# 5 "BORING" AI Automations To Sell For $1.5K+ Each in 2025

## Метаданные

- **Канал:** Nick Saraev
- **YouTube:** https://www.youtube.com/watch?v=jBF48jNWPJE
- **Дата:** 20.04.2025
- **Длительность:** 23:19
- **Просмотры:** 265,798

## Описание

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Summary ⤵️
This video breaks down 5 simple yet high-ROI AI automations you can sell for $1.5K+ each using Make.com or N8N—no flashy tools, just highly sellable boring proven systems.

My software, tools, & deals (some give me kickbacks—thank you!)
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Why watch?
If this is your first view—hi, I’m Nick! TLDR: I spent six years building automated businesses with Make.com (most notably 1SecondCopy, a content company that hit 7 figures). Today a lot of people talk about automation, but I’ve noticed that very few have practical, real world success making money with it. So this channel is me chiming in and showing you what *real* systems that make *real* revenue look like.

Hopefully I can help you improve your business, and in doing so, the rest of your life 🙏

Like, subscribe, and leave me a comment if you have a specific request! Thanks.

Chapters
00:00 Introduction
00:27 Search-intent scraping system
06:22 AI Automation #2
10:19 AI Automation #3
13:59 AI Automation #4
19:51 AI Automation #5
22:03 Outro

## Содержание

### [0:00](https://www.youtube.com/watch?v=jBF48jNWPJE) Introduction

Here are five boring AI automations that you could sell today for 1,500 bucks a pop or more. These are not flashy chat bots. They're not AI agents. What they are unsexy but very simple straightline automations that can add value to virtually any business. I sold systems just like this when I scaled my automation agency to $72,000 per month. They're going to be in a blend of make. com and n and I'll show you exactly how to build them over the course of the next few minutes. And by the end of the video, you'll have added five more great systems to your arsenal. Let's get started. The first system is a search

### [0:27](https://www.youtube.com/watch?v=jBF48jNWPJE&t=27s) Search-intent scraping system

intent scraping system. What the system does is it finds lists of high quality companies that are hiring for a specific position. Then it scrapes those companies, gets CEOs, decision makers, and founders at those businesses, and then adds them to a cold email sequence using a bunch of automation. I'll run you through how all of this stuff works, but first, for context, let's say you're a company that wants to hire somebody. Where do you go? Well, you basically have two major options, at least in the United States. The first is a website called Indeed and the second is LinkedIn. So what companies do when they're hiring for a role like an SDR sales development representative is they'll create a post on LinkedIn looking for people to fill. Floor coding warehouse just did this. Fulcrum just did this. Blanco Technology Group just did this. Right now what we can do is we could take this page aka this URL and we can actually pump it into a scraper created on a service called Appify. If we do this, what we can do is we can actually go through the previous page automatically and scrape the company name, the company website, the description of the job, everything that they're looking for, their compensation, and we could add it to a big spreadsheet. Okay. Once we have it in a big spreadsheet, we can fire it off to one of many email verification services. The system uses one called any mailfinder. And then on the back end of the system, we can do some additional things like research the decision maker, give us some more context about them autonomously before finally adding them to a cold email service. This video is going to be using instantly, but you can also swap with whatever you want. Um, smart lead is a big one. There are a few other ones as well. So, in terms of how the system works under the hood, as you can see, the very first thing we do is we run an actor. Now, I'm putting in a hard-coded URL here, but I'm sure you guys can imagine you guys could swap out this URL however you like. That URL is equivalent to the URL of the LinkedIn job, the post that we just showed you a minute ago. Okay. From there, we will get the data set items of that post, aka this run just occurred. It deposited a bunch of data into this data set on Appify. And what we're doing is we're just extracting it here. The end result is we have a giant list of companies. Okay, Air Garage. We have a company LinkedIn URLs, company logos, location, salary info, and so on and so forth, just repeated for all of the jobs that we've scraped. So in this case, I think we scraped 30 or 33 or something. From there, I have a bunch of filters set. These filters just check to see does the website exist because we need the website to proceed. Is the website linkedin. com? Sometimes LinkedIn posts ad posts that go to careers. linked, so this just filters them out. Are the number of employees at the business less than 150? This might be useful if you're looking to work with small businesses like I am. Assuming that they are okay, what we do is we jump into a database. Now, I'm using Google Sheets as my database. You guys can use whatever you want. What this Google Sheets database basically does is it just checks to see, have we added the same company name to the database before? If so, don't proceed. What we want to do is we only want to pitch companies once. We don't want to continuously pitch people over and over again every time the scraper runs. And this is a very simple design pattern that allows us to do that. So, assuming that the job we're scraping doesn't already exist in this big database with the company name and the title and the tracking ID and so on and so forth, then what we do is we actually add them to the database right over here. Okay? So, mapping all of the fields from that app search. And as you can see at the end of it, we get a ton of data. Finally, I'm using an open AI or an artificial intelligence module, GPT4 mini in this case, but you guys could swap this out with whatever model you guys want to basically filter out jobs that don't contain or include things that I'm looking for. So, I left this very vague here, but essentially, you can use this filter to determine whether or not a job is relevant to us. Now, think about the logic here. We're only filtering jobs that aren't already in our database. So we're significantly reducing the token count that we're feeding into the artificial intelligence and we're ensuring that we're only filtering jobs that are new. Now once we filter the job, what we finally do is we search for a decision maker using a service that I really like called any mailfinder. You guys could swap this out with any API call to any similar enrichment service. What we're doing is we're looking specifically for CEOs that operate at the domain. And then I'm also passing some additional information, row number, company name, and campaign status. Then I'm also adding what's called a web hook URL. This just allows us to set up a web hook somewhere else and then watch and wait to see all the results roll in. You don't need to do this, but I do it because I like making sure these systems are pretty airtight. Okay. Now, once we're done with that, what we do is we go over to this second scenario here that's waiting with a web hook. If I run this, we'll catch all the data currently in the queue. And this sends a node right off to Perplexity, which is an artificial intelligence service that allows us to browse the web. Very similar to GPT web search, but this is sort of the first iteration of it. And assuming that we get some information on who the prospect is. So, what I'm doing is I'm actually just feeding in a bunch of messages about who the person is and seeing if we can look them up. What we receive as a result is we get a bunch of data on who that person is. In this case, Molina, where she's based out of, how long she's been working, and so on and so forth. And we do is we actually feed this information into an AI module that generates a customized icebreaker that allows us to reach out to her. Instead of just saying, "Hey, Melina, how's it going? I'd really like to sell you something. " What we do is we use AI to take that web search data and then build us an extraordinarily customized icebreaker. So the end result of this is hey Molina admire how Propel Champions diversity and digital talent because diversity is important to her. Also love your thought leadership on the future of tech because I think she published some podcast or something. From there we add that to the database. We actually update the row that we had earlier with a bunch more information aka full name all of their data basically over here. And then underneath what we do is we add an icebreaker and then a link. We do an HTTP request instantly, which in our case is just going to a test campaign. This test campaign is already set up and basically is just queued up to send the icebreaker as well as a couple of other points of information. So the end result is we now have an email sequence that is crafted, ready to go and automatically already sending emails completely autonomously. All we need to do is just add a LinkedIn search URL to the end of the system. This is going to say something like, "Hey Molina, admire how you do X, Y, and Z. I really love to get in touch with you because I saw that you were hiring for a certain position. I think that I have a solution that might be able to solve your problem for significantly less money and make it really, really easy for you to take care of this. Here's what the solution would look like. The second system is an AI

### [6:22](https://www.youtube.com/watch?v=jBF48jNWPJE&t=382s) AI Automation #2

podcast repurposing engine. Now, the way that this works, and I built this out on my channel just a couple weeks ago, so if you guys are already familiar with this, this is why. You enter the URL of a podcast. Once podcast, it will then transcribe the entire podcast, extract anything of relevance, and then use that to develop highquality social media posts that you guys could publish on Instagram, LinkedIn, or Facebook. So, let's give this a go. I'm going to go over here and go Nick Sarrive Jack Roberts YouTube podcast because we had a podcast published on his channel just a little while ago. I'm going to copy this URL and then I'm going to paste this into my system. Okay, what this is going to do is if we go back here, we're now getting the transcript via an Aify actor. Ampify being the same scraping service that I was using before. And you'll notice that it comes up quite often because I'm a big fan of Apify. I think it makes my life a lot easier. As you can see, the one we're using is called YouTube Transcript Ninja. So, it's actually now extracting the transcript for that video specifically. Because this is a little bit longer of a video, I think this one was 40 minutes. You might have to wait a minute or two. After this is done, you'll see we've now finished with the scrape. You now have a bunch of data over here which if I make JSON, one of the items is a long transcript from start to finish where Jack and I talk about, you know, AI and automation and so on and so forth. From there, I feed this transcript into artificial intelligence open AI with a prompt like you take as input a long meandering transcript and you identify the 10 most interesting engaging points. Then you generate a bunch of JSON containing these points in the following structure. Okay, I give it some rules. I give it a quick little data dump and then it goes and it actually generates me a list of 10 engaging points. Once it's completed, we have a list of these 10 points on the right hand side. So there's a transcript of the paragraph of interest. There's some context and then there's some feedback that I have the model actually give me on how we could make it better. There's a deep explanation of that section. Then there's a short image description because I want to generate some images on this later on. From there, we use a split out node. This just turns our data from one item into 10 items, which allows us to loop over them and iterate them. Then from here, what we do is we pass them through three separate nodes. The first is an Instagram post generator. The second is the LinkedIn post generator. And the third is a Facebook post generator. So I'll only open the Instagram one for brevity, but essentially we take as input a section of a transcript right over here. We tell AI, hey, I want you to write it like an Instagram post. Then it goes out and does it. After that, we generate an Instagram image. Okay. Now, the way we're doing this is with Dolly. This is by no means the best AI image generator on the market. In fact, I think as of right now, it's probably kind of somewhere near the worst. But the end result is we get some cute looking image like this. In our case, I think we were talking about the labyrinth or something. And I've chosen to use a watercolor bunnies as my style and my theme for maybe my blog or something. So that's what that looks like. And then we take all of that information. Then we add it to a Google sheet called my little Instagram sheet right over here. And this is now just basically an entry into a database that a later workflow can use to automatically publish on my behalf. We'll do the same thing for both LinkedIn and for Facebook. And the way that I've separated this is I have multiple tabs here, one for LinkedIn, one for Facebook. And these posts are sort of custom curated to that specific platform. You know how sometimes LinkedIn posts are longer than Instagram posts which tend to be short. That's sort of the idea. Now after that what we have is we have a schedule trigger node in the second half of this workflow. What this does is every day let's check it at 7 a. m. This goes through the previous sheet that I'm showing you over here. And it actually looks to see hey which row does not have a posted on value? Probably this one here. So we haven't actually posted this yet. It then goes and it publishes this to the specific platform of interest. So if it's Instagram, it'll use the Instagram graph API or the Metagraph API to post on Instagram. If it's LinkedIn, it'll do an HTTP request and then publish directly to LinkedIn. Then it's Facebook, it'll publish to Facebook. I don't want to have to go through and then delete them all from my Instagram, LinkedIn, and Facebook. So I'm not actually going to run this one, but in that way, what we get is we get a fully contained system that allows us to generate a list of, let's say, 10 blog posts and then just drip them out one at a time. The next system I built in

### [10:19](https://www.youtube.com/watch?v=jBF48jNWPJE&t=619s) AI Automation #3

make. com. And this is one of the simplest and most straightforward systems you could sell to basically any business on planet Earth that operates using invoices. I've shown this a few times just because I think it really is important for people to realize the systems that make money do not have to be these big, complex, scarylooking agents that you guys see all over YouTube right now. In reality, usually it's the very simple systems like this that you guys can actually generate substantial ROIs on cuz they look a lot less complicated because you're typically able to explain them and interpret them for the business owner. And because in this case this does something very useful. Let me explain. So over here I have a Google sheet. This Google sheet is filled with just a big list of invoices that I've sent with different days. Okay. So this was an invoice we sent to TechCore Solutions. This is another one we sent to Global Dynamics Corp. This is Quantum Analytics. Another one we sent to Blue Sky. Hopefully you guys could tell, but all of these are examples. I just had AI generate me a bunch. The value here though is you can actually have this hook up directly to basically any payment processor out there. Stripe, QuickBooks, Zero, whatever the heck you guys are using, you guys can actually just use that as the data source instead of the Google sheet. Okay. Now, as we see, there's a date sent column over here on the lefth hand side. What this automatic invoice collection system does is it basically allows us to follow up on invoices that haven't been paid yet. And I can't overstate just how much money is usually tied up in unpaid invoices for the average B2B agency or manufacturing company or so on and so forth. So, this is a really big deal. It allows us to make a lot of money for the company very quickly. Okay, so the way that this works is we start by searching through the rows in that database. This is what you'd replace with, let's say, something like Stripe or Zero or whatnot. And specifically, we're checking to see if the status field is equal to overdue. So basically, back in the Google sheet, there's this little status field. And if the status field is equal to overdue, then we return it. Okay? So there's overdue, overdue. For the purpose of this demo, why don't I just say that they're all overdue. So what happens is we'll return these. Okay? And now because they're all overdue, we're going to get a big list of results. See 20 here. Then what we do is we grab the date. Okay, we basically see how many days has it been since we've sent that invoice. So if it's been 7 days, what we do is we go up this top route and then we create this email. If it's been 14 days, we go down this second route and we create another email. If it's been 21 days, we create another one. If it's been 28, 35, 42. And the value here is all of these email modules are basically just writing a follow-up in slightly different words. So all you have to do as a business is one time write your sequence of follow-up emails, have some source that runs every day like I showed you a minute ago and then you just send people follow-up emails every day assuming or rather every week or so, whatever sort of cadence you want um with this system. So if what I do here is let me just limit this to I don't know the first five so I don't send a ton of emails. If I run this, this will pull up all of the sheets. They'll then grab all of the dates. Now, what it's going to do is it's going to look for ones that are seven, I guess, 14, 21, and 28. I'm realizing now that I don't think we actually have any that are those exact dates. So, let me just grab all of them and see if there's one. All right, so it looks like we now have a couple here. It looks like it identified one invoice that has not been followed up with in 21 days. Then, it identified another one that has been outstanding for 28 days. Okay, so I don't know exactly which ones these would be. I just quickly updated the date field here so I could do this demo. But the end result is we send an email to this person, Rachel, saying, "Apologies. I know I followed up about this a few times now, but just wondering about that invoice I sent. Let me know if there's anything you need on it, please. " So, you can imagine, you know, after 28 days of still waiting for somebody to pay your invoice. Communication like this is fair and reasonable. If it's been 21 days, maybe William checking in on my invoice from a couple weeks ago. All good. This is a very simple and easy Lego block that you could slot into any business to more or less immediately add value. This next

### [13:59](https://www.youtube.com/watch?v=jBF48jNWPJE&t=839s) AI Automation #4

system is a cyclic content generator that returns as output a Google doc with an extraordinarily wellressearched and in-depth article. This was one of the very first systems that I've ever published on YouTube. I built that back in make. com I think about a year and a half ago when I was just getting up and running on this platform and it had great results and a lot of people have used this since. But there's still a ton of value with the system. So, let me run you through what it looks like. In order to run this, what we do is we click this test workflow button. This little form will pop up and it'll ask us for two things, a keyword and an email. The keyword is what you want to create a blog post about. So in my case, maybe I'm running a blog that does travel stuff and I want to optimize for the keyword Scotland cabins. Okay? And I'm just going to send myself this email as a result. You click submit and immediately after you click submit, the very first thing that happens is we run an open AI search that goes out on the internet. I'm communicating with this via API that goes out on the internet and then it actually finds blog posts that other people have written about this keyword. Then it builds outlines using their content. Okay, this is really cool. It's like a parasite sort of style system. So 10 of Scotland's best designed luxury cabins, one in Edinburg and so on and so forth. Okay, it actually goes and it finds a bunch of them. After that, what we do is we find specific citations from those blog posts. Then we ask for high-ranking articles about Scotland cabins and the outlines in markdown format. So we're doing we're basically building this structured outline. After that, we extract and we format these outlines, okay, using another OpenAI call. From there, what we do is we actually progressively generate better and better outlines. So now I'm feeding this into another OpenAI module which generates a high quality comprehensive outline for a topic given a crappier outline over here. And then what we do is we separate that into sections. So now instead of it just being one big outline, what we actually have is we have an item containing an array. Inside of that array is the intro, the next step, the next step. And this is really the key and the reason why this is a cyclic generator. We split these out into separate items and we actually have AI write us a section for every item. So we're actually passing in a single heading into AI and having it write an additional or rather a new section. Um so we're piecing together a whole article just uh you know heading by heading. Now obviously because we're writing this in this case 10 times this section takes substantially longer than most other sections. But while this is running let me explain the rest to you. Here is a limit node. This limit node is simply a little preventative bug fixing measure that I put in because sometimes I had so many headings in the system that it was difficult for me to keep track of it. What we do after is we aggregate all of these sections together and then we actually summarize the below article section in three sentences returning the output in JSON. Now the reason why we're doing this is kind of nifty and what I want to do is I want to take this summary and I actually want to use it to generate an image for that section. Once I have the image to that section, what I'm doing is I'm actually going to feed in the previous section plus the image to construct a new blog section with both an image and the text itself. So, we're being kind of nifty here and we're I don't know if you want to call this hacky or ingenious. I prefer ingenious, but basically what we're doing is we're constructing the article section by section. We're generating an image for every section. The current lowest hanging fruit here, which sometimes occurs, is the OpenAI image module will time out or rate limit if you don't currently have a high enough tier. So, if you guys are a tier one and you find that this happens from time to time, just spend a little bit of money and upgrade to tier two. I think you need like $30 or $50 on the card. If you use AI as much as um I do, this shouldn't necessarily be a big deal. You'll use that $50 reasonably quickly. So, after that, we feed in all of the objects into the OpenAI generate an image module. After the images are done generating, we get their outputs here. We'll actually merge the outputs of the previous node and that image node, and then we'll get a picture alongside a section. So, let me actually show you what that might look like. Looks something like this. In my case, I'm just using pen handdrawn illustrations, which, you know, end up being okay. I wouldn't say they're the best in the world, but they're also not the worst. Then we do some data processing nodes. We aggregate those 10 items to one item. Then we convert them all into HTML before creating an HTML text file. And then this section right over here, this is just something you have to do if you want to get like a Google doc um to uh be generated nicely with good formatting. If I go over here to where it says share link and email, what you end up with is an email notification basically inviting you to share the link. So, if I go over here to my Gmail, we'll see over here that an item has been shared right over here. The ultimate guide to luxury and unique cabins in Scotland. So, I'm just going to open this with a Google doc cuz that's HTML back there. Once we open it with a Google Doc, you'll get something that looks like this. Now, the images aren't entirely perfectly sized or whatnot. If you wanted to go that extra step, you would have to make an HTTP call to the Google Docs API. This is reasonably simple to do. I just, you know, I didn't want to spend God knows how long fiddling over the various API configuration parameters. Also, I personally like I'm not a big fan of just publishing stuff like this immediately. I do like to have a human in the loop when I publish blog content because I personally find I think anybody here that's done any sort of publishing will find if you just sprinkle a few minutes of somebody's time going over an article fixing even minor mistakes. You improve the end output like the quality of that article multiple orders of magnitude versus if it's just entirely automated. So, this is somebody that's run a $92,000 a month content writing company that used AI to do this sort of stuff. I still stand by it, but you know, having slight little formatting issues or whatnot when people read the article is not the end of the world. But, your end result is something like Scotland has been has long been a favorite destination for travelers seeking self-catering holidays, offering visitors the flexibility and comfort of a home away from home experience set against stunning natural landscapes. In recent years, cabins and self-catering rentals have notably increased in popularity with approximately 31% of visitors choosing cabins as their preferred accommodation. That's pretty cool and interesting. Thank you very much, AI. As you guys can tell, you guys could actually generate some pretty high quality content with this, assuming that you guys get the image prompt down. And there are a variety of other image generators that you guys could use for this. There's nothing wrong with publishing this directly to, let's say, WordPress if you guys wanted to, or some other blog service, web flow or whatnot. All you would have to do is just replace that last set of modules with instead of, in my case, generating a Google doc, just publish directly to WordPress with whatever logic is required in order to

### [19:51](https://www.youtube.com/watch?v=jBF48jNWPJE&t=1191s) AI Automation #5

do that. Okay. And then the last system here is a simple email categorization system I built in make. com and I've showed this a couple of times. This is one of the simplest systems to get your foot in the door at, you know, a small to mid-size business because it handles an issue that most founders have been in personally. And that's where you just get a ton of emails. Your email ends up being the bottleneck of your company and your whole business. And so what this system does is this basically just watches for a new email that comes in, forwards it over to a web hook, and then it categorizes it using artificial intelligence. And I'll show you this in real time in a moment. This AI is responsible for giving it one of four labels. Sponsorship requests, people selling me stuff, invoices, and receipts, and worthwhile. This is just for my own business. Obviously, you would categorize this however you want for the client that you're working with. Then I give it some additional instructions. And then all it does is it moves the email to the right category, marks it as red. Yeah, that's uh more or less it. So, let me show you what this actually looks like. If I run this once, we're now currently waiting for that web hook to come in, right? I'm just going to open my second email here. My first is going to email my second. Say, "Hi, I'd like to sell you something. Hello, this is me selling you something. Please reply, please. " not too far off from uh some of the messages I actually get. Okay, so I just sent myself an email. Uh that email is coming. Gmail just takes a second because it has to like hold on to it for 5 seconds to give you the ability to undo it. Okay, we then returned the email here with a bunch of text underneath. Then finally, we categorize that email. What you'll see is the end result is the category is people selling me stuff. So the email categorization system did a good job. It is not worthwhile. So then what we do? We just move them into that label. Now you have to move using this module's parlance but in Gmail it's called add a label. So now if I go back here let me give this a quick little refresh. Go down to people selling me stuff. You'll see that this has now been marked as people selling me stuff. And then as you can see like it's it's gray which shows that I've now looked at it or whatever. Same thing here. We no longer see in the main inbox and my life is a lot easier as a result of simple systems like this. So are all of the business owners I've sold them to. The cool thing with this system is you can actually just show the client how to update the filter and then you can have them play around with the filter. It's actually pretty simple. You record a simple loom. you say, "Hey, you just jump right over here into this. " Or what you could do is you could pull it from, let's say, a Google sheet or a Google doc or something and have them edit it there. Variety of different means with which you can, but this also just gives them a level of control over the end result in the system and makes them feel like they're more of a part of

### [22:03](https://www.youtube.com/watch?v=jBF48jNWPJE&t=1323s) Outro

it. Awesome. You guys could sell any of these systems for $1,500 a pop or more. I've seen people sell a couple of these systems for more than 10K, and I myself have sold my content generator system for at least that amount on a couple of occasions. There's a lot of value in simple linear left to right flows like this, as I'm sure you guys could tell. Not everything has to be fancy chat bots or AI agents. You guys can print money so long as you solve a customer problem and not just like pitch them any solution. So my hope is now you guys have five more ways to solve customer problems here. And I walked you through it from start to finish. So you guys should know everything that you need to about all the systems. You guys will find templates and blueprints to literally every one of these systems inside of Maker School. It's my zero to1 automation community with daily accountability to walk you through everything you need to know in order to get your very first customer in this niche. I will literally give you a dayby-day road map. So, however long you stay in my program, you will have a list of tasks to do on day one, two, and so on and so forth. We have over 2,000 people in the community as of the time of this recording, and I increase the price every 100 members. So, if you've been on the fence, let this be the sign and permission you need in order to take that next step and dive into the lovely world of AI and automation. Aside from that, really appreciate everybody watching my videos till the end. If you're still here, you're a real one. Like, comment, subscribe. Do whatever you can to help me bump up to the top of the AGO. I'll catch you on the next video. Thanks so much.

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*Источник: https://ekstraktznaniy.ru/video/12262*