Guide · 35 min · Working audit
An actionable website audit for ChatGPT, Google AI Search, Gemini, Perplexity, and other AI platforms. Start with 15 essential checks, identify what needs attention, and build a practical plan to improve your brand's discoverability.
Updated: October 2026 | Includes a quick audit, complete checklist, scoring framework, and 90 day action plan
Your website may be technically healthy, ranking well on Google, and publishing useful content. But how do you know whether AI assistants can find the right information about your company?
More importantly, when someone asks an AI assistant which products or services to consider, what does it actually say about your brand?
These are two different problems.
The first concerns whether your information is accessible, accurate, and useful. The second concerns whether that information contributes to your visibility in AI generated answers.
This checklist helps you investigate both.
It covers the fundamentals you can control, such as your website, product information, and content, alongside the things you can measure but cannot directly control, such as AI recommendations, competitor mentions, and citations.
You don't need to complete all 90 checks today.
Start with the first 15. They will help you identify the most obvious problems. Use the complete audit afterward to investigate the areas that deserve closer attention.
These are the checks I would prioritize before asking a marketing team to launch a larger GEO initiative.
Allow approximately 10 to 15 minutes for an initial review if you already have access to your website, Search Console, and basic marketing information. Verifying every finding may require additional work.
1. Important pages are accessible. Open your homepage, primary product pages, and key use case pages. Confirm they load publicly without unintended login requirements or errors.
2. Search crawler access is intentional. Check yourdomain.com/robots.txt. Review Googlebot, OAI-SearchBot, and other relevant crawlers against your company's access policy.
3. Your important pages can be indexed. Use Google Search Console's URL Inspection tool to check for accidental noindex instructions, canonical problems, or indexing errors.
4. Your homepage clearly explains your business. Someone unfamiliar with the company should understand what you sell, who it serves, and the main problem it solves.
5. Your product information is complete. Important features, integrations, supported use cases, and limitations should be explained on accessible pages.
6. Your brand information is consistent. Review company descriptions, product names, locations, and relevant facts across your website and major external profiles.
7. Important buyer questions have useful answers. Review whether your website addresses the questions customers ask during research and evaluation.
8. Your comparison content helps buyers make decisions. Where appropriate, explain how your product compares with alternatives, including genuine tradeoffs.
9. Your claims have supporting evidence. Check case studies, product documentation, benchmarks, testimonials, and research for verifiable information.
10. Important external profiles are accurate. Review relevant software directories, review platforms, partner listings, and industry profiles.
11. You've identified relevant buyer prompts. Select real questions from sales conversations, customer research, and buying situations.
12. You've checked how AI describes your company. Test relevant questions on the AI platforms your customers use and review the responses.
13. You know which competitors appear. Look beyond your own mentions. Identify competitors that repeatedly appear in relevant recommendations.
14. You can inspect the evidence behind the results. Preserve prompts, responses, model or platform details, dates, and cited sources.
15. You have an improvement plan. Assign responsibility for the most important findings, document changes, and schedule a comparable review.
Don't immediately commission 20 new blog posts.
A blocked product page, inaccurate product information, and a missing comparison article are very different problems.
Fix technical barriers first when they affect commercially important pages. Then prioritize missing or inaccurate information that matters to actual buyers.
A missing recommendation on one AI platform may justify investigation, but it is not proof that something on your website is broken.
A useful audit should help teams understand progress. It should not pretend to predict their position in ChatGPT.
For each applicable checkpoint, use this scale:
| Status | Points | Meaning |
|---|---|---|
| Missing | 0 | The requirement is absent or materially broken |
| Partial | 1 | Some information or implementation exists, but gaps remain |
| Complete | 2 | The checkpoint is sufficiently addressed and verified |
| Not applicable | Excluded | The checkpoint does not apply to your business |
Your audit completion score is:
Points earned ÷ maximum points for applicable checks × 100
For example, if 13 of the 15 essential checks apply, the maximum available score is 26. If you earn 17 points, your audit completion score is approximately 65%.
That does not mean you have a 65% chance of being recommended by AI.
It means your organization has addressed approximately 65% of the available points in this internal assessment.
A company could score highly while accidentally blocking its most valuable product page from search indexing.
Another could score relatively poorly because it has not created every possible comparison article, even though its website already answers its customers' needs.
So keep two separate outputs:
Audit completion: How many applicable practices have been addressed?
Issue priority: Which problems create the greatest practical risk or opportunity?
Classify your findings as Critical, High, Medium, or Low.
A critical issue could be an accidental noindex on an important product page. A high priority issue might be inaccurate information about a core product capability. A missing low value glossary page could be low priority.
The score gives you structure.
The prioritized findings tell you what to do.
Use the following sections when you need a more thorough assessment.
The first four sections examine information your company can directly influence. The following sections cover external credibility, AI visibility, measurement, and prioritization.
Each checkpoint should produce one of three outcomes: verified, not applicable, or an issue requiring action.
12 checks | Recommended owners: SEO, engineering, web operations
Before improving content, determine whether your important information can be reached and processed.
Google's official AI search guidance confirms that standard SEO fundamentals remain relevant to AI Overviews and AI Mode.
For Google AI features, eligible pages must meet normal Search requirements. There is no separate technical GEO certification.
OpenAI also publishes documentation distinguishing its search crawler from its crawler used for potential model training.
That distinction is important when reviewing access policies.
01. Review robots.txt. Confirm important search crawlers aren't unintentionally blocked from valuable public content.
02. Review PerplexityBot access. Check whether your crawler policy matches your intended discoverability and content access decisions.
03. Separate search access from training access. Review OAI-SearchBot and GPTBot independently, in consultation with legal and security stakeholders where needed.
04. Inspect CDN and firewall restrictions. Check whether automated requests encounter unintended bot challenges, rate limits, or access blocks.
05. Verify HTTP responses. Important public pages should return appropriate success responses rather than errors, misleading redirects, or inaccessible content.
06. Check indexability. Use Search Console to investigate unintended noindex instructions, excluded URLs, and other indexing problems.
07. Review canonical URLs. Make sure commercially important pages do not incorrectly point to unrelated or less suitable canonical versions.
08. Maintain XML sitemaps. Include appropriate canonical URLs and exclude irrelevant, redirected, or intentionally nonindexed pages.
09. Test JavaScript rendering. Confirm essential information remains accessible after rendering and investigate whether important content is missing from initial HTML.
10. Keep important information accessible. Product capabilities and core explanations should not be available exclusively after login, inside images, or through inaccessible interactive elements.
11. Check mobile accessibility. Important content and navigation should work correctly on common mobile devices.
12. Review page performance. Fix serious usability and loading problems on important pages, prioritizing actual visitor experience.
Pick three pages: your homepage, primary product page, and most valuable commercial landing page.
For each, inspect the HTTP response, robots access, indexing status, rendered content, and important internal links.
If the pages pass those checks, you have verified something meaningful.
You have not established that an AI assistant will recommend your product, but you've reduced the risk of avoidable accessibility problems.
Useful references: Google Search Console URL Inspection, Google JavaScript SEO guidance, and OpenAI crawler documentation.
8 checks | Recommended owner: Product marketing
Your website should make it easy for a reader to understand your business.
Consider these two homepage descriptions.
Unclear: "Helping modern enterprises unlock the future of intelligent operations."
Clear: "Workflow automation software for finance teams managing approvals, compliance, and reporting."
The second communicates a product category, target audience, and business use case.
That helps potential customers understand whether the company is relevant.
It also provides explicit information that search and retrieval systems can use.
13. Write a clear company definition. Explain what the business sells, who it serves, and the main problem it addresses.
14. Test your homepage's first impression. Ask someone unfamiliar with the business to explain what the company does after a brief review.
15. Use consistent category language. Avoid describing the same product as unrelated categories across different pages without explanation.
16. Check basic company facts. Keep relevant company names, locations, product names, and contact details consistent.
17. Maintain a trusted brand fact sheet. Give internal teams one reliable source for approved descriptions and key company facts.
18. Explain target audiences. Clarify relevant customer sizes, industries, business functions, and user groups.
19. Clarify geographic availability. State supported countries, languages, service regions, and important geographic restrictions where relevant.
20. Explain your product structure. Distinguish the company, platform, products, modules, and individual solutions.
Inconsistent category descriptions are worth investigating because they can confuse potential buyers.
But don't interpret one unusual AI classification as proof of an entity problem.
Compare your website information with several actual AI responses. Identify recurring inaccuracies before deciding what needs correction.
11 checks | Recommended owners: Product marketing and product management
This section is especially valuable for B2B SaaS companies.
A surprising amount of commercial content talks about product benefits without clearly explaining what a product actually does.
For example:
"Our seamless enterprise platform transforms customer relationships."
That sentence does not tell a buyer which integrations, workflows, or requirements the platform supports.
Specific information is more useful.
21. Explain your product's purpose. Describe the actual problem it solves and the outcomes customers can reasonably expect.
22. Document important capabilities. Describe core functionality in enough detail for customers to understand it.
23. Explain integrations. Identify supported systems, available functionality, setup requirements, and limitations.
24. Make pricing understandable. Explain pricing structure, qualification requirements, or how customers can obtain accurate pricing.
25. Publish relevant security information. Document verifiable certifications, security practices, compliance information, and data handling where appropriate.
26. Explain relevant limitations. Make material product restrictions, unsupported functionality, or eligibility conditions understandable.
27. Clarify ideal customer fit. Explain which customers and situations are best suited to the product.
28. Explain when another solution may be preferable. Where relevant, acknowledge situations the product is not designed to address.
29. Create useful use case pages. Document actual customer situations rather than producing generic industry pages.
30. Improve important integration pages. Prioritize integrations that matter commercially and are insufficiently explained.
31. Keep product information current. Review product changes, pricing, availability, and important claims on a defined schedule.
Suppose your product integrates with Salesforce.
A weak page says:
"Seamless Salesforce integration."
A useful page explains:
What information can be synchronized? Is the integration one way or two way? Which plan is required? Are there limitations? How does authentication work? Where can users find setup instructions?
You don't need every technical detail on a marketing landing page.
But the underlying information should exist, be accurate, and be easy to locate.
24 checks | Recommended owners: SEO and content marketing
This is where many companies waste time.
They create content around phrases like "best software", "alternatives", and "AI tools" without considering whether the article offers anything a buyer couldn't already find elsewhere.
A page is valuable when it helps someone answer a real question or make a better decision.
That principle matters more than producing a large number of pages.
Google's 2026 AI search optimization guidance specifically emphasizes original, useful content and discourages scaled content created mainly to manipulate search visibility.
32. Answer the main question clearly. Readers should not need to scroll through promotional introductions to find the central answer.
33. Make important explanations understandable in context. Use clear headings and complete explanations rather than relying on fragmented keyword statements.
34. Cover necessary details. Address the important considerations required to understand the subject, without adding material merely to increase word count.
35. Research customer information needs. Use sales conversations, support questions, and buyer research alongside keyword tools.
36. Group related buyer questions. Organize discovery, comparison, requirements, and validation questions into meaningful themes.
37. Avoid one page per prompt variation. Don't produce near identical pages for every wording of the same question.
38. Organize related resources logically. Build clear relationships between foundational guides, specialist articles, and product content.
39. Create fair comparison resources. Explain relevant differences between products, including tradeoffs and suitable audiences.
40. Evaluate alternatives content. Publish alternatives pages when they help real buyers understand meaningful choices.
41. Keep industry pages specific. Explain industry requirements, workflows, and use cases rather than replacing one industry name with another.
42. Publish glossary content selectively. Define concepts that genuinely require explanation and connect them to relevant resources.
43. Include concrete examples. Show how a concept works in real or clearly labeled illustrative situations.
44. Incorporate genuine experience. Use appropriately attributed insights from practitioners, customers, product teams, or direct research.
45. Publish original research when available. Share credible findings from company data, customer research, or experiments, with appropriate privacy protections.
46. Explain research methodology. State sample size, data collection period, measurement method, and important limitations.
47. Reference reliable primary sources. Use official documentation, original studies, and directly relevant evidence when making factual claims.
48. Put statistics in context. Explain who was studied, when, and what the numbers actually demonstrate.
49. Maintain relevant dates. Review content where product specifications, regulations, statistics, or platform features change.
50. Keep a content review schedule. Assign owners and review dates to important pages.
51. Use tables when comparison helps. Make pricing, product capabilities, requirements, or competing approaches easier to evaluate.
52. Use images for explanation. Favor original diagrams, annotated examples, and useful data visualizations over decorative imagery.
53. Improve internal linking. Connect relevant educational content to deeper resources and appropriate product pages.
54. Identify orphan pages. Find valuable pages that lack adequate internal links and improve their discoverability.
55. Resolve overlapping search intent. Review unnecessary duplication and competing pages that serve essentially the same purpose.
Imagine your website already has a strong article explaining customer data platforms.
But AI responses repeatedly associate your company with a narrower use case than the product actually supports.
The answer might be to improve an existing product page, customer story, or use case resource.
It may not require another educational article.
This is why content decisions should begin with identified information gaps rather than arbitrary publishing targets.
16 checks | Recommended owners: Growth, SEO, product marketing
This is where the audit becomes specific to AI visibility.
You can have a technically healthy website and excellent content without knowing how AI assistants represent your company.
To investigate, you need a repeatable sample of real buying situations.
65. Define your prompt sample. Select questions relevant to your products, customer segments, and priority markets.
66. Prioritize commercial questions. Give more attention to buyer situations that connect to meaningful business opportunities.
67. Organize prompts by intent. Separate discovery, problem solving, comparison, alternatives, requirements, and validation.
68. Evaluate platforms individually. Don't assume ChatGPT, Gemini, Claude, Perplexity, or Google AI experiences produce equivalent answers.
69. Track brand mentions. Record whether your company or relevant product names appear.
70. Distinguish recommendations. Identify when an answer presents your product as suitable rather than merely naming it.
71. Record recommendation position where meaningful. Capture placement within actual ordered shortlists without implying every answer has a stable ranking.
72. Record citations. Preserve referenced domains and URLs where the platform exposes them.
73. Compare competitive Share of Voice. Use consistent prompts, engines, and competitor definitions when comparing brand presence.
74. Review brand descriptions and sentiment. Identify favorable, unfavorable, neutral, and inaccurate claims in context.
75. Examine competitor presence. Identify cases where relevant competitors appear and your company does not.
76. Assess response variation. Repeat selected prompts to see whether patterns are recurring or isolated.
77. Test company identification. Ask relevant questions that reveal how accurately the platform understands your products and category.
78. Examine competitor recommendations. Investigate which requirements or evidence are associated with recommended alternatives.
79. Investigate relevant omissions. Look for questions where your product appears suitable but remains absent.
80. Review unfavorable narratives. Check whether repeated negative descriptions reflect genuine limitations, outdated information, or factual errors.
Suppose you market software for customer support teams.
An initial prompt sample could include questions like these:
| Intent | Example buyer question |
|---|---|
| Discovery | What customer support platforms are suitable for a growing SaaS company? |
| Comparison | How does Freshdesk compare with Zendesk for a team of 30 agents? |
| Requirements | Which support platforms offer HubSpot integration and multilingual capabilities? |
| Alternatives | What are some affordable alternatives to Zendesk? |
| Validation | What limitations should a company consider before choosing Freshdesk? |
These are illustrative questions. They are not verified records of actual customer searches.
The most useful prompt library comes from real customer research and is updated when products, markets, or buying requirements change.
For a deeper explanation of why a brand mention is not necessarily an endorsement, read Brand Mentions vs Citations in AI Search.
10 checks | Recommended owners: Marketing analytics, RevOps, growth
Good measurement makes the audit repeatable.
Without consistent evidence, a team may celebrate an increase in mentions without knowing whether the improvement reflects a real pattern, a changed prompt sample, or a model update.
81. Store the exact prompts. Preserve the question text and relevant parameters used during each test.
82. Preserve the actual responses. Keep the full output rather than relying exclusively on summary scores.
83. Record the comparison set. Document which brands and products count as competitors.
84. Preserve cited sources. Save citations, domains, and available source URLs.
85. Maintain a consistent benchmark. Compare a defined prompt sample across comparable time periods, noting changes in methodology.
86. Segment results appropriately. Separate engines, markets, languages, buyer intents, and product categories where relevant.
87. Review AI referral traffic. Monitor identifiable AI platform referrals in analytics, while recognizing attribution limitations.
88. Examine branded search trends. Use branded search as supporting context, not proof that AI visibility caused changes.
89. Ask customers how they discovered you. Include AI research where relevant in customer interviews and self reported attribution.
90. Connect findings to commercial evidence. Investigate relationships with qualified leads, pipeline, and customer acquisition without inventing causal links.
In June 2026, Google introduced dedicated Generative AI performance reporting in Search Console.
Google confirmed worldwide availability as of August 31, 2026.
These reports provide insight into page impressions within Google's generative AI search features, including AI Overviews and AI Mode.
That information should be part of your reporting.
But it does not replace AI response analysis across other platforms.
A page appearing in a Google AI feature is not the same as your company being recommended by ChatGPT.
Likewise, an increase in ChatGPT recommendations does not automatically mean more qualified traffic or pipeline.
| Reporting area | What to show |
|---|---|
| Website health | Important accessibility, indexing, and content issues |
| AI presence | Mentions, recommendations, and relevant visibility trends |
| Competitors | Significant differences within the defined prompt sample |
| Sources | Important cited domains and opportunities for better evidence |
| Brand accuracy | Repeated factual errors or positioning issues |
| Marketing actions | Changes completed, owners, and next steps |
| Business outcomes | Referral, search, and CRM observations where available |
For executive reporting, focus on what changed, why the findings matter, and what decision should follow.
Don't overwhelm leadership with scores that cannot be explained.
MentionX documents its own measurement definitions in the MentionX Methodology.
Completing the checklist is useful.
Knowing which three problems to address first is where it becomes valuable.
Start by reviewing the findings against four questions.
How commercially important is the issue?
A missing explanation on a high value product page matters more than a low traffic article that is unrelated to your main business.
How strong is the evidence?
A repeated pattern across relevant responses is more useful than a single unexpected answer.
Can your team influence it?
You can rewrite inaccurate product documentation. You cannot dictate how an independent AI model will answer every question.
What effort is required?
A small copy correction may take one afternoon. Publishing original research or improving third party reputation may require several months.
| Priority | Example finding | Initial action |
|---|---|---|
| Critical | Core product page unintentionally set to noindex | Resolve the indexing restriction and validate |
| High | Major product integration undocumented | Work with product team to improve documentation |
| High | Recurring inaccurate description of a key capability | Investigate supporting sources and correct controlled information |
| Medium | Weak comparison content for an important buyer segment | Improve an existing comparison or create one where justified |
| Medium | Inconsistent descriptions across external profiles | Correct relevant listings |
| Low | Minor glossary formatting issue | Address during normal content maintenance |
This approach avoids pretending that every checklist item has the same importance.
It also prevents teams from investing in easy cosmetic changes while more substantial information gaps remain unresolved.
Consider a fictional software company called NorthstarOps.
NorthstarOps provides workflow automation for finance teams.
Its website ranks reasonably well for several relevant search terms, but an AI visibility audit identifies an interesting pattern.
When buyers ask general questions about workflow automation, NorthstarOps is sometimes mentioned.
When buyers ask specifically about Microsoft Dynamics integration for financial approval workflows, the company is rarely recommended.
The marketing team investigates the difference.
Its product does support Microsoft Dynamics. However, the integration page consists of two paragraphs and a short feature list.
Meanwhile, competing products have public documentation explaining supported workflows, permissions, configuration, and limitations.
This is a meaningful information gap.
SEO and web: Confirm the existing integration page is accessible, indexable, and properly linked from relevant product content.
Product marketing: Rewrite the page to explain the integration in language that potential buyers can understand.
Product and engineering: Verify supported capabilities, configuration requirements, and limitations before publication.
Content marketing: Connect the integration page to a relevant finance workflow guide if it provides additional value.
Analytics: Record the original AI responses, monitor subsequent results, and compare any changes within the same prompt sample.
The improvement is useful even if an AI recommendation doesn't immediately change.
Buyers now have clearer information about a commercially important capability.
And the team has identified a specific problem, implemented a relevant fix, and established a way to measure subsequent observations.
That's a much stronger marketing process than publishing another generic blog article and hoping AI visibility improves.
A complete audit can produce more issues than one team can reasonably address.
I recommend starting small.
Review the first 15 checkpoints.
Confirm critical accessibility issues, identify obvious gaps in product information, and collect an initial set of buyer questions.
Assign one person to consolidate findings.
Output: An initial issue list with Critical, High, Medium, and Low priorities.
Complete the detailed audit sections most relevant to your business.
Run your chosen buyer prompts across appropriate AI platforms.
Store the responses, competitors, and cited sources.
Review existing content and identify the most important information problems.
Choose three to five improvements with clear owners.
Output: Baseline measurements, documented evidence, and an approved action plan.
Address technical blockers first.
Then improve incomplete product information, commercially important comparison content, customer evidence, or relevant external profiles.
Avoid expanding work simply because there are 90 checks.
The goal is to improve the issues that matter.
Output: Completed changes, updated pages, and a record of what was modified.
Repeat the original benchmark under comparable conditions.
Review changes in recommendation patterns, brand descriptions, citations, and competitor presence.
Assess whether the underlying information has improved.
Look for supporting signals in referral traffic and customer research.
Document uncertainty. Don't attribute every movement to your own changes.
Output: A concise management report showing findings, completed improvements, observed changes, and proposed next steps.
There are several popular techniques worth putting in perspective.
The llms.txt format is intended to provide machine readable information for systems that choose to use it.
It may have a role in specific workflows.
However, Google's official guidance states that Google Search does not use llms.txt as a special visibility or ranking mechanism.
It should not take priority over important accessibility or content problems.
Structured data remains useful for established SEO applications.
But Google explicitly says there is no special schema required for its generative search features.
Use accurate, supported structured data where it serves a legitimate purpose.
Don't invent a proprietary AI schema and assume it improves recommendations.
Question and answer content is useful when it reflects real customer needs.
But forcing every article into an FAQ format can make content repetitive and less readable.
There is no established universal paragraph length or content format that guarantees AI inclusion.
Creating separate articles for small variations of the same buyer question is unlikely to make a useful content strategy.
It can also create duplication and unnecessary maintenance.
Prioritize substantial content that helps readers understand decisions.
Fake reviews, fabricated social discussions, undisclosed paid endorsements, and irrelevant placements create trust and compliance risks.
They are not a defensible substitute for genuine customer evidence.
A statistic is useful when it helps explain a claim and comes from a reliable source.
A number inserted merely to make content look authoritative is not.
If your company publishes original research, explain the methodology, dates, sample, and relevant limitations.
An AI search optimization checklist is an audit framework covering website accessibility, product information, content quality, external credibility, and visibility within AI generated answers.
It helps marketing teams identify issues worth investigating and prioritize improvements.
The terms overlap substantially.
Generative Engine Optimization, or GEO, generally refers to efforts to improve how brands and information appear within generative AI responses.
AI search optimization may refer more broadly to search experiences that use generative AI, including Google AI Overviews and conversational assistants.
You can improve public information, review crawler policies, publish accurate product documentation, and assess how ChatGPT represents your company.
OpenAI documents crawler controls that affect ChatGPT Search discovery.
However, no website change guarantees a recommendation.
For more detail, see How ChatGPT Recommends Brands.
Usually not.
Many important improvements are existing SEO and content practices, such as fixing indexing problems, improving page clarity, and documenting product capabilities.
More specialized monitoring may require additional tools or analytical processes.
There is no validated universal score that predicts success across AI platforms.
Use a checklist score to track audit completion and identify internal gaps, not as a substitute for observing actual AI responses.
An initial comprehensive audit followed by quarterly reviews is a practical starting point for many businesses.
Important technical changes, major product launches, or meaningful shifts in AI visibility may justify additional checks.
The appropriate cadence depends on your market and resources.
There is no reliable universal timeframe.
Technical problems can sometimes be corrected quickly, while updated information may take longer to be discovered or reflected in generated responses.
Establish a baseline and measure changes without promising a fixed outcome.
Not automatically.
Strong SEO fundamentals remain important.
Start by identifying the additional work required for AI visibility, then decide whether it belongs within existing marketing activities or justifies a separate investment.
A completed checklist is not the finish line.
The important outcome is knowing something about your business that you didn't know before.
Perhaps your website has a genuine technical problem.
Perhaps your product information doesn't adequately explain an important capability.
Perhaps competitors have better evidence for particular buyer requirements.
Or perhaps AI assistants are describing your business inaccurately.
Those findings can guide better marketing work.
You don't need to optimize every page for every possible AI question.
You need to understand where your company is relevant, what information supports that relevance, and where meaningful gaps exist.
Make your information accessible. Make your capabilities understandable. Make important claims verifiable. Then measure how your brand appears in AI discovery and improve what the evidence tells you.
That is the purpose of this checklist.
If you're new to GEO, start with our Practical Guide to Generative Engine Optimization, which explains the broader strategy, measurement principles, and marketing implications.
If you want to understand how AI visibility differs from existing search reporting, read AI Visibility vs SEO.
For a detailed explanation of the underlying measurements, see What Is AI Visibility? and Brand Mentions vs Citations in AI Search.
B2B marketing leaders can also explore AI Visibility for B2B SaaS for examples specific to software discovery and evaluation.
The first part of this checklist can be completed with your existing SEO, website, and marketing tools.
The AI visibility sections require a different form of evidence: actual responses generated by AI platforms.
MentionX supports AI visibility measurement across ten platforms and search experiences, with available coverage depending on the selected plan.
It helps marketing teams examine brand mentions, recommendations, competitor presence, sentiment, and cited sources, with access to the underlying AI responses.
Those findings can help teams identify and prioritize information gaps, although they cannot guarantee changes to AI recommendations.
You can review the MentionX Methodology to understand the platform's measurement definitions and limitations.
Start with the evidence. Then decide what deserves optimization.
