How to Audit Your Brand’s Visibility on LLMs: A Step-by-Step Framework

Most marketing teams have never checked what major AI systems actually say about their brand. They assume the brand’s positioning is reflected accurately — the same way it appears on the website, in the deck, in the sales pitch. Often it isn’t.

AI systems build their picture of a brand from training data and retrieved web content, not from the brand’s own self-description. The result is sometimes accurate, sometimes outdated, sometimes vague, and occasionally wrong in ways that matter. A brand that positions itself as the enterprise-grade option in its category may be described by AI as a mid-market tool because that’s what the third-party content AI has access to says. A brand that repositioned two years ago may still be characterized by AI using its old positioning because the training data predates the pivot.

An LLM brand visibility audit is how you find out where you actually stand. This article builds the framework — what to check, how to check it, how to score it, and what to do with what you find.

What an LLM Brand Visibility Audit Measures (And What It Does Not)

An LLM brand visibility audit measures four things: whether AI systems recognize your brand, whether they describe it accurately, how they characterize it in tone and positioning, and whether your brand appears when buyers ask category-level questions without naming you directly.

It does not measure traditional SEO metrics, website traffic, or ad performance. It is not a marketing audit in the broader sense — for that, our B2B marketing strategy audit guide is the right starting point. This audit is specifically about what AI systems know and say about your brand.

It also does not measure what individual buyers think of your brand based on their own experience. It measures the AI-mediated layer — what a buyer who consults an AI assistant as part of their vendor research encounters when your brand comes up, or what they encounter when your brand doesn’t come up at all.

That scope is narrow enough to be actionable. The audit produces findings in each of the four dimensions, and each dimension points to a different category of improvement investment.

Which LLMs to Include in Your Audit

Not all AI systems are equally relevant to every B2B category, and auditing all of them is neither practical nor necessary. For most B2B companies, the right audit scope covers three to four platforms.

ChatGPT is the most widely used AI assistant by volume and is a training-data-driven system. What it says about your brand reflects its training data — which means it may be months or years behind current events, recent repositioning, or new product launches. Because ChatGPT doesn’t show its sources, auditing it reveals what the AI believes but not always why it believes it. Still, given its reach and the volume of B2B research that happens in it, it belongs in every audit.

Perplexity is the most analytically useful platform for auditing purposes because it displays the sources it draws from alongside its answers. When Perplexity describes your brand, you can see which publications, reviews, and content it is pulling from to form that description. That citation transparency makes it the highest-value platform for understanding the information landscape your brand exists in.

Google AI Overviews matter because they appear directly in Google search results — in the position above organic results that buyers are already running. A buyer who searches “best [category] tools for [use case]” and sees an AI Overview that doesn’t include your brand has received a pre-filtered shortlist before they’ve visited a single website. Given that these Overviews sit inside an existing search behavior, they have significant reach.

Gemini is worth including if your buyers are heavy Google Workspace users, since Gemini is integrated into that environment. For companies where the buyer base skews heavily toward enterprise organizations using Google products, it’s a relevant addition.

Start with the first three. Add Gemini if you have specific reason to believe your buyers use it.

The Four-Part Audit Framework

The audit runs four sequential checks. Each one answers a different question about your brand’s AI presence, and each one builds on the one before.

Part 1: Brand Recognition

The first check answers the most basic question: does the AI know your brand exists?

Run these direct queries across each platform:

• “What is [Your Brand]?”
• “Tell me about [Your Brand] — what do they do?”
• “Who are [Your Brand’s] main customers?”
• “What is [Your Brand] known for?”

What you are looking for: does the AI produce a substantive, specific answer, or does it produce a vague, hedged response — or no response at all?

A clear, confident answer with accurate basic facts (founded, what the company does, who it serves) indicates baseline recognition. A response that hedges heavily (“I don’t have detailed information about this company”) or that produces factual errors on basic facts indicates weak entity signals. A complete absence of recognition — the AI says it doesn’t have information about the company — indicates an entity clarity problem that needs to be addressed before anything else.

Part 2: Brand Accuracy

The second check answers whether what AI systems say about your brand is actually true — and whether it reflects your current positioning or an outdated version of it.

Run these queries:

• “What products or services does [Your Brand] offer?”
• “What type of companies does [Your Brand] typically work with?”
• “What are [Your Brand’s] main strengths?”
• “How would you describe [Your Brand’s] positioning in the [category] market?”

Compare the AI’s answers against your current positioning, your actual product offering, and your current target customer profile. Mark any discrepancy between what the AI says and what is currently accurate.

Pay particular attention to: descriptions that reference products or features you’ve sunset, customer profiles that no longer match your ICP, positioning language that reflects how you described yourself three or four years ago, and factual errors about company size, founding date, or core functionality.

Inaccuracies in this section are not random — they trace back to specific third-party sources that are still in circulation and still being drawn from. Those sources are the remediation target.

Part 3: Brand Sentiment

The third check answers how AI systems characterize your brand in terms of quality, authority, and fit — the qualitative impressions that shape buyer perception beyond basic facts.

Run these queries:

• “Is [Your Brand] a good option for [your target use case]?”
• “What are the pros and cons of [Your Brand]?”
• “What do customers typically say about [Your Brand]?”
• “How does [Your Brand] compare to other options in the [category] space?”

What you are looking for: is the overall tone of the AI’s characterization positive, neutral, or negative? Does the AI describe your brand as a credible, established option — or as an emerging player, a niche tool, or a budget alternative? Does the characterization align with how you intend to be positioned?

Sentiment issues are often sourced from review aggregators (G2, Capterra, Trustpilot), analyst commentary, or press coverage that the AI is drawing from. When Perplexity surfaces citations in response to sentiment queries, the citation list is a direct map to which sources are shaping the AI’s perception of your brand.

Part 4: Competitive Context Visibility

The fourth check answers the most commercially significant question: does your brand appear when buyers ask category-level questions without using your name?

Run these queries — without naming your brand in the prompt:

• “What are the best [category] solutions for [your target customer type]?”
• “Which [category] vendors are worth evaluating for [your primary use case]?”
• “What do companies like [your target customer profile] typically use for [the problem you solve]?”
• “If I was evaluating [category] tools, which ones should I look at?”

Note every brand that appears in the AI’s answers, how your brand does or does not appear, and where in the response your brand falls relative to competitors.

This is the check with the most direct commercial implication. A brand that passes Parts 1–3 but fails Part 4 — the AI knows the brand exists and describes it accurately, but doesn’t recommend it when buyers ask for recommendations — is winning the brand recognition battle while losing the brand discovery battle. Those are different problems with different solutions.

The Prompts to Use at Each Stage

Running the audit effectively requires prompts that produce diagnostic responses rather than generic ones. A few principles for writing effective audit prompts:

Be specific about context. “What is [Your Brand]?” is a recognition check. “What is [Your Brand] and what kind of companies is it best suited for?” is a more diagnostic recognition check because it reveals whether the AI has a precise picture of your target customer, not just that the company exists.

Use buyer language, not marketing language. The prompts should sound like what a buyer would actually type, not like what your marketing team would write. “What are the pros and cons of [Your Brand] for a mid-market SaaS company?” is closer to buyer language than “What are [Your Brand’s] key differentiators?”

Vary the phrasing of the same underlying question. AI outputs have some variability, and a single prompt run once may not be representative. Run the same underlying question with two or three different phrasings across a session and note whether the characterization is consistent. Inconsistency across phrasings is itself a diagnostic signal.

Include negative-framing prompts. “What are the main criticisms of [Your Brand]?” and “What types of companies should probably not use [Your Brand]?” reveal how AI characterizes your brand’s limitations — information that buyers doing due diligence will encounter whether you like it or not.

How to Document and Score Your Audit Results

The documentation format should be consistent enough to compare across months and across platforms, but simple enough that completing it doesn’t become the obstacle to running the audit.

For each query and each platform, record:

• Query text (exact wording)
• Platform (ChatGPT / Perplexity / Google AI Overviews / Gemini)
• Date
• Audit part (Recognition / Accuracy / Sentiment / Competitive Context)
• Result summary (2–3 sentences describing what the AI said)
• Accuracy rating (Accurate / Partially accurate / Inaccurate / No information)
• Characterization rating (Strong and specific / Vague / Neutral / Negative)
• Citation sources (Perplexity only — note the publications cited)
• Red flags (anything that warrants immediate attention)

A scoring system helps translate the raw documentation into a summary assessment. For each of the four audit parts, score on a simple three-point scale: Strong (the AI’s response in this dimension is accurate, specific, and favorable), Adequate (the AI has a basic picture but it’s vague, partially outdated, or inconsistent), or Weak (the AI has significant gaps, inaccuracies, or unfavorable characterizations in this dimension).

The four-part score gives you a diagnostic profile rather than a single composite number — which is more useful because different scores point to different remediation investments.

What Good Looks Like vs. What’s a Red Flag

Understanding what the audit results actually mean requires a benchmark. Here is what a healthy AI brand presence looks like across each audit dimension — and what signals a problem worth acting on.

Brand Recognition — Strong: The AI produces a specific, accurate, confident description of your company in response to direct brand queries. It knows your founding context, what you do, who you serve, and what you’re known for. It doesn’t hedge or disclaim limited information.

Brand Recognition — Red flag: The AI says it doesn’t have reliable information about your company, produces significant factual errors on basic facts, or confuses your brand with another company. This indicates an entity clarity problem — the AI’s training data doesn’t have enough clean, consistent information about your brand to form a confident picture.

Brand Accuracy — Strong: The AI’s description of your products, positioning, and target customer aligns closely with your current reality. If you repositioned twelve months ago, the current positioning is reflected.

Brand Accuracy — Red flag: The AI describes products you’ve discontinued, a target customer profile you moved away from, or a positioning angle that was accurate three years ago but isn’t now. This indicates the AI is drawing from outdated sources that are still in circulation and weighted in its training data or retrieval.

Brand Sentiment — Strong: The AI characterizes your brand as a credible, established option for your target use case. It surfaces specific strengths that align with your intended positioning. Criticisms it surfaces are reasonable and not disproportionately damaging.

Brand Sentiment — Red flag: The AI characterizes your brand as a budget option, an emerging player, or a niche tool in contexts where you’re competing for enterprise or mid-market consideration. Or it surfaces a pattern of critical commentary that doesn’t reflect your current product quality. These sentiment issues trace to specific sources that are worth identifying and addressing.

Competitive Context Visibility — Strong: Your brand appears consistently in category-level queries across at least two of the three primary platforms, typically in the first half of whatever list the AI produces. The characterization in these category answers is specific enough to be meaningful — not just a name-drop, but a description of what you’re good for.

Competitive Context Visibility — Red flag: Your brand is absent from category-level queries while two or three competitors appear consistently. Or your brand appears in category queries but is characterized so generically that it conveys no specific reason to evaluate you. Absence from category-level queries is the most commercially significant red flag in the audit — it means buyers who haven’t already heard of you are being given a shortlist that doesn’t include you.

How to Use Audit Findings to Prioritize Improvement Efforts

The audit produces a diagnostic profile. Translating that profile into a prioritized improvement plan requires knowing which findings matter most.

If Brand Recognition is Weak, fix it first — before anything else. Nothing else in the improvement plan matters if AI systems don’t have a confident, accurate picture of what your company is. Start with entity signals: check your Wikipedia page (create one if you’re notable enough and don’t have one), update your Wikidata record, implement Organization schema markup on your website, and ensure your brand’s name, description, and category are consistent across all public directories and profiles.

If Brand Accuracy is the primary issue, trace the inaccurate sources and replace them. On Perplexity, the citation list in response to accuracy queries often points directly to the outdated sources being drawn from. On ChatGPT, look at which older third-party content about your brand still ranks well in search. Those are your remediation targets. Publishing clear, current, well-indexed content that corrects the record is the primary fix — but be patient, because the correction takes longer in training-data-driven systems than in retrieval-based ones.

If Brand Sentiment is the issue, audit your review and analyst presence. Sentiment issues most often trace to review aggregator scores, analyst commentary, or press coverage patterns. Check your G2, Capterra, and Trustpilot profiles: are reviews current and representative? Are there patterns in negative reviews that the AI may be amplifying? Improving review profile health and building a presence in analyst coverage both contribute to sentiment improvement over time.

If Competitive Context Visibility is the issue, that is the highest-priority content and earned media investment. Absence from category-level queries means your content authority and citation profile in AI-relevant sources are not strong enough to earn inclusion in synthesized category answers. The fix — building deeper content on high-intent category topics and earning citations in the publications AI systems draw from — is the most sustained investment of the four, and the one with the longest feedback loop. The AI visibility improvement guide covers the specific levers in detail.

How Often to Repeat the Audit

The initial audit establishes a baseline. Subsequent audits measure whether the baseline is changing and in which direction.

Run the full four-part audit twice a year. The feedback loop on AI visibility improvements is long enough — particularly for training-data-driven systems where changes take months to reflect — that quarterly auditing produces more noise than signal. Semi-annual audits, with consistent documentation that allows direct comparison between runs, give you enough time to see whether investments are moving the needle without running the audit so infrequently that significant shifts go undetected.

Between full audits, run a lighter monthly check focused on Competitive Context Visibility — the Part 4 queries are the highest-signal dimension for ongoing tracking and the most likely to shift in response to competitive activity or content investments. A twenty-minute monthly check on the category-level queries is enough to catch significant changes between full audits.

For the measurement infrastructure that places this audit within a broader analytics cadence, the marketing analytics stack guide covers how to build a consistent measurement program that includes both traditional metrics and newer signals like AI brand visibility.

FAQ

How long does a full LLM brand visibility audit take to run?

The first audit takes longer because you’re building the query set and documentation format from scratch — budget a half day for a thorough first pass across three or four platforms. Subsequent audits using the same query set and documentation structure take two to three hours. The time investment is front-loaded; the ongoing process is lighter.

What if our brand doesn’t appear in any AI answers at all? Where do we start?

Start with entity clarity. If a major LLM doesn’t mention your brand even in response to direct brand-name queries, the most likely cause is weak entity signals — the AI doesn’t have enough clean, consistent information about your brand to include it confidently. Check your Wikipedia presence, Wikidata entity record, and schema markup on your website. These are the foundational signals that tell AI systems what your brand is and what it does, and they’re the right starting point before investing in content or earned media.

Should we include Google AI Overviews in the audit, or focus only on standalone AI assistants?

Include both. Google AI Overviews appear directly in organic search results and affect brand visibility at the exact point where buyers are running searches about your category. Standalone AI assistants are increasingly used for research tasks that happen before buyers reach a search engine. The two audit tracks use similar query structures but may produce different results — and the differences are themselves informative about where your AI visibility is strongest and weakest.

What score qualifies as “good” in an LLM brand visibility audit?

There is no universal standard yet — the field is too new for settled benchmarks. A useful frame: if your brand appears in more than 60% of category-level queries across the AI platforms your buyers use, and the characterization is accurate and specific rather than vague or generic, you’re in a reasonable position. Benchmark against your main competitors more than against an absolute scale — the competitive context matters more than hitting an arbitrary threshold.

How do we handle a situation where the AI describes our brand inaccurately?

The short-term options are limited — you cannot directly submit corrections to AI training data. The medium-term solution is to publish clear, accurate, authoritative content that gives AI systems better source material: updated product pages, current press releases picked up by credible publications, and structured data that precisely describes what your company does. For retrieval-based systems like Perplexity, accurate and well-indexed current web content can shift the characterization relatively quickly. For training-data-driven systems like ChatGPT, the correction takes longer and depends on the next model update incorporating the newer, more accurate sources.

The Bottom Line

An LLM brand visibility audit answers a question most marketing teams have never asked: what do AI systems actually say about your brand when buyers consult them? The answer is often different from what the marketing team assumes — sometimes subtly, sometimes significantly.

The audit is not complex. It requires about half a day the first time through, a consistent documentation format, and enough pattern recognition to translate what you find into a prioritized set of improvements. What it produces is a specific, evidence-based picture of your brand’s AI presence — not a general sense that AI is important, but a concrete understanding of exactly where your brand is visible, where it’s invisible, and where it’s being characterized in ways that may be costing you consideration.

If you want to run an AI visibility audit as part of a broader brand and marketing assessment, let’s talk.