Introducing Self-Healing Web Scrapers

Imagine you're a data analyst, eagerly awaiting the latest market data from your nightly web scraping jobs. Instead of getting fresh data, you’re seeing and error message: "Selector not found."
The website you're scraping has (once again) changed its layout and your scrapers broke. Sound familiar?
This scenario happens daily in companies worldwide and highlights one of the biggest pains with web scraping: constant need for maintenance.

At Kadoa, we developed self-healing web scrapers using large language models. Our scrapers adapt to changes and fix themselves on the fly, significantly reducing setup and maintenance costs. This is a massive improvement in the efficiency and cost-effectiveness of any web data project.

The Challenges of Traditional Web Scraping

Before going into how our self-healing web scrapers work, let's first explore the core steps and challenges of the traditional approach for extracting web data.

Manual Rule-Based Setup

Imagine trying to describe a painting to someone who can't see it. That's what setting up a traditional web scraper is like. You painstakingly define hard-coded rules for each data point you want to extract:

"Look for a div with class 'product-title'. Inside, you'll find an h2 tag. That's the product name."

This process is slow, tedious, and requires technical expertise. For complex websites or large-scale projects, setup can take days or even weeks. The more dynamic the website, the more effort required.

Constant Maintenance

Because of this fragility, traditional scrapers require constant attention from developers during operations. You can't just set it and forget it. Instead, you're caught in a never-ending cycle of monitoring, fixing, and updating. The main reasons why scrapers break are:

CSS selectors change
Data on website changes format or structure
Scrolling or pagination issues
Getting blocked
Website not available

You can imagine how much effort goes into operating a large number of scrapers and handling all of these interruptions.

Scaling Limitations

As your data needs grow, so does the complexity of managing your scrapers. Each new website adds another potential point of failure to your system and creates additional maintenance. This is why large-scale web scraping across hundreds or thousands of sources has traditionally been prohibitively expensive.

How We Built Self-Healing Scrapers with AI

At Kadoa, we've built a system that generates scrapers that adapt, learn, and heal themselves. Here's a quick demo of our internal sandbox, where we can simulate hundreds of different test scenarios, such as layout changes or website errors:

Auto-Detection of Available Website Data

We use multimodal LLM to automatically identify and categorize data on a webpage. This helps with a super efficient setup of new scrapers as you just have to submit the URL, customize the proposed data schema, and run it. Once configured, you can then reuse the created data schema as a template for other similar websites of the same type.

Automated Scrolling & Navigation

Websites are rarely static, single-page affairs. We use LLMs to detect all available navigation elements on websites to hande infinite scrolling, pagination, and multi-level navigation automatically.

Generating Scraper Code

We've developed an AI agent that writes and updates scraping code on the fly. Instead of relying on brittle, hard-coded rules, our system generates extraction logic that adapts to changes in website structure. If a website changes, we regenerate the required code and validate it against the previously extracted data to make sure everything still works with high accuracy.

Automated Anti-Blocking

Our browser agent is built to mimic human browsing patterns, making it adept at avoiding detection by anti-bot measures. Our system varies its behavior, manages cookies and sessions, and even solves CAPTCHAs when necessary.

Robust Error Handling

We've put a lot of effort into automated error detection and recovery mechanisms. When a change is detected, our system doesn't just fail; it analyzes the problem, attempts multiple recovery strategies, and often fixes itself without human intervention.

Scalable Architecture

Doing this for a single website is one thing, but handling thousands of websites simultaneously is a whole different challenge. Our cloud-based infrastructure automatically scales to meet demand and to ensure consistent performance regardless of the number of sources being scraped.

The Result: Autonomous Web Scrapers

By combining these innovative approaches, we've created a self-healing scraping system that:

Reduces setup time from days to minutes
Cuts maintenance costs by up to 90%
Scales effortlessly to handle hundreds or thousands of data sources

This opens up many new use cases for web data insights. Even non-technical users can now effortlessly extract data from hundreds of websites, and we've only begun to scratch the surface of what's possible with unstructured data.

Interested in seeing how this works for your specific needs? Schedule a free demo today!