This is not a customer claim about achieving AI search performance. It is a general workflow for making the evidence in reports, white papers, and IR materials easier to inspect.
Where does the problem appear?
A long PDF may contain important figures while its web page provides only a title and download link. Readers cannot judge the scope before opening it, and automated systems may lose the relationships in tables and charts.
Organize claims and evidence first
List the questions, main claims, supporting sources, and exceptions. Add a unit, period, population, and source to every figure. Explain tables and charts in text. Remove non-public evidence or lower the claim to a supportable scope.
Connect structured content to the original
Feat AI publicly presents a direction for structuring PDFs, data, and web pages for AI understanding and citation. Connect the result to the original document, author, review date, and change history. Do not guarantee citation or ranking in any AI service.
Test with realistic questions
Use questions real readers ask. Check that every figure matches the original and that no condition or old version has been mixed into the answer. Correct the source and structure together when a test exposes an error.
Pre-publication checklist
- Every central claim has an inspectable source.
- Units, periods, and denominators remain visible.
- The author, review date, and original link are present.
- Unsupported quantitative performance claims are excluded.
- AI citation and ranking are not promised.
- Update ownership is assigned.
The objective is a verifiable evidence structure, not extra copy written only for an answer engine.