Joshua Ochia

AI-Powered Website Validation & Industry Detection

Large-scale website validation and industry classification using AI and multi-layered web scraping.

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Overview

We worked with a client that required large-scale website validation and enrichment for a dataset of 50,000+ websites. The goal was to verify whether each website was reachable and valid, detect the company’s industry using AI, and extract key social media links such as LinkedIn and Facebook.

Due to the scale and variability of websites, the system needed to be highly reliable, cost-efficient, and resilient against anti-bot protections.

The Challenge

The client faced several challenges:

  • Validating tens of thousands of websites efficiently
  • Handling websites with aggressive bot protection
  • Balancing accuracy vs cost when scraping
  • Extracting consistent data (industry, LinkedIn, Facebook) from highly unstructured content
  • Preventing high failure rates during scraping

The Solution

We designed a multi-layered scraping and validation pipeline combined with AI-based industry detection.

Key Concepts

  • Tiered scraping strategy to optimize cost and accuracy
  • AI-driven industry classification from website content
  • Automated social media link extraction
  • Fault-tolerant processing for large datasets

Scraping & Validation Strategy

To maximize success while minimizing cost, we implemented a fallback-based scraping system using three third-party services:

Scraping Priority Order

  1. ScraperAPI (Primary)

    • Fast and cost-effective
    • Used for the majority of websites

  2. CloudScraper (Secondary Fallback)

    • Bypasses common bot protections (e.g., Cloudflare)
    • Triggered when ScraperAPI fails

  3. ScrapingBee (Last Resort)

    • Most accurate but also the most expensive
    • Used only when both primary methods fail

This approach ensured:

  • High success rate
  • Controlled operational costs
  • Reliable data extraction at scale

AI Industry Detection

Once a website was successfully scraped:

  • Website content was processed using AI models
  • The system classified each website into an industry category
  • AI handled variations in content structure, language, and terminology

This removed the need for rigid keyword-based classification and improved accuracy across diverse websites.

Data Enrichment

In addition to validation and industry detection, the system automatically extracted:

  • LinkedIn company pages
  • Facebook business pages

These links were identified by:

  • Scanning HTML content
  • Analyzing metadata and outbound links
  • Normalizing URLs for consistency

Scalability & Reliability

  • Designed to process 50,000+ websites efficiently
  • Retry and fallback logic reduced failure rates
  • Graceful handling of timeouts, blocked requests, and invalid domains
  • Modular architecture allowed easy addition of new scraping providers

Results & Impact

  • ✅ Successfully validated and enriched 50,000+ websites
  • 📈 High scraping success rate despite bot protections
  • 💰 Optimized costs through tiered scraping strategy
  • 🤖 Accurate AI-based industry classification
  • 🔗 Reliable extraction of LinkedIn and Facebook links

The client gained a clean, enriched dataset ready for downstream use in analytics, sales, and outreach workflows.

Tech Stack

  • Python
  • AI / NLP Models for industry classification
  • ScraperAPI
  • CloudScraper
  • ScrapingBee
  • Async job processing
  • PostgreSQL (for structured storage)

Conclusion

By combining a cost-aware scraping strategy with AI-powered classification, we delivered a scalable and resilient website validation system. This solution allowed the client to enrich a massive dataset with high accuracy while keeping operational costs under control.