What Does AI Know About Your Brand? The AI Brand Audit Framework

An AI Brand Audit is a systematic assessment of what large language models like ChatGPT, Claude, Google’s Gemini, and Perplexity say about your brand, products, leadership, and competitive positioning. Developed as a companion framework to the Get Cited methodology, the AI Brand Audit identifies factual inaccuracies, knowledge gaps, and opportunities to shape how AI systems represent your business when users ask about you directly.

Every company now has an AI reputation—what language models state as fact when someone asks “What does [Company X] do?” or “Is [Product Y] any good?” Most businesses have no idea what AI systems are saying about them. This guide provides the framework for finding out and fixing what you find.

Why Your AI Reputation Matters Now

AI systems are answering questions about your business right now, and you probably don’t know what they’re saying.

When a potential customer asks ChatGPT “What does [your company] do?” or “Who is [your CEO]?” or “Is [your product] worth it?”—they get an answer. That answer comes from somewhere. It may be accurate. It may be outdated. It may be completely wrong. It may confuse you with someone else entirely.

This is the new word-of-mouth. Except instead of asking a friend, people ask AI. And AI answers with confidence regardless of whether it’s correct.

The Scale of AI-Assisted Decision Making

ChatGPT alone has over 200 million weekly active users as of late 2024. Google’s AI Overviews appear on a substantial percentage of search queries. Perplexity handles millions of searches daily. Microsoft Copilot is embedded across enterprise productivity tools.

These aren’t niche tools anymore. They’re where a growing percentage of information-seeking happens. And every one of those interactions potentially involves questions about businesses, products, and people—including you.

When someone asks AI “What CRM should I use?” or “Who’s the best accountant in [city]?” or “Is [your product] better than [competitor]?”—AI provides an answer. If you’re not part of that answer, or if you’re characterized unfavorably, you’re losing opportunities you’ll never know about.

Traditional marketing could measure impressions, clicks, and conversions. AI-assisted decisions happen invisibly. Someone asks, gets an answer, makes a decision—and you never see the interaction. The only way to influence it is proactively.

The Compounding Problem

Wrong information in AI systems doesn’t stay contained. It spreads and reinforces:

• Training data persistence: Inaccuracies baked into model training persist until the next training run—often 6-18 months
• Cross-model contamination: AI-generated content citing wrong information becomes source material for other AI systems
• User trust: People increasingly treat AI answers as authoritative, not requiring verification
• Search integration: Google’s AI Overviews, Bing’s Copilot, and Perplexity surface AI-generated answers as primary results

A competitor’s blog post with wrong information about your company could become what AI “believes” about you. An outdated Wikipedia paragraph could define your business for millions of AI interactions. A disgruntled Glassdoor review could shape how AI characterizes your company culture.

You can’t fix what you don’t know. The AI Brand Audit gives you visibility into what’s being said.

The Opportunity

The inverse is also true: businesses that actively shape their AI presence gain compounding advantages.

When AI systems accurately represent your value proposition, your differentiators, your expertise—every AI-assisted query becomes a brand touchpoint working in your favor. Your positioning gets reinforced thousands of times daily across AI interactions you’ll never see.

The businesses doing this work now, while competitors remain unaware, establish the AI narratives that become default. Like early SEO adopters who claimed search territory before others understood the game, early AI reputation managers shape the information landscape before it solidifies.

The Five Layers of AI Brand Knowledge

AI systems construct knowledge about your brand across five distinct layers. A complete AI Brand Audit examines each layer systematically.

Layer 1: Entity Recognition

Entity recognition refers to whether AI systems recognize your brand as a distinct entity and can correctly identify what you are.

What to assess:

• Does AI know your company/brand exists?
• Can it distinguish you from others with similar names?
• Does it correctly categorize what type of entity you are (company, person, product, organization)?
• Does it associate the correct basic attributes (industry, location, founding date)?

Common problems:

• Entity confusion: AI conflates you with another company or person with a similar name
• Non-recognition: AI has no knowledge of you and either says so or hallucinates information
• Miscategorization: AI thinks you’re a different type of entity than you are
• Outdated identity: AI knows an old version of your company (pre-pivot, pre-rebrand, pre-acquisition)

Entity recognition is foundational. If AI doesn’t recognize you correctly, nothing else matters.

Layer 2: Factual Accuracy

Factual accuracy measures whether the specific claims AI makes about you are correct.

What to assess:

• Are stated facts about your company accurate (founding date, location, size, leadership)?
• Are product/service descriptions correct?
• Are claimed features and capabilities real?
• Are pricing or business model descriptions accurate?
• Are historical facts (acquisitions, milestones, partnerships) correct?

Common problems:

• Outdated information: Facts that were once true but no longer are
• Hallucinated details: Specific claims AI invented with no source
• Merged information: Facts from different entities incorrectly combined
• Exaggerated or understated claims: Scale, capabilities, or achievements misrepresented

Factual inaccuracies range from minor (wrong founding year) to catastrophic (claiming you offer services you don’t, or attributing competitor features to you).

Layer 3: Sentiment and Positioning

Sentiment and positioning refers to how AI characterizes your brand qualitatively—the tone, associations, and comparative framing it uses.

What to assess:

• Is the overall characterization positive, negative, or neutral?
• What attributes does AI associate with your brand?
• How does AI position you relative to competitors?
• What strengths and weaknesses does AI cite?
• Does AI recommend you, and for what use cases?

Common problems:

• Negative sentiment from outdated issues: Past problems that have been resolved still define AI’s characterization
• Competitor-favorable framing: AI positions competitors as superior or your category leader
• Missing differentiation: AI describes you generically without your actual value propositions
• Wrong audience association: AI recommends you for use cases that aren’t your strength

Sentiment issues are harder to fix than factual errors because they emerge from aggregate source analysis rather than discrete wrong facts.

Layer 4: Knowledge Gaps

Knowledge gaps are important information about your brand that AI systems don’t know but should.

What to assess:

• Does AI know your current products/services?
• Does AI know your leadership team?
• Does AI know your notable clients, case studies, or achievements?
• Does AI know your geographic presence or expansion?
• Does AI know recent developments (new offerings, pivots, partnerships)?

Common problems:

• New information missing: Launches, hires, or changes from the past 6-18 months
• Depth gaps: AI knows you exist but has shallow knowledge of what you actually do
• Leadership gaps: AI knows the company but not the people
• Proof gaps: AI knows your claims but not your credibility (awards, clients, results)

Knowledge gaps represent opportunity. Every gap is information you can create and publish to fill the void.

Layer 5: Source Attribution

Source attribution examines where AI systems are getting their information about you.

What to assess:

• What sources does AI cite when discussing you (if it cites sources)?
• What sources likely informed AI’s knowledge based on the specific claims it makes?
• Are the sources authoritative and current?
• Are there problematic sources disproportionately influencing AI’s understanding?
• What sources exist that AI should be drawing from but isn’t?

Common problems:

• Over-reliance on one source: Wikipedia or one media article dominates AI’s understanding
• Competitor sources: AI’s knowledge comes from competitor comparison content
• User-generated content: Reviews, forums, or social media disproportionately shape perception
• Outdated sources: Old articles from years ago remain primary source material

Understanding sources reveals where to focus remediation efforts.

How to Run the AI Brand Audit

The AI Brand Audit follows a systematic process: query multiple AI systems, document responses, analyze patterns, and score brand health across the five layers.

Step 1: Select Your AI Systems

Test across multiple major language models to understand the full picture:

Primary systems to audit:

• ChatGPT (GPT-4): Largest user base, often the default “AI” people use
• Claude (Anthropic): Growing user base, different training data
• Google Gemini: Powers Google’s AI features and has deep integration with search
• Perplexity: Explicitly designed for search, cites sources
• Microsoft Copilot: Bing integration, enterprise deployment

Each system has different training data, different knowledge cutoffs, and different retrieval mechanisms. What one knows, another may not.

Step 2: The 25-Question Brand Diagnostic

Ask each AI system these questions about your brand. Document every response verbatim.

Entity Recognition Questions:

1. What is [Company Name]?
2. Who founded [Company Name]?
3. What does [Company Name] do?
4. Is [Company Name] the same as [Similar Company/Common Confusion]?
5. Where is [Company Name] headquartered?

Factual Accuracy Questions:

6. When was [Company Name] founded?
7. Who is the CEO of [Company Name]?
8. What products or services does [Company Name] offer?
9. How big is [Company Name] (employees, revenue, scale)?
10. Who are [Company Name]’s main customers or clients?

Positioning Questions:

11. What is [Company Name] known for?
12. How does [Company Name] compare to [Competitor A]?
13. How does [Company Name] compare to [Competitor B]?
14. What are the strengths of [Company Name]?
15. What are the weaknesses or criticisms of [Company Name]?

Recommendation Questions:

16. Should I use [Company Name] for [your primary use case]?
17. Who should use [Company Name]?
18. What are alternatives to [Company Name]?
19. Is [Company Name] good for [specific customer segment]?
20. Would you recommend [Company Name] for [specific need]?

Knowledge Depth Questions:

21. Who is on the leadership team at [Company Name]?
22. What recent news is there about [Company Name]?
23. What awards or recognition has [Company Name] received?
24. What is [Company Name]’s pricing/business model?
25. What makes [Company Name] different from competitors?

For personal brands, adapt the questions:

• Who is [Your Name]?
• What is [Your Name] known for?
• What has [Your Name] written/created/built?
• What is [Your Name]’s background?
• Is [Your Name] credible on [your topic]?

Step 3: Document and Compare Responses

Create a spreadsheet with:

• Question asked
• Response from each AI system
• Accuracy assessment (Accurate / Partially Accurate / Inaccurate / Unknown / Hallucinated)
• Notes on discrepancies

Look for patterns:

• Consistency across systems: Do all AI systems say the same thing? Consistent answers (even wrong ones) suggest strong source material. Inconsistent answers suggest weak or conflicting sources.
• Confidence levels: Do AI systems hedge (“I believe” / “According to some sources”) or state facts definitively? Low confidence suggests thin information.
• Source citations: When AI cites sources (Perplexity always does, others sometimes do), what sources appear? Are they authoritative and current?

Step 4: Score Your AI Brand Health

Rate each of the five layers on a 1-10 scale:

Entity Recognition (1-10): – 9-10: AI immediately recognizes you correctly, no confusion – 6-8: AI knows you but may have minor confusion or categorization issues – 3-5: AI partially recognizes you, significant confusion exists – 1-2: AI doesn’t know you or completely misidentifies you

Factual Accuracy (1-10): – 9-10: All major facts correct, minor details at most – 6-8: Core facts correct, some outdated or wrong details – 3-5: Mix of accurate and inaccurate information – 1-2: Majority of stated facts are wrong or hallucinated

Sentiment & Positioning (1-10): – 9-10: Positive characterization, accurate differentiation, favorable comparisons – 6-8: Generally accurate positioning, some gaps in differentiation – 3-5: Neutral or generic characterization, poor differentiation – 1-2: Negative sentiment, unfavorable positioning, competitor-favorable framing

Knowledge Depth (1-10): – 9-10: Comprehensive knowledge of products, team, achievements, recent developments – 6-8: Good foundational knowledge, gaps in recent or detailed information – 3-5: Shallow knowledge, major gaps in important areas – 1-2: Minimal knowledge beyond basic existence

Source Quality (1-10): – 9-10: AI draws from authoritative, current, owned sources – 6-8: Mix of good sources with some problematic ones – 3-5: Over-reliance on third-party or outdated sources – 1-2: Primary sources are competitors, critics, or unreliable

Total Score: Add all five layers for a score out of 50.

• 40-50: Strong AI brand presence—focus on maintenance and optimization
• 30-39: Moderate presence—targeted improvements needed
• 20-29: Weak presence—significant remediation required
• Below 20: Critical gaps—foundational work needed

Fixing What You Find

Remediation strategies differ by problem type. Some fixes show results in weeks (retrieval-based systems). Others take months or longer (training-based knowledge).

Fixing Entity Recognition Issues

Entity confusion requires establishing clear disambiguation signals across the web.

Strategies:

• Claim and optimize your Wikipedia page: If you’re notable enough, Wikipedia is the single most influential source for AI entity understanding. Ensure your page is accurate, current, and clearly distinguishes you from similar entities.
• Claim Wikidata entry: Wikidata is a structured knowledge base that many AI systems reference. Ensure your entry exists and contains correct structured data.
• Consistent NAP (Name, Address, Phone): Ensure your business name appears identically across all web properties, directories, and citations.
• Schema markup: Implement Organization, Person, or appropriate schema on your website to provide structured entity data.
• LinkedIn optimization: LinkedIn profiles are heavily weighted in AI training data. Ensure company and personal profiles clearly establish identity.

Fixing Factual Inaccuracies

Factual errors require correcting source material and creating authoritative new content.

Strategies:

• Identify the source: If you can determine where wrong information originated (often visible in Perplexity citations), address it at the source when possible.
• Create authoritative corrections: Publish clear, definitive content on your own properties stating the correct facts. Use explicit language: “[Company] was founded in [correct year]” not vague references.
• Update Wikipedia: If Wikipedia contains errors, correct them with proper citations to reliable sources.
• Press coverage: Earned media with correct information creates authoritative sources AI systems weight heavily.
• Update third-party profiles: Crunchbase, LinkedIn, industry directories—anywhere that lists facts about you should be accurate.

Fixing Sentiment and Positioning Issues

Sentiment requires shifting the balance of information available about you.

Strategies:

• Create comparison content: Publish your own honest comparisons between you and competitors. Define the terms of comparison favorably.
• Amplify positive signals: Case studies, testimonials, awards, and client logos—create content that provides proof points for positive positioning.
• Address historical issues directly: If past problems still define perception, create content acknowledging the issue and explaining what changed.
• Expert positioning: Publish thought leadership that establishes your expertise and authority in your space.
• Review management: Google reviews, G2, Capterra, Trustpilot—these influence AI sentiment. Actively manage your review presence.

Filling Knowledge Gaps

Knowledge gaps require creating the content that should exist but doesn’t.

Strategies:

• Leadership pages: Individual pages for key executives with bios, credentials, and quotes. AI systems often can’t answer “Who runs [Company]?” because the information isn’t clearly published.
• Product/service pages: Clear, detailed pages for each offering with explicit descriptions using definitional language.
• News and announcements: Publish press releases and announcements for developments. “Company X announces partnership with Y” becomes knowledge AI can cite.
• About page depth: Most About pages are too thin. Include history, milestones, key facts, and proof points.
• Awards and recognition page: Consolidate achievements with dates and context.

The Long Game: Training vs. Retrieval

Understand the two pathways for AI knowledge:

Retrieval (faster): Systems like Perplexity and Google’s AI Overviews retrieve current web content in real-time. Changes to web content can influence these answers within days to weeks of indexing.

Training (slower): ChatGPT and Claude’s core knowledge comes from training data snapshots. Changing what these systems “know” requires your content being included in future training runs—typically 6-18 months out.

Optimize for both:

• Create content designed for retrieval now (will influence Perplexity, Google AI Overviews quickly)
• Build the authoritative web presence that will be captured in future training data (will influence all systems eventually)

The Competitive Intelligence Angle

The same audit framework applies to competitors, revealing strategic opportunities.

Running Competitor Audits

Ask the same 25 questions about your top 3-5 competitors. Document:

• What AI says about their strengths and weaknesses
• How AI positions them vs. you
• What AI recommends them for
• What gaps exist in AI’s knowledge of them

Finding Positioning Opportunities

Competitor audits reveal:

• Claims you can make that competitors can’t: If AI doesn’t associate certain strengths with competitors, you can own that positioning.
• Weaknesses AI already perceives: Don’t attack—simply emphasize your strength in areas where AI sees competitor weakness.
• Knowledge gaps to exploit: If AI doesn’t know your competitor’s pricing or limitations, ensure it knows yours (favorably framed).
• Category framing: How AI describes the category you’re in. Can you influence that framing to favor your approach?

Monitoring Competitor AI Presence

Make competitor AI audits a quarterly practice. Track:

• Changes in how AI describes competitors
• New information AI learns about competitors
• Shifts in AI recommendations
• Sources being cited about competitors

What You Can and Cannot Control

Honest assessment of limitations is essential. AI brand management isn’t magic—it’s influence with constraints.

What You Can Control

• Your owned content: Website, blog, official publications
• Your structured data: Schema markup, Wikidata, knowledge panels
• Your profiles: LinkedIn, Crunchbase, industry directories
• Your earned media: Press coverage you pursue
• Your review presence: Actively managed review profiles

What You Can Influence (But Not Control)

• Wikipedia: You can suggest edits, but the community decides
• Third-party content: You can request corrections, but compliance isn’t guaranteed
• Reviews and sentiment: You can encourage satisfied customers, but can’t control what they write
• Media coverage: You can pitch, but journalists write what they write

What You Cannot Control

• AI model architecture: How AI systems weight sources and synthesize information
• Training data selection: What gets included in future training runs
• Other companies’ content: Competitor content about you
• Historical content: Old articles, posts, and pages that remain indexed
• Model hallucinations: AI confidently stating things that have no source

The Right Mindset

Think of AI brand management like SEO or PR—a practice of ongoing influence, not a set-and-forget solution. You’re working to shift probabilities, not flip switches. Consistent effort over time compounds into stronger AI presence.

Building the Ongoing Practice

An AI Brand Audit isn’t a one-time project. AI systems evolve, information changes, and competitors adapt. Build a sustainable practice.

Quarterly Audit Cadence

Every quarter:

• Re-run the 25-question diagnostic across all major AI systems
• Compare to previous quarter’s responses
• Identify new issues or improvements
• Update your AI Brand Health scorecard
• Adjust remediation priorities

Trigger-Based Audits

Run additional audits when:

• You launch new products or services
• Leadership changes occur
• You receive significant press coverage
• Competitors make major announcements
• You notice concerning AI responses in the wild

Integration With Existing Practices

Connect AI brand management to existing workflows:

• Content strategy: Every new content piece should consider AI extraction and brand representation
• PR and communications: Brief PR teams on AI brand goals; earned media influences AI knowledge
• Product marketing: Product launches should include content explicitly designed for AI knowledge
• Executive communications: Leadership thought leadership contributes to AI brand presence

Getting Started Today

You can run a basic AI Brand Audit in 30 minutes. Here’s your immediate action plan:

Today (30 minutes):

Open ChatGPT, Claude, and Perplexity. Ask each: “What is [your company]?” and “Who is [your name]?” Document what they say. Note any inaccuracies or gaps.

This week (2 hours):

Run the full 25-question diagnostic across all five AI systems. Create your documentation spreadsheet. Score your AI Brand Health.

This month:

Identify your top 3 remediation priorities based on audit findings. Begin creating or correcting content to address them.

Ongoing:

Establish quarterly audit cadence. Monitor AI responses for your brand in regular usage. Integrate AI brand considerations into content and communications planning.

The businesses that understand AI brand management now—while competitors remain unaware—will define how AI systems represent their industries, their categories, and their competitive positioning. The narratives being established today become the defaults AI systems repeat tomorrow.

The Strategic Advantage Window

This moment is analogous to early SEO, early social media, or early content marketing—a window where first movers establish positions that become exponentially harder to displace.

Consider the dynamics:

Source authority compounds: Once your content becomes what AI systems reference, that reference creates more authority, which leads to more reference. The first authoritative voice on a topic tends to remain the default voice.

Training data locks in: When your correct, well-positioned content gets captured in the next training run of ChatGPT or Claude, it becomes what millions of instances of that AI “know” about you—for the next 12-18 months minimum.

Competitors remain unaware: Most businesses aren’t auditing their AI presence. They’re still focused exclusively on traditional search rankings and social metrics. This asymmetry is temporary.

Category definitions are forming: How AI describes your industry, your category, and the criteria for evaluating solutions in your space is being established now. The businesses that provide clear frameworks get to define the terms.

The window won’t last. Within 18-24 months, AI brand management will be as standard as social media management. The question is whether you’ll be the established authority by then or playing catch-up.

Your AI reputation is being written right now. The only question is whether you’re the author or a bystander.

David Cosgrove is a digital marketing consultant with over 30 years of experience. He developed the AI Brand Audit framework alongside the Get Cited methodology to help businesses navigate AI-powered search and reputation management.