A beautifully designed chart comparing tiers, features, or competitors communicates nothing to a language model, because the differentiating facts live inside pixels rather than parsed text or markup — the model simply cannot extract "Tier B includes 24-hour support" from an image. Generative engines answer comparison questions by pulling structured, tabular facts they can quote verbatim, which means any business relying on graphical data visualization for its key differentiators is functionally invisible in exactly the queries — "which plan includes X," "how does Y compare to Z" — where being cited matters most. The fix is re-encoding those comparisons as accessible HTML tables paired with Table and structured Product/Offer schema, and as plain-language equivalents in an llms.txt briefing, so the same facts a designer put into a chart become facts a model can retrieve, quote, and attribute correctly. This does not mean abandoning the visual; it means giving every visual a machine-legible counterpart underneath.
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Alt text helps but is rarely sufficient — it typically summarizes the image rather than reproducing every data point. A parallel structured table or schema markup with the same figures is required for models to extract and cite specific comparison facts accurately.
Product schema with nested Offer entities for pricing tiers, combined with a semantic HTML table using proper th/td scoping, gives both search crawlers and AI retrieval systems a clean, unambiguous structure to parse and quote from.
Tables map directly to structured retrieval — each cell is an isolated, attributable fact — while prose comparisons require the model to infer relationships, increasing the risk of misquoting figures or omitting the business from the answer entirely.
Proof & data
Most AI-visibility tools only watch — they report where you are absent and stop there. AI HALO does the work that changes the answer, then re-scans to prove it.
Measured live across ChatGPT · Claude · Gemini · Meta AI · Grok · DeepSeek — we ask the models your buyers’ real questions, before and after.
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