Frequently Asked Questions

No. Traditional search engine optimization focuses primarily on ranking web pages in indexed search results for specific keyword queries, with the goal of earning direct clicks. AI citation analysis focuses on entity comprehension and fact verification: can an AI system extract unambiguous facts about what you do, who you serve, and why you are trustworthy in order to directly synthesize an answer or cite you as a trusted source?

This tool is designed for business owners, marketing directors, digital agencies, professional service firms, and SaaS companies who want to understand why their business may or may not be surfaced when prospective clients ask conversational AI tools (like ChatGPT, Perplexity, Claude, or Google AI Overviews) for recommendations.

In this context, AI visibility describes how clearly and completely your website presents the evidence AI systems look for: your identity, services, locations, trust signals, and structured data. It's a measure of your site's own readiness, not a measurement of how often you're actually mentioned inside AI answers, which we don't track.

Yes, the scan and your overall AI Visibility Score are completely free, with no credit card required and no account needed to see your initial results. To unlock your full detailed report, we ask for a name, email, and phone number so we can send it to you.

We scan only the publicly accessible pages of the URL you submit to generate your report. If you unlock the full report, the contact details you provide are stored so we can deliver it and follow up. See our Privacy Policy for the full details.

No. Modern generative AI models use complex, probabilistic algorithms and dynamic retrieval pipelines that change constantly. No diagnostic tool can guarantee third-party rankings or citations. AI Citation Analysis provides actionable diagnostic findings on your website's evidence quality and structural clarity so you can eliminate ambiguities that cause automated systems to skip your site.

When you submit a domain, our automated scanner crawls your publicly accessible pages, starting from your homepage and following internal links, and parses the raw HTML and structured markup it finds. It checks entity indicators, schema definitions, text clarity, navigation structure, and trust signals to assess how easily an automated parser can extract essential business facts.

Up to 50 publicly accessible pages per scan, the same public entry points and internal links an AI crawler would typically reach. This focuses the audit on the fundamental entity footprint search and retrieval systems inspect during initial discovery.

Websites that render all their primary content via complex client-side JavaScript, lock essential details behind interactive widgets/modals, or use aggressive bot-blocking configurations may prevent automated parsers from reading complete text. Our report highlights when essential information cannot be extracted cleanly.

Yes. If a server uses bot protection such as Cloudflare or a CAPTCHA challenge, refuses the request outright, or rate-limits it, the scanner may be unable to inspect the site. The scanner only accesses publicly accessible, unprotected web pages, the same way any other automated crawler would.

This means a technical or access issue, such as a blocked request, a DNS or certificate problem, or a timeout, kept us from evaluating that item, not that we confirmed it's missing. An inaccessible page is an access limitation, not automatically a sign of a lower-quality website.

Most scans complete in under 60 seconds. Because the analysis runs in real time against your live public pages, exact timing depends slightly on your website's server response speed.

The score is a diagnostic readiness index from 0 to 100, built from five weighted categories: Clarity, Identity, Trust, Expertise, and Discoverability (the C-I-T-E-D framework). A high score means your site provides clear, machine-readable evidence of who you are, what you offer, and why you are credible. A lower score points to missing signals or structural ambiguities.

Humans judge websites through visual design, typography, imagery, and implied context. AI systems, however, read raw text, hierarchical heading structures, and semantic data. If a website looks gorgeous but uses vague marketing slogans ('Empowering the future'), hides contact details in images, or lacks structured service specifications, an automated system cannot verify what the company actually does.

'Not Applicable' means that particular check doesn't apply to a business shaped like yours, for example a multi-location rule when you operate from one address. 'Could Not Be Checked' means an access or technical issue stopped us from evaluating it. Neither means the information is false, only that it wasn't scored either way.

Yes. On-page website clarity is a foundational requirement for AI citation, but AI answer engines also synthesize information from external sources such as third-party reviews, press, directories, and industry databases. A high score ensures your own website is not the bottleneck preventing AI models from understanding you.

Even in the same industry, two websites often differ substantially in how they format their entity details, whether they declare Schema.org markup, how explicitly they define service areas, and whether they publish verifiable credentials. Small structural differences compound into noticeable score variances.

No. We don't query ChatGPT, Perplexity, Gemini, Claude, or Google AI Overviews, and we don't monitor actual citations or mentions of your business inside their answers. Instead, we analyze your website's own publicly accessible signals, the readiness factors that make your information easier for automated systems to find and understand.

No. AI Citation Analysis doesn't query ChatGPT or any other AI assistant, so we can't show you a live answer or tell you whether one currently mentions your business. What we can show you is how clearly your website presents the evidence those systems would need to.

C-I-T-E-D is the five-category framework every scan is scored against: Clarity (are your services and locations stated plainly?), Identity (can your business be reliably identified?), Trust (are credible proof points published?), Expertise (does your content show real depth?), and Discoverability (is your data structured for machines to parse?).

An AI visibility audit checks whether the evidence AI systems look for, business identity, services, trust signals, and structured data, is clearly stated and machine-readable on your site. You can run one for free above: it's sometimes also called an AI readiness audit, and it measures your website's readiness, not actual citations.

In general, and not based on tracking your specific competitors, businesses tend to appear more often when they have a clearer entity profile across the web: unambiguous service descriptions, explicit local context, structured markup, and third-party validation such as directory listings or press coverage that gives AI models higher confidence to cite them.

AI models often store knowledge about well-known brands in their parametric memory from training data, allowing them to name a company. However, during real-time retrieval, answer engines tend to cite websites that offer direct, authoritative, cleanly structured answers to the specific query. If your site is hard to parse, a model may name your brand while citing a directory or competitor's page instead.

Not necessarily. Traditional Google search often ranks pages based on historical backlink authority and general domain metrics. AI answer engines, by contrast, look for granular, synthesized answers. A page ranking #3 on Google might be favored by an AI engine ahead of #1 if its text is more direct, structured, and factual.

They can, in a different way than traditional SEO: mentions across authoritative industry sources, independent reviews, and reputable directories can serve as entity-verification signals elsewhere on the web. AI Citation Analysis itself doesn't check backlinks; it scores only your own website's evidence.

Schema.org structured data (JSON-LD) provides an explicit, machine-readable data layer. While schema alone will not force an AI engine to cite you, it eliminates ambiguity regarding your business type, address, operating hours, service catalog, and leadership, making automated fact extraction significantly more reliable.

Generative AI models are non-deterministic, and answer engines frequently test different retrieval sources, query expansions, and settings. Changes in prompt phrasing, user location, search engine index updates, and model versions all contribute to natural answer variation, which is part of why we measure readiness rather than promising a specific outcome.

A 'Needs Attention' finding (an internally failed check) indicates a high-priority gap where an essential signal, such as a primary service definition, entity identity, or basic machine-readable structure, is entirely absent or unreadable by automated parsers.

A 'Minor Issue' finding (an internal warning) means information is present but ambiguous, poorly structured, or incomplete. For example, your services may be mentioned casually in a paragraph but lack clear, dedicated scope descriptions that automated engines require for high-confidence matching.

Treat the report as an objective diagnostic checklist. Focus on the items needing attention first. In most cases, making your website copy more descriptive, adding explicit contact and service details, and implementing standard Schema markup will resolve the majority of issues.

No tool can speak for proprietary black-box AI engines like OpenAI, Google, Anthropic, or Perplexity. Our report evaluates the objective prerequisite signals: is your website providing the clear evidence that any retrieval and synthesis engine requires to trust and cite you?

Yes. Once you update your website's copy, structure, or metadata, you can run a fresh scan immediately to verify whether the new evidence is being correctly recognized and parsed.