We'll

set the method to get. But where are we

getting something from? Well, we're

going to make a request to VP's API to

get the call that just happened. If we

check out the VP documentation, we can

see that we need to send our request to

this URL where the last part of the URL

is the ID of the call we're fetching. So

let's copy that URL and paste it into

the HTTP module. Adding on the call ID

from the vari modules output

in the header section. Here's where we

add instructions like putting a label on

a package by putting authorization in

the name field. This just means we're

saying we have the authorization to get

this info and the proof is the value of

the header itself. Another way to think

of that proof is that we have a key to

unlock this special box that contains

the information we're requesting.

That key is the API key that we generate

from VPY. So in the value field, we'll

type bearer. Then paste that key next to

it. This roughly translates to the

person who possesses or bears. This key

has permission to access what they are

requesting. Finally, we'll say yes to

passing the response. This means the

module will break apart the response

into structured fields that we can

easily use later in the scenario. Let's

make sure this is working by running

this module only. Here at the bottom,

it's asking for the call ID that we want

to get, which we can just grab off of

the last time the VAP module made a

call. Placing that calls ID here and

hitting save will now cause this HTTP

get request to fire off. And we quickly

see that it was a success. We now have

access to all of this data from our

call, including the summary, which as

you recall is the result of that prompt

we added into VP to summarize the call

for a sales team. We also have access to

the analysis, which tells us if the call

was successful. This is perfect since

we'll use both pieces later in the

scenario to update the lead's air table

record with their call summary and

interest level. So now that we're

monitoring the call results, let's start

implementing those next steps. First, we

want to determine whether the call was

answered. So we'll set up a router to

create paths for both answered and

unanswered calls. When someone answers

the call and engages with your voice

assistant, it's a valuable interaction

that your sales team needs to know

about. Let's set up a filter to check if

someone answered the call. On the first

route, we'll add a filter with a

condition that checks for calls where

the data do the ended reason

equals customer ended call. Among all

the data points available, this is our

most reliable indicator that the call

was answered and didn't go to voicemail.

On the second route, we'll do the

opposite and set the condition to filter

for cases where data ended reason does

not equal customer ended call.

On the answered route, we want to know

if the lead was interested in being sent

a proposal or not. So we'll add another

router that splits into two paths based

on the call analysis, specifically the

success evaluation. Remember earlier

when we told Varie to evaluate if the

call was a success based on whether the

lead wanted a proposal or not. That

evaluation is contained in this data

here. Since this data is simply true or

false, we'll check whether the analysis

is equal to true success or false

failure on each route. For both routes,

we want to update the lead's record with

the call summary and note whether they

were interested in a proposal. So on

each route we'll add an air table update

a record module. On the interested route

we'll target the lead contacts based by

this lead's ID and update the contacted

on field two. Now add the call summary

and check yes on interested.

We'll do the same for the not interested

route

but check no for interested. Now, our

sales rep can instantly see when the

lead was contacted and what transpired

during the call. The system

automatically documents each

interaction, just as a human rep would

take notes after speaking with the

prospect. By the way, in case you're

curious, this process technically

performs a put request under the hood

where it puts or updates something about

the record without replacing it all

together. Of course, not all of our VP

calls will be answered. This happens,

people are busy or they screen unknown

numbers. That's why we build in a

backup. On the notwans answered route,

we'll keep the steps from our initial

build where we send that lead an email

to schedule a call and we let our sales

team know about them. With this all

locked in place, let's run the scenario

to make sure it's working. Hey, it's Ben

from Edge AI. Is now a good time to

quickly talk about your business needs?

Yes, it is. Great. Thank you. I'd love

to understand a bit more what led you to

reach out to us. Are there specific

processes or tasks you're looking to

automate? Yeah, looking to uh implement

AI agents.

That's exciting. Implementing AI agents

can really streamline operations. Um are

there particular tasks or workflows at

your company that you find especially

timeconuming or manual which you're

hoping to improve with automation? Yeah,

just hoping to um have the agents

trained on the learning material so that

they can help my students.

That makes a lot of sense. Training AI

agents to assist your students could

really enhance their learning

experience. Thanks for sharing that.

Would it be helpful if I sent a quick

proposal your way showing how we could

support you in developing those AI

agents? Yeah, that would be perfect.

Excellent. I'll send the proposal to the

email we have on file. Thanks again.

We've done a lot. Let's zoom out for a

second to recap. With our current

scenario, we now have a system that

detects qualified leads, places an AI

powered voice call with no manual

effort, waits, listens, and reacts

depending on whether someone answered

and whether they were interested or not,

logging the outcome accordingly. As of

now, the voice assistant works, but it's

not as capable as it could be. Without

knowing our lead's name or anything

about their company and needs, the

agents ability to pitch is pretty

limited. Our next step is to enhance our

scenario by researching our lead with

open AI and passing those insights to

our Vappy Assistant, allowing it to

personalize each call. Essentially,

we're going to have a a search feature

that is going to not only just use the

voice agent for uh doing the research

and getting more information, we're

going to use OpenAI's uh search models.

So, when people fill out that form we

made, then we're going to use OpenAI to

research the internet for that lead and

get some information on that. Then,

we're going to send a call to them again

using Vappy. This time it's going to be

personalized with the information that

we got from that web search. So it's

really, hey, we know this about you, but

what else? We're looking for this

information on you. You can really get a

a a complete picture of who this person

is before we even booked them in for a

call with our sales team. So with the

VIP module we're currently using,

unfortunately, we're not able to feed

anything into it, at least not at the

time of this recording based on the VIP

module's current setup, but we can solve

for this by switching out the VP module

with a more custom approach using an

HTTP module. So, let's drag it out of

the workflow and unlink it. And since

we'll be borrowing some of the values

from it soon

inside a new HTTP make a request module.

Since we're making a manual request out

to the Vappy API, we need to specify the

URL just like we did in our existing

HTTP module. So, we can go ahead and

copy that URL from the get module. Since

we're simply placing a Vappy call and

not retrieving an existing one, we won't

need the calls ID. So, we'll leave that

off. And we'll change the method type to

post. The header remains the same,

but we need to add a second item where

the name is content type. Value is

application/json.

Content type is like labeling the

envelope you're sending.

Application/json means inside this

request, the data is structured like a

JSON object. It's like writing English

or Spanish on the outside of a letter so

the recipient knows what language to

expect when reading it. Continuing down

the module, we'll then set body type to

raw, which means we're manually writing

out the data we want to send to VP. And

we set content type to application/json,

which again tells VPY to expect data

formatted as JSON. And I know many of

you don't know what JSON is, but it's

less intimidating than you might think.

It's really just a pair, the key and the

value. Just like in a spreadsheet where

you have the keys like phones, name, and

email. Then you have the values, the

actual data that goes with each key.

It's an easy to read way to structure

and share data. In JSON, everything sits

inside curly brackets. This is called an

object. Both the keys and values need to

be in quotes with commas separating each

key value pair. Don't worry if you're

not a JSON expert. Many free online

tools can help you check if your JSON

formatting is correct. So back in make

down in the body of our HTTP call out to

Vappy, we're going to add some JSON to

save some time. We'll grab some values

from the VPY module we were using

earlier, including the assistant ID,

which we'll paste into the JSON.

And we'll also grab the VPY phone number

ID off that old VP module and paste

reuse it in our new HTTP module. We're

just doing it a bit more manually. The

main difference here is in this

assistant override section. As it

sounds, we are overriding the assistant

within custom variables. These are

essentially placeholders that will be

replaced by the lead's name, company,

and the research we perform for each of

them. This way, when we say, "Hey, Vapy,

call this lead." We're also saying, "And

here's info about them to use on the

call." Now, that we're going to be

sending these variables into the

assistant, we need to head back over to

the Vappy dashboard and tell our

assistant Ben to be expecting that

information and instruct them on how to

use it. In the first message, we can add

the first name variable so our assistant

can greet our lead by name. In the

updated prompt, we'll inform Ben that he

will receive custom data via variables

such as the lead's first name, company,

and company research. And to use these

details to personalize everything on the

call with the goal of pitching more

effectively. With our assistant ready to

receive all that custom info, we just

need to perform research on our lead.

And for that, we'll be using OpenAI.

Let's add an Open AI. Create a chat

completion module in line just before we

make the Vappy call. You'll need to set

up the connection with your OpenAI

account and make sure get an OpenAI API

key and also have some credits to use

for this.

To do this, you can log into your

account at platform.opai.com

login and click on API keys. Then create

a new one, making sure to copy and save

it in a safe place. Then you'll click

over to billing and add some credits,

making sure you have an active credit

card set up. With that connection set

up, we'll select the model. Since we're

doing research, I'm choosing GPT40 mini

search preview. By the time you watch

this, there may be other options. The

important thing is to choose a model

that can do search. In the messages

section, we'll set the role to user,

which just means this message is coming

from a user, you in this case. The text

content is where we place the prompt.

Essentially, we're telling it to serve

as a research assistant that uses our

leads information to generate a summary

of how an AI automation agency could

help them with their needs. Let's see

how it works by running this module only

giving it an example company name and

notes for testing purposes.

As you can see, it's running and

performing research for us. But notice

how the result is formatted in

paragraphs. While that might look fine,

it actually causes a problem because

when we try passing this result into our

HTTP module, it needs to follow strict

formatting rules. Those paragraph breaks

can quietly break things behind the

scenes and cause the system to reject it

since it won't be valid JSON. There are

a couple ways to fix this. We could

either demand that chat GPT gives us our

summary in JSON, or we could add a text

passer in between the GPT and HTTP

modules, which removes those line breaks

for us. Since there may be other

situations when you need to transform

data in your future workflows, it's

helpful to get some practice passing

text. So, let's add the text passer

replace tool. For the pattern, we'll add

this regular expression. This

essentially finds any line breaks, so we

can replace them with a new value, which

we'll leave blank. In other words,

instead of the line break, it will be

replaced by nothing, aka removed. We

want global match to be set to yes, so

that it finds all line breaks, not just

the first one, then no, for the rest of

the options. Finally, we'll select the

text we need to pass the result from

open AI's research. With this

reformatting in place, we can now

reference that clean text inside the

company research field of our HTTP body.

Finally, we'll say yes to pass the

response and close out this module.

Notice how we still have this red error

icon on the second HTTP module. It's

broken because it's trying to reference

the call ID from the original VP module

that no longer exists because we deleted

that module. Instead, it needs to

reference the call ID from our new HTTP

module, which manually makes that VP

call. Since we haven't yet run that new

VP replacement module, we'll run it in

isolation, feeding it a phone number and

whatever info just to get it to run.

Once it runs, we can now access and

reference the ID field from it for

future runroughs of this workflow. We're

now ready for the big payoff. Make sure

you add a new lead to your Air Table

with a valid company it can research.

Let's run the scenario to make sure

everything is working properly.

Hey Mark, it's Ben from Edjai. Is now a

good time to talk about your business

needs? Yeah, sure is.

Awesome. I'm following up on the form

you submitted. We build custom AI agents

and automations for companies like View

Mastery. Anything from customerf facing

bots to uh internal workflow

optimization. I'd love to understand a

bit more about what led you to reach

out. What sort of processes or tasks are

you looking to automate? I'm looking to

implement AI agents to facilitate the

learning journey on my e-learning

platform. That sounds like a great

initiative. Enhancing the learning

journey for your students can really

make a difference. Are there any

specific bottlenecks or manual tasks

you're hoping to reduce with these AI

agents? Just hoping to increase the

effectiveness of our content by

supplementing it with these AI agents.

Got it. So, you're looking to enhance

the effectiveness of your content by

providing real-time support to to your

students. That's a smart approach,

especially with the extensive library

you have at View Mastery. Thanks for

sharing that. Would it be helpful if I

sent a quick proposal your way showing

how we could support your goals? Yeah,

that would be perfect.

Perfect. I'll send that to the email we

have on file. Thanks again, Mark. All

right. Thanks. Exciting. Our scenario is

working and our voice assistant is now

empowered to perform dynamic pitches

based on our lead's unique information.

We're almost there. Finally, within

build 3, we take this about as far as we

can within a beginner tutorial like

this. Um, and this is a really, really

powerful thing once you've added in

these extra features. So, after the call

again, we're going to analyze that and

deal with if they answered or didn't

answer. And long story short, if they

answered and they said that they were

interested and said, "Hey, yes, can you

please send me a proposal?" Then we're

going to take all of this research that

we've done and all the information that

we got from the phone call in order to

generate them a custom proposal and

saying hey look this is what we want to

kick off with you because you need to

make a proposal in order to start any

kind of services or most kinds of

services but we can automate the

generation of a proposal which can take

hours and hours and hours for businesses

and we can use all of this information

we collected and they've said yes hey

can you send it over and then we use an

application called Panda do and we can

create a template of a document for our

agency in this case and it's going to

use AI in this case we're going to use a

chatbt uh node here on make and it's

going to take all of this information

and write a personalized proposal on how

we would kick things off with them of

what we're proposing in terms of the

scope of work for them like we will do

this this it sounds like you need this

we can do this this is roughly how much

it's going to cost etc and then using

panda do we can send that as a e-ign

link so that they're ready to sign and

we get notifications about if they've

viewed it if they've signed it etc and

so by the time you've done this we've

automated everything from the initial

point of contact where the leader said

that they're interested in our services

to learning more about them to

determining if they're qualified for our

offer uh to sending them a custom

proposal and ultimately for them signing

the dock through Panda do and they're

ready to kick things off with us. So

that is an explanation of of build 3 and

what we're really trying to go here. I

hope this been helpful to clarify things

for you um because this is really really

powerful if you can wrap your head

around it. So please stick with it.

Once a lead has expressed interest, it's

the perfect moment to harness that

momentum and transform it into something

concrete, a tailored business proposal.

Why wait for someone on your team to do

this manually when we already have all

the context we need? With the help of

OpenAI and Panda do, we can generate,

send, and log a custom proposal without

anyone lifting a finger. So, in the

final section of this course, we'll be

tacking on a proposal generator to the

end of our workflow. We'll use OpenAI to

create the custom text to plug into a

proposal generator.

We're going to be using Panda Do as our

tool for creating and sending proposals.

It allows you to create templates with

placeholders that can be filled in

dynamically from your automation

workflows. Here's how to create and

configure your template in Panda Do. Log

into the Panda dashboard. Just create an

account if you don't yet have one. Then

go to templates and click plus template.

For sake of ease, we can select an

existing template to remix such as one

of these business proposal or

advertising sales proposal templates.

I'm going to use a template I already

created here. Within a Panda Do

template, you are able to drop in

tokens, which are basically placeholders

that can be replaced with actual values,

such as your client's company name. In

this case, you can set up this template

however you'd like, but as you can see,

I've set mine up like this with a client

introduction section addressing them by

name. In the goals and plan section,

I've left room to insert information

about my lead's goals and the services

I'll recommend and a plan for how I'll

implement things. so you can see how it

works. We'll create those as variables.

Over here in the sidebar, we'll add

proposal.goals

will be a paragraph or two summarizing

the client's top priorities.

Proposal.services

will be a bulleted list of the

recommended services.

Proposal.implementation

will be a concise execution plan to

deliver the above. In the pricing

section, I've already added placeholders

here. proposal pricing and for a

breakdown of services and costs and

proposal.total,

the full estimated project total.

Down in the agreement section, we're

requesting signature and including the

leads info. Once everything is in place,

name it something relevant and save it

because you'll soon be using this

template inside your makes panda dooc

module. Optionally, you could spend some

time styling this template with a logo,

brand colors, etc. Remember, this

document will be client-f facing, so

make it look and feel as professional as

the service you're offering. With your

Panda do template ready and tokenized

correctly with placeholders, we're ready

to include it in the final sequence of

our make scenario. We'll add the Panda

Do create a document module, set up a

connection with our Panda Do account,

name the document based on the company

we're sending it to, and select the

proposal template we created earlier.

We'll fill in these values, giving the

module the lead's email to send this to

and include all of the necessary info

about our client, like the company name

and the client's first and last name.

For all these proposal tokens, we'll be

generating these values with AI in a

moment.

For now, we'll scroll down and say yes

to send a document because we want to

email this Panda doc to our lead. Fill

in the subject line using their name and

company, write a short message,

then hit save. Next, we'll use OpenAI,

create a chat completion module to help

us write a clear, convincing proposal.

We'll select a quick and efficient GPT

model, and in the prompt, we feed it

instructions about its role as a sales

expert with context about the services

our mock company offers like AI

automation and agent-based systems,

information about the client, including

the company research we did earlier in

the workflow, and the summary of the

call our voice assistant had with them.

Then we clarify its task to identify the

most relevant services we can offer them

and we demand the output to be in JSON

format so we can make easy use of it in

the modules after this.

If we go ahead and run this module only

passing in some dummy data for the call

summary and company research.

We'll see it efficiently generates this

relevant info organized as JSON like we

requested. We're almost ready to plug

these values into the Panda doc module,

but we first need to add a pass JSON

module to prep the message content

output from OpenAI, breaking it up into

discrete values that are available as

variables we can inject into our

proposal document in the next step. To

plug these proposal values into their

slots in the Panda do module, we first

need the JSON module to run. While we

could run our entire scenario, there's a

quicker way. go into our OpenAI module,

grab the expected JSON format, and paste

it as dummy data into the JSON module to

test it. Perfect. Now, we can see it's

passing correctly, and we can now go

into our Panda Do module and plug those

variables into their corresponding token

slots.

Finally, we want to bring things full

circle by updating the leads air table

record so that our sales team can see

when the proposal was sent.

So in the proposal sent on field, we'll

add a timestamp with now. This keeps

everything tidy. So when someone on your

team looks at a lead, they know a lead

was created, they were qualified, they

were called, including a call summary,

they were interested, a proposal was

sent. No one has to chase a status

update. It's all there in one glance

automatically. Let's do a final run of

this scenario to make sure all of the

moving pieces are working correctly.

Hey Brad, it's Ben from Edge AI. Is now

a good time to quickly talk about your

business needs? Yeah, it is. Great.

Thanks. I came across some info about

View Mastery. It sounds like you're

really focused on helping developers

learn VDJs efficiently. We build custom

AI agents and automations for companies

like like yours. Anything from customerf

facing bots to internal workflow

optimization. I'd love to understand a

bit more about what led you to reach

out. Are there specific processes or

tasks you're looking to automate? Yeah,

I'm looking to implement AI agents for

learning who are trained on the content

library of my learning platform.

Uh, that makes a lot of sense. Creating

AI agents that can assist learners by

leveraging your content library sounds

like a great way to enhance engagement.

Are there particular bottlenecks or

manual tasks you're hoping to reduce

with these AI agents? Yeah, just hoping

for uh the students to be able to

comprehend and apply the concepts more

effectively with the agents.

That's a smart approach. Empowering

students to better understand and apply

concepts with AI support can really

boost their learning experience. Thanks

for sharing that. Would it be helpful if

I sent a quick proposal your way showing

how we could support new mastery with

tailored AI solutions? Yes, please go

ahead and do that. Perfect.

I'll send the proposal to the email we

have on file. Thanks again, Brad. Thank

you. You're welcome, Brad. Thanks for

your time today. If you have any

questions later or want to discuss

further, just reach out. Have a great

day. The workflow seemed to have worked

as planned. Inside Air Table, we can see

that the proposal sent on date was

added. Now, let's check the email that

we told Panda do to send the proposal

to. Heading into that email, we can see

that it worked perfectly. Our proposal

dock arrived which the client can open

up to go view their proposal which we

can verify has been properly drafted

detailing the client's specific needs

and our plans to help them. From here

the client can sign the document, date

it and finish it out.

An added benefit of Pandanda is that we

don't need to create additional

automation steps in make.com to track

when leads view or sign proposals. Panda

handles this automatically by sending

notification emails to our email

whenever a lead views or completes a

proposal. With this final stretch,

you've created a full system that not

only identifies and qualifies leads, it

translates interest into action. Of

course, there are many other steps you

could add to this scenario. For example,

the delay we added after the call takes

place works in most cases, but if the

call exceeds 5 minutes, the system would

incorrectly mark it as not answered,

even if it was successful. The foolproof

solution would be to implement a web

hook that listens for the call to end,

but that's outside the scope of this

beginner build. You could even add a

whole extension to this workflow where

you detect when a lead signs the

proposal and then onboard them with an

automated client orientation workflow. I

encourage you to get creative and add on

to it to learn and challenge yourself.

For now, I want to share some

troubleshooting tips to keep in mind as

you go off on your own and build your

own workflows.

As you take your next steps and start

building your own AI powered

automations, I want to be completely

transparent. You will encounter issues.

platforms will change, tutorials may

become outdated, and unexpected problems

arise. This isn't a flaw in the system.

It's a natural part of working in a

rapidly evolving field. The truth is,

even experienced developers spend a

significant portion of their time

troubleshooting. I can't count how many

times I found myself yelling at my

screen because some seemingly simple

thing wouldn't work. It's part of the

process for everyone. There's an old

saying, give a man a fish and you feed

him for a day. Teach a man to fish and

you feed him for a lifetime. This sums

up our approach to technical education.

If you are spoonfed every solution,

you'll end up just copying existing

systems and won't be prepared for real

world challenges and you won't develop

ways to differentiate yourself either.

So, think of technical problem solving

as a muscle. Right now, it might be

underdeveloped and using it feels

uncomfortable. That's normal. But with

consistent exercise, tackling problems,

finding solutions, learning from

mistakes, this muscle will grow

stronger. It's a gradual process that

develops through experience. Each

problem you solve builds your expertise,

making future challenges less daunting.

While you can't rush this journey, you

can fully embrace it. When you feel that

sense of being out of your depth, try to

recognize it not as failure, but as

growth. You're in uncharted waters.

You're pushing your limits and expanding

your capabilities. The most valuable

learning happens precisely in these

moments of struggle. So with that growth

mindset, let's cover some tools you can

use for troubleshooting. You probably

won't be surprised that I encourage you

to leverage AI as you develop your

skills. If you run into errors in your

workflow by heading over to chat GPT or

a similar LLM, you can lean on its

ability to support you in solving the

issues you encounter. The trick here is

to describe your issue in detail.

Remember to give enough context so the

AI can grasp what you're building, the

specific step you're on, and the

expected outcome versus the problem

you're experiencing. Include

screenshots, error codes, and let it

search for the latest information.

There will be times your issues are a

little more complicated. For complex

problems, you can even try the deep

research mode to get comprehensive

information on specific platforms or

issues. Sometimes the first suggested

solution won't be the right one. But as

you engage in back and forth dialogue,

as you see me doing here, where I am

giving it more info,

the AI gains more clarity about your

issue and can better narrow down

potential solutions.

And if you truly want to learn, don't

just do what it says without curiosity.

Ask it to help you understand why the

issue happened and why the new solution

ultimately worked. If you are hitting a

wall, it's possible that others have

already hit that same wall and climbed

over it. So, by doing a traditional

Google search, you can look for existing

threads or community posts about similar

issues. Many problems you'll encounter

have already been solved by others. Of

course, YouTube is another great

resource. Search for recent tutorials

that show updated interfaces and

workflows or that highlight key details

that could help resolve your issue. We

now have access to tools like Google AI

Studio that can watch your screen and

provide real-time support as you work.

Think of this as an experienced guide

peering over your shoulder and helping

you step by step. This eliminates the

need to manually type out your problem

and provide screenshots to chat GPT

since you can give the tool permission

to observe what's on your screen and

guides you through solutions using voice

assistance. Take a look at how powerful

this is as it helps diagnose what's

causing this error. I'm getting an error

on this HTTP module where I'm trying to

make a post request out to the VP API.

Any idea where I should start looking

for a solution to this error? I see the

workflow you've created and that you are

receiving an error when trying to make a

post request to the VAPI API. Are there

any error messages that you can share?

It's a 400 bad request error. Do you see

the information here in red? Okay, I see

that it's a 400 bad request error. The

message indicates there's an issue with

the JSON formatting of your request

body. It seems like there's an

unexpected character, either an expected

or after array element in JSON at

position 1167, line 12, column 51. Would

you like me to help you review the JSON

structure in your request to identify

any syntax errors or missing commas or

brackets?

Yes. Can you notice anything in here

that is wrong?

Looking at the JSON, it seems like the

error might be in the company research

field where you have an array containing

a single element and that element seems

to be an expression. If you're trying to

pass a single string there, try removing

the curly bracket. Also, can you double

check that all your keys and values are

properly formatted as strings? Learning

to read and apply documentation is a

critical skill. Each platform has

detailed guides that are regularly

updated. Often, you'll find information

there that can help solve your issue. If

it feels overly technical, you can

circle back to asking something like

chat GPT to help you understand the

documentation and how it applies to your

specific issue. Finally, there are

plenty of online communities like the

ones me and my team run related to the

platforms you're using. Join Discord

servers or other forums, search for

similar issues, or even ask questions

directly. You'll be surprised by how

helpful and supportive these communities

can be. If your goal is to build real

automations for real clients, things

won't always go according to plan.

platforms will change and requirements

will shift. The ability to adapt,

troubleshoot, and find solutions is what

separates successful builders from those

who give up early. The most successful

people in this field aren't those who

never encounter problems. They're the

ones who persist through difficulties,

who see obstacles as puzzles to be

solved rather than roadblocks to

progress. So, when you hit that

inevitable moment where something isn't

working, and you feel stuck, remember

this is normal. Take a breath. Step away

if needed. Then come back and work

through your troubleshooting toolkit.

Each time you solve a problem, you're

not just fixing that specific issue.

You're becoming better at solving all

future problems. This resilience and

problem solving ability will be just as

valuable to your success as any

technical skill you learn. So, embrace

the journey of building your technical

skills. In the next and final section of

this course, I'll show you how to sell

your newly built systems to real world

clients.

Now that you understand how AI

automations work and can build them for

yourself, let's talk about actually

making money with these skills. But

first, let me destroy a huge

misconception that many people have. You

don't need to build the next chat GBT or

create some revolutionary AI startup to

make some money in the AI space. The

real opportunity is much simpler.

Helping businesses to understand and

implement AI automations like what

you've just learned. This is how I

monetize my AI automation skills, and it

has been the most explosive growth I've

ever experienced in my career. The good

news is, if you've made it this far in

the video, you're much closer to being

able to tap into this starving market

for AI automation services than you

think. But don't take my word for it.

Let's hear from some of the world's most

famous and successful businessmen. If I

was 25 years old today in 2024, what

would I do? What's a good sector to get

involved in? What business would I get

involved in? I think everything is

looking at AI now in a different way.

And I think AI growth is going to be

exponential. So, anything to do with AI.

Now, what could that be? in the simplest

form is helping people use the

technology. There's going to be a

massive amount of people wanting to use

it that don't know how to and they're

willing to pay to solve that painoint.

So, is that consulting? Not really. It's

implementation and execution and so

helping a business do that transfer into

a world where they're controlling their

data and getting information from it.

Now, the majority of businesses in

America, for example, are between 5 and

500 employees. So, they're small

businesses. They create 62% of the jobs.

They want to use AI. You should help

them solve for that and they'll pay you.

Mark Cuban is saying the same thing as

well. That the biggest opportunity right

now is helping small to mediumsiz

businesses who don't understand AI and

automation yet but desperately need it

to survive. And they're right. Here's

why. According to recent data, there are

1.7 million businesses in the US alone,

making between $500,000 and $10 million

per year. These are small businesses

which make up 62% of all jobs in the

USA. These businesses know that they

need AI automation to stay competitive,

but they don't have the time to learn it

themselves. And there's basically no one

there to help them at all. All of the

big consulting firms are looking at

other big businesses, and no one serving

this small business market who still

need AI automations just as much as

anyone else. So basically, almost all

small businesses are starving for AI

automation services like education

services in order to help them to

understand what AI and automation is and

why they need them. Then there's

consulting services which help them to

identify where AI automation can help

them the most. And finally, there is

actual implementation services to help

them build and maintain their AI

automation systems. Right now, based on

data I've collected in my community, for

every person offering AI automation

services, there are over 1,100

businesses in the USA alone that need

help. 1 to 1,100. The market is

completely untapped and will be for a

very long time. And that's where you

come in, which is helping these

hardworking small business owners to

understand and implement AI automations

so that they can keep up and survive

through this AI revolution just like

you. We've seen this exact same pattern

happen when the internet came out. The

companies that help businesses to adapt

to the web made fortunes as the

companies that they helped. I personally

spotted this opportunity in early 2023

and started Morningside AI, my AI

automation agency. And since then, I've

generated over $5 million selling AI

products and services. And as a company,

we're only just getting started. And the

best part is that, as we've proved in

this video already, that you don't need

to be a technical genius to understand

AI and even to build your own AI

automations. You just need to be one

step ahead of the businesses that you're

going to help. Let me show you the three

specific ways that you can start making

money with your AI automation skills.

So, there are basically three services

that you can provide to help businesses

with AI automation. First, there's

education. This is teaching businesses

about AI automation, running your own

workshops or presentations, or training

their teams and creating courses.

Businesses are desperate for someone who

can explain this stuff in simple terms

to you, just like I've done for you in

this video, what the hell AI is, what

automation is, and what it can do for

them. And after watching this video and

a few others that I've made on this

channel, you'll know more than enough to

start educating businesses and helping

them to sort of move from where you were

at the start of this video to where

you're going to be at the end of it.

I'll be covering which videos those are

in a moment. And secondly, you have

consulting services. And this is where

you analyze a business's operations and

show them where AI automations can help

them save time or make extra money.

You're essentially being their AI

automation strategist. For example, you

could recommend a lead qualification

workflow like the one you just made to

help a struggling sales department. And

third is implementation. This is where

you actually build and deploy AI

automation solutions for businesses. Or

better yet, like my agency, you can do

all three of these. You can do

education consulting and

implementation. But you don't have to do

it all at once. It took us like over two

years to get here. So there's no rush.

And believe it or not, there are people

with only a few months of experience in

the AI automation space selling all of

these kinds of services right now.

Education consulting and

implementation. And the demand from

businesses is increasing insanely fast

right now because they're all switching

on and realizing they need to do

something about this right now. But

here's the thing. You still have one

problem, which is that you still don't

really know enough. You're close, but

you're not quite there. The way to make

money in the AI automation space or with

any services really is to create what's

called a knowledge gap between yourself

and the people that you're trying to

help. Your knowledge gap is your money

maker. Businesses will pay you in

proportion to how much more you know

about AI automations workflows and their

business applications than they do. So

now while this video has taught you a

lot, your knowledge gap is still fairly

small. But we can fix that. Let me break

down exactly what you need to do to

extend your knowledge gap so that you

can start monetizing this skill.

This video is step one. As long as

you've been taking notes and followed

all of the tutorials by building the

automations alongside me, you're already

far ahead of most people who have no

idea about how to build AI automations.

Step two is building even more

experience with AI automation so that

you are more familiar with the platforms

and better understand different ways

that they are being used to help

businesses. I've only given you a taster

here. This is a foundational knowledge

is the point of this video. But to do

this and extend your knowledge gap, you

can take my free course on school where

you'll build another 5 to 10

automations. Um, and the link to join

will be in the description below this

video. And this will further expand your

knowledge gap without paying a dime.

Once you've done that, you'll have what

I call a foundational knowledge. So you

understand the core AI concepts. You can

build basic solutions yourself. And you

know what's possible for businesses

right now. And then comes the big

decision. Do you want to go deeper

technically or do you want to start

monetizing what you already know? As

we've already covered, building and

implementing AI automations is only one

of the services that you can sell.

Naturally, the technical skills needed

to make money in implementation,

actually building AI automation systems

are far greater than just having a

foundation, you know, so you need to be

a lot more skilled in terms of

experience and being hands-on with the

technology to deliver high-quality

services for your clients. However, with

a good foundation, you're basically

ready to start having a crack at selling

AI automation, education, or consulting.

So this decision of whether you go to

education and consulting or actually

building it depends on what you're

really interested in. I'll use myself as

an example. I've always loved making

things from like block houses when I was

a kid to like brewing beer with my my

granddad to tinkering with engines. So

when I hit this foundational level in

early 2023 with my skills, I naturally

kind of just dove deeper into the

technical side. I kept building more and

more complex AI automations which led me

to starting Morningside AI where we now

build AI solutions for clients. But

here's the thing. A lot of people aren't

like me. And chances are you aren't

either. They don't get much of a buzz

out of the building side of things. And

because of this, many of you going to be

better at teaching and actually working

with people to help them understand and

get value out of this technology than

actually building stuff. But that

doesn't mean that you can't make money

in the space for you. Using the

foundational knowledge that you'll have

after you finish that free course to

actually sell AI, automation,

educational consulting makes way more

sense. So the key is being honest about

your strengths and your interest and

setting real expectations on how

technical you want to get in your career

by picking whether you want to sell

educational consulting or development

services. You have a hard stop on how

much you need to learn before you start

taking action. So this essentially

prevents you from getting stuck in an

endless learning phase. I see it all the

time where people just keep, oh, I don't

know enough. I need to keep learning and

they're forcing themselves to do

something they're not really good at or

they don't want to do and they're

procrastinating when they could be out

there making money. So, in summary, your

options from here are if you love

building and want to learn more, then

just keep going. Keep following that

interest and that energy. Watch my free

course tutorials in the description on

my free school. And then after you've

completed all of those, go on to build

your own projects and then ones for

friends and family. Try solving your own

problem or the people around you solving

their problems with AI workflow

automation. And within 2 to 3 months of

doing that, you'll be ready to start

trying to sell implementation to the

people around you properly. However, if

by now you haven't fallen in love with

the building process, then it's probably

best that you just finish off your

foundation by doing the builds in my

free course and then get started on

monetizing. Like, you don't need to keep

forcing yourself to learn more and more

and more and more. You already know

enough cuz you have a knowledge gap

between yourself and your clients and

you can start to help them by monetizing

that knowledge gap.

So, once you're clear on what type of AI

automation business owner you are going

to be, getting your first few clients is

actually pretty straightforward. And

there are two main ways of doing this.

The first is through what are called

warm connections. And this is by far the

easiest way to start. Instead of

expecting strangers to trust you with

your automation expertise, you can start

with people who already know you and

already trust you. What you're going to

be doing is basically making a big list

of all the people you know, all of the

connections and friends that you have

and acquaintances even, and reaching out

to them systematically to say, "Hey, I'm

doing this AI automation thing. Would

you be interested in having a chat to

see how we could help you or your

business?" And it's just consistently

starting those conversations with these

people in your network. And then

eventually, one of those doors is going

to open and become your first client.

I've covered this exact process many

times here on the channel. So on the

school post for this video, I'm going to

add the all of my complete guides for

warm outreach, including resources

directly from my AAA accelerator program

that will help you to fast track your

first few clients. The second way is

what I call the community content

flywheel. So this is how you build

long-term momentum with getting clients

for your business. Firstly, you need to

get inside my free community on school

and then immediately start creating

content about what you're learning. So

this could be uh making YouTube

tutorials about building automations. It

can be LinkedIn posts about workflow

automation tips and tricks or whatever

platform you prefer. But here's the key

is that you need to share this content

back into the community. And we have

over 160,000 members in my free school

with the biggest AI business community

in the world. And that gives you an

instant audience of people who are

interested in the same things to help

you get traction with your content as

fast as possible. So let me give you a

perfect example of this working in

action. We have a guy called Rory

Ridges, a young guy from the UK, and he

joined my free community and followed

this exact process. He took the free

course in the community. He learned the

basics and started posting simple

tutorials on make.com and relevance AI

and literally just started sharing the

automation workflows that he had learned

from my videos. Like he wasn't trying to

reinvent the wheel. He was just saying,

"Oh, I learned this cool thing. I'm

going to make a video on it." But every

time that he made a tutorial or a video,

he would share it back into the

community. The community would watch it,

give him feedback, and many of them

would go and subscribe to his channel

and become regular viewers. So, this not

only helped him to grow his channel

faster, but it started to position him

as an automation expert to his potential

clients. So, now his YouTube channel

brings him in enough leads to support

his growing AI automation agency. He's

basically started the same flywheel that

took me from zero subscribers and 0 with

AI to on track to making $10 million

this year and over 500,000 subscribers

in just 2 years. So, essentially, the

community gives you an audience. The

content gives you credibility and

together they bring you clients

consistently. So, on my free school,

there's going to be a post for this

video, and I'm going to leave all of the

links to my complete guide for creating

content and generating leads, just like

Rory and I have done. I've done a video

on it, ton of resources. You can find

that all on the free school.

What's really important to notice with

both of these methods is that they start

with giving value first. Whether it's

helping your warm connections to

understand and implement AI automation

for free or sharing your workflow

automation knowledge for free through

content, you have to start giving before

you get. Now, I know all this businessy

stuff may feel a little bit overwhelming

or out of reach for some of you, but you

will seriously be amazed at what baby

steps add up to in this AI automation

space. You've already taken the first

step by watching and following through

on this video. So, congratulations. All

you need to do now is keep this momentum

going. And the next step for all of you

is pretty clear now. You need to jump in

my free community. Go in there and drop

an introduction post. Let everyone know

who you are, what you'd like to do with

your AI automation career, and then

start working through my free course

material. It's there for a reason. And

I've poured everything that I've learned

about AI automations and building AI

automation businesses into videos like

these and they're all in a nice sequence

for you to work through on school each

time you complete a video. You can click

the little check box and keep stacking

those small wins and keep that momentum

going until you get to where you want to

go. And all of the resources I've

mentioned in this last chapter, the

selling part of this video will be on a

school post for this video within school

if you go to the YouTube resources tab

and then it should be right there. And

don't forget to check all of those

resources out there. And of course, if

you've made it this far, could you

please do me a big favor and just leave

a like on this video? You can drop a

comment below. Tell me what you like the

most or what you'd like to see next. And

click the share button and send it to

any loved ones or family or friends or

anyone so that they can start learning

these valuable automation skills for the

future as well. So, all of these actions

just help my videos like these reach

more people in the YouTube algorithm and

I'd really appreciate it cuz I put a lot

of work into these for you. And of

course, subscribe to the channel for

more content like this, helping you

understand AI automations, how to build

them, and more importantly, how to build

businesses and make money around this

incredible opportunity that is AI. If

you want to check out my 4-hour guide on

how to build AI agents, I've got that up

here. It's just like this one, but on AI

agents. Got a ton of great feedback from

it. So, if you want to keep learning,

that's a great place to go. But aside

from that, guys, thank you so much for

watching. That's all for the video and

I'll see you in the next