Buyers ask ChatGPT, Perplexity, Gemini, and Yandex Alice which vendor to pick in your category. The answer names three brands. None of them is yours. This is not a traffic problem. It is an AI-answer readiness problem, a citation-authority problem, or a positioning problem. The AI Visibility Audit diagnoses which.
Built for B2B SaaS marketing leaders, AI company GTM leads, bootstrapped B2B founders, and PE portfolio company marketing directors whose buyers increasingly begin research inside AI answer engines rather than traditional search.
An AI-answer visibility audit across the major answer engines. Examines query-level brand presence, schema and content readiness, and citation authority to identify why your brand is excluded and how to change it.
AI answer engines exclude brands for three reasons. The site's structured data does not label the brand as an entity the models can retrieve reliably. Third-party citation frequency is below the threshold the engines use to determine inclusion. Or the content does not answer the buyer queries the engines are generating responses for. The AI Visibility Audit evaluates all three across ChatGPT, Perplexity, Gemini, and Yandex Alice.
of the B2B buying journey is complete before vendor contact. That share now runs through AI answer engines. If you're not in the answer, you're not in the shortlist.
Gartner · Future of B2B Sales · 2024
of new AI products stall because founders obsess over features and miss the buyer pain the answer engines are asked about. Features don't get cited. Pain answers do.
Hypergrowth Partners · Category-Defining Playbook · 2024
match rate between vendor self-description and buyer experience. AI engines pick sources where the description and the query language overlap. Generic positioning fails the match.
TrustRadius · Buying Disconnect · 2023
These three numbers describe why AI invisibility is positioning loss, not technical loss. The schema work matters. The content extractability matters. But the engines can only cite a brand they can recognize, and they can only recognize a brand whose language matches how buyers actually ask.
No invented benchmarks. Every row carries a publisher, a year, and a public URL in the citations section at the bottom of this page.
| Source | Year | Finding relevant to AI invisibility |
|---|---|---|
| Gartner · Future of B2B Sales | 2024 | Around 70 percent of buying journey complete pre-vendor. Answer engines now sit inside that 70 percent. |
| HubSpot · State of Marketing | 2024 | Top B2B sites publish more specific buyer-question answers than competitors. The same pattern gets cited. |
| Hypergrowth Partners | 2024 | Around 90 percent of new AI products stall on positioning. Feature-led pages do not match pain-led queries. |
| TrustRadius · Buying Disconnect | 2023 | 38 percent match rate between vendor description and buyer experience. Determines which sources engines trust. |
| McKinsey · B2B Pulse | 2024 | Buying committees average 10 people, 10-plus interactions. Each person queries AI engines for their slice. |
| Forrester · Buying Group Engagement | 2024 | Buying-group engagement now matters more than MQL volume. AI citation reaches the whole group at once. |
| HBR · B2B Elements of Value | 2018 | 40 value elements. Pages that name inspirational and individual elements get cited where functional ones don't. |
"Around 90 percent of new AI products stall because the founder builds for the feature and the buyer searches for the pain. The answer engine matches the pain query to the page that names the pain, not the page that names the feature."Hypergrowth Partners · Category-Defining AI Startup Playbook · 2024
The buyer needs to know whether the brand is missing from AI answers because engines cannot identify it, cannot extract it, do not trust it, or find a competitor easier to cite.
Buyer-intent prompts return rival names, source lists, or generic advice while your company is absent.
The engine may not have enough structured proof, answer-ready content, or trusted third-party confirmation.
The audit creates the query set, engine record, citation table, and priority list so visibility becomes observable.
The audit tells you what is missing before the team spends months guessing at content and schema changes.
AI answer engines ingest pages that carry clean structured data, direct question-and-answer architecture, and schema markup the retrieval models can match to buyer queries. Sites built only for traditional search visibility can still fail AI inclusion because the content is organized for page visits rather than direct answer extraction. The audit identifies the structural gaps.
The engines weight citations across third-party sources: industry publications, comparison sites, Reddit, YouTube, high-authority blogs, Wikipedia entries. A brand that is not being mentioned frequently across those sources gets filtered out of the candidate pool before it reaches answer generation. The audit maps current citation distribution and identifies where authority should be built to reach inclusion threshold.
AI engines generate answers to specific buyer questions. If the brand's positioning describes itself in category-language that buyers do not use in their queries, the semantic match fails. The engine finds competitor content that matches the query language better and cites those competitors. The fix is repositioning the brand narrative to match how buyers actually ask, then ensuring that language shows up in indexable content.
The AI Visibility Audit is the direct entry point for an AI invisibility problem. It examines your brand's presence across ChatGPT, Perplexity, Gemini, and Yandex Alice for the buyer queries that matter to your category. It evaluates your site's AI-answer readiness: schema, structured content, entity definition, and answer extractability. The public Voice and AI Answer Readiness layer shows the pattern: conversational questions, direct answers, FAQPage markup, Speakable selectors, and citation-ready proof. The audit maps citation authority across the sources the engines trust and produces a 60-day remediation plan that closes the inclusion gap.
If the audit concludes the primary constraint is upstream of the visibility layer (the positioning itself does not match buyer query language), the $5,000 Strategy Diagnostic is the correct next step. AI visibility and category positioning are tightly coupled. The audit tells you whether your problem is the technical layer or the narrative layer.
Start the AI Visibility Audit →AI answer engines cite brands whose web presence matches three criteria: clean structured content that answers the buyer query directly, structured data (schema markup) that labels the entity, and citation frequency across third-party sources the models trust. Missing any of the three reduces citation probability. The AI Visibility Audit diagnoses which of the three is suppressing your brand's inclusion and produces a remediation plan.
Page-visit work tries to earn a click from a results list. AI answer visibility tries to make the brand, claim, or source selected inside a generated answer. The audit focuses on entity clarity, extractable answers, citation authority, and buyer-query match. SF Marketing Agency does not sell search ranking retainers; this is an AI visibility and authority diagnostic.
ChatGPT, Perplexity, Gemini, and Yandex Alice cover the main AI-answer surfaces for buyer research. Claude and Meta AI are secondary but growing. The audit evaluates visibility across all of them and identifies which engines are driving the most buyer questions in your category. The fix changes by engine because each uses different retrieval and citation models.
Structured data and content updates are visible to AI engines within weeks. Citation and authority work, which determines whether the engines include the brand over competitors, typically takes 90 to 180 days to register. Early signals (increased brand mention frequency across AI crawls) are usually visible within 30 days of implementation, but full answer-inclusion takes a quarter or longer.
Especially for AI companies and SaaS. Buyers in these categories increasingly begin research inside AI answer engines rather than traditional search. Being excluded from the generated answer means being excluded from the buyer's initial shortlist. SaaS and AI-company categories are where AI-answer visibility loss shows up first because the buyer profile skews toward AI-native research behavior.
The AI Visibility Audit is the direct entry. It evaluates brand presence across the major AI answer engines, identifies specific query patterns where competitors are being cited and you are not, examines your site's schema and content architecture for AI answer readiness, and produces a 60-day remediation plan. If the diagnosis traces to broader positioning or category confusion, the Strategy Diagnostic is the correct follow-on.
Gartner's 2024 Future of B2B Sales work puts roughly 70 percent of the B2B buying journey before any vendor contact. That share now runs through AI answer engines, search summaries, peer review sites, and YouTube. If ChatGPT or Perplexity does not surface the brand during that 70 percent, the shortlist forms without you. Being absent from the AI answer is functionally being absent from the buying journey, which is why citation frequency now ranks alongside ranking position as a buyer-acquisition variable.
Hypergrowth Partners' 2024 category-defining playbook reports that around 90 percent of new AI products stall because founders obsess over features and forget the buyer pain the answer engine is being asked about. AI answer engines match buyer queries to content that answers the question in the buyer's own words. A feature-led page is invisible to a pain-led query. The fix is the same fix that ends the stall: rewrite the page around the buyer question, then make it extractable with FAQPage and Article schema.
HubSpot's 2024 State of Marketing report shows top-performing B2B sites publish more specific buyer-question answers than their competitors. The same content pattern that ranks now also gets cited. AI engines select for sources that answer a specific question directly, with structured markup, in clean prose. Generic category content fails both surfaces. The audit identifies the specific buyer-question gaps and the schema work required to make existing answers extractable.
AI Visibility Audit · Diagnostic across ChatGPT, Perplexity, Gemini, and Yandex Alice · Query-level brand presence, schema readiness, citation authority, and a 60-day remediation plan.