WEBVTT

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Start here. What if I told you the simplest online

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business isn't about complex products or viral

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videos. It's just about organizing information.

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Exactly. Yeah. We're not talking about some complicated

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sauce platform here. We're talking about selling

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simple structured data files. Think of them like

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ultra specialized phone books and selling them

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for maybe twenty nine forty nine dollar a year.

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This model works because people pay. to skip

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the tedious work, the collection part. And we

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are diving into how AI tools can automate pretty

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much this entire process. We're turning hours

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of manual research into, well, minutes of automated

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generation, which makes this a genuinely viable,

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profitable side business for almost anyone. Welcome

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to the Deem Dive. Our mission today is pretty

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straightforward. We want to unpack the business

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model of selling these highly organized, specific

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data libraries. We have a clear five step plan

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for you. Yeah, that five step plan is really

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the core of it. Find, collect, filter, enrich,

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and sell. Simple steps. We'll show you some real

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world examples like building an AI tool database

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or maybe a blog niche research file. And we'll

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show you how you can get your first sale probably

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within two weeks if you follow the system. OK,

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let's unpack this then. We're in the information

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age. Almost all data is free somewhere. So why

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does selling organized data files actually work?

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Why do professionals pay for this? It really

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boils down to efficiency. Yeah. And trust, I

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think, professionals, marketers, bloggers, researchers,

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they need specific facts. Like right now, they

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could spend days trying to scrape and verify

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data themselves. Right. Or they could just pay

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you, say, $40 to get a clean, ready -to -use

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file instantly. The value you're providing is

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basically time -saved, pure and simple. And that

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time -saving is powered by the secret weapon

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here, which is AI. Using today's LLM tools, you

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don't collect data by hand anymore. You automate

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almost everything. This means you're creating

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saleable products in minutes, maybe hours, not

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weeks. That automation capability is absolutely

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crucial. It makes this whole thing accessible

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for anyone, even if you don't have deep coding

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experience. The barrier to entry for building

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a sophisticated data product, it's just collapsed.

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So if we distill it down, customers aren't really

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buying information itself. They're buying convenience.

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Spot on. They want convenience. They're paying

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money to save their valuable time. Simple as

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that. Okay. So every successful data library

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follows that exact five -step process you mentioned.

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Find an idea, collect the raw data, filter it,

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enrich it, and finally, package and sell it.

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Right. Let's start with finding a hot idea. That's

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step one. Right now, a database of AI tools for

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content writers. That's gold. Seriously, this

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niche is constantly changing. New tools pop up

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daily. Writers will happily pay a subscription

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to avoid that manual searching every day. So

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the actual collection process step two, that's

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where the automation really kicks in. You mentioned

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using a method called recursive brainstorm. with

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modern tools, maybe open source ones like Simpler

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LLM. And just for clarity, Simpler LLM is an

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example of an open source framework. It's good

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for reliable, structured batch processing with

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AI. Yeah, and recursive brainstorm sounds a bit

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intimidating, maybe. But it's really just asking

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the AI for, say, five ideas. Then you ask it

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to expand those five into five more. then repeat.

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Okay. It's kind of like growing a tree, right?

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Yeah. But with data points, you can generate

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hundreds of records really quickly this way,

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maybe in an hour. Okay. So for our AI tools example,

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the prompt, the instruction to the AI needs to

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demand structured output. Things like the tool

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name, a short sentence description, and the starting

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press. That structured output is then easily

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saved into like a CSV format, right? Like a simple

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spreadsheet. Exactly. And then comes step three.

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Filtering. This is critical. You can't skip this.

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The AI might just invent tools. We call that

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hallucination, or it might miss the actual pricing

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data. So you absolutely must manually check,

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say, the first 20 or 30 rows yourself, just to

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assess the initial quality. OK, wait. If the

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AI is prone to making things up, doesn't that

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manual checking start to eat into the time saved?

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Where's the tipping point where automation still

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clearly wins out? Yeah, that's a really good

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point. It's a bit of friction there. Automation

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wins because we only manually check that first

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tiny fraction, just to sample. After that, we

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use simple scripts to automatically delete rows

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that are obviously bad like rows with an empty

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tool name or maybe a zero dollar price. But honestly,

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I still wrestle with prompt drift myself sometimes

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when filtering that initial junk data. It happens.

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And for listeners who haven't run big AI jobs,

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when you say prompt drift, you mean the AI starts

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kind of forgetting the rules you set, right?

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It loses focus and starts giving you, well, junk

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data instead of the structured stuff you asked

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for. Exactly that. It's like the model gets tired

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or loses context and forgets the structure you

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requested. So that manual check just verifies

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we have a strong, consistent baseline before

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we let the automated cleanup run. Gotcha. So

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we check those initial rows manually to validate

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the AI's quality and its output consistency.

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Precisely. You need that baseline check. Okay,

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then step four, enrichment. You call this the

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value generator. This turns a basic list. which,

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let's be honest, probably no one pays for, into

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a premium, actionable product. The kind that

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gets you into that, say, $49 territory. Yeah,

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you got to think like the buyer here. The blog

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writer or the marketer, they need more than just

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a name and a price. That's not enough. They want

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the website URL. They want to know if it offers

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a free trial. They want to know what specific

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marketing task the tool is best for, you know,

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actionable stuff. Right. So we use an enrichment

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script for this, which is just another automated

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batch job. basically, that runs through every

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single row of our existing file and asks the

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AI to find and add these new details. Exactly.

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And this transforms the data from just a list

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into immediately actionable information. It lets

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the customer make purchasing decisions right

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away without doing any more research themselves.

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It turns a static list into a competitive edge

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for them. That sounds incredibly powerful for

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scaling this up. But what about the cost? What

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does enriching a massive file, say 100 ,000 rows,

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actually cost in AI credits or? API calls. That's

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the beautiful part, really. For 100 ,000 rows

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of a fairly light enrichment like this, we're

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talking maybe $10 to $50. Depends on the AI model

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and complexity, sure, but it's negligible compared

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to the potential revenue. Whoa. Imagine scaling

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that enrichment script to process, I don't know,

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hundreds of thousands of niche entries automatically.

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The possibilities. Yeah. That's impressive. And

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if we connect this back to the bigger picture,

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this method works for pretty much any data type,

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right? Take your second example, building a blog

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niche ideas database. Right. For blog niches,

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enrichment would mean using the AI to add maybe

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the specific target audience for that niche,

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and perhaps three example blog post titles for

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each niche idea it finds. But we can make it

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even better than just the AI, right? This is

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where you mentioned bringing in a non -AI tool.

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Absolutely. This is key. We use a data API. Something

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like Keywords Everywhere is a popular one. We

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use it to pull in a objective, quantifiable data,

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specifically the monthly search volume for each

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niche. Okay, and just for clarity, a data API

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is simply a way for your script to programmatically

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ask for and pull in real -time numbers, like

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search volume, from some third -party provider.

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Yeah, and that API connection is so important

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because it adds objective external validation

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to your data. It's the difference between saying,

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hey, this is probably a good niche, and saying,

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this niche gets 50 ,000 monthly searches. That

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measurable data is why people will pay significantly

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more. It removes guesswork. Got it. So connecting

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to a data API adds those valuable, hard metrics

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that really justify a higher price tag. Definitely.

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It adds that layer of credibility. OK. So now

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you have valuable, enriched data. The product

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is essentially built. What does this all mean

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for monetization? How do you actually sell it?

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You mentioned three ways. Yeah, three main options.

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Option one is the easiest kind of the entry point.

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Selling it as a simple digital file. You just

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save it as a clean Google sheet or an Excel file

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and then upload it to a marketplace like Gumroad.

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Gumroad handles all the payments, the delivery.

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It's perfect for a beginner just trying to validate

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the idea quickly. OK, simple file. Option two,

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you said, is the more professional move, building

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a simple web application around your data. Yeah,

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exactly. This lets people actually interact with

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the data, search it, sort it, filter it, like

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filtering those AI tools by price range or by

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feature. people will pay significantly more for

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that professional experience and usability. Right,

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the interaction adds value. For sure. And you

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can build this today without needing extensive

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coding skills yourself. There are tools like

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v0 .dev by Vercel or Replit's AI features. You

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basically just describe the searchable database

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you want, and the AI generates the necessary

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code for you. You're selling a search experience,

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not just a spreadsheet anymore. Interesting.

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OK, and the third way, the most technical approach.

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That's turning your organized data into an API.

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an application programming interface. This is

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essentially a technical pipeline. It allows other

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businesses or developers to automatically pull

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your data directly into their own apps or workflows.

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OK, so B2B. Yeah, mostly B2B. Listing your API

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on a marketplace like Rapid API is excellent

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for generating that sticky, reliable monthly

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subscription income from other companies. But

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look, my solid advice is always start with Gumroad.

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Get those first few sales. Prove there's market

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demand. If you hit consistent sales, then it

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makes sense to invest the time and maybe a bit

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of money in building the web app or the API.

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Don't overbuild early. Makes sense. An API is

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definitely ideal if your target customers are

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developers or larger businesses needing that

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direct integration. Absolutely. Know your customer.

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So this repeatable five -step method, find, collect,

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filter, enrich, sell it, really seems to work

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for any specific data set. What other examples

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have you seen out there in the market that stand

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out? I know I've seen things like influencer

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contact lists or directories of drop shipping

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suppliers. Even hyper -specific real estate data

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seems to be sold successfully this way. Oh yeah,

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tons of examples. To find ideas yourself, a great

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place to look is online communities. Like, go

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browse subreddits such as Airsauce or niche marketing

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forums. See what specific data people are complaining

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about missing or what they mention manually tracking

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in spreadsheets. That's often a goldmine. Or

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just solve your own Okay, before we finish up,

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let's cover the most important tips for actually

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achieving long -term success with this. Tip one

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you emphasized. Focus relentlessly on specificity.

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Go niche. Don't sell just general keywords. Sell

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something like keyword databases specifically

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for high -end boutique pet stores. Specificity

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is easier to market, you can target better, and

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you can charge significantly higher prices, right?

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Absolutely, specificity equals higher perceived

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value. Tip two is all about retention, especially

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if you want subscriptions. Keep your data fresh.

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You need to set up automated scripts to run,

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maybe weekly or monthly, to update the information.

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Stale data kills recurring revenue. And tip three

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is always, always prioritize quality over quantity.

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100 deeply accurate verified tools will always

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beat 5 ,000 messy broken entries, always. Okay,

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but I want to challenge the pricing assumption

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a little. Isn't $49 potentially too high for

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just a single CSV file, even an enriched one?

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Won't people just try to find the information

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themselves if the barrier to entry for them seems

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low? That's a fair question and it comes up a

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lot, but you have to understand the buyer's mindset

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here. Usually they're a professional. They value

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their time. often in terms of billable hours.

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So $49 an hour is actually negligible if it saves

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them, say, five hours of tedious research and

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verification work. Okay. The higher price actually

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communicates that your data is already curated,

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validated, and ready to use. It signals quality.

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If you price it too low, like $5, you kind of

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suggest it might be junk data that I'll have

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to re -verify anyway. Price signals value. Don't

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undercharge. That makes sense. Price is a signal.

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And tip four you mentioned is critical, especially

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for anyone maybe short on time. Test the market

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first. Yes. Create a minimum viable version,

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maybe just 100 to 500 records. Package it simply.

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Put it on Gumroad for, say, $15 or $19. If people

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actually buy that small initial file, then you

00:12:17.019 --> 00:12:18.940
know it's worth investing the resources to build

00:12:18.940 --> 00:12:21.639
the huge comprehensive version. Exactly. Testing

00:12:21.639 --> 00:12:23.559
the market first with a small data set. That

00:12:23.559 --> 00:12:25.519
is the single most important strategic step.

00:12:25.860 --> 00:12:28.159
Do that before investing weeks into full production.

00:12:28.440 --> 00:12:31.399
Validate first. OK, wrapping up. What's really

00:12:31.399 --> 00:12:35.419
fascinating here, I think, is how AI has democratized

00:12:35.419 --> 00:12:38.220
this role of the information broker. It feels

00:12:38.220 --> 00:12:40.919
accessible now. The big idea seems to be that

00:12:40.919 --> 00:12:43.500
a valuable business can often be incredibly simple

00:12:43.500 --> 00:12:46.779
at its core. Finding useful information, rigorously

00:12:46.779 --> 00:12:49.679
organizing it, and then selling convenience back

00:12:49.679 --> 00:12:51.940
to professionals who value their time. Yeah,

00:12:51.960 --> 00:12:54.600
totally. We learned the system is perfectly repeatable.

00:12:54.840 --> 00:12:58.379
Find, collect, filter, enrich, and sell. That's

00:12:58.379 --> 00:13:01.159
the loop. And crucially, the really heavy lifting

00:13:01.159 --> 00:13:03.259
parts, the collection and the enrichment, those

00:13:03.259 --> 00:13:06.120
are now largely handled by AI and fairly cheap

00:13:06.120 --> 00:13:08.980
API services. That proves the model is practical

00:13:08.980 --> 00:13:11.399
now and highly viable for building some serious

00:13:11.399 --> 00:13:14.059
side income or even a full business. So if you're

00:13:14.059 --> 00:13:15.799
listening and you're ready to jump in, here is

00:13:15.799 --> 00:13:18.860
your simple maybe two week action plan. This

00:13:18.860 --> 00:13:21.500
week, choose your specific niche, really define

00:13:21.500 --> 00:13:23.639
it, and write down exactly who is going to buy

00:13:23.639 --> 00:13:25.879
this data. Get your basic tools ready. That probably

00:13:25.879 --> 00:13:28.639
means an LLM account, maybe OpenAI or Anthropic,

00:13:29.200 --> 00:13:31.399
and perhaps a simple setup with Python or VS

00:13:31.399 --> 00:13:32.860
Code if you want to automate scripts, though

00:13:32.860 --> 00:13:35.080
you could start manually. Yep. Then next week,

00:13:35.399 --> 00:13:37.450
write your first prompt. Create just a small

00:13:37.450 --> 00:13:40.389
data set. 100 to 200 records is fine to start.

00:13:40.809 --> 00:13:43.269
Filter it. Enrich it. Don't aim for perfection

00:13:43.269 --> 00:13:45.490
here. Just go for getting it done. Get something

00:13:45.490 --> 00:13:48.350
out there. Right. And then within 14 days, roughly,

00:13:48.669 --> 00:13:51.350
upload that clean and rich data to a free Gumroad

00:13:51.350 --> 00:13:54.309
account. Set a price point. Maybe test that $29

00:13:54.309 --> 00:13:57.029
level and publish it. Your entire goal initially

00:13:57.029 --> 00:13:59.409
is just chasing that first sale. That's the proof

00:13:59.409 --> 00:14:02.230
point. Proof that people actually want your organized

00:14:02.230 --> 00:14:04.370
knowledge. Exactly. Get that validation. Yeah.

00:14:04.629 --> 00:14:06.720
And here's a final pro... thought for you to

00:14:06.720 --> 00:14:09.720
explore on your own. Once you build one successful

00:14:09.720 --> 00:14:12.059
data library and you've got the process down,

00:14:12.919 --> 00:14:14.659
how many different niches could you potentially

00:14:14.659 --> 00:14:17.820
serve using the exact same automation and enrichment

00:14:17.820 --> 00:14:20.240
scripts you already built? Think about that leverage.
