How Web Crawlers Extract and Transform Unstructured Web Data for AI Applications

The web is overflowing with unstructured data. By 2023, the total amount of data generated globally had reached a staggering 64 zettabytes (ZB), and it’s expected to grow to 175 ZB by 2025. To put that in perspective, 1 ZB equals one sextillion (10 to the power of 21) bytes, and we’re generating around 2.5 quintillion bytes of data daily, from social media posts to IoT devices.

This massive and exponentially increasing surge in unstructured data presents both a challenge and an opportunity for organizations. The challenge is clear: how can you turn this vast sea of unstructured information into something clean, well structured, and actionable? The opportunity: the right tools can automate much of this process, allowing businesses to extract, transform, and leverage web data for a range of AI-driven applications.

Whilst web scraping tools like Firecrawl can extract raw web data and convert it into formats like Markdown for AI models, this is just the first step. A more comprehensive approach is often required to automate the entire unstructured data ETL process, streamlining data transformation and enabling the seamless development of AI applications.

In this article, we’ll explore why web data is so useful, how it can be leveraged to build sophisticated AI products, and how Instill Core simplifies this process with its advanced web crawling capabilities coupled with its versatile all-in-one AI platform.

The full details, code, and results of this experiment can be found in the accompanying notebook in the Instill AI Cookbook. Launch it with Google Colab using the button below:

Why Extract and Process Web Data for AI-Powered Insights?

Web data is dynamic, decentralized, and ever-changing, making it a goldmine for real-time insights, provided you can capture and structure it effectively. Whether for market research, competitive analysis, or creating a knowledge base for AI models, the ability to process web data efficiently gives organizations an edge in adapting to fast-paced environments.

Here are some powerful use cases that highlight the potential of extracting and processing web data:

• 📄 Insurance Due Diligence: Using large language models (LLMs), organizations can automatically identify discrepancies or causes for concern between policy details and content found on business websites.
• 🎯 Sales Leads: Extract web data to identify potential customer profiles or discover emerging market trends.
• 🏆 Market Competition Research: Analyze competitors’ websites to gain insights into product positioning, pricing strategies, and strategic direction.
• 📈 Product and Price Monitoring: Automatically track and adjust pricing based on competitors’ activities and supply-demand shifts.
• 🏛️ Regulatory Compliance Monitoring: Stay compliant by automating the extraction of changes from government websites or legal documents.
• 🔬 Semantic Exploratory Data Analysis: Leverage web data to uncover key topics, trends, or sentiment shifts in industry discussions, blogs, or forums.

Each of these use cases demonstrates how processed web data can unlock value across various domains, from market insights to automated workflows.

How to Leverage Web Data for Advanced AI Use Cases

Once web data is extracted and processed, the possibilities for AI applications multiply. Businesses can build high-value solutions like retrieval-augmented generation (RAG) systems, augmented data catalogs, AI-driven market insights, and agentic AI systems. These applications naturally fall into two main categories:

1. Create a Knowledge Base or Data Catalog

Extracted web data is an ideal foundation for building AI-ready knowledge bases or data catalogs. Chunking and embedding web content allows businesses to enhance decision-making processes by quickly retrieving relevant, up-to-date information. Here are some examples of how this can be applied:

• RAG for Enhanced Decision-Making

  RAG systems fetch relevant data from a knowledge base in real-time, allowing AI models to generate more accurate, context-rich responses and minimize the risk of hallucinations. This is particularly useful in a wide range of applications, from customer support to legal research.

• Semantic Search and Discovery

  Embedding web content enables AI systems to perform semantic searches, allowing deeper understanding of queries and more precise results. This is a powerful capability when analyzing complex topics or documents, like legal, financial, or research materials. By focusing on semantic meaning rather than keywords, AI can uncover the most relevant insights.

  Semantic search applications can be easily built by making use of Instill Artifact’s Retrieve Chunks API endpoint.

• Unsupervised Learning for Market Insights

  Unsupervised learning on web data embeddings enables businesses to extract valuable insights that might not be immediately apparent. For example, clustering competitor content could reveal emerging trends or product positioning strategies, offering critical market insights.

2. Enable AI Pipelines to Search the Web in Real-Time

AI systems become exponentially more powerful when they can access real-time data from the web. By integrating web search capabilities into AI pipelines, businesses can ensure their AI applications remain up-to-date, contextually aware, and relevant. Here are some examples of how this can be applied:

• Corrective RAG

  Corrective-RAG (CRAG) enhances traditional RAG by incorporating a self-grading mechanism, ensuring that only the most relevant data is used. If the retrieved documents meet a threshold of relevance, CRAG refines them by breaking down the content into “knowledge strips” and filtering out irrelevant parts. If documents fall below the threshold or are insufficient, CRAG supplements retrieval with real-time web search and content extraction. Checkout the paper here.

• Automated Discovery and Summarization

  Adding web search to AI pipelines enables automated discovery and summarization. Whether tracking competitor product updates or regulatory changes, AI systems can continuously crawl, extract, and summarize critical data points, providing real-time insights for decision-makers.

👉 See our previous blog post, where we show how to combine Google Search and Web Scraping with OpenAI’s GPT-4o model to generate structured output summaries in one end-to-end pipeline based purely on user queries.

How Instill Core Simplifies Web Data Processing for AI Applications

Turning raw web data into AI-ready insights requires a seamless unstructured extraction-transformation-load (ETL) process. Instill Core’s Pipeline offers a customizable platform to streamline this workflow from start to finish, eliminating the complexities of manual data processing.

Instill Core’s Web Operator is a modular component within Pipeline that automates the crawling, extraction, and transformation of web data. Its performance is on par with other web crawling services, such as Firecrawl, delivering fast, reliable results for large-scale data needs. Here are some of its standout features:

• ⚡ Fast Performance: Capable of crawling up to 100 pages in 2 seconds, allowing you to handle websites with speed.
• 🌐 Support for Multiple URLs: Crawl several websites simultaneously, scaling effortlessly to match large web crawls.
• 📝 LLM-Friendly Output: Generates clean HTML or Markdown, ideal for feeding into language models.
• 🎥 Comprehensive Media Extraction: Extracts not just text, but also images, audio, and video, creating rich datasets.
• 🔗 Metadata and Link Extraction: Captures important metadata and internal/external links for a more complete view of the web content.
• 🎛️ Customizable Extraction: Use CSS selectors to fine-tune the extraction process, ensuring precision and control.
• 🔄 Asynchronous Design: Ensures scalability and optimized performance, even with complex, large-scale datasets.
• ⌨️ Scraping Support for Dynamic Content: Fetch dynamic content by simulating user actions like scrolling, with future support for clicks, screenshots, and keyboard inputs (like Firecrawl Actions)

These features make Instill Core a robust solution for converting unstructured websites into structured AI-ready data, ensuring efficient scalable workflows without the heavy lifting.

See it in Action on Instill Core

Check out our Semantic web insights Jupyter Notebooks in the Instill AI Cookbook repository.

In this example, we crawl the WebMD website to extract and convert its content into Markdown format. Next, we chunk this data into manageable pieces and use Jina CLIP V1 to create semantic embeddings, which we analyze and visualize to gain insights. It’s a hands-on demonstration of how to transform raw web data into actionable insights.

The Ultimate Platform for Unstructured Data ETL - Instill Core

Unlike standalone web crawling services, such as Firecrawl, Instill Core offers so much more than just its exceptional Web Operator - it’s a full-stack all-in-one AI solution for performing unstructured data ETL and building AI-powered applications.

With an ever-growing suite of modular components, Instill Core allows businesses to build fully customizable pipelines to handle a plethora of different data modalities, from video to websites. The platform’s flexibility ensures that you can efficiently process and analyze unstructured data without juggling multiple fragmented tools, and without the burden of managing complex infrastructure.

Conclusion

Instill Core eliminates the complexity of processing unstructured web content, offering an out-of-the-box solution for converting web data into AI-ready insights. By integrating complex elements like crawling, chunking, and embedding into one seamless platform, businesses can easily harness the full power of web data in their AI-driven applications. From market research to real-time agentic AI systems, Instill Core makes the process simpler, faster, and more scalable.