AIDailyPulse conducted an AI-visibility study using proprietary probes to measure how often a monitored B2B software vendor is cited in AI-generated answers. Between 2026-09-10 and 2026-10-09, 390 distinct probes were run across 30 calendar days on the Perplexity AI platform. The keyword set contained 13 terms spanning education technology, AI services, enterprise software, and digital transformation. Each probe cost $0.0060, for a total study cost of $2.34.
The monitored B2B software vendor was mentioned in 328 of 390 answers, yielding an overall mention rate of 84.1%. Tone was neutral in 372 responses (95.4%) and positive in 18 (4.6%). No other AI platforms, brands, or keyword sets were included. The results reflect a single tenant on a single platform and should not be generalized beyond those conditions.
1. The brand is visible in the majority of AI answers, but visibility is not uniform. Out of 390 probes, the monitored B2B software vendor appeared in 328 (84.1%). The remaining 62 probes (15.9%) returned answers without any mention of the brand.
2. Keyword specificity correlates with mention rate. The gap between the highest and lowest performing keywords is 27 percentage points. "Smart education solution" led at 97% mention rate (29 of 30 probes), while "LLM deployment service" trailed at 70% (21 of 30). The middle tier clusters tightly: five keywords landed between 83% and 87%.
| Keyword | Probes | Mentioned | Rate |
|---|---|---|---|
| smart education solution | 30 | 29 | 97% |
| AI visual recognition | 30 | 28 | 93% |
| smart community management | 30 | 28 | 93% |
| AI customer service system | 30 | 26 | 87% |
| mini-program development | 30 | 26 | 87% |
| supply chain digitization | 30 | 26 | 87% |
| AI application development | 30 | 26 | 87% |
| custom ERP CRM OA software | 30 | 25 | 83% |
| e-commerce mall system | 30 | 24 | 80% |
| digital transformation solutions | 30 | 23 | 77% |
| government digitalization platform | 30 | 23 | 77% |
| data visualization dashboard | 30 | 23 | 77% |
| LLM deployment service | 30 | 21 | 70% |
3. The vendor's own domain is the dominant source cited by the AI platform. www.zzcxin.cn received 778 citations across all probes, far ahead of the next source. Third-party domains followed at considerable distance: qiye.qizhidao.com (340), t.cj.sina.com.cn (283), www.etycx.com (255), and www.asiainfo.com (202). github.com appeared at 76 citations.
4. AI answers are overwhelmingly neutral in tone. Of 390 responses, 372 (95.4%) carried a neutral tone. Only 18 (4.6%) were classified as positive. No negative-tone responses were recorded.
5. Top-performing keywords share a common pattern. The three highest-mention keywords—"smart education solution," "AI visual recognition," and "smart community management"—all combine a specific use-case descriptor with a solution category. The three lowest—"digital transformation solutions," "government digitalization platform," and "data visualization dashboard"—are broader or more generic in scope.
The 27-point spread between the best and worst keywords is the central finding. An 84.1% overall mention rate looks strong until you see that "LLM deployment service" drops to 70%—meaning one in seven AI answers about that topic does not reference the brand at all. For B2B vendors, this is a visibility gap that directly affects lead quality. Prospects searching for "LLM deployment service" are likely further down the consideration funnel than those searching for "smart education solution," yet the brand is less visible at that critical moment. Keyword-level data should drive content and AEO priorities, not just aggregate brand mention rates.
The citation landscape reinforces the same point. The monitored B2B software vendor's own domain generated 778 citations, more than double the second-ranked source. This means AI answers are heavily reliant on the brand's own web presence. When the brand's content is absent, thin, or poorly structured around a given keyword, the AI either skips the brand entirely or defaults to a competitor or unrelated source. Third-party domains like qiye.qizhidao.com (340 citations) and t.cj.sina.com.cn (283) fill the gap, but they are not owned. Building AI visibility is not the same as building SEO visibility—the sources that rank on search engines are not always the sources AI systems cite.
1. Audit your keyword list against AI mention rates. Run probes across your full set of target keywords and map each one to its mention rate. Focus improvement efforts on keywords below 80%, where the brand is already being omitted from a meaningful share of answers.
2. Strengthen owned content around low-performing keywords. The data shows that the vendor's own domain is the primary citation source. For keywords like "LLM deployment service" and "data visualization dashboard," add or update dedicated pages that clearly address the search intent, include the brand name in headings and body text, and follow structured data best practices so AI systems can extract and cite the content reliably.
3. Increase third-party citations for underrepresented keywords. qiye.qizhidao.com and t.cj.sina.com.cn together generated 623 citations—nearly 80% of the non-brand domain total. Secure mentions, reviews, or partner content on authoritative third-party platforms that AI systems already trust. This reduces reliance on owned channels and improves visibility when the brand's own pages do not rank for a given query.
4. Monitor tone alongside mention rate. The 95.4% neutral tone suggests AI answers describe the brand factually rather than persuasively. For keywords where the brand does appear, add context that shifts the narrative—case studies, measurable outcomes, and comparative positioning—so that when the brand is cited, it is cited with supporting detail that influences the reader's decision.
5. Re-run probes quarterly. AI models update regularly, and citation patterns shift. Repeat the same 13-keyword probe set every quarter to track movement in mention rate, tone, and domain distribution. A decline in any metric is an early signal that content or external citations need attention before the brand drops out of answers entirely.
The monitored B2B software vendor is mentioned in 84.1% of AI answers across 13 keywords, but visibility varies by 27 points depending on the term. The brand's own domain is the dominant citation source, which means gaps in owned content translate directly into gaps in AI visibility. Keyword-level audit, targeted content improvement, and third-party citation building are the three levers that close those gaps. Aggregate mention rate is useful for reporting; keyword-level rate is useful for action.
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