Plain-language answers about Get Cited AI, AI visibility, AEO, audit reports, pricing, technical setup, and next steps.
Get Cited AI is an AI visibility audit that reviews how clearly a business website can be read by tools like ChatGPT, Gemini, and similar answer engines. It looks at crawlable pages, structured data, and discovery files so a business can see whether AI systems can understand and describe it correctly.
Get Cited AI is built for businesses that rely on their website to explain services, locations, expertise, and credibility online. It is especially useful for companies that want to know whether AI-generated recommendations can identify them accurately when prospects ask category, location, or comparison questions.
Get Cited AI audits a website by checking the public pages and machine-readable signals that AI systems can access without logging in. The audit reviews items like structured data, crawlable content, discovery files, and page clarity to show what AI can read today and where information gaps exist.
Get Cited AI looks for the signals that help answer engines interpret a business website, including clear service pages, organization details, location information, schema markup, robots directives, sitemaps, and AI-readable files. It also checks whether key information is easy to crawl, understand, and cite.
Get Cited AI focuses on the website signals that influence AI visibility and can also compare those signals with how AI systems commonly describe businesses. The point is not only to observe an answer once, but to understand the underlying reasons an AI platform may mention, skip, or misunderstand a company.
To start an audit, you typically need a business name, website URL, contact email, location, and a short description of what the business does. That information helps connect the audit to the right website and gives the report enough context to evaluate whether key business details are visible.
AI visibility is the likelihood that tools like ChatGPT, Gemini, and similar systems can find a business, understand what it does, and mention it accurately in generated answers. It depends on whether a website presents trustworthy, crawlable, machine-readable information that supports clear business identification and explanation.
AEO, or Answer Engine Optimization, is the practice of shaping website content and technical signals so answer engines can extract reliable facts quickly. Instead of focusing only on blue links, AEO helps AI systems identify entities, summarize services, match user questions, and cite the right business information.
AI visibility matters because more buyers are asking AI tools for recommendations, summaries, and comparisons before they visit a website directly. If a business is hard for answer engines to interpret, it may be omitted from those early decisions even when the company has strong services and a solid reputation.
AI tools tend to mention businesses when the underlying web signals are clear, consistent, and easy to verify. They look for understandable service pages, location details, entity information, schema, citations, and other trustworthy context that helps the model answer a user question without guessing or conflicting signals.
A good-looking website can still be invisible in AI if its key facts are buried, inconsistent, blocked, or hard for machines to parse. Visual design helps people, but answer engines also need clean text, structured entities, crawlable pages, and consistent supporting signals before they can mention a business confidently.
AI systems tend to trust websites that publish clear business details, consistent naming, identifiable service pages, readable location information, structured data, and accessible technical files like sitemaps. They also benefit from pages that explain expertise plainly, because direct language reduces ambiguity when an answer engine summarizes the business.
The audit report typically shows whether key AI-readability signals are present, weak, or missing across the site. It highlights issues around discoverability, trust, and business clarity, then points to the pages or files involved so the findings can be interpreted as practical technical and content actions.
The audit scores measure how well a website supports AI understanding in a few core areas, such as discoverability, clarity, trust, and technical accessibility. A higher score means the site gives answer engines more reliable signals, while a lower score usually means important facts are missing, inconsistent, or difficult to parse.
The best way to read the report is to start with the weakest areas first, because those are the biggest barriers to AI visibility. Review the summary, note the lowest scores, then look at the specific findings tied to pages, files, or missing elements before choosing which fixes to handle first.
A low trust score usually means the website does not give answer engines enough reliable evidence to describe the business confidently. Common causes include weak organization details, missing schema, vague service pages, inconsistent naming, or a lack of machine-readable context that helps AI verify what the company does.
A low discoverability score means important pages or signals may be hard for AI systems to find or access. This can happen when sitemaps are missing, crawl paths are unclear, robots directives are restrictive, or core business information is not published on pages that answer engines can easily read.
Yes. A useful AI visibility report should translate technical findings into a clear next-step order rather than just listing problems. That usually means fixing the highest-impact gaps first, such as missing organization details, weak service explanations, incomplete schema, or discovery files that prevent AI systems from understanding the site.
Get Cited AI currently offers a free AI visibility check so a business can see whether its website is understandable to ChatGPT, Gemini, and other AI tools. The free check is designed to surface current visibility issues before a company decides how it wants to address the findings.
After you submit the form, the audit processes the website information and returns a report tied to that submission. The report is intended to show what AI can read, where confidence or clarity is weak, and which technical or content items should be reviewed first.
The audit usually takes only a short time to run once the site details are submitted, because it is evaluating public pages and technical signals rather than performing a full redesign. Exact timing can vary by website size, but the process is built around a quick diagnostic rather than a long engagement.
You do not need to install anything before running the audit. The check works against the public version of a website and evaluates what AI systems can already access. That means the most useful preparation is simply making sure the website URL and business details you submit are accurate.
You can run the audit for most public business websites as long as the site is live and accessible without special logins. The results are most useful when the website clearly represents a real business, because answer engines rely on public evidence and readable business details to form responses.
If a report already exists for the same submission context, the system may return the existing results instead of creating a duplicate record. That behavior helps keep reporting consistent and avoids generating multiple versions of the same audit when the website and contact details have not materially changed.
Schema markup is structured data added to a webpage so machines can interpret business facts more reliably. It helps answer engines recognize entities such as organizations, services, locations, products, and frequently asked questions, which makes it easier for an AI system to summarize a site without guessing.
llms.txt is a plain-text discovery file intended to help large language model systems understand the most important parts of a website. It can point AI tools toward key pages, explain what the site covers, and reduce ambiguity by presenting a concise machine-readable map of useful resources.
ai.txt is not a universal requirement, but it can give AI systems a simple, direct explanation of what a website does and how it should be referenced. When implemented carefully, it works like a concise machine-readable guide that supplements pages, schema, sitemaps, and other discovery signals.
Yes. robots.txt can affect AI visibility because it controls whether crawlers are allowed to access certain parts of a site. If important sections are blocked, answer engines may miss pages, files, or supporting details that would otherwise help them identify, trust, and describe the business accurately.
Some AI systems can interpret JavaScript-rendered content, but heavy client-side rendering still adds risk because not every crawler processes pages the same way. Critical business facts are safest when they appear in server-delivered HTML, structured data, and plainly readable page copy that does not depend on interaction.
Most business websites benefit from publishing a crawlable sitemap, a clear robots.txt file, organization details, strong service pages, and useful structured data. Sites focused on AI discoverability may also publish llms.txt or ai.txt so answer engines can find a cleaner summary of key pages, topics, and business context.
After the audit, start with the lowest-scoring areas and fix the issues that block understanding first. In many cases, that means clarifying service pages, improving business identity details, adding missing schema, and publishing technical files that help answer engines find the most useful pages on the site.
AI visibility usually improves when a website becomes easier for machines to interpret consistently. Practical steps include writing clearer service descriptions, adding organization and FAQ schema, tightening location details, publishing discovery files, and making sure the most important pages answer real customer questions in direct language.
It makes sense to rerun the audit after major website edits, technical changes, or new content initiatives that affect how the business is described. A recurring review every few months can also help, because AI visibility depends on whether current site signals still reflect the company accurately and completely.
Many AI visibility issues can be fixed internally if someone on the team can edit website content, publish structured data, and update technical files. Content clarity, organization details, FAQ pages, sitemaps, and discovery files are often manageable changes when the site platform allows direct editing.
AI systems do not update on one fixed schedule, so timing depends on crawling, indexing, and model behavior. Some changes can be reflected relatively quickly if the pages are easy to crawl, while broader citation and recommendation patterns may take longer as answer engines revisit and reinterpret the site.
If competitors appear in AI answers and your business does not, compare how clearly each site explains services, locations, expertise, and technical structure. Competitors often win visibility because their pages are easier to crawl and summarize, not simply because they are larger or better known overall.