Start here. What if I told you the simplest online
business isn't about complex products or viral
videos. It's just about organizing information.
Exactly. Yeah. We're not talking about some complicated
sauce platform here. We're talking about selling
simple structured data files. Think of them like
ultra specialized phone books and selling them
for maybe twenty nine forty nine dollar a year.
This model works because people pay. to skip
the tedious work, the collection part. And we
are diving into how AI tools can automate pretty
much this entire process. We're turning hours
of manual research into, well, minutes of automated
generation, which makes this a genuinely viable,
profitable side business for almost anyone. Welcome
to the Deem Dive. Our mission today is pretty
straightforward. We want to unpack the business
model of selling these highly organized, specific
data libraries. We have a clear five step plan
for you. Yeah, that five step plan is really
the core of it. Find, collect, filter, enrich,
and sell. Simple steps. We'll show you some real
world examples like building an AI tool database
or maybe a blog niche research file. And we'll
show you how you can get your first sale probably
within two weeks if you follow the system. OK,
let's unpack this then. We're in the information
age. Almost all data is free somewhere. So why
does selling organized data files actually work?
Why do professionals pay for this? It really
boils down to efficiency. Yeah. And trust, I
think, professionals, marketers, bloggers, researchers,
they need specific facts. Like right now, they
could spend days trying to scrape and verify
data themselves. Right. Or they could just pay
you, say, $40 to get a clean, ready -to -use
file instantly. The value you're providing is
basically time -saved, pure and simple. And that
time -saving is powered by the secret weapon
here, which is AI. Using today's LLM tools, you
don't collect data by hand anymore. You automate
almost everything. This means you're creating
saleable products in minutes, maybe hours, not
weeks. That automation capability is absolutely
crucial. It makes this whole thing accessible
for anyone, even if you don't have deep coding
experience. The barrier to entry for building
a sophisticated data product, it's just collapsed.
So if we distill it down, customers aren't really
buying information itself. They're buying convenience.
Spot on. They want convenience. They're paying
money to save their valuable time. Simple as
that. Okay. So every successful data library
follows that exact five -step process you mentioned.
Find an idea, collect the raw data, filter it,
enrich it, and finally, package and sell it.
Right. Let's start with finding a hot idea. That's
step one. Right now, a database of AI tools for
content writers. That's gold. Seriously, this
niche is constantly changing. New tools pop up
daily. Writers will happily pay a subscription
to avoid that manual searching every day. So
the actual collection process step two, that's
where the automation really kicks in. You mentioned
using a method called recursive brainstorm. with
modern tools, maybe open source ones like Simpler
LLM. And just for clarity, Simpler LLM is an
example of an open source framework. It's good
for reliable, structured batch processing with
AI. Yeah, and recursive brainstorm sounds a bit
intimidating, maybe. But it's really just asking
the AI for, say, five ideas. Then you ask it
to expand those five into five more. then repeat.
Okay. It's kind of like growing a tree, right?
Yeah. But with data points, you can generate
hundreds of records really quickly this way,
maybe in an hour. Okay. So for our AI tools example,
the prompt, the instruction to the AI needs to
demand structured output. Things like the tool
name, a short sentence description, and the starting
press. That structured output is then easily
saved into like a CSV format, right? Like a simple
spreadsheet. Exactly. And then comes step three.
Filtering. This is critical. You can't skip this.
The AI might just invent tools. We call that
hallucination, or it might miss the actual pricing
data. So you absolutely must manually check,
say, the first 20 or 30 rows yourself, just to
assess the initial quality. OK, wait. If the
AI is prone to making things up, doesn't that
manual checking start to eat into the time saved?
Where's the tipping point where automation still
clearly wins out? Yeah, that's a really good
point. It's a bit of friction there. Automation
wins because we only manually check that first
tiny fraction, just to sample. After that, we
use simple scripts to automatically delete rows
that are obviously bad like rows with an empty
tool name or maybe a zero dollar price. But honestly,
I still wrestle with prompt drift myself sometimes
when filtering that initial junk data. It happens.
And for listeners who haven't run big AI jobs,
when you say prompt drift, you mean the AI starts
kind of forgetting the rules you set, right?
It loses focus and starts giving you, well, junk
data instead of the structured stuff you asked
for. Exactly that. It's like the model gets tired
or loses context and forgets the structure you
requested. So that manual check just verifies
we have a strong, consistent baseline before
we let the automated cleanup run. Gotcha. So
we check those initial rows manually to validate
the AI's quality and its output consistency.
Precisely. You need that baseline check. Okay,
then step four, enrichment. You call this the
value generator. This turns a basic list. which,
let's be honest, probably no one pays for, into
a premium, actionable product. The kind that
gets you into that, say, $49 territory. Yeah,
you got to think like the buyer here. The blog
writer or the marketer, they need more than just
a name and a price. That's not enough. They want
the website URL. They want to know if it offers
a free trial. They want to know what specific
marketing task the tool is best for, you know,
actionable stuff. Right. So we use an enrichment
script for this, which is just another automated
batch job. basically, that runs through every
single row of our existing file and asks the
AI to find and add these new details. Exactly.
And this transforms the data from just a list
into immediately actionable information. It lets
the customer make purchasing decisions right
away without doing any more research themselves.
It turns a static list into a competitive edge
for them. That sounds incredibly powerful for
scaling this up. But what about the cost? What
does enriching a massive file, say 100 ,000 rows,
actually cost in AI credits or? API calls. That's
the beautiful part, really. For 100 ,000 rows
of a fairly light enrichment like this, we're
talking maybe $10 to $50. Depends on the AI model
and complexity, sure, but it's negligible compared
to the potential revenue. Whoa. Imagine scaling
that enrichment script to process, I don't know,
hundreds of thousands of niche entries automatically.
The possibilities. Yeah. That's impressive. And
if we connect this back to the bigger picture,
this method works for pretty much any data type,
right? Take your second example, building a blog
niche ideas database. Right. For blog niches,
enrichment would mean using the AI to add maybe
the specific target audience for that niche,
and perhaps three example blog post titles for
each niche idea it finds. But we can make it
even better than just the AI, right? This is
where you mentioned bringing in a non -AI tool.
Absolutely. This is key. We use a data API. Something
like Keywords Everywhere is a popular one. We
use it to pull in a objective, quantifiable data,
specifically the monthly search volume for each
niche. Okay, and just for clarity, a data API
is simply a way for your script to programmatically
ask for and pull in real -time numbers, like
search volume, from some third -party provider.
Yeah, and that API connection is so important
because it adds objective external validation
to your data. It's the difference between saying,
hey, this is probably a good niche, and saying,
this niche gets 50 ,000 monthly searches. That
measurable data is why people will pay significantly
more. It removes guesswork. Got it. So connecting
to a data API adds those valuable, hard metrics
that really justify a higher price tag. Definitely.
It adds that layer of credibility. OK. So now
you have valuable, enriched data. The product
is essentially built. What does this all mean
for monetization? How do you actually sell it?
You mentioned three ways. Yeah, three main options.
Option one is the easiest kind of the entry point.
Selling it as a simple digital file. You just
save it as a clean Google sheet or an Excel file
and then upload it to a marketplace like Gumroad.
Gumroad handles all the payments, the delivery.
It's perfect for a beginner just trying to validate
the idea quickly. OK, simple file. Option two,
you said, is the more professional move, building
a simple web application around your data. Yeah,
exactly. This lets people actually interact with
the data, search it, sort it, filter it, like
filtering those AI tools by price range or by
feature. people will pay significantly more for
that professional experience and usability. Right,
the interaction adds value. For sure. And you
can build this today without needing extensive
coding skills yourself. There are tools like
v0 .dev by Vercel or Replit's AI features. You
basically just describe the searchable database
you want, and the AI generates the necessary
code for you. You're selling a search experience,
not just a spreadsheet anymore. Interesting.
OK, and the third way, the most technical approach.
That's turning your organized data into an API.
an application programming interface. This is
essentially a technical pipeline. It allows other
businesses or developers to automatically pull
your data directly into their own apps or workflows.
OK, so B2B. Yeah, mostly B2B. Listing your API
on a marketplace like Rapid API is excellent
for generating that sticky, reliable monthly
subscription income from other companies. But
look, my solid advice is always start with Gumroad.
Get those first few sales. Prove there's market
demand. If you hit consistent sales, then it
makes sense to invest the time and maybe a bit
of money in building the web app or the API.
Don't overbuild early. Makes sense. An API is
definitely ideal if your target customers are
developers or larger businesses needing that
direct integration. Absolutely. Know your customer.
So this repeatable five -step method, find, collect,
filter, enrich, sell it, really seems to work
for any specific data set. What other examples
have you seen out there in the market that stand
out? I know I've seen things like influencer
contact lists or directories of drop shipping
suppliers. Even hyper -specific real estate data
seems to be sold successfully this way. Oh yeah,
tons of examples. To find ideas yourself, a great
place to look is online communities. Like, go
browse subreddits such as Airsauce or niche marketing
forums. See what specific data people are complaining
about missing or what they mention manually tracking
in spreadsheets. That's often a goldmine. Or
just solve your own Okay, before we finish up,
let's cover the most important tips for actually
achieving long -term success with this. Tip one
you emphasized. Focus relentlessly on specificity.
Go niche. Don't sell just general keywords. Sell
something like keyword databases specifically
for high -end boutique pet stores. Specificity
is easier to market, you can target better, and
you can charge significantly higher prices, right?
Absolutely, specificity equals higher perceived
value. Tip two is all about retention, especially
if you want subscriptions. Keep your data fresh.
You need to set up automated scripts to run,
maybe weekly or monthly, to update the information.
Stale data kills recurring revenue. And tip three
is always, always prioritize quality over quantity.
100 deeply accurate verified tools will always
beat 5 ,000 messy broken entries, always. Okay,
but I want to challenge the pricing assumption
a little. Isn't $49 potentially too high for
just a single CSV file, even an enriched one?
Won't people just try to find the information
themselves if the barrier to entry for them seems
low? That's a fair question and it comes up a
lot, but you have to understand the buyer's mindset
here. Usually they're a professional. They value
their time. often in terms of billable hours.
So $49 an hour is actually negligible if it saves
them, say, five hours of tedious research and
verification work. Okay. The higher price actually
communicates that your data is already curated,
validated, and ready to use. It signals quality.
If you price it too low, like $5, you kind of
suggest it might be junk data that I'll have
to re -verify anyway. Price signals value. Don't
undercharge. That makes sense. Price is a signal.
And tip four you mentioned is critical, especially
for anyone maybe short on time. Test the market
first. Yes. Create a minimum viable version,
maybe just 100 to 500 records. Package it simply.
Put it on Gumroad for, say, $15 or $19. If people
actually buy that small initial file, then you
know it's worth investing the resources to build
the huge comprehensive version. Exactly. Testing
the market first with a small data set. That
is the single most important strategic step.
Do that before investing weeks into full production.
Validate first. OK, wrapping up. What's really
fascinating here, I think, is how AI has democratized
this role of the information broker. It feels
accessible now. The big idea seems to be that
a valuable business can often be incredibly simple
at its core. Finding useful information, rigorously
organizing it, and then selling convenience back
to professionals who value their time. Yeah,
totally. We learned the system is perfectly repeatable.
Find, collect, filter, enrich, and sell. That's
the loop. And crucially, the really heavy lifting
parts, the collection and the enrichment, those
are now largely handled by AI and fairly cheap
API services. That proves the model is practical
now and highly viable for building some serious
side income or even a full business. So if you're
listening and you're ready to jump in, here is
your simple maybe two week action plan. This
week, choose your specific niche, really define
it, and write down exactly who is going to buy
this data. Get your basic tools ready. That probably
means an LLM account, maybe OpenAI or Anthropic,
and perhaps a simple setup with Python or VS
Code if you want to automate scripts, though
you could start manually. Yep. Then next week,
write your first prompt. Create just a small
data set. 100 to 200 records is fine to start.
Filter it. Enrich it. Don't aim for perfection
here. Just go for getting it done. Get something
out there. Right. And then within 14 days, roughly,
upload that clean and rich data to a free Gumroad
account. Set a price point. Maybe test that $29
level and publish it. Your entire goal initially
is just chasing that first sale. That's the proof
point. Proof that people actually want your organized
knowledge. Exactly. Get that validation. Yeah.
And here's a final pro... thought for you to
explore on your own. Once you build one successful
data library and you've got the process down,
how many different niches could you potentially
serve using the exact same automation and enrichment
scripts you already built? Think about that leverage.