Structured data guide / 2026

Structured Web Data for AI Applications in 2026

Structured data makes downstream AI work safer and simpler. An application can render a product, post, or feed based on an explicit type while omitting data that a source did not expose.

Quick answer

Use semantic entity types instead of page-specific parsers.

Schema-v1 distinguishes documents, articles, products, posts, profiles, videos, audio, and feeds so applications can validate common fields without coupling to layouts.

curl --get 'https://extractor.sh/api/extract' \
  --data-urlencode 'url=https://example.com/' \
  --data-urlencode 'format=json'

Available data

What you can extract

Semantic entity types
Shared metadata fields
Optional source-specific attributes
Nested items for profiles and feeds

AI workflows

Where normalized data helps

Typed UI rendering
Knowledge graphs
Validation before LLM prompts
Cross-source analytics

AI-ready output

Markdown for models. JSON for systems.

Raw HTML consumes tokens on navigation, scripts, styling, and interface labels. Clean Markdown keeps the readable hierarchy for LLM prompts and RAG chunks. Normalized JSON is better when your application needs an explicit semantic type, source, author, publication date, media, attributes, and collection items.

Always retain the canonical URL from the response. AI-generated summaries should remain traceable to the public source, especially when the underlying page can change.

Boundaries

Public data only

Normalization does not make a source authoritative.
Missing data is omitted rather than inferred.
Consumers must handle nullable and optional fields.

extractor.sh does not bypass CAPTCHAs, login walls, paywalls, access controls, or regional restrictions. Review the source’s terms and applicable law before collecting or reusing data.