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Case Study: In 90 days, a Taiwan Machine Tool Company's AI Citation Rate Rose from 0 to 34%

GEO success story from a medium tool factory: In 90 days, the number of brand names cited by AI rose from 0 to 34% in the context of the procurement of ChatGPT, Perplexity and Gemini. How can you copy?

Tenten GEO TeamPublished 2025-01-235 min read
A five-axis CNC processing centre is surrounded by soft and lavender purple beams in a warm-coloured light, like the brand of the manufacturing industry in the AI search.

AI quotes not from more blogs. The tool factory in this site pulls the AI citation rate from 0 to 34% in 90 days, and we don't have a major change in the web set-up, and we don't break the ad -- the real pointer is to tear the question of the buyer asking AI down to a point where the answer to each question is a paragraph that AI can clean out.

Starting point: The brand is equal to non-existent in AI

This manufacturer does five axes of the CNC processing centre, which accounts for 70 percent of the revenue, and most of the clients are East and South-East Asian and European molds, high-class suppliers. The situation was typical when they came to Tenten: Google passed its natural ranking, the network visited thousands of times a month, but the business started to pay back one thing: more and more overseas shopping said, “I asked ChatGPT first.” The question is, when we actually ask, AI's answer is never to them.

We're using a set of fixed shopping situations to cross ChatGPT, Perplexity, Gemini, three times each, record whether brands are mentioned, considered recommended, attached. The results of the first baseline test were quite straightforward: 38 questions, and the number of times cited was zero. The competition, which includes two Japanese and a Taiwan-based business, has stabilized among the problems of the "Recommendation of Small and Medium Quantities Modeling Devices" in the Taiwan Five Axis Processing Center.

We asked 38 real questions.

Most of the content of the manufacturing website is written for yourself: a schedule, company evolution, flowers. These pages are almost useless in the face of AI, because the purchase does not ask "what year your company was founded." The first step of the GEO audit is to sort out the questions that buyers really throw to AI on their decision-making path, and then examine whether there is a good enough answer to be drawn.

We split these 38 questions into three layers, and this is the skeleton that follows:

  • Situation level: like "Small and medium-volume alloy parts should use three or five axes" "What are the plant conditions to prepare before the 5-axis processing centre enters the field" -- the buyer asked when the demand was cleared.
  • Comparative Level: "What are the brands of Taiwan's Five Axis Processing Center?"
  • Standard level: "How much does the main axis turn fit for hard matter high-speed cutting" "Performance ±0.005 mm represents what in practice" -- technical questions are asked when a customs check.

In contrast, their original website answered only a fraction of the hierarchy, and the answer was buried in a PDF-type book, and AI could not read it. The situational and comparative levels -- the first and most frequently asked AI -- are completely blank. This is the structural reason for the zero citation rate.

Three things, get the citation rate moving in 90 days.

We didn't change a hundred things at a time. First of all, there are three of the highest-rated items, each of which corresponds to the list of questions.

Tool factory 90 days GEO Run Flowchart: Retrieve from the acquisition point, extractable content to reference tracking three steps
90-day running in three segments: pre-characterize questions, then change answers, and end up staring at AI quotes a week.

One, change the answer to "AI."

Each scenario and comparison question received an 80-to-150-word answer that leads with the conclusion and lives in the corresponding page text, not a catalog or video. The goal was a self-contained passage: one idea per paragraph, no hidden context, and no facts trapped in images. We also kept comparisons honest. By acknowledging the advantages of Japanese equipment in some high-rigidity applications, we made AI more willing to list the manufacturer as a 'high-value Taiwanese option.'

ii. Complementing structured data and verifiable facts

AI will try to confirm its credibility before citing a source. We use product specifications, authentications, real exchange cases to structured data, and to clearly identify the verifiable facts on the page, such as precision numbers, suitable materials, process size ranges. The vague word for marketing ( "predominant quality") has been deleted and replaced with a specific number. The AI engine prefers to verify the content, a step that has led to the most rapid recovery in the citation rate for the issue of the hierarchy.

Three, in AI visibility monitoring, one week, only the quoted page.

We use AI visibility monitoring to run the same group of 38 questions every week, documenting which problems are starting to appear in the brand, at the top of the line, and whether there are links. This has the effect that the team no longer changes the content by feeling: the answer to which page has gone into AI strengthens it, the page or the blank paragraph. The iterative speed of GEO depends on how fast you see the backlash, which is much more useful than watching Google Analytics once a week.

And the manager of the company's sales said, "I used to write like I threw it in a black hole, and now every week I see what's been bit by AI and what's not yet." It's not until the team knows what's next."

90-day numbers

On day 90, we reran the baseline test. Across 38 procurement questions, the brand's AI citation rate reached 34 per cent, with comparison queries performing best because that layer had received the most complete coverage. Organic search traffic to the official website also rose slightly; content that AI can extract usually aligns better with traditional search intent too. The clearest business signal came from overseas buyers who began saying, "I saw AI recommend you," during the first call. That had not happened once in the previous 90 days.

How can you copy this?

If you're in the manufacturing business or B2B SaaS, you want to know what your current AI citation rate is, it's easy to start with: listing 30 to 40 clients who really are going to lose to AI, go to ChatGPT and Personal, ask each other, and count how many times. Most of the companies that do it for the first time, the numbers are so low that they're not comfortable: that's the opportunity, because most of the competitors have not filled this.

You can shorten the discovery work by booking 30 minutes for a GEO diagnostic session. We will measure the current citation baseline, identify the most important gaps, and show which three actions deserve priority in the next 90 days.

Frequently asked questions

What's AI's citation rate? How do you measure it?
AI citation refers to a group of buyers asking questions about the ratio of their brand. The practice is to set a set of 30 to 40 shopping sentences, and in ChatGPT, Perplexity, Gemini, the number of times the brand is mentioned and recommended divided by the number of questions.
90, can you really get 34% from 0?
This is the practical result of a Taiwan tool factory, provided that the content gap is large and the method is correct. The key is to clean up the shopping questions, re-form the answers into paragraphs that can be extracted cleanly by AI, and track the text every week. The starting speed is different from the intensity of the competition.
Manufacturing doing GEO, what's the first step?
Start by taking stock of the purchases that clients would actually throw to AI, and go to ChatGPT and Perplexity and ask them, how many times did the brand come up and get the reference benchmark? Most companies have lower volumes for the first time, which is the easiest entry point to pull out.

READY WHEN YOU ARE

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