- [Topic 1]
- [Topic 2]
- [Topic 3]
- [Topic 4]
- [Page Title]: [URL]
The GetCited Framework is a five-step system for making any business visible and citable in AI search engines like ChatGPT, Perplexity, Claude, and Gemini. The five steps are: open the door by unblocking AI crawlers, introduce yourself with an llms.txt file, speak the language using AI-optimized schema markup, answer the questions your customers are asking with restructured content, and measure and improve by tracking AI citations over time. This framework did not come from guesswork. Anthony spent months studying which websites get cited by AI engines and which ones get ignored, audited dozens of brands across industries, and distilled the patterns into these five steps. The framework is published in full in Chapter 4 of the GetCited ebook, and this article walks through every step in detail so you can implement it yourself, starting today. If you follow all five steps in order, you will go from invisible to citable. If you skip steps or do them out of order, you will waste time fixing problems that should have been solved earlier. The sequence matters because each step builds on the one before it. There is no point optimizing your content for AI if AI crawlers cannot access your site. There is no point adding schema markup if your content does not answer the questions people are actually asking. This framework gives you the right work in the right order, and that is what separates businesses that show up in AI answers from the ones that wonder why they do not.
Why Most Businesses Are Invisible to AI Search
Before we get into the framework itself, it is worth understanding the scale of the problem.
Most businesses are invisible in AI search. Not partially visible. Not underperforming. Completely absent. When a potential customer asks ChatGPT, Perplexity, or Claude a question about your industry, your category, or even your specific product type, there is a high probability that the AI does not mention you at all. It mentions your competitors instead, or it mentions nobody and gives a generic answer that sends the buyer down a path that never includes your brand.
This is not because your business is small or unknown. Companies with strong brands, solid Google rankings, and millions in annual revenue discover this problem every day. Traditional SEO success does not translate automatically to AI visibility. The factors that make a website rank well on Google overlap with AI citation factors in some areas, but diverge sharply in others. Google evaluates backlinks, domain authority, and keyword relevance. AI engines evaluate whether your content directly answers the question being asked, whether it is structured in ways the model can parse, whether the model can access your pages at all, and whether your site communicates its identity in a machine-readable format.
The gap between these two sets of criteria is where businesses fall through. A company might invest $20,000 per month in SEO and rank on page one for 50 keywords, but if its robots.txt blocks AI crawlers, it earns zero AI citations. A company might produce ten blog posts per week, but if those posts are written as narrative essays without clear question-and-answer formatting, the AI will skip them in favor of a competitor's FAQ page that states the answer in the first sentence.
The GetCited Framework addresses this gap systematically. It covers the technical, structural, and content requirements that determine whether AI engines can find you, understand you, and cite you. It is not a collection of random tips. It is a sequence of five steps where each step unlocks the value of the next.
How the GetCited Framework Was Built
Anthony did not invent this framework from theory. He built it from evidence.
The process started with a simple question: why do some websites get cited by AI search engines and others do not? To answer that question, he studied the websites that AI engines actually cite in their responses. He ran queries across ChatGPT, Perplexity, Claude, and Gemini, documented which sources appeared in the responses, and then analyzed what those cited sources had in common.
The pattern became clear quickly. The websites that earned consistent citations shared five characteristics. They allowed AI crawlers to access their content. They had some form of machine-readable identity file. They used structured data markup that AI models could parse. Their content was organized around direct answers to specific questions. And they tracked their AI visibility over time, making adjustments as the landscape changed.
The websites that were invisible were missing at least two of these five characteristics, and most were missing three or four.
From there, Anthony audited dozens of brands, covering everything from small local businesses to mid-market B2B companies to enterprise organizations. The audit process confirmed that the same five factors predicted AI visibility across every industry, every company size, and every AI platform. A local plumber who allowed AI crawlers, created an llms.txt file, added schema markup, and restructured their service pages to answer common questions was earning citations from Perplexity. A Series B SaaS company with a $2 million marketing budget was invisible because their robots.txt blocked every AI crawler by default.
The evidence was consistent enough to formalize into a framework. The five steps are arranged in priority order, from the most fundamental (can AI even see your site) to the most ongoing (are you tracking and improving). That order is not arbitrary. It reflects the actual dependency chain that determines AI visibility.
Step 1: Open the Door
The first step in the GetCited Framework is making sure AI crawlers can actually access your website. This sounds basic, and it is. But it is also the single most common reason businesses are invisible in AI search.
AI search engines send automated crawlers to read your website. These crawlers have specific names. OpenAI sends GPTBot to gather content for ChatGPT. Anthropic sends ClaudeBot to gather content for Claude. Perplexity sends PerplexityBot. Google uses Google-Extended for its AI features including Gemini. These crawlers respect your robots.txt file, which is a text file at the root of your website that tells automated bots what they can and cannot access.
Here is the problem. Many websites have robots.txt configurations that were set up years ago, before AI search existed. These configurations often include blanket rules that block all bots except the ones the webmaster specifically allowed, which typically means only Googlebot for traditional Google search. When AI crawlers show up, they read the robots.txt file, see that they are not allowed in, and leave. They do not force their way in. They do not try again later. They simply skip your site and move on to the next one.
Our audit data shows that nearly one in five websites actively blocks at least one AI crawler through their robots.txt configuration. Many of these blocks are completely unintentional. The site owner has no idea it is happening. They set a restrictive robots.txt policy years ago to block spam bots, and that same policy now blocks the AI crawlers that could be driving qualified traffic and citations to their business.
How to Audit Your robots.txt
Open your browser and go to yourdomain.com/robots.txt. Read through the file line by line. You are looking for two things.
First, check whether there is a blanket block. A line that reads User-agent: * followed by Disallow: / blocks every bot that is not specifically allowed elsewhere in the file. If your robots.txt has this pattern and does not include explicit Allow rules for AI crawlers, then GPTBot, ClaudeBot, PerplexityBot, and Google-Extended are all blocked.
Second, check for specific blocks. Look for lines like User-agent: GPTBot followed by Disallow: /. Some websites specifically block individual AI crawlers, often because the site owner read an article about AI companies scraping content and decided to block them without understanding the implications for AI search visibility.
What to Fix
If AI crawlers are blocked, update your robots.txt to allow them. The simplest approach is to add explicit allow rules for each AI crawler:
User-agent: GPTBot
Allow: /
User-agent: ClaudeBot
Allow: /
User-agent: PerplexityBot
Allow: /
User-agent: Google-Extended
Allow: /
If you want to allow most pages but block specific directories like admin panels or staging environments, you can use more targeted Disallow rules for those specific paths while keeping the general Allow in place.
This fix takes about two minutes. It is the highest-impact two minutes you will spend on AI visibility because nothing else in this framework matters if AI crawlers cannot see your pages. You can have the best content in your industry, perfect schema markup, and a flawless llms.txt file, and none of it will produce a single AI citation if the crawlers are locked out at the front door.
Common Mistakes at This Step
Some businesses take a partial approach. They unblock GPTBot but leave ClaudeBot blocked. Or they unblock the crawlers for their homepage but keep the blog, resource center, or product pages blocked. This creates a fragmented visibility profile where you show up in ChatGPT but not Claude, or you show up for brand-name queries but not for the industry questions where citations really matter.
Unblock all four major AI crawlers across your entire site. The only pages you should block are genuinely private areas like admin dashboards, internal tools, staging environments, and customer login portals. Everything that is publicly meant for human visitors should be accessible to AI crawlers too.
Another common mistake is assuming this is done once and forgotten. CMS updates, security plugin changes, and developer modifications can silently alter your robots.txt. Check it monthly to make sure your AI crawler access has not been reverted.
Step 2: Introduce Yourself
Once AI crawlers can access your site, the next step is telling them who you are and what matters most. You do this with an llms.txt file.
The llms.txt file is a relatively new standard, but it is quickly becoming one of the most important files on your website for AI visibility. Think of it as your machine-readable elevator pitch. It sits at the root of your domain (yourdomain.com/llms.txt) and provides large language models with a structured summary of your business, your expertise areas, and the pages you want them to prioritize.
Without an llms.txt file, AI crawlers have to figure out your site on their own. They crawl your pages, parse your content, and make their best guess about what your business does and which pages are most important. Sometimes they guess correctly. Often they do not. They might index your About page as your most important content while missing your detailed comparison guide that answers the exact questions buyers are asking.
With an llms.txt file, you take control of that process. You tell the AI directly: here is what our company does, here are the topics we are authoritative on, and here are the specific pages that matter most.
What Goes in Your llms.txt
A good llms.txt file includes several key elements.
Organization identity. Your company name, a one-paragraph description of what you do, and your primary area of expertise. Keep this factual and specific. Do not use marketing language or vague claims. Instead of "We are the leading platform for innovative business solutions," write something like "GetCited is an AI visibility platform that helps businesses get cited in AI search engines including ChatGPT, Perplexity, Claude, and Gemini."
Topic authority. List the specific topics your website covers authoritatively. If you are a cybersecurity company, list the specific areas: endpoint protection, threat detection, incident response, compliance frameworks. This helps the AI model understand which queries your site is relevant for.
Key pages. Link to the 10 to 20 most important pages on your website. These should be the pages that contain your most comprehensive, factual, and useful content. Not your homepage (unless your homepage is genuinely your most informative page). Not your pricing page. The pages where a human or an AI would find the definitive answer to a question in your area of expertise.
Content structure. If your site is organized into clear categories or sections, describe that structure. This helps the AI model navigate your site more efficiently and understand the relationship between different pieces of content.
Why This Step Comes Second
The ordering in the GetCited Framework is deliberate. The llms.txt file only works if AI crawlers can access it, which is why Step 1 (opening the door) comes first. If GPTBot is blocked in your robots.txt, it will never read your llms.txt file no matter how well-crafted it is.
But once the door is open, the llms.txt file becomes your most efficient tool for communicating with AI. It takes less than an hour to create, it requires no technical implementation beyond uploading a text file to your web root, and it immediately improves how AI models understand your site.
Our data shows that 92 percent of websites do not have an llms.txt file. That means implementing one puts you ahead of the vast majority of your competitors before you even touch your content or schema markup.
How to Create Your llms.txt File
Create a plain text file named llms.txt and place it at the root of your domain so it is accessible at yourdomain.com/llms.txt. Here is a simplified structure:
# [Your Company Name]
## About
[One-paragraph factual description of your business]
## Topics
- [Topic 1]
- [Topic 2]
- [Topic 3]
- [Topic 4]
## Key Resources
- [Page Title]: [URL]
- [Page Title]: [URL]
- [Page Title]: [URL]
Keep the language plain and factual. The llms.txt file is not a marketing document. It is a reference document for machines. Write it the way you would write a Wikipedia entry about your company, not the way you would write a sales landing page.
Upload the file, verify it is accessible in your browser, and move to Step 3.
Step 3: Speak the Language
The third step in the GetCited Framework is adding AI-optimized schema markup to your website. Schema markup is structured data that translates your content into a format that machines understand natively. Without it, AI engines have to interpret your content from raw HTML and natural language. With it, they can extract key facts, relationships, and attributes directly, without interpretation.
Schema markup has been around for years in the context of traditional SEO. It powers the rich results you see in Google search, like star ratings on reviews, recipe cards with cooking times, and event listings with dates and venues. But schema markup for AI visibility requires a different approach than schema markup for Google rich results. The types of schema that matter, the depth of implementation, and the specific properties you include are all different when the goal is AI citations rather than Google featured snippets.
The Three Essential Schema Types for AI Visibility
For most businesses, three types of schema markup will cover the majority of AI visibility needs.
Organization schema on your homepage. This is the foundational schema that establishes who you are. It tells AI engines your official company name, your URL, your logo, your contact information, your social media profiles, and a description of what you do. Think of it as the structured data version of your llms.txt identity section. It reinforces the same information in a format that every AI model and search engine can parse automatically.
Organization schema should include your legal name, your common name (if different), your founding date, your industry, your primary URL, and links to your official social profiles. Every major AI engine reads Organization schema, and it is one of the signals they use to verify that a source is legitimate and authoritative.
Article schema on your content pages. Every blog post, guide, resource page, and thought leadership piece should have Article schema that specifies the headline, author, publication date, last modified date, and a description of what the article covers. This helps AI engines understand what each piece of content is about, when it was published, whether it has been updated recently, and who wrote it.
The author information is particularly important. AI models use author signals as part of their quality assessment. An article with a named author who has credentials in the topic area carries more weight than an article with no attribution. If your content is authored by subject matter experts, Article schema lets you communicate that expertise directly to the AI model.
FAQ schema on your product and service pages. FAQ schema is arguably the most directly impactful schema type for AI visibility. It structures question-and-answer pairs in a format that AI engines can extract and cite directly. When an AI model is looking for the answer to a specific question, a page with FAQ schema that contains that exact question and a clear answer is significantly more likely to be cited than a page that buries the same information somewhere in a paragraph of marketing copy.
FAQ schema works on product pages, service pages, pricing pages, and any page where you can anticipate the questions a potential customer would ask. Each FAQ item should contain a genuine question (phrased the way a real person would ask it) and a direct, factual answer. Do not stuff FAQ schema with dozens of questions. Include the five to ten most important questions for each page, and make sure every answer is substantive enough to stand on its own.
Implementation Details That Matter
Schema markup is implemented by adding JSON-LD code to the <head> section of your web pages. JSON-LD is the format recommended by Google and the format that AI engines are most proficient at parsing. If your site uses a CMS like WordPress, Shopify, or Webflow, there are plugins and native features that can generate schema markup for you. If your site is custom-built, your developer will need to add the JSON-LD manually or through a template system.
The most important implementation detail is accuracy. Schema markup that contains incorrect information is worse than no schema markup at all. If your Organization schema says you were founded in 2010 but you were actually founded in 2015, you are feeding the AI model bad data about your company. If your Article schema says a post was published in 2024 but it was actually published in 2022, the AI model will misunderstand how current your information is. Audit your schema regularly to make sure it reflects reality.
You can validate your schema markup using Google's Rich Results Test or the Schema.org validator. These tools will flag errors in your implementation and help you verify that the structured data is being read correctly.
Why Schema Is Step 3 and Not Step 1
Businesses often want to jump straight to schema markup because it feels like a concrete technical fix. But schema is Step 3 in the GetCited Framework for a reason. Schema only works if AI crawlers can access your site (Step 1) and only delivers maximum value when the AI already has context about your business identity (Step 2). If you implement perfect schema markup on a site that blocks GPTBot, none of it matters. If you implement FAQ schema on pages that do not actually answer the questions well (which is a Step 4 issue), the schema will point the AI to mediocre answers that it may choose not to cite anyway.
Schema is the translation layer. It takes your content and your identity and converts them into machine-native format. But it requires the foundation of access and identity to be in place first.
Step 4: Answer the Questions
Step 4 is where the GetCited Framework shifts from technical optimization to content strategy. The first three steps ensure that AI engines can find you, understand who you are, and parse your content efficiently. Step 4 is about making sure your content is worth citing in the first place.
AI search engines answer questions. That is their primary function. A user types in a question or describes a problem, and the AI generates an answer by synthesizing information from the sources it has access to. When the AI cites a source, it is because that source provided content that directly addressed the query in a way that was clear, comprehensive, and authoritative.
This means your content strategy for AI visibility needs to be fundamentally organized around questions. Not keywords. Not topics. Questions. Specifically, the questions your customers and potential customers are asking when they are in the research and buying process.
Identify the Questions That Matter
Start by listing every question a customer might ask before purchasing your product or service. Not just the obvious ones. All of them.
There are several categories of questions to cover.
Category questions. What is [your product category]? How does [your category] work? Who needs [your category]? These are top-of-funnel questions that establish your category authority.
Comparison questions. What is the best [category] for [use case]? How does [Product A] compare to [Product B]? What are the alternatives to [Competitor]? These are mid-funnel questions where AI citations have the most direct revenue impact.
Implementation questions. How do I set up [your product]? What does it cost to implement [your category]? How long does it take to get results from [your category]? These are bottom-of-funnel questions that drive purchase decisions.
Problem questions. How do I fix [problem your product solves]? Why is [symptom] happening? What causes [issue your product addresses]? These are the entry points where buyers discover they need a solution.
Build a master list of 50 to 100 questions across these categories. You can generate this list from your sales team's call notes, your customer support tickets, your existing keyword research, and by simply asking your AI tools directly: "What questions do people ask about [your category]?"
Restructure Content to Lead with the Answer
This is the single most important content change you can make for AI visibility: lead with the answer.
Traditional content marketing follows a narrative structure. You start with the problem, build context, develop the argument, and arrive at the answer somewhere near the end. This works well for human readers who are reading an article start to finish. It works terribly for AI citation purposes.
AI models scan content to find the passage that best answers the user's query. If the answer is buried in paragraph seven of a 2,000-word post, the AI might miss it entirely or find a different source that states the answer immediately. The most citation-friendly content structure puts the definitive answer in the first paragraph, then uses the rest of the page to provide context, evidence, examples, and nuance.
Look at the first paragraph of this article. It directly states what the GetCited Framework is, names all five steps, and explains where it came from. If an AI model needs to answer the query "what is the GetCited Framework," it can extract that answer from the first paragraph without reading anything else. The remaining 3,000+ words provide depth and detail for humans who want the full picture, but the AI already has what it needs from the opening sentences.
Apply this principle to every page on your site. For every page, ask yourself: if someone read only the first paragraph, would they have a complete answer to the question this page addresses? If not, restructure.
Build Dedicated Comparison Pages
Comparison queries are some of the highest-value queries in AI search. When a buyer asks "what is the best CRM for small businesses" or "how does Hubspot compare to Salesforce," they are actively in the buying process. The AI's answer directly influences their shortlist.
Building dedicated comparison pages for your most important competitive matchups is one of the highest-ROI content investments you can make for AI visibility. These pages should be factual, balanced, and comprehensive. They should cover features, pricing, use cases, strengths, and limitations for both your product and the competitor's. They should include a clear recommendation at the top (yours, presumably, but with honest caveats).
AI models tend to cite comparison content that is thorough and fair rather than content that is aggressively biased. If your comparison page reads like a hit piece against the competitor, the AI may skip it in favor of a third-party review that covers both sides. If your comparison page reads like an honest assessment that happens to conclude your product is the better fit for specific use cases, the AI is much more likely to cite it.
Use Clear Headings That Match Queries
Headings serve a dual purpose in AI-optimized content. For human readers, they break up the text and provide navigation. For AI engines, they signal what each section of the page is about.
When your heading matches the query the user asked, the AI model can quickly identify the relevant section and extract the answer. A heading like "How Much Does AI Visibility Optimization Cost?" directly matches the query "how much does AI visibility optimization cost" and signals to the AI that the following section contains the answer.
Use H2 headings for major questions and H3 headings for sub-questions. Write them in natural language, the way a real person would phrase the question. Avoid creative or clever headings that obscure the content. "The Price of Being Seen" might sound better in a magazine article, but "How Much Does AI Visibility Cost?" is far more effective for AI citation purposes.
Cover Topics Comprehensively
AI models prefer to cite sources that cover a topic thoroughly rather than sources that provide partial information. If your page about AI visibility covers three aspects of the topic but a competitor's page covers seven aspects, the competitor's page is more likely to be cited because it gives the AI model more material to work with.
Comprehensive coverage does not mean long content for the sake of length. It means addressing every dimension of the topic that a reader might care about. For a product category page, that includes what the product does, who it is for, how it works, what it costs, how it compares to alternatives, how to implement it, what results to expect, and what the limitations are. For an educational article, it includes defining the concept, explaining why it matters, showing how to implement it, providing examples, addressing common objections, and answering related questions.
The goal is to make your page the single best source for every query you want to own. When an AI model encounters a query and scans its available sources, your page should be the one that provides the most complete, accurate, and useful answer. That is what earns the citation.
Content Quality Signals AI Models Evaluate
Beyond structure and comprehensiveness, AI models assess content quality through several signals that are worth understanding.
Specificity. Content that includes specific numbers, dates, examples, and facts is more citable than content that speaks in generalities. "Our audit data shows that 18.9% of websites block at least one AI crawler" is more citable than "many websites block AI crawlers."
Recency. Content that has been recently published or updated is preferred over older content, especially for topics where the landscape is changing quickly. Update your key pages regularly and make sure your Article schema reflects the updated date.
Attribution. Content authored by named individuals with relevant expertise carries more weight than anonymous content. Byline your content, link author names to author bio pages, and include credentials that are relevant to the topic.
Corroboration. AI models cross-reference information across multiple sources. If your content makes a claim that is supported by other credible sources across the web, it is more likely to be cited. If your content makes claims that contradict everything else on the web, the AI model may treat it as unreliable. Back up your claims with data and cite your own sources where possible.
Step 5: Measure and Improve
The final step of the GetCited Framework is the one that makes it sustainable. Measuring your AI visibility and making iterative improvements is not optional. It is the difference between a one-time project that fades and an ongoing practice that compounds over time.
AI visibility is not static. The AI models are updated regularly. New competitors enter the conversation. Content that was cited last month might not be cited this month because a newer, better source appeared. Queries that were not relevant to your business six months ago might be driving significant buyer behavior today. Without measurement, you are flying blind.
What to Track
Citation presence across platforms. Know whether you are being cited in ChatGPT, Perplexity, Claude, and Gemini for your most important queries. Each platform uses different models, different training data, and different citation logic. You might be well-represented in Perplexity but completely absent from Claude. Platform-specific data tells you where to focus your efforts.
Query-level performance. For each query you want to win, track whether you are being cited, which competitor is being cited instead, and what content is driving the competitor's citation. This is the most actionable data you can have. It tells you exactly which content gaps to fill and which pages to improve.
Competitor citation tracking. Know who is beating you and for which queries. If a competitor consistently outranks you in AI citations for a specific topic, study their content. What are they doing differently? Is their content more comprehensive? Better structured? More recently updated? More factually specific? Use their strengths as a blueprint for improving your own content.
Improvement over time. Track your AI visibility score monthly. A score that rises over three to six months tells you your efforts are working. A score that plateaus tells you it is time to look for new queries to target, new content to create, or technical issues that have crept in.
How to Measure
Manually checking AI citations is possible but impractical at scale. You would need to type every important query into every AI tool, document the results, note which sources were cited, and repeat the process monthly. For a business targeting 50 queries across four platforms, that is 200 manual checks per month.
GetCited automates this process. The platform runs your queries across all major AI search engines, documents which sources are cited for each query, tracks changes over time, and identifies the specific gaps between your current visibility and the visibility you need. It turns what would be a week-long manual audit into an automated dashboard you can review in minutes.
Whether you use GetCited or a manual process, the key is consistency. Check monthly. Record the data. Identify trends. Make adjustments. Then check again.
The Monthly Review Process
Here is a practical monthly review process that takes the measurement data and turns it into action.
Week 1: Run the audit. Measure your current AI visibility across all platforms for all target queries. Record the results and compare them to last month.
Week 2: Analyze the gaps. Identify which queries you lost, which ones you gained, and which competitors gained ground. For each lost or weak query, identify the root cause. Is it a content gap? A structural issue? A competitor who published something better?
Week 3: Implement fixes. Update existing content to address gaps. Publish new content for queries where you have no coverage. Fix any technical issues (like robots.txt changes that reverted, or schema markup that broke during a site update). Update your llms.txt file if your content landscape has changed.
Week 4: Document and plan. Record what you changed, why you changed it, and what result you expect. Set targets for next month. Share results with stakeholders.
This cadence is not arbitrary. It mirrors the typical reindexing timeline for major AI platforms. Changes you make in week 3 are likely to be reflected in AI search results within two to four weeks, which means your next month's audit will capture the impact of this month's work.
Why This Is an Ongoing Practice
The most important mindset shift in Step 5 is understanding that AI visibility is not a project with a finish line. It is an ongoing practice, the same way SEO has been an ongoing practice for the past 20 years.
Businesses that treat AI visibility as a one-time project will see initial gains that erode over time as competitors catch up, content becomes stale, and AI models update their training data. Businesses that treat it as a monthly practice will see compounding gains as their content library grows, their citation count increases, and their brand becomes more deeply associated with their category in the AI models.
The companies winning in AI search right now are the ones that started six months ago and have been iterating every month since. Six months from now, the winners will be the companies that start today and commit to the process.
Putting the GetCited Framework into Practice
The five steps of the GetCited Framework are designed to be implemented in order, but they do not all require the same level of effort.
Step 1 (Open the Door) takes 10 to 30 minutes. Check your robots.txt, fix any blocks, and move on. This is a one-time fix with periodic checks.
Step 2 (Introduce Yourself) takes one to two hours. Write your llms.txt file, upload it, verify it loads, and move on. Update it quarterly or whenever your content landscape changes significantly.
Step 3 (Speak the Language) takes one to five days depending on the size of your site and your technical resources. Implementing schema markup across an entire website is a meaningful technical task, but most CMS platforms have plugins that simplify it considerably. Expect to spend the most time on FAQ schema, since each page needs its own set of questions and answers.
Step 4 (Answer the Questions) is the most time-intensive step and the most ongoing. Content restructuring and creation is not a one-time task. The initial phase, where you identify your target queries, restructure existing content, and fill the most critical gaps, typically takes two to four weeks of focused effort. After that, it becomes a monthly process of identifying new queries, creating new content, and updating existing pages.
Step 5 (Measure and Improve) requires a few hours per month, assuming you are using a tool like GetCited to automate the measurement. If you are doing it manually, budget a full day per month for the audit and analysis.
For most businesses, the entire framework can be fully implemented within 30 to 60 days. The technical steps (1 through 3) can be completed in the first week. The initial content work (Step 4) takes two to four weeks. And the measurement cadence (Step 5) begins immediately and continues indefinitely.
Common Objections and How to Address Them
When introducing the GetCited Framework to organizations, several objections come up repeatedly. Here is how to address each one.
"We already do SEO, so we should be covered."
SEO and AI visibility optimization overlap but are not the same. SEO focuses on ranking pages in traditional search results. AI visibility focuses on earning citations in AI-generated answers. A site can rank on the first page of Google and be completely invisible to AI search engines. The technical requirements are different (AI crawler access versus Googlebot access), the content format requirements are different (direct answers versus keyword-optimized narratives), and the measurement methods are different (citation tracking versus rank tracking). Most companies need both SEO and AI visibility work, and the GetCited Framework is specifically designed to complement existing SEO efforts rather than replace them.
"We don't want AI companies using our content."
This is a legitimate concern, and it deserves a thoughtful answer. By blocking AI crawlers, you are not preventing AI from knowing about your industry or answering questions about your category. The AI will still answer those questions. It will just use someone else's content to do so. Blocking AI crawlers does not protect your content. It protects your competitors by removing you from the conversation. The businesses that allow AI access and optimize for AI citations are the ones that control how they are represented in AI-generated answers. The businesses that block AI access have no control at all.
"This seems like a lot of work for something that might not last."
AI search is not going away. Every major technology company on the planet is investing billions of dollars in AI search infrastructure. Usage is growing exponentially across every demographic and every industry. The question is not whether AI search will matter. It is whether you will be visible when it does. The GetCited Framework is designed to produce results that compound over time. Each step builds on the previous one, and each month of measurement and improvement makes your position stronger. The work you do today does not expire next quarter. It accumulates.
"We'll wait until things settle down."
Waiting is the most expensive option. AI search is a first-mover-advantage environment. The businesses that establish citation presence early build momentum that is increasingly difficult for latecomers to overcome. AI models develop associations between brands and topics over time. The longer you are cited as an authority in your category, the stronger that association becomes. The longer you wait, the more ground your competitors gain, and the more work you will need to do to catch up.
"Our developers don't have time for this."
Steps 1 and 2 require almost no developer time. Updating robots.txt is a two-minute task. Creating and uploading an llms.txt file is a one-hour task. Step 3 (schema markup) does require some developer involvement, but most CMS platforms have plugins that handle the technical implementation. Step 4 (content) is a marketing and content team responsibility, not a developer responsibility. Step 5 (measurement) requires no developer resources at all. The total developer time for the entire framework is measured in hours, not weeks.
The Bigger Picture: Why This Framework Matters Now
AI search is doing to organic discovery what Google did to the Yellow Pages. It is fundamentally changing how people find, evaluate, and choose businesses. The companies that adapted to Google early dominated their categories for 15 years. The companies that dismissed Google as a fad spent decades trying to catch up.
The same dynamic is playing out right now with AI search. The GetCited Framework gives any business a clear, proven path to visibility in this new landscape. It is not theoretical. It is not speculative. It is based on the patterns that Anthony identified by studying which sites earn citations and which do not, validated across dozens of audits, and structured into a sequence that works for businesses of any size and any industry.
The five steps are simple to understand and straightforward to implement. Open the door. Introduce yourself. Speak the language. Answer the questions. Measure and improve. Do them in order. Do them thoroughly. And then keep doing Step 5 every month for as long as AI search exists.
If you want to see exactly where your business stands right now, before implementing any of these steps, start with an AI visibility audit through GetCited. It will show you which of the five steps you need most urgently and give you a baseline to measure your progress against.
The framework is here. The tools exist. The only question is whether you will implement it now or wish you had six months from now.
Frequently Asked Questions
How long does it take to see results from the GetCited Framework?
Technical changes from Steps 1 through 3 can produce visible results within two to four weeks, as AI crawlers reindex your site with new access permissions, identity information, and schema markup. Content changes from Step 4 typically take four to eight weeks to show up in AI citations, since AI models need time to process and incorporate new content into their knowledge base. Most businesses see measurable improvement in their AI visibility scores within 60 to 90 days of fully implementing all five steps.
Can I implement the steps out of order?
You can, but you will waste effort. The steps are sequenced based on dependency. Schema markup (Step 3) is pointless if crawlers cannot access your site (Step 1). Content optimization (Step 4) delivers weaker results without schema to help AI models parse it (Step 3). The framework is designed so each step amplifies the one after it. Starting at Step 1 and working forward gives you the fastest path to results.
Does the GetCited Framework work for local businesses or only enterprise companies?
The framework works for any business that wants to be visible in AI search, regardless of size. A local plumber benefits from AI crawler access, an llms.txt file describing their service area, schema markup on their service pages, content that answers local customer questions, and monthly visibility tracking just as much as an enterprise SaaS company does. The scale of implementation differs, but the five steps apply universally. Some of the most dramatic before-and-after results Anthony observed during his research came from small businesses that went from completely invisible to consistently cited.
What tools do I need to implement this framework?
Steps 1 and 2 require no specialized tools beyond a text editor and FTP or CMS access. Step 3 can be implemented with free CMS plugins for WordPress (like Yoast or Rank Math), built-in features in Shopify or Webflow, or manual JSON-LD coding for custom sites. Step 4 requires content creation resources but no special tools. Step 5 is where dedicated tooling makes the biggest difference. Manually tracking AI citations across multiple platforms is time-consuming and error-prone. GetCited was built specifically to automate Step 5, providing automated citation tracking, competitor monitoring, and gap analysis across all major AI search platforms.
How is the GetCited Framework different from regular GEO advice?
Most GEO advice available online is a collection of individual tips without a unifying structure or priority order. The GetCited Framework is different in three ways. First, it is sequenced. The order matters because each step depends on the ones before it. Second, it is based on empirical evidence from auditing dozens of real businesses rather than theoretical best practices. Third, it is comprehensive. It covers the full stack of AI visibility, from technical access to content strategy to ongoing measurement. Most GEO articles cover one or two pieces of the puzzle. The GetCited Framework covers all five and explains how they fit together.