Ask an AI assistant for your price and something counter-intuitive can happen. The model reads the number straight off your brochure, sees it perfectly, and still will not put your name on the answer. A vision model can look at a picture and make out a figure. The step that decides who gets credited is a separate one, and it runs on text the engine retrieved and indexed, not on what the model's eye can see in a graphic.
When the Crane Index™ scored UK websites for AI readability in October 2026, the readable text layer came out strong, averaging 86 out of 100, so where a fact is live text on the page an engine lifts it without trouble. The gap sits elsewhere. Fewer than three sites in a hundred publish what they actually sell as structured data a model can select, a Product, a Service or an Offer. That gap suggests the facts that decide a sale are often left where the citation step cannot reach them, and a price set inside an image is the hardest case, because there the fact is not plain text. Ranking is no longer proof the fact is working.
Ranking a file is not the same as being quoted from it
Google's Search Central documentation lists PDF among the file types it indexes and can rank, and that documented position is the foundation of the standard SEO view: if the file turns up in results, it is doing its job. That logic held while search returned a list of links and the buyer clicked through to read the document. It breaks the moment the answer, not the link, is what the buyer sees. Ranking means Google will return the file somewhere in a list. Being quoted means an answer engine can lift your fact and hand it back with your name on it. A file can pass the first test and fail the second, and the second is the one that now closes the sale.
Retrieval runs on text, and that is where your name is attached
AI Overviews and AI Mode answer in a sentence or two and attribute that sentence to the source it came from. Attribution runs on what the engine retrieved and indexed as text, so a price that sits only inside an image leaves no indexed string to attach your name to, and the credit is more likely to go to a page that states the number in plain words.
The obvious objection in 2026 is that multimodal models can read an image directly. They often can, at the moment they compose an answer. Attribution is a separate step, and a number the model can see but the engine never retrieved as text has nothing for a citation to point to. What decides it is whether the engine can retrieve your price as text in the first place.
For a document, that comes down to conversion. Google indexes a text version of the file: it extracts the words and, where text is missing, applies OCR to the images. Whatever does not come through as characters is absent from that copy. A PDF whose words are real, selectable text can be read and quoted like any page; the exposure is a deciding figure set as artwork, such as a price typeset into a designed table, a menu laid out as a graphic or a scanned tariff, on the page or inside the file. Our scan draws the same line: the readable-text score is high because engines lift what resolves to characters. If your price is live text you are fine. If it exists only as an image, you are relying on a conversion you cannot see.
The fix is small, so the real gap is the audit
The remedy is a single edit: put the figure on the page as live text in your CMS, and keep the file as the downloadable brochure for anyone who wants one. So why does a deciding fact still end up trapped in a download? Because ranking hid the failure. The file appeared in results, the conventional scorecard read green, and no one checked whether the number inside it survived as text a machine could repeat.
The check is quick and needs no tools. Open the page a buyer lands on and try to highlight the price with your cursor. If it will not select, it is a picture, not text, and an answer engine cannot retrieve it either. The free Crane Index readability read does the same job across a whole page at once, scoring how much of your content resolves to text a machine can lift. Run either over the facts that decide a sale, the price, the current menu, the key specification, the opening hours, and confirm each one exists as readable text on an ordinary page, not only inside a file and not inside an image in that file.
Will the engines close this gap for you? Attribution may well improve, and models already read images well at the point of answering. The retrieval and citation layer is the slower half, and it still rewards a clean, retrievable text string. The fix costs nothing and takes minutes, so it is worth doing now rather than waiting on a change you do not control. The fact you cannot find in plain text is the one an AI answer hands to someone else.
