How to Extract Structured Data from Any Website with AI
You need product names and prices from an e-commerce site. Job titles and companies from a conference speakers page. Research paper titles, authors, and abstracts from a journal. Contact information from a directory.
Traditional web scraping gives you HTML or text. You still need to write parsers to extract the specific fields you want. With AI-powered extraction, you describe the data you want and the tool figures out how to extract it.
The Old Way: CSS Selectors
// Site-specific, breaks when markup changes
const title = document.querySelector('.product-card h2.title')?.textContent
const price = document.querySelector('.product-card .price-current')?.textContent
const rating = document.querySelector('.product-card .stars')?.getAttribute('data-rating')
This works until the site redesigns. Then your selectors break. For each new site, you write new selectors. You maintain a growing library of site-specific extraction code.
The New Way: Schema-Based Extraction
Firecrawl supports structured extraction — you define a schema for the data you want, and it extracts matching data from any page:
import Firecrawl from '@mendable/firecrawl-js'
const app = new Firecrawl({ apiKey: 'fc-...' })
const result = await app.scrapeUrl('https://example.com/speakers', {
formats: ['extract'],
extract: {
schema: {
speakers: [{
name: 'string',
title: 'string',
company: 'string',
topic: 'string'
}]
}
}
})
console.log(result.extract.speakers)
// [
// { name: "Jane Smith", title: "CTO", company: "Acme AI", topic: "RAG at Scale" },
// { name: "Bob Lee", title: "Staff Eng", company: "DataCo", topic: "Vector Search" },
// ...
// ]
No CSS selectors. No site-specific code. The same extraction schema works across different conference websites, job boards, product listings — any site with similar data.
Try Firecrawl FreePractical Applications
Lead generation. Extract company names, contact info, and descriptions from directories and conference pages. Feed directly into your CRM.
Price monitoring. Define a schema for product name, price, availability, and shipping cost. Apply it across multiple e-commerce sites.
Research aggregation. Extract paper titles, authors, abstracts, and publication dates from academic databases. Build a structured research database.
Job market analysis. Extract job titles, companies, requirements, and salary ranges from job boards. Track hiring trends in your industry.
Content aggregation. Extract article titles, authors, dates, and summaries from news sites and blogs. Build topical feeds without RSS.
Why This Matters for AI Applications
Structured data is more useful than markdown for certain AI workflows:
Function calling. If your AI agent needs to compare products, route leads, or make decisions based on specific fields, structured data is directly usable. Markdown requires an additional extraction step.
Database ingestion. Structured data maps directly to database rows. No parsing needed.
Analytics. You can run queries, aggregations, and trend analysis on structured data. "Show me all speakers from AI companies" is a simple filter on structured data. It's an LLM call on markdown.
Try Firecrawl FreeCombining Approaches
The most powerful pattern combines markdown extraction with structured extraction:
- Markdown for full content — feed to RAG pipelines, summarizers, and content analysis
- Structured extraction for specific fields — feed to databases, CRMs, and analytics tools
Firecrawl supports both in a single API call, letting you get clean content and structured data from the same page.
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