Everyone’s Building AI. Nobody’s Talking About Getting the Data

Most people think AI is about the model. They obsess over GPT-4, Claude, or whatever shiny new algorithm just dropped. They think wrong.

The real bottleneck isn’t the AI. It’s the data.

While everyone’s arguing about which model is best, smart builders are quietly scraping the web for training data. They’re not waiting for public datasets or paying thousands for clean data.

They’re getting it themselves. And they’re using proxies to do it at scale.

Here’s how to scrape any website and get AI-ready data without getting blocked.

The Problem with Web Scraping (And Why Most People Fail)

Web scraping sounds simple: write a script, hit a website, grab the data. But try to scrape Amazon for product data or LinkedIn for contact info, and you’ll hit a wall fast.

Here’s why most scraping attempts fail:

1. IP blocking is instant Hit a site too many times from the same IP, and you’re done. Most sites block you after 10-50 requests.

2. Rate limiting kills scale Even if you don’t get blocked, sites slow you down to a crawl. What should take minutes takes hours.

3. Geographic restrictions Many sites serve different content based on location. Scrape from the wrong country, and you miss half the data.

4. Bot detection is smarter Modern sites use fingerprinting, CAPTCHAs, and behavior analysis. Your basic scraper looks nothing like a real user.

This is where proxies become essential. Not just any proxies — residential proxies that make your requests look human.

Enter RoundProxies: The Scraper’s Secret Weapon

RoundProxies solves the core problem: making your bot traffic look like real user traffic.

Here’s what makes RoundProxies different:

Datacenter IPs from real devices Instead of datacenter IPs that scream “bot,” you get IPs from actual homes and mobile devices. Sites can’t tell the difference.

Global IP rotation Need data from different countries? RoundProxies has IPs from 100+ locations. Scrape like you’re a local user anywhere in the world.

Automatic rotation Every request comes from a different IP. No manual switching, no getting flagged for repeat visits.

Built for scale While free proxies die after 50 requests, RoundProxies handles millions. I’ve scraped entire e-commerce catalogs without a single block.

The 5-Step Process to Scrape Any Website

Step 1: Choose your target and understand the structure

Before writing a single line of code, spend 30 minutes exploring the site manually.

• What pages have the data you need?
• How is the data structured? (JSON, HTML tables, dynamic loading?)
• Are there obvious bot protection measures?

Actionable tip: Use your browser’s developer tools (F12) to inspect the HTML structure and network requests. This saves hours of trial and error later.

Step 2: Set up RoundProxies

Sign up for RoundProxies and get your credentials. Configure your scraper to rotate through their proxy pool. Test with a few requests to make sure everything connects.

I recommend starting with their residential proxy pool — it’s the most reliable for avoiding blocks.

Step 3: Build your scraper with proper headers

Your scraper needs to look human. That means:

• Real browser headers (User-Agent, Accept, etc.)
• Random delays between requests (2-5 seconds)
• Following redirects and handling cookies

Example setup in Python:

headers = {

    ‘User-Agent’: ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36’,

    ‘Accept’: ‘text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8’,

    ‘Accept-Language’: ‘en-US,en;q=0.5’,

    ‘Accept-Encoding’: ‘gzip, deflate’,

    ‘Connection’: ‘keep-alive’,

}

Step 4: Extract and clean the data

Raw scraped data is messy. You’ll get:

• HTML tags mixed with text
• Inconsistent formatting
• Missing values
• Duplicate entries

Clean it up immediately:

• Strip HTML tags
• Normalize text (remove extra spaces, fix encoding)
• Handle missing data consistently
• Remove duplicates

Step 5: Format for AI training

AI models need structured, consistent data. Convert your scraped content into formats like:

• JSON for structured data
• CSV for tabular data
• Plain text files for language models
• JSONL for large datasets

Pro tip: Include metadata like scrape date, source URL, and data confidence scores. Your future self will thank you.

Real-World Example: Scraping E-commerce Data

I helped a client build a price monitoring system for electronics. Here’s what we scraped:

• Product names and descriptions
• Current prices and discounts
• Customer review counts and ratings
• Stock availability
• Seller information

The setup:

• Target: 5 major e-commerce sites
• Volume: 50,000 products daily
• RoundProxies: Residential proxy pool with US/EU IPs
• Result: 99.7% success rate, zero blocks

The key was treating each site differently:

• Amazon required mobile user agents
• eBay needed specific geographic IPs
• Walmart had aggressive rate limiting

RoundProxies handled all of this automatically through their rotation system.

Common Pitfalls (And How to Avoid Them)

1. Scraping too fast Even with proxies, hitting a site with 100 requests per second looks suspicious.

Solution: Add random delays. 2-5 seconds between requests keeps you under the radar.

2. Ignoring robots.txt Some developers think robots.txt doesn’t matter for scraping. Legally, you’re probably fine ignoring it. Practically, sites that care about robots.txt also have better bot detection.

Solution: Check robots.txt first. If they’re blocking crawlers, expect stronger protection.

3. Not handling JavaScript Many sites load data dynamically. Your basic scraper misses this entirely.

Solution: Use tools like Selenium or Playwright for JavaScript-heavy sites. RoundProxies works with both.

4. Storing raw HTML Raw HTML files are huge and mostly useless. Extract what you need immediately.

Solution: Parse and clean data during the scraping process, not after.

Making Your Data AI-Ready

Scraped data isn’t automatically good training data. Here’s how to make it AI-ready:

Consistency is everything AI models hate inconsistent data. If you’re scraping product descriptions, make sure they all follow the same format.

Quality over quantity 1,000 high-quality, clean records beat 10,000 messy ones. Spend time on data cleaning.

Label your data If you’re training a classifier, you need labels. Plan your labeling strategy before you start scraping.

Version your datasets Keep track of when you scraped what. Data goes stale, and you’ll need to re-scrape regularly.

The Legal Side (Don’t Skip This)

Web scraping exists in a legal gray area. Here’s what you need to know:

Publicly available data is usually fair game If it’s on a public webpage without login requirements, you’re probably fine.

Check terms of service Some sites explicitly prohibit scraping. This doesn’t make it illegal, but it could violate their terms.

Don’t overload servers Aggressive scraping that impacts site performance could be considered a DoS attack.

When in doubt, ask Many companies are happy to provide data access through APIs if you ask nicely.

Why This Matters Now

AI is moving fast, and data is the competitive advantage. Companies with better training data build better models. Period.

While your competitors are waiting for public datasets or buying expensive data licenses, you can be building custom datasets tailored to your exact needs.

The tools are available. RoundProxies makes the technical barriers minimal. The only question is whether you’ll use this advantage or let someone else grab it first.

Your next move:

1. Pick a data source relevant to your AI project
2. Set up RoundProxies
3. Build a simple scraper
4. Start collecting data today

The AI revolution isn’t just about better algorithms. It’s about better data. And better data comes from getting it yourself.

Stop waiting for the perfect public dataset. Start scraping.