To audit website extractability for AI systems, compare the content a permitted visitor can see with the source HTML and the extracted text. Identify missing main content before changing prompts or adding schema. Successful extraction is a content-access check, not a guarantee of AI citations.
Compare the three representations
| Representation | Check |
|---|---|
| HTTP source | Does the main body exist before JavaScript runs? |
| Authorized rendered DOM | Which required sections appear only at runtime? |
| Reader output | Do supporting paragraphs, tables and code survive? |
| Source identity | Are URL and access time attached to the extracted record? |
Use content assertions instead of a length threshold
A long extraction can consist mostly of navigation or repeated interface labels. Write a small list of source content that must survive: the main heading, a distinctive paragraph, a required table cell and a code token. Compare those items across representations.
The owned fixture checks supplied with this revision cover a main-text span and structure. They do not establish how any AI engine will rank or cite the page. For dynamic content, choose a bounded rendering configuration and record what remained missing.
Turn a failure into a specific fix
Use the GEO measurement workflow to observe citations and exposure separately. Structured data can clarify supported page information, but cannot replace readable content or prove future citation inclusion.
- If useful text is absent from the source HTML, verify the required runtime section.
- If extraction loses an accessible table or code block, retain that failure case for the parser or rendering review.
- If the source is unavailable, record the access or transport result instead of accepting empty text.
- Rerun the same content assertions after a change.
Reference documentation: www.searchcans.com — /reader-api; developers.google.com — /search/docs/appearance/ai-features.
