How I Use Apify Scrapers to Feed Real Projects With Real Data

Estick Silva — Wed, 15 Apr 2026 22:28:15 +0000

Most developers know web scraping exists. Few actually build it into their workflow in a way that scales.
This is how I do it, across three different projects, using actors from ParseForge on Apify.

The setup

Apify works like an app store for scrapers. You find an actor, configure your inputs, run it, and get structured data back in JSON or CSV. No infrastructure to manage, no proxies to rotate manually.
The part most tutorials skip is actor quality. Not every actor on the platform is maintained or reliable. After testing several options, ParseForge became my default because the output is consistent and the data structure is clean enough to pipe directly into the next step without heavy transformation.

Project 1: Investor research with PitchBook data

The goal was simple: build targeted outreach lists for early-stage startups by filtering investors by industry, stage, and geography.
The flow looks like this:

Run the ParseForge PitchBook scraper with your target filters
Export the results as JSON
Clean and deduplicate with a simple script
Load into a spreadsheet or CRM for outreach

What used to take a full day of manual research now runs in under an hour. The data includes investor profiles, portfolio companies, and contact signals that would otherwise require a paid PitchBook subscription to access at scale.

Project 2: Amazon product tracking

For a client project I needed to monitor competitor listings across hundreds of products: prices, ratings, review counts, and availability changes over time.
The flow:

Feed a list of ASINs into the ParseForge Amazon scraper
Schedule it to run daily via Apify's built-in scheduler
Store results in a simple database
Diff the output against the previous day to detect changes

The result is a lightweight competitive intelligence system that runs automatically and flags anything worth looking at. No manual checking, no missed price drops.

Project 3: A Reddit-aware AI agent

This one is more experimental but probably the most interesting.
The idea was to build an AI that can participate in Reddit communities in a way that feels native, not spammy. The problem with most bots is that they generate text without understanding context. They get downvoted into oblivion because they sound like bots.
To fix that, I needed behavioral data from the actual community.
The flow:

Run the ParseForge Reddit scraper targeting specific subreddits
Pull posts, comment threads, upvote patterns, and post timing
Feed that data into a language model as context and training signal
The model learns vocabulary, tone, which topics get engagement, and when to post

The scraper gives me structured thread data that the model can actually learn from. It understands that a question phrased one way gets engagement while the same question phrased differently gets ignored. It learns the difference between communities that tolerate self-promotion and ones that do not.
The output is not a bot that blasts content. It is an agent that understands the environment it is operating in.
None of that works without clean, reliable input data. If the scraper gives inconsistent output, the model learns the wrong patterns.

It wouldn't hurt to take a look at Scraper Apify.

Most Founders Look for Investors the Wrong Way

Estick Silva — Wed, 15 Apr 2026 17:30:22 +0000

Most Founders Look for Investors the Wrong Way

Most founders think the hard part is finding investors.
It is not.

What actually slows people down is not access, but direction.

You can spend weeks searching, building lists, sending emails, and still get no real traction. Not because there are no investors out there, but because you are talking to the wrong ones.

This is where most founders get stuck.

They assume the process is about volume. More emails, more contacts, more effort. But in reality, it is about precision.

Reaching out to 20 highly relevant investors will always outperform contacting 200 random ones.

The problem is that most people do not have a clear way to identify who is actually relevant. So they default to manual search, scattered data, and guesswork.

That approach feels productive, but it rarely leads to results.

The usual approach

When people start looking for investors, they typically do this:

Search random lists online
Send cold emails without clear targeting
Try platforms like PitchBook

This feels like progress, but it is inefficient.

You spend hours researching, switching between tabs, collecting fragmented information, and trying to connect the dots manually.

Platforms like PitchBook solve part of the problem, but they are expensive and not always accessible early on.

So most founders end up somewhere in the middle. Too much manual work, not enough structured insight.

He real problem

The problem is not access to investors. The problem is relevance.
The only investors that matter are the ones who:

invest in your industry
operate in your geography
match your stage

Everything else is noise.

What actually works

The biggest shift is moving from manual search to structured data. Instead of guessing, you define what you need and filter for it.

With the right dataset, you can:

identify investors that actually fit your startup
build clean, targeted lists
focus only on high-quality outreach

This changes the process completely.

You stop chasing investors and start approaching the right ones.

A practical way to do it

There are different ways to get structured investor data.

One approach that works well is using scraping-based tools that aggregate this information and let you filter it easily.

For example, I have been using a PitchBook investors scraper available on Apify, built by ParseForge.

It allows you to:

access a large pool of investor profiles
filter by industry, location, and investor type
export the data for outreach or analysis

It is not a replacement for due diligence, but it removes most of the manual work at the beginning.

Why this matters

Most founders waste time trying to “find investors”.

The real advantage comes from:

targeting correctly
working with structured data
moving faster than others

Once you have that, outreach becomes more predictable.

Conclusion

Finding investors is not about having access to exclusive platforms.

It is about using data in a smarter way. When you stop searching randomly and start filtering intentionally, the process becomes faster, clearer, and much more effective.

If you'd like to try this tool, here it is: Pitchbook Scraper | Investor Data

DEV Community: Estick Silva

How I Use Apify Scrapers to Feed Real Projects With Real Data

The setup

Project 1: Investor research with PitchBook data

Project 2: Amazon product tracking

Project 3: A Reddit-aware AI agent

Most Founders Look for Investors the Wrong Way

Most Founders Look for Investors the Wrong Way

The usual approach

He real problem

What actually works

A practical way to do it

Why this matters

Conclusion