Four sweet spots for AI in web scraping

Since ChatGPT launched in late 2022, Large Language Models (LLMs) have gone from a breakthrough to a widely used tool—handling everything from text summarization to code generation, deep research to media creation—all made possible by their training on massive volumes of web data.

Naturally, then, the web is abuzz with chatter and real-world examples of how AI could be used in web scraping.

• Some developers are generating spider code in tools that accept natural language input.

• Others are calling cloud-hosted, AI-driven crawling and scraping services via API.

• Some hook a working crawler into different LLM-powered extraction tools.

Beyond LLMs, other types of AI—such as machine learning (ML) models for detecting product listings or automating ban detection—are increasingly being embedded into scraping workflows.

Finding the sweet spot

Such possibilities are exciting–so exciting, in fact, that some people believe AI fundamentally changes the whole practice and traditional foundation of web scraping.

Feeds on X and LinkedIn these days are lit up with prophesies about the end of conventional scraping, with a growing number of people coming to see the ability to conjure up web data using LLM prompts as signalling a wholesale transformation.

While there is no doubt that we are seeing the dawn of a new era for web scraping, the reality is more nuanced.

LLMs can’t replace every current scraping setup, but they help you cover more ground.

At Zyte, we’re focused on blending AI with traditional scraping, so you can use new tools alongside the reliable systems you already know. Based on our observation, here are four scenarios where we think AI adds the best value.

1. Intelligent crawling

Scraping begins with knowing which pages to visit. When sites have clear URL patterns or sitemaps, rule-based crawlers work well. But what if the structure isn’t clear?

Say you need to gather course descriptions from a university website. Some live under the “Faculty” section, others under “Research,” or hidden in news stories and PDFs — there is no sitemap, no pattern, and inconsistent markup. It is, sadly, a case that’s all too common among websites:

• Pages aren’t linked clearly.

• Data is spread across varied page types.

• It’s easier to describe what you want to scrape than how to find it.

Here, LLMs can help by reading page content to guide discovery. You can prompt an LLM to:

• “Find pages with news announcements”

• “Locate policy pages”

• “Identify pricing pages with tables”

This shifts scraping into a search-and-understand task instead of crawl-and-parse.

How do you implement this? Here are some examples:

• Zyte’s AI spiders come with ML-powered intelligent crawling built into Scrapy Cloud. Given a starting URL, they can either follow a user-specified crawl strategy or autonomously determine the best path forward by identifying pagination links, product or item links, and relevant sub-category links while avoiding irrelevant paths. Zyte’s AI spiders also support search-based discovery by detecting and interacting with search forms through ML-powered form recognition and input detection.

• You can build a custom workflow with language model integration frameworks such as LangChain that integrates with browser automation through libraries like Selenium. You can visit the starting links, render the pages, then ask the LLM to find the relevant links to extract or follow based on the content of the page.

LLMs can be integrated into your spiders as an intelligent discovery engine when structure fails. But how can they be used on those pages themselves?

2. Fuzzy extraction

Most scraping tutorials will teach you how to gather data from neat, well-structured pages with consistent layouts – the sort of scenarios that rule-based methods like XPath, CSS selectors, or JSON APIs handle well.

Unfortunately, most real world websites are not that organised. For example:

• An ecommerce listing might show stock volume and a shipping estimate without using any meaningful selectors, like  div.stock or span.shipping, for a scraper to target.

• A customer’s product review might combine price, durability, and delivery feedback all in one block, without any useful differentiation.

• A job listing page may blend requirements, education, and skills in a single paragraph, complicating extraction of distinct fields.

These are “fuzzy” targets—loosely structured data that brittle rules can’t reliably extract.

Another type of challenge arises from semantic variation: different pages describe the same thing in different ways:

A rules-based approach to handling this type of human variation makes life as a scraper very difficult. You end up having to write more rules for more edge cases, with more maintenance required, without ever exhaustively covering off all variations in linguistic expression.

Traditionally, you could attempt to solve for that variation in the post-processing stage with regex, taxonomies, and lookup tables with the help of fuzzy logic libraries like FuzzyWuzzy or RapidFuzz.

But now, this task can be moved upstream, during extraction itself, to AI-powered extraction systems which can infer, parse, and standardize fields on the fly.

What does this look like in practice?

• Zyte API’s /extract endpoint includes automatic extraction that lets you describe a set of custom attributes for output by describing your target field in natural language (“extract furniture type”), receiving a structured data response.

• You can parse raw HTML pages, then run chains to extract structured values using a combination of language model integration frameworks like LangChain and services like Unstructured that are designed to make processing of unstructured documents for Retrieval Augmented Generation (RAG) easier.

Look for this sweet spot when:

• Pages vary a lot, even on the same site.

• The layout is flat but the information is semantically rich.

• Content is user-generated or narrative-heavy.

Fuzzy extraction makes scraping work more like human-reading than machine-powered parsing.

3. Scaling spider creation and orchestration

Traditionally, scaling scraping has meant building a custom spider for each site. This multiplies site-specific logic and anti-bot work. Maintenance becomes a second job.

LLM-assisted scraping changes all this. It lets you automate spider generation, replacing manual work that scales linearly with the number of sites.

You can:

• Prompt an LLM to generate scaffolding for each domain.

• Automatically analyze page markup to identify the selectors the spider should use.

• Analyze webpages to craft a catch-all logic the spider can use when the page structure changes.

With these benefits at hand, upfront spider perfection is no longer required. This speeds iteration, cuts repetitive work, and makes scraping infrastructure more flexible. Refining one LLM-based spider costs less than monitoring a thousand fragile spiders.

In practice:

• Code generators: Use chat prompts inside AI-enabled IDEs to write site-specific spiders on demand. For example: “Write a Scrapy spider that navigates websitea.com, websiteb.com, and websitec.com, and follow all product pages, and extracts price, name, and reviews.”. Low-code AI-powered application builder services like Lovable or Replit can also assist less technical users in bootstrapping spiders quickly.

• General-purpose browser agents combined with LLM interpreters: Run browser agents that scroll and click, then pass page content to LLMs trained to parse it. Tools like Skyvern and Stagehand can act as the backbone, feeding content to the parsing backends like OpenAI function calling, LlamaIndex, and LangChain’s output parsers.

• Zyte’s AI spiders: Upload URL lists to Zyte’s homegrown supervised models for Products, Articles, or Jobs. You’ll get a set of spiders that automatically crawl pages, extract structured data, as well as adapt to site-specific challenges, all in one go.

4. Intelligent quality assurance (QA)

Once a spider is deployed, the fun begins: keeping it running. Pages change and on-page content comes and goes over time. Traditionally, this can mean silent failures of selector targeting, until a human reviews the output or, worse, a critical pipeline breaks downstream–killer discoveries for data quality

Data validation libraries such as pydantic, jsonschema, and numpy can already be used to ensure scraped records align with a defined schema.

But, beyond these rule-based approaches, now machine learning techniques can help tackle more advanced data quality assurance tasks.

• Classification: For tasks such as verifying if a captured string is truly a product name, lightweight classification models using spaCy and fastText bring natural language understanding to bear.

• Anomaly detection: Tools such as YData’s ydata-profiling to detect data quality issues such as anomalies that may indicate changes in website structure or unexpected content variations.

• Cleaning and transformation: Data pre-processing tasks such as cleaning column names, removing empty rows or columns, encoding categorical variables, and filtering data based on conditions can be tackled using libraries like pandas and pyjanitor.

By integrating AI tools and techniques into your web scraping workflows, you can establish a more proactive and intelligent QA layer. This approach also paves the way for the development of self-healing data pipelines, in which “health” scores applied to scraped data can trigger refinement or rebuilding of scraping systems.

Considerations when scraping with AI

We have seen all the different ways AI—whether through machine learning techniques or cutting-edge neural networks such as LLMs—can aid in web scraping use cases. But even in those four situations where AI provides an edge, there are still real-world considerations, depending on the type of AI you choose.

LLM-specific considerations

• Cost: LLMs consume tokens for every page you feed them and every structured response they generate. This is a potential limiter on the idea that LLMs can take over the entirety of scraping. But Zyte has identified and mitigated this concern by implementing a pre-processing strategy that uses a fine-tuned LLM to identify only the most relevant document nodes for processing, and in effect, significantly shrinking-down the data the LLM needs to interrogate.

• Runtime: Passing prompts to LLMs and getting responses during the web data collection process will add processing time.

• Predictability: Flexibility can equal unpredictability. LLMs don’t return the same answer to the same prompt every time. Achieving consistency of output may require fallback logic.

General ML considerations

• Data quality: Like any AI system, the quality of input data heavily impacts output quality. If not managed correctly, noisy markup, incorrect labels, or insufficient examples can hinder model performance.

• Model drift: Machine learning models can degrade over time as the training dataset expands. Periodic retraining and evaluation is key to maintaining performance.

• Maintenance: Integrating ML into your scraping stack means managing models, pipelines, and potentially retraining infrastructure. All of these add operational overhead beyond traditional scraping.

AI brings both capabilities as well as operational complexity into web scraping. Fortunately, many of these challenges are temporary or can be mitigated with the right architecture, or by leaning on specialist scraping platforms that embed AI directly into their products and infrastructure.

The real wisdom lies in knowing when to rely on traditional scraping techniques, when to go ML- or LLM-first, and when to augment your stack with purpose-built tools from domain experts.

A blended future

AI has lifted the ceiling of what’s possible in scraping. You can now find pages when structure is ambiguous, extract meaning from unstructured content, and scale setup without handcrafting every spider.

You can plug the growing palette of LLM tools into your own scraping workflows—enhancing how you crawl, extract, or handle bans. Many teams are already doing this with great results.

Web scraping has always evolved with the web. LLMs are undeniably a critical part of the next chapter, but not the final one.

The right question is never "should I use AI?" It’s "what’s the smartest way to get the data I need?"