Comparison tables present structured, unambiguous data — options in rows, attributes in columns — that AI engines and featured snippets extract cleanly into side-by-side answers. When someone asks 'X vs Y' or 'best tools compared', a well-formed table is far more likely to be surfaced than the same information buried in prose.

It inspects every table element on the page and applies heuristics: does it have a header row (th cells or a thead), does it have multiple columns and multiple data rows, and do the dimensions suggest options-versus-attributes layout. Tables that meet these criteria are flagged as likely comparison tables; trivial layout tables are not.

Use real table markup with a clear header row of th cells (ideally inside thead), keep one concept per column, label the first column with the items being compared, and avoid using tables purely for visual layout. Proper headers let machines map each cell to a row and column, which is what enables extraction into rich results and AI answers.

A table without a header row gives machines no way to know what each column means, so the data is hard to interpret and rarely extracted. Adding th header cells turns an opaque grid into structured, machine-readable data. The detector flags missing headers so you can fix the most common table-SEO mistake.

For genuinely data-rich tables, structured data such as Dataset schema can reinforce meaning, though clean semantic HTML tables are the foundation. Start by ensuring the table markup itself is correct with proper headers; layered schema is a bonus for large or reusable datasets, not a substitute for good HTML.

It focuses on real table elements, because those are what search engines and AI systems reliably parse as tabular data. Grids built only with styled div elements are not detected and, importantly, are not understood as tables by machines either — which is itself a reason to use proper table markup for comparison content.

No. Detection and classification are entirely rule-based, run on our server by parsing the page HTML — no LLM or external AI API is called. The page is analyzed only to count and classify tables and is not stored or used for training.