How do web scraping APIs convert HTML to structured JSON data?

Web scraping APIs convert HTML to structured JSON by using AI models that analyze page content and extract specific data points according to your requirements. Instead of writing complex parsing rules, you define a JSON schema or provide a natural language prompt, and the API returns clean structured data. Firecrawl’s LLM extraction uses AI to intelligently extract data from any HTML structure without brittle CSS selectors or XPath expressions.

Web scraping APIs convert HTML to structured JSON by combining rendering, content extraction, and AI-powered parsing. The process starts by scraping the page to get fully rendered HTML, then uses language models to understand the page content and extract specific data matching your schema. Rather than writing manual parsing logic with CSS selectors, you simply define what data you want—field names, types, and descriptions—and the AI finds and extracts it. This approach works across different page layouts and HTML structures without requiring custom parsing code for each website.

The most reliable way to extract structured data is by defining a JSON schema that describes exactly what you want. You specify field names, data types (string, boolean, number, array), and optionally add descriptions to guide extraction. The API then analyzes the page content and returns data matching your schema structure.

Firecrawl’s JSON mode accepts schemas in OpenAI’s JSON Schema format. For example, to extract company information, you define fields like company_mission (string), supports_sso (boolean), and is_open_source (boolean). The API finds these data points on the page regardless of where they appear in the HTML structure or how they’re formatted.

For more flexible extraction, you can use natural language prompts instead of strict schemas. Simply describe what data you want to extract, and the AI determines the appropriate structure. This works well when you don’t know the exact structure in advance or want the model to decide how to organize the data.

With Firecrawl, you can pass a prompt like “Extract product details including name, price, and features” without defining a schema. The AI analyzes the page, identifies relevant information, and returns it in a logical JSON structure. This is faster for exploratory scraping or when page structures vary significantly.

AI-powered extraction works by feeding the page content to language models trained to understand web page structure and semantics. These models recognize common patterns—product names, prices, descriptions, contact information, article titles—even when HTML markup varies between sites.

The models consider both the text content and its context: HTML tags, CSS classes, proximity to other elements, and semantic meaning. This allows them to differentiate between a price and a date, identify which text is a product name versus a description, and understand hierarchical relationships like product variants or nested categories.

Traditional HTML parsing uses CSS selectors or XPath expressions to locate specific elements. This approach breaks when websites change their HTML structure, add new CSS classes, or reorganize content. Each website requires custom parsing logic, and maintenance becomes burdensome as sites evolve.

AI-powered extraction is resilient to HTML changes. Since the model understands content semantically rather than relying on specific selectors, it can find data even when the page structure changes. You write one schema that works across multiple sites with similar content types, rather than maintaining site-specific parsing rules.

Web pages often contain nested or repeated data structures—product listings with multiple items, articles with authors and tags, or company profiles with employee lists. Structured extraction handles these patterns by supporting arrays and nested objects in your schema.

For example, extracting multiple products from a listing page, you define a schema with an array of product objects, each containing fields like name, price, and image URL. The API identifies all products on the page and returns them as a structured array, preserving relationships between related data points.

While JSON provides structured data, you might need multiple formats from the same scrape. Firecrawl’s scrape endpoint supports multiple output formats in a single request: markdown for readable text, HTML for structure, JSON for data, and screenshots for visual capture.

This multi-format approach is useful when you need both the structured data and the full page content. For instance, extract specific product details as JSON while also getting the full product description as markdown, or capture structured article metadata while preserving the complete article text.

AI extraction accuracy depends on how clearly you define your requirements. Descriptive field names and detailed schema descriptions help the model identify correct data. For example, “product_price” is clearer than “price,” and adding a description like “The current selling price in USD” further guides extraction.

When using prompts, being specific improves results. Instead of “extract company info,” try “extract the company’s mission statement, whether they support SSO authentication, and if they’re open source.” The more specific your instructions, the more accurate the extracted data.

Web scraping APIs convert HTML to structured JSON using AI models that understand page content semantically rather than relying on fragile CSS selectors. You define data requirements through JSON schemas or natural language prompts, and the API returns clean structured data matching your specification. Firecrawl’s LLM extraction handles this automatically, working across different HTML structures without custom parsing code. The approach is more resilient to website changes than traditional parsing, supports complex nested data structures, and can be combined with other output formats. For best results, use descriptive schema definitions or specific prompts to guide the AI in identifying and extracting the exact data you need.