Per-Table Grade
Each table gets an A to F grade and a 0-100 extractability score built from header, scope, cell-value, and merge checks.
Comparison Table Extractability Auditor for AI Retrieval
TL;DR: Comparison Table Extractability Auditor checks score how cleanly ai can extract your html comparison tables. so you can confirm the current issue, understand when it matters, and move directly into the next fix without leaving the browser.
Grade every HTML table on how reliably an answer engine can read it.
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cheerio-based walk of every HTML table on the page Header and scope validation (th vs td, scope=col/row) Alt-less image and icon/glyph cell-value detection colspan/rowspan merged-cell soup scoring Div-grid and role=table masquerade detection
What does Comparison Table Extractability Auditor do?
Comparison Table Extractability Auditor finds the HTML tables on a page and grades how well an AI model, scraper, or answer engine can turn each one into structured data. It walks every <table> with cheerio, inspects header semantics, cell values, and merge structure, and assigns a per-table grade with concrete fixes.
Why it matters for AI SEO: Comparison and pricing tables are exactly the content AI Overviews, ChatGPT, and Perplexity love to quote, but only when the markup is machine-readable. A table that looks perfect to a human can be invisible to a retriever if its Yes/No cells are alt-less images, icon-font checkmarks, or a <div> grid pretending to be a table.
Header and scope checks: It verifies that columns and rows use real <th> elements with scope=col/scope=row, not styled <td> cells, so each value stays bound to the right label when the grid is flattened.
Icon and image value detection: It flags cells whose only value is an alt-less <img>, an unlabeled SVG, an icon-font glyph, or a bare check/x character, the most common reason a feature matrix extracts as a wall of blanks.
Structure and fakes: It penalizes colspan/rowspan soup that makes the grid ragged, and surfaces div-grids and role=table look-alikes that masquerade as tables without any semantic markup at all.
Use cases: Audit pricing pages, best-of roundups, spec sheets, and feature matrices before publishing, then rerun to confirm your fixes raised the grade.
When should you use Comparison Table Extractability Auditor?
Comparison Table Extractability Auditor is most useful when you need a direct answer on a live URL or draft before you change templates, ship content, or rerun a wider audit.
So verwenden Sie dieses Tool
- 1 Enter the URL of a page that contains a comparison, pricing, or spec table.
- 2 Run the extractability audit.
- 3 Review the per-table grade, failed checks, and div-grid suspects.
- 4 Apply the suggested header, scope, and cell-value fixes, then rerun.
What should you do next?
After making tables extractable, validate answer extractability and structured data to round out AI retrieval readiness. Then move to the related checks below to confirm the fix on the live canonical page.
Was Sie bekommen
Hidden-Value Finder
Catches Yes/No cells encoded as alt-less images, unlabeled SVGs, icon fonts, or bare check/x marks that extract as blanks.
Targeted Fix List
Returns the exact markup changes per table plus a ranked list of the highest-impact fixes across the whole page.
Frequently Asked Questions
Antworten zu Comparison Table Extractability Auditor
The biggest culprits are non-text cell values and missing header semantics. When a Yes/No column uses alt-less images, icon-font checkmarks, or unlabeled SVGs, a text extractor sees an empty cell. When every cell is a styled <td> with no <th> headers or scope attributes, the model cannot tell which value belongs to which feature. Heavy colspan/rowspan merges and <div> grids posing as tables compound the problem.
Yes. It flags containers that imitate a table visually but use markup an extractor often skips, including elements with role=table or role=grid that are not real <table> elements, and <div>/<ul> blocks with table-like class names holding repeated row-classed children and no semantic <table> inside. These are reported separately as div-grid suspects with the offending selector.
Because an icon glyph carries no text the model can read. A Font Awesome or material-icon checkmark, an inline SVG with no aria-label or <title>, and a bare ✓/✗ character all render visually but extract as nothing. Pair each marker with visually-hidden text or an aria-label (so ✓ reads as Yes and ✗ reads as No), or use a plain text value, and the cell becomes machine-readable.
Run it on any live page that contains a comparison, pricing, or specification table. The auditor fetches the rendered HTML server-side, walks every <table> element, and grades each one. It works best on best-of roundups, feature matrices, and pricing pages where a clean, extractable table directly improves your odds of being quoted in AI answers.