AI brand visibility: 87.4% of answers named the monitored B2B software vendor

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AI brand visibility: 87.4% of answers named the monitored B2B software vendor

What we measured

AIDailyPulse analysed 390 AI probes conducted on Perplexity across 30 distinct days, from 2026-09-17 to 2026-10-16. The study used a fixed set of 13 keywords covering software development, digital transformation, AI services, education, government, commerce, and business operations.

Each probe cost $0.0060, for a total measured cost of $2.34. The monitored B2B software vendor was counted as mentioned when its brand appeared in the AI answer. The study also recorded the tone of each answer and the domains cited by Perplexity.

This is a focused measurement, not a market-wide benchmark. It covers one AI platform, one monitored brand, one tenant, one 13-keyword set, and a 30-day period. The results therefore show how often the monitored B2B software vendor appeared under these specific conditions; they do not establish visibility across other AI engines, broader query categories, or competing brands.

What the data shows

The central result is clear: the monitored B2B software vendor appeared in 341 of 390 answers, producing an overall mention rate of 87.4%.

That rate describes presence, not necessarily recommendation strength, prominence, or buying intent. An answer can mention a vendor briefly, include it among several options, or discuss it in a neutral way. The measurement answers a narrower question: did the brand appear at all?

#### 1. Visibility was high, but not uniform across keywords

The strongest results came from two keyword groups: “smart education solution” and “AI visual recognition.” Each produced 29 mentions from 30 probes, for a 97% mention rate.

At the other end of the range, “LLM deployment service” produced 22 mentions from 30 probes, or 73%. That 24-percentage-point gap shows why an overall visibility figure can hide important differences between topics.

Six keyword groups reached at least 90% visibility. Three more reached 87%, while four fell below 85%. For content and demand-generation teams, this makes the keyword-level view more useful than the headline average alone.

#### 2. The answers were overwhelmingly neutral

Perplexity’s answers used a neutral tone in 353 of 390 probes, or 90.5%. Positive tone appeared in 36 answers, or 9.2%, while negative tone appeared once, or 0.3%.

The combination of high mention frequency and mostly neutral tone matters. Being named often does not automatically mean the engine frames the vendor as a preferred provider. The data indicates broad recognition within the tested prompts, but relatively few explicitly positive descriptions.

That distinction is especially important for B2B software companies. A neutral inclusion may help a buyer discover a vendor, but it may not communicate differentiation, trust, technical fit, or superiority over alternatives.

#### 3. The weakest topic was also the most specialised

“LLM deployment service” had the lowest mention rate at 73%, with 22 mentions from 30 probes. “Data visualization dashboard” followed at 80%, with 24 mentions. “Digital transformation solutions” and “government digitalization platform” each reached 83%, with 25 mentions apiece.

These results suggest that visibility may vary when a keyword becomes more specialised, more technical, or less closely aligned with the source material available to the AI engine. The data does not identify the cause, so it would be inappropriate to conclude that topic complexity alone explains the lower rates. It does, however, show where further investigation should begin.

A practical next step would be to compare the actual answer wording for the 22 LLM deployment probes with the 29 answers for each of the two 97% topics. That review could reveal whether the gap reflects missing category language, stronger competitors, different source coverage, or inconsistent descriptions of the monitored B2B software vendor.

#### 4. Citation volume was concentrated in a small number of domains

The most-cited domain was www.zzcxin.cn, with 714 citations. It was followed by qiye.qizhidao.com with 353, and t.cj.sina.com.cn with 206.

The vendor’s own domain is not listed among the ten most-cited domains in the supplied data. That does not prove that the vendor’s own domain was never cited; it only means it was not included in this top-ten list.

The concentration of citations also shows why mention rate and citation profile should be tracked separately. A brand may appear in many answers while the evidence shaping those answers comes mainly from third-party sites. For content teams, the question is not only whether the engine names the vendor, but also which sources provide the context around that name.

#### 5. Daily measurement gives a repeatable baseline

The 390 probes were distributed across 30 distinct days, with the same 13-keyword set used as the measurement frame. This creates a baseline for future comparison.

The study does not provide daily mention rates, so it cannot show whether visibility rose or fell on particular dates. It does establish a starting point against which later runs can compare overall visibility, keyword-level performance, tone, and cited domains.

That repeatability is the main value of the dataset. A single AI answer is volatile and difficult to interpret. Repeated probes across a defined period provide a more useful operational signal, even when the measurement remains limited to one platform and one tenant.

Why it matters for your brand

For B2B and SEO teams, the 87.4% overall mention rate indicates that the monitored B2B software vendor was already discoverable across the tested topic set. However, the spread from 97% for “smart education solution” and “AI visual recognition” to 73% for “LLM deployment service” shows that visibility is not a single site-wide property. It changes by problem, product category, and wording.

That has direct implications for AEO. Traditional SEO often focuses on rankings and clicks, while AI-answer measurement asks whether a model includes a company in its response. The most useful workflow combines both: identify the topics where the brand appears consistently, find the topics where it disappears, and examine how the engine describes it when it does appear. The tone data adds another layer. With 90.5% of answers classified as neutral, increasing the number of mentions is only one objective; improving the accuracy and usefulness of those descriptions is another.

The citation data points to a second issue: source dependence. The top cited domain received 714 citations, compared with 81 for the tenth-ranked domain. That uneven distribution means AI visibility can be influenced by a relatively small set of frequently cited sources. B2B marketers should therefore monitor not only their own pages, but also the external pages, directories, profiles, technical repositories, and publisher content that AI engines use to form answers.

How to act on it

1. Create a keyword-level visibility dashboard. Track mention rate separately for all 13 keywords rather than relying only on the 87.4% aggregate. Use the existing rates as the baseline, especially the 97% high and 73% low.

2. Investigate the weakest category first. Review every answer generated for “LLM deployment service,” where the monitored B2B software vendor appeared in 22 of 30 probes. Compare those answers with the 29 of 30 results for “AI visual recognition” and “smart education solution.”

3. Separate presence from sentiment. Record whether each answer is neutral, positive, or negative. The current baseline is 353 neutral, 36 positive, and 1 negative, so future gains should be assessed not only by more mentions but also by more useful and accurate descriptions.

4. Audit the cited-source ecosystem. Review the domains appearing around the brand in AI answers. The supplied top-ten list includes high-volume sources such as www.zzcxin.cn, qiye.qizhidao.com, and t.cj.sina.com.cn. Check whether those pages describe the vendor accurately and whether important capabilities are missing.

5. Repeat the same measurement design. Keep the platform, tenant, keyword set, and probe logic consistent when comparing future periods. Add other platforms or keyword groups only as separate measurement layers, so changes can be attributed clearly.

Bottom line

Across 390 Perplexity probes over 30 days, the monitored B2B software vendor was named in 87.4% of answers, but visibility ranged from 97% to 73% by keyword and was neutral in 90.5% of cases. The practical lesson is simple: measure brand presence by topic, inspect the tone and sources behind each mention, and treat the overall percentage as a baseline rather than a complete AEO performance score.