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LLM Brand Audits: What OperatorIQ's $197 AI Visibility Scan Actually Checks

"I've been writing about AI visibility for six months and I have no idea if ChatGPT ever recommends us."

That sentence gets said in a lot of Slack channels right now. Maybe yours.

You've read the think-pieces. You've set up Google Alerts. You've even asked ChatGPT "are you familiar with [your company]?" and gotten a vague, technically-accurate non-answer. What you don't have is a concrete list of where you appear, where you don't, and what to do about it.

That's what the LLMRadar Audit is for. Here is what it actually checks.

The 5 Things the $197 Audit Checks (With Named Metrics)

Most teams skip straight to monitoring when they should start with a baseline. You can't monitor drift if you don't know where you started.

The LLMRadar Audit runs across ChatGPT GPT-4o, Claude, Perplexity, and Gemini. It checks five things on every pass.

1. Brand mention frequency

This is the simplest measure: across 20+ buyer-intent queries, how often does your brand name appear in the AI response at all? Not ranked, not cited as a source. Just mentioned.

Frequency is measured as a percentage. A score of 0% means none of the tested queries triggered a mention. A score of 85% means your brand appeared in 17 of 20 queries. The gap between 0% and 85% is not random. It is fixable.

2. Citation source (which of your URLs the LLM is pulling from)

When an AI assistant mentions you, it is usually pulling from a specific page. It might be your homepage, your G2 profile, a Capterra review, a Reddit thread, or a blog post. It is almost never pulling from the page you think it should be.

The audit identifies which URL is being cited for each mention. This matters because the page that gets cited controls the description the AI generates. If Claude is citing your 2023 "About Us" page, it is describing 2023-you to buyers in 2026.

3. Competitive placement (does AI recommend you or a competitor in the same breath)

This is the check that stings most. For each query where your brand is absent, the audit records which brands appear instead.

Look, you might not care that you're absent from a query you've never heard of. You'll care a lot when you see that your three closest competitors are cited in the same breath for your primary category query, and you're not there.

The competitive placement report names the competitors, names the queries, and names the position. First, second, third, or absent.

4. Query category mapping (brand queries vs. category queries vs. competitor queries)

Not all queries are equal. There are three types the audit tests.

Brand queries are searches where someone already knows your name: "what does [your product] do" or "is [your product] worth it." These should return accurate, positive mentions. If they don't, you have an entity definition problem.

Category queries are searches where buyers are evaluating options: "best AI visibility tools for SaaS" or "how do I know if my brand is cited by AI assistants." These are the queries that generate new awareness. Missing from these means you're invisible at the top of the funnel.

Competitor queries are searches that name a rival: "[competitor] vs [your product]" or "alternatives to [competitor]." These are the highest buyer-intent queries in your category. A buyer typing this is close to a purchase decision.

The audit maps where you appear across all three types so you know which category of gap to fix first.

5. Sentiment and context (is the mention favorable or just a warning)

Getting mentioned is not always good. An AI that mentions your brand as "a tool some teams use, though reviews note reliability concerns" is worse than no mention.

The audit records the sentiment of every mention: favorable, neutral, or negative. It also flags contextual accuracy issues. If Perplexity describes your product as a project management tool when you're a sales intelligence tool, that's a vocabulary misalignment problem. One blog post fix can correct it within weeks.

Why Most SaaS Teams Audit the Wrong Thing

Here's what most SaaS teams check when they get worried about AI visibility: they open Google Search Console and look at organic impressions.

That is the wrong measurement. Entirely.

Google Search Console measures whether Google's crawler indexed your pages and whether those pages appeared in Google search results. That is a ranking system. LLM citation is a retrieval system. The two are related but not interchangeable.

A brand can rank page 1 on Google for "best AI visibility tools" and receive zero citations in ChatGPT, Claude, or Perplexity. This happens constantly. Ranking well requires keyword density, backlinks, and Core Web Vitals. Getting cited in AI responses requires structured schema markup, entity consistency across review platforms, and vocabulary alignment between your product description and the questions buyers type into AI assistants.

The monitoring platforms (Otterly.ai, Profound, Quoleady) will tell you whether your citation rate is going up or down over time. They won't tell you why, and they won't tell you what page to fix first. That's the audit's job.

Makes sense. You wouldn't monitor a car engine without first knowing what's wrong with it.

What a Good Result Looks Like vs. a Bad One

A good result from the LLMRadar Audit: brand mention frequency above 70% on category queries, citation source pointing to your product page or a high-quality review profile, first or second position in competitive placement queries, and all three query types (brand, category, competitor) returning favorable mentions.

A bad result looks like this. Real language, composited from actual audit outputs.

A SaaS tool in the AI content category runs the audit. Perplexity returns the following for "best tools for AI-assisted content marketing": "Jasper, Copy.ai, and Writer are the most commonly used tools for this workflow. Some teams also use Notion AI for drafts." The brand is not named. For the brand query "what does [their product] do," ChatGPT returns a description that's two years out of date: "a content repurposing tool for social media teams," when the product has since pivoted to enterprise content ops.

The bad result is two distinct problems. First: category query absence, which requires entity signal fixes (schema, review profiles, updated descriptions). Second: brand query inaccuracy, which requires updating the page that's being cited and republishing the structured data.

Different problems, different fixes, different timelines. The audit tells you which is which.

How Long Does an LLM Audit Actually Take?

You can do a DIY version. Here is what that looks like in practice.

Open ChatGPT, Claude, Perplexity, and Gemini in four tabs. Write 5-7 buyer-intent queries relevant to your category. Run each query in each engine. Copy the outputs into a spreadsheet. Note whether you're mentioned, your position, which competitor took your spot, and whether the description is accurate. Repeat with brand queries and competitor queries.

That process takes 3-5 hours. Done carefully, it gives you a reasonable baseline.

The $197 LLMRadar Audit runs the same battery, across 20+ queries and all four engines, and delivers a prioritized fix list within 48 hours. The fix list is ordered by expected citation impact, not alphabetically or by effort. You know what to do first.

The monitoring subscriptions (typically $50-500 per month) start tracking from the day you subscribe. They don't give you a historical baseline, and they don't tell you what to fix. They're useful after the audit, not instead of it.

If you want to know exactly where you stand with ChatGPT, Claude, and Perplexity, see the LLMRadar Audit. $197, results in 48 hours.

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Christine Johnson is the founder of OperatorIQ. She runs an autonomous AI venture studio that ships daily content, manages a live skill library, and handles client fulfillment without hiring.

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Originally published on OperatorIQ on 2026-06-29.