SEO (Google search) vs. GEO (generative-AI search): how do they differ?
“Find a company that can machine titanium to a tolerance within ±0.01 mm and has a track record in medical device components.”
Until recently, a sourcing requirement like this was beyond what a Google search box could handle. The buyer had to break it into keywords such as “titanium machining medical device,” open the resulting links one by one, and judge for themselves whether each company fit.
That is changing. With generative AI such as ChatGPT, you can describe your sourcing requirement in plain sentences. The AI reads multiple company sites and returns only the firms that match — summarized, with source URLs attached. For prospects, “finding a supplier” is shifting from scanning a list of links to receiving a shortlist the AI has assembled.
So what happens to a manufacturer’s website? Does a site that ranked highly on Google get cited, as-is, in the AI’s answer? FinMark Edge tested this question with Claude Fable 5 and real-world searches.
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How buyers search is starting to change
Traditionally, a buyer combined keywords, searched, and checked the matching links one at a time. With generative AI, that flow is shifting toward “describe the requirement in sentences and receive a shortlist the AI has compiled.”
The question is simple: does a site that ranked highly on Google also get cited, as-is, in the AI’s answer? — FinMark Edge tested exactly this with real searches.
Method: 15 sourcing scenarios, 4 tested in practice
We first defined 15 search scenarios modeling real B2B inquiries to small and medium manufacturers. For each scenario we prepared a matched pair: a “prompt” (a natural-language request) to enter into generative AI, and a set of “keywords” to enter into Google search.
From these, we ran four scenarios of differing character — (1) titanium × ±0.01 tolerance × medical devices, (2) stainless, small lot, short lead time, (3) VA/VE proposals, and (4) comparison of difficult-to-cut materials — in both the keyword and prompt formats, then matched up which pages were displayed and cited. Together they cover four buyer motivations: precision-driven, terms-driven, proposal-driven, and material-comparison.
What we found: the bar for “pages that get picked” is different
The results were clear. The two approaches surface a different cast of pages, for different reasons.
Google search (keyword type)
Driven by titles and a page’s prominence. The top spots fill with industry portals and comparison/ranking sites; an individual company’s page appears only when its title matches the keywords. What you get is a “list of links,” and the user must click to judge which firm meets the requirement.
Generative-AI search (prompt type)
The AI reads the body text and extracts only pages whose text actually states the conditions — “±0.01 tolerance,” “medical-device track record,” “VA/VE proposals available.” It then presents an “answer” pairing each company name with a source URL.
In other words, the selection criterion is no longer title-matching or site authority, but whether the body text contains a sentence that directly answers the requirement.
In the four tested scenarios, the firms cited by the AI were: a company that wrote “medical device parts / high precision (0.01 or better) / titanium” in plain text on its track-record page; a company whose FAQ heading “Can you provide VA/VE proposals?” was answered directly with “Yes”; and a company that listed its ISO certification and medical-device manufacturing license number in the body text. Conversely, sites with impressive equipment and experience that lived only inside images, PDFs, or abstract catch-copy did not appear in the AI’s answer at all.
5 writing traits shared by the cited pages
Across the four tested scenarios, the pages cited by generative AI shared a set of common traits.
Numbers are written in the body text
Not “high precision” or “short lead time,” but “tolerance ±0.01 mm,” “from 1 piece,” “quote within 2 hours.” AI can’t match conditions against vague adjectives. The numbers must sit in the HTML body text — figures buried inside imaged case studies or PDF catalogs appear not to be picked up.
Material codes and industry names appear verbatim
Buyers write “SUS316,” “Inconel 718,” “medical device,” “semiconductor manufacturing equipment” into their prompt. Only when the same terms exist in your body text and headings can the AI connect the two. “Handles various stainless steels” does not answer a niche prompt like “SUS316.”
Content is structured to answer questions
An FAQ format — a heading “Can you provide VA/VE proposals?” followed by “Yes. We propose process conversions as well” — matches the structure of the prompt itself, and showed a markedly higher citation rate. Anticipate the questions buyers would put to AI, turn them into headings, and answer them. That alone changes how citable you are.
Dedicated pages exist by material and application
Sites built one-theme-per-page — “titanium machining,” “difficult-to-cut machining,” “VA/VE case studies” — were the ones cited. A site that crams everything onto the home page has no “answer page” for a specific condition, making it hard for AI to cite.
Grounds for credibility are spelled out in prose
ISO certification, medical-device manufacturing license number, in-house equipment, annual number of quotes handled. When such verifiable facts appear in the body text, AI treats the firm as one it can “recommend with evidence.” Because generative AI is designed to answer while citing sources, it tends to favor pages where the grounds are written out.
Want to know whether your site is in a “citable-by-AI” state?
FinMark Edge checks whether your strengths are expressed in a form AI can read, and proposes a prioritized list of improvements. Feel free to get in touch.
So what is actually different from SEO?
Traditional SEO, boiled down, was “a contest of keywords and authority.” You targeted high-volume terms, put them in your title, and collected backlinks. For a small or mid-sized manufacturer, that meant fighting portals and big players on the same field — an uphill match.
Generative AI, by contrast, does not care about search volume. Even a niche condition searched only a few dozen times a month — like “aluminum thin-wall reduce distortion” — will get cited if the buyer writes it into a prompt and the answer is written in your site’s body text. A niche, specific strength becomes the weapon — and for SMEs whose edge lives in concrete conditions (supported materials, machining precision, industry-specific know-how), this is arguably a tailwind rather than a threat.
The catch: that strength has to be written in a form AI can read — plain text, numbers, proper nouns, question-and-answer structure. “Craftsmanship” that isn’t written on the web might as well not exist, as far as AI is concerned.
Takeaway: first, learn how AI reads your own site
The first step is easy. Put the conditions your customers would type into ChatGPT or Gemini as plain sentences: “Which companies can machine [your strongest material] to [a given precision] and have a track record in [your industry]?” Does your company show up in the answer? If not, which competitor’s page is cited — and on the strength of which wording? Most of what your site needs to fix is right there.
In the age of generative-AI search, a manufacturer’s website is shifting from something to “be found” to something to “be cited by AI.” Give your site words that match your technical ability. That is the first condition for staying on the shortlist in this new era.