Public benchmark pilot - AI Visibility and GEO
AI Visibility Audit Sample + B2B AI Visibility Mini Index
This page shows the company-level method AuraCite uses before a 14-day AI Visibility Audit and includes a real public pilot: 300 date-stamped AI and Google checks across Gemini, Perplexity, ChatGPT, Claude, and Google AI Overviews.
1. What the audit measures
AuraCite treats AI visibility as a measurable answer quality problem, not a vague branding exercise. The audit records the prompt, engine, locale, date, answer position, cited source, competitor mentions, and whether the answer can be supported by public evidence.
2. B2B AI Visibility Mini Index: first public pilot
On 8 May 2026, AuraCite ran a public company-level pilot across 10 established B2B software brands. The purpose was not to rank those brands as winners or losers. The purpose was to test a practical audit question: when AI systems answer buyer questions, do they mention the brand, and do they cite the brand's own official domain?
The pilot sample covered Ahrefs, Semrush, HubSpot, Salesforce, Notion, Asana, monday.com, Webflow, Miro, and Zapier.
These names describe the public sample set only. No benchmarked brand participated in, sponsored, or endorsed this pilot, and this page does not rank them as winners or losers.
| Surface | Full-run scope | Brand mentioned | Official domain cited | Google-specific signal |
|---|---|---|---|---|
| Gemini | 60 LLM responses | 98.3% | 58.3% | Not applicable |
| Perplexity | 60 LLM responses | 96.7% | 48.3% | Not applicable |
| ChatGPT | 60 LLM responses | 93.3% | 25.0% | Not applicable |
| Claude | 60 LLM responses | 98.3% | 21.7% | Not applicable |
| Google AI Overviews | 60 Google AI Overview checks | 98.0% when an AI Overview appeared | 41.7% | AI Overview present in 85.0% of tested queries; official domain in organic top 10 for 65.0% |
Method note: the pilot used date-stamped, unpersonalized checks across assistant answers and Google AI Overview results. Collection tooling and raw traces are retained internally; raw answers and raw AI Overview text require human review before publication.
3. Prompt and query set
The pilot used six repeatable company-level buyer-intent classes per brand. AuraCite stores the detailed collection templates internally with the raw traces; the public version shows the intent layer so the method is understandable without exposing operational details.
| Intent | Public intent description | What we evaluate |
|---|---|---|
| Brand definition | Basic entity and category understanding. | Basic entity recognition, category clarity, official-domain citation. |
| Buyer evaluation | How a buyer might evaluate the brand for a relevant category. | Strengths, limitations, buyer-fit language, support sources. |
| Alternatives | Which competitors or substitutes appear around the brand. | Competitor set, comparison context, official vs third-party sources. |
| Category shortlist | Whether the brand appears in non-branded category discovery. | Unaided category visibility and whether the official site is used as evidence. |
| Problem/solution | Whether the brand is associated with high-intent business problems. | Unaided problem-solution visibility and competitor substitution risk. |
| Source evaluation | Which public sources support or shape the answer. | Whether AI and Google use official pages, documentation, pricing, help centers, or third-party sources. |
4. What the pilot means for AI and Google Search
The headline finding is not that large brands are invisible. They are usually mentioned. The sharper visibility gap is source control: answer engines and Google AI Overviews frequently rely on third-party sources instead of official company pages.
| Finding | Why it matters | AuraCite audit implication |
|---|---|---|
| Brand mentions are high. | Large B2B brands are already known to AI systems, so simple mention tracking is not enough. | Measure source quality, description accuracy, competitor context, and official-domain citation. |
| Official-domain citation is inconsistent. | If AI answers cite comparison blogs, directories, or random explainers, the brand loses control over evidence and framing. | Strengthen canonical entity pages, product pages, method pages, help pages, and evidence-backed answer sections. |
| Google AIO has two separate gates. | A page can rank organically but still fail to become an AI Overview citation. | Track AI Overview presence, official-domain citation, and organic top-10 presence separately. |
| Measurement should be bounded and repeatable. | A compact prompt and query matrix makes the audit reproducible without turning a pilot into an overclaimed market ranking. | Use fixed prompts, dates, locales, raw traces, human review, and internal cost controls before publishing conclusions. |
5. Source-gap analysis
Most AI visibility problems are source problems. If public pages do not clearly explain what the company does, who it serves, why it is credible, and which evidence supports the claims, AI systems have to infer too much.
| Gap | Why it matters for AI answers | Typical fix |
|---|---|---|
| No canonical entity page | AI systems cannot identify one authoritative company profile. | Create a factual company/entity page with Organization and SoftwareApplication schema. |
| No answer-first category page | AI systems prefer passages that directly answer buyer prompts. | Add short definitions, comparison tables, FAQ sections, and direct use-case answers. |
| Weak citation sources | AI answers cite pages that contain verifiable facts, not only marketing slogans. | Publish method notes, public sample reports, data-backed examples, and source-quality pages. |
| Google snippet ineligibility | Google AI features depend on indexable, snippet-eligible content. | Keep pages crawlable, avoid restrictive snippet directives, and align visible content with structured data. |
6. Google and AI readiness checklist
Google's public Search Central guidance for AI features emphasizes the same durable foundation as classic organic search: indexed pages, snippet eligibility, helpful content, and structured data that matches visible page content. AuraCite adds AI-answer-specific measurement on top: which engines mention the brand, which sources they cite, and where official pages lose citation share to third-party sources.
| Signal | Status to verify | Evidence to collect |
|---|---|---|
| Crawling | Important pages are allowed in robots.txt and present in sitemap.xml. | Status codes, sitemap entries, robots rules, server-rendered HTML. |
| Indexing and snippets | Pages are indexable and not blocked by restrictive snippet directives. | Meta robots, canonical, title, description, visible text, Search Console status where available. |
| Structured data | Schema.org JSON-LD matches visible content. | Organization, SoftwareApplication, Article, FAQPage, BreadcrumbList, and product/service offers where appropriate. |
| Answer fit | Important buyer questions are answered directly on the page. | H2/H3 questions, short definitions, tables, FAQs, examples, and methodology notes. |
| Source quality | Claims are source-backed and dated. | Author, date, method, limitations, internal/external references, update cadence. |
Sources used for the public method
- Google Search Central: AI features and your website
Used for Google AI Overview and AI Mode eligibility basics: indexability, snippet eligibility, helpful content, and visible-content alignment. - Google Search Central: robots meta tag and snippet controls
Used for the point that snippet controls such asnosnippetandmax-snippetaffect Google Search surfaces including AI Overviews and AI Mode.
7. Sample fix pack
| Priority | Fix | Mechanism | Expected result |
|---|---|---|---|
| P0 | Publish a canonical entity page and keep it current. | Improves brand/entity clarity for Google, AI crawlers, and LLM retrieval. | Higher likelihood of accurate brand descriptions. |
| P0 | Add a public sample audit and methodology page. | Gives AI engines a concrete source for how the company thinks and measures. | Better source quality for category and audit-intent prompts. |
| P1 | Convert sales claims into evidence-backed answer sections. | Reduces unsupported claims and increases passage usability. | More trustworthy AI summaries and better buyer comprehension. |
| P1 | Map Google Search Console queries to page owners. | Finds low-CTR, position 4-20, and cannibalization opportunities. | More precise content refreshes and stronger Google discovery. |
8. What AuraCite does next
A full AuraCite AI Visibility Audit applies this same structure to a specific company, category, market, and language. The public pilot proves the measurement pattern; a customer audit adds approved internal context, Google Search Console data, owned-source review, Answer Quality checks, and a prioritized implementation plan.
For publication, AuraCite treats this page as a cautious proof asset: the aggregate numbers can be cited, but raw answers, raw AI Overview text, and brand-level conclusions require human review before they are turned into public claims.
Request a focused AI Visibility Audit
Use AuraCite to identify where AI answer engines mention, omit, misdescribe, or weakly cite your brand. Start with a small company-level snapshot, then validate the findings in a 14-day audit.
Request an AI Visibility Audit or run the Free AI Brand Check.