Normalization guide / 2026
Web Data Normalization for AI Agents in 2026
Normalization is not forcing every page into a bland document. It makes common concepts predictable while preserving meaningful differences between articles, products, profiles, videos, and feeds.
Quick answer
Normalize what repeats; preserve what is source-specific.
Use shared fields such as title, URL, author, date, content, and media across responses. Add optional attributes only when a public source exposes them.
curl --get 'https://webstractor.com/api/extract' \
--data-urlencode 'url=https://example.com/' \
--data-urlencode 'format=json'Available data
What you can extract
- Stable shared fields
- Semantic entity types
- Optional structured attributes
- Source URLs and content
AI workflows
Where normalized data helps
- Unified search UIs
- Cross-source retrieval
- Data validation
- Agent memory and indexing
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
- Unavailable public fields remain unavailable.
- Normalized output should not hide provenance.
- Consumers must handle nullable values.
webstractor.com 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.
Ready to try it?