The one-sentence conclusion: this Suzhou manufacturer's overall mention rate across 12 AI engines was 49.2% (counting only the client's domestic brand), which looks like a passing result—but when broken down by engine, Doubao (345 million monthly active users) and DeepSeek (127 million), two gateways with over 100 million users each, scored only 39% and 11% respectively, while DeepSeek had already begun recommending the company's competitors by name for comparable questions.
Correction (2026-09-24): (1) The original said "We used YinJen to continuously monitor this client for 11 days"; corrected to: the client ran the monitoring itself with YinJen; YinJen is a product of Suzhou ZhiMaHang Technology Co., Ltd. (ZhiMaHang for short), and ZhiMaHang only provides the tool. (2) The original gave an overall mention rate of 50.2% (263/524); corrected to: counting only the client's domestic brand, 49.2% (258/524), with the per-engine figures in the title and text updated accordingly; 22 of the 524 answers were incomplete and are counted as not mentioning the brand. (3) The original called Doubao and DeepSeek "the two engines with the largest user bases"; corrected to: Doubao (345 million monthly active users) and DeepSeek (127 million) are two gateways with over 100 million users each. (4) The original said the highest and lowest engines differed "roughly twenty-fold"; corrected to: the multiplier has been removed. (5) The original gave "manufacturer recommendation" questions a hit rate of 54%–75%; corrected to: 52.7% (238/452). (6) The original said "selection knowledge" questions had "only 1 hit across 19 questions"; corrected to: the most-measured selection question was run 19 times, and the brand was mentioned only once (1/19); the number 19 has also been removed from Step 3. (7) The original described Newrank's study as covering "474,000 Doubao citations" and title match as "the only significant variable"; corrected to: 474,006 is the number of source items collected, 176,215 of them cited by Doubao, and 0.23 was the relatively strongest correlation, which the source itself calls weak. (8) The original said AI was "citing only other companies' technical content", that "AI was using competitors' content to educate this company's customers", and that being present "meant little more than making up the numbers"; corrected to: among the answers to selection questions that left a citation record, none cited the client's own website.
Background: The Anxiety Began with a Casual Conversation
The client is an electronic-components manufacturer in Suzhou. Its main products are network transformers and integrated RJ45 connectors, and its customers are primarily hardware engineers at communications-equipment and industrial-control manufacturers.
The concern began with something small. A purchasing engineer at a long-standing customer casually mentioned that, before selecting a product, he now asks DeepSeek, "How should I choose a gigabit network transformer?" The owner went back and tried it himself. The answer was highly professional and cited technical content from several peer companies, but it did not mention his company once.
This was not an isolated case. CNNIC's 57th report (February 2026) showed that China had 602 million generative-AI users, with a penetration rate of 42.8% and year-on-year growth of 141.7%. Data from a Zhihu Research Institute white paper, as reported by China News Service, was even more direct: 81% of consumers would partially adopt AI recommendations when making a purchase. Engineers in B2B procurement chains are among the earliest groups to turn "ask AI" into a professional habit.
Another figure is worth considering alongside those numbers: as of October 2025, about 70% of Baidu mobile search results pages contained AI-generated content, according to Baidu's Q3 earnings disclosure. Even when users are still "searching," more and more of the first screen is written by AI. An SEO ranking no longer automatically means being seen.
That raises the questions: what exactly does AI say about you? How often does it mention you? When it does not mention you, whom does it mention instead? Asking a few questions manually cannot answer them; a systematic checkup is required.
Checkup Method: 43 Questions, 12 Engines, 11 Days, 524 Valid Runs
The client ran the monitoring itself with YinJen for 11 days (first measured run 2026-06-30, last 2026-07-10; the account itself was created on 06-21):
- Question set: the client's question library holds 344 entries in total; 43 questions were actually deployed in this period, divided into two main categories—"manufacturer recommendation" questions, such as "recommended network-transformer manufacturers," and "selection knowledge" questions, such as "what is the difference between 100 Mbps and gigabit network transformers?";
- Engine coverage: Doubao, DeepSeek, Kimi, Qwen, ERNIE Bot, Tencent Yuanbao, Zhipu Qingyan, StepFun, ChatGPT, Claude, Gemini, and Perplexity—12 engines in total;
- Measurement method: automatically asked real questions of each engine every day and recorded the answers, producing 524 valid measured runs over 11 days (each question-engine pair ran 1.4 times on average, 4 at most; a further 236 runs failed and 82 were skipped, neither of which is counted; 22 of the 524 answers were incomplete—under 100 characters of body text, 13 of them from Doubao—and all are counted as not mentioning the brand);
- Evaluation rule: an answer counted as one "mention" if it explicitly mentioned the brand or its official website; mention rate = number of mentions ÷ number of measured runs.
(YinJen is a product of Suzhou ZhiMaHang Technology Co., Ltd. (ZhiMaHang for short), and ZhiMaHang only provides the tool; the case data has been anonymized throughout.)
Finding 1: 49.2% Overall, but an Order-of-Magnitude Gap Between Engines
Counting only the client's domestic brand, across 524 measured runs the brand was named 258 times, an overall mention rate of 49.2%. Viewed alone, that number seems acceptable. Broken down by engine, however, the results describe two entirely different worlds. The 10 engines with usable sample sizes are listed below (the figures in parentheses are mentions over measured runs):
- Qwen — mention rate: 87% (47/54)
- Tencent Yuanbao — mention rate: 83% (38/46)
- Claude — mention rate: 70% (37/53)
- ERNIE Bot — mention rate: 68% (39/57)
- Zhipu Qingyan — mention rate: 63% (34/54)
- Kimi — mention rate: 53% (19/36)
- Doubao — mention rate: 39% (23/59)
- ChatGPT — mention rate: 15% (7/47)
- DeepSeek — mention rate: 11% (6/57)
- Gemini — mention rate: 2% (1/46)
Two further engines, StepFun (8 runs, 63%) and Perplexity (7 runs, 29%), had samples too small to compare and are listed here for completeness only.
The highest result was Qwen at 87%; the lowest was Gemini at 2%. That comparison deserves a caveat: Gemini's 2% is 1 hit out of 46 runs, so a single additional hit would shrink the ratio noticeably. What the contrast shows is that engines are not in the same order of magnitude—not a precise multiple. "AI visibility" is never one number; it is one number per engine. Looking only at the average hides the most consequential weaknesses.
Finding 2: The Weakest Results Were on Two Gateways With Over 100 Million Users
The significance becomes clear only when mention rates are considered alongside user volume. According to QuestMobile's 2026 Q1 data, native AI apps had 446 million monthly active users: Doubao had 345 million and DeepSeek had 127 million, so each is a gateway with over 100 million users. Those are precisely the two engines on which this client scored only 39% and 11%.
Using the same measurement basis, the top-performing Qwen had 166 million monthly active users, less than half of Doubao's total. On these two gateways, the client was mentioned fewer than four times out of ten questions at best, and at the low end only a little more than once. Visibility needs to be weighted by traffic; once weighted, that 49.2% immediately shrinks.
Finding 3: Present for "Recommendations," Absent from "Knowledge"—Losing During Demand Formation
The contrast was even sharper when the results were split by question type:
- "Manufacturer recommendation" questions: a hit rate of 52.7% (238/452). When users directly asked "Which supplier is good?", the client often appeared on the candidate list;
- "Selection knowledge" questions: the most-measured selection question was run 19 times, and the brand was mentioned only once (1/19).
Before engineers decide "which supplier to contact," they spend substantial time understanding "how to choose." During this demand-formation stage, among the answers to selection questions that left a citation record, none cited the client's own website.
Finding 4: DeepSeek Was Already Naming Your Competitors
The most striking record came from DeepSeek. For a comparable "manufacturer recommendation" question, it recommended other peer brands by name while omitting this brand.
Tracing the citation sources shows which pages the AI trusted. This record does not mean that "AI does not understand the industry." It means competitors' content had already entered DeepSeek's source pool while yours had not. A blank space does not remain blank forever; the only question is who fills it first.
Optimization Roadmap: Address Weaknesses One Engine at a Time
Different engines have different source structures, so the actions should differ as well. Our roadmap for this client had three steps:
Step 1, DeepSeek: open structured content on the official website. Turn product specification tables, selection guides, and application-scenario FAQs into clearly structured, crawlable pages so the engine at least has your content available to cite. This is the first priority for climbing above 11%.
Step 2, Doubao: Toutiao-ecosystem sources plus titles aligned with query intent. A sampled study published by Newrank in May 2026 (165 prompts; 474,006 source items collected, 176,215 of them cited by Doubao) found that, among Toutiao articles already cited by Doubao, the number of citations had almost no correlation with the account's follower count; the relatively strongest correlation was with whether the title matched the keywords and intent of the question (coefficient 0.23), which the source itself calls weak. There is no need to spend heavily building a large account. Distribute content within the Toutiao ecosystem and phrase titles as the questions users actually ask—"How should I choose a PoE network transformer?" rather than "Our product receives another certification"—to align more closely with the citation mechanism.
Step 3, turn the missed selection questions into content topics. Every unanswered "selection knowledge" question is a ready-made technical-content topic. Fill them one by one so AI can begin citing you during the demand-formation stage.
One boundary must be made explicit: these actions are fundamentally about "giving AI credible content it can cite." No one can guarantee indexing or rankings. In March 2026, CCTV's 315 Gala exposed the gray market for GEO "AI poisoning," in which just 11 advertorial articles could make AI recommend a fictitious product. That route is neither acceptable nor sustainable. There is only one compliant approach: keep producing genuine content, then use monitoring to verify whether each step works.
How to Run the Same Checkup for Your Own Brand
Every step in this checkup can be reproduced for your own brand:
- Download YinJen for macOS or Windows: https://zhimahang.com/yinjen/download ;
- Build your own question set. Think about how customers ask for "recommendations," but more importantly, how they ask "how to choose";
- Enable automatic daily measurement and run it for two to three weeks to obtain per-engine mention rates, a Visibility Score, and comparisons of competitor share of voice;
- Use citation-source tracing to identify whom AI currently trusts, then address weaknesses engine by engine using the roadmap above.
The 14-day free trial requires no credit card, and the Creator plan starts at ¥29.9/month. See https://zhimahang.com/yinjen for details. First understand your real position across 12 engines; then decide where to focus.
FAQ
Q: What is AI visibility (mention rate)? A: An explicit mention of a brand or its official website in an AI answer counts as one "mention." Mention rate = number of mentions ÷ number of measured runs, measuring how frequently a brand is seen in AI answers.
Q: How long should monitoring run, and how many questions are needed for useful results? A: This case covered 43 questions, 11 days, and 524 valid measured runs (the client's library holds 344 questions; only 43 were deployed in this period). We recommend covering both "manufacturer recommendation" and "selection knowledge" questions and measuring continuously for two to three weeks, because a single query is too susceptible to chance.
Q: Can content optimization guarantee an AI recommendation? A: No, and no one can make that guarantee. The "AI poisoning" gray market exposed by CCTV's 315 Gala demonstrates precisely the risk of shortcuts. What you can do is continue producing genuine content and use monitoring to verify the results.
Further reading
