Master AI Automation: Full Course to Thrive in the AI Revolution

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 [email protected],

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