"I typed our product name into ChatGPT and it either didn't know us or described us wrong. How does a competitor with half our features end up recommended instead of us?"
That's the exact question founders ask before running an AI brand audit. And it's the right question. ChatGPT and Perplexity aren't being arbitrary. They're running a fast, implicit checklist on every brand they consider mentioning. Your competitor passes the checklist. You don't. The question is which items you're failing.
An AI brand audit maps exactly that: which signals LLMs can find about your brand, which ones are missing or broken, and what that means for your citation likelihood when a buyer asks the AI to recommend a tool in your category.
What an AI Brand Audit Actually Measures
A traditional SEO audit looks at keyword rankings, backlinks, and technical site health. An AI brand audit looks at something different: how much structured, verifiable, cross-referenced information about your brand exists in the sources LLMs draw from when they generate recommendations.
LLMs don't search the web in real time when a buyer asks "what's a good project management tool for remote teams?" (Perplexity does, to a degree, but even then it weights authoritative prior mentions heavily.) They synthesize from training data and, where available, retrieval-augmented sources. The brands that appear in those answers are the ones that showed up clearly and consistently across multiple authoritative inputs before the model formed its opinion.
Seven signals determine whether that happens for your brand. Here's what each one means in practice, and what a weak score looks like.
Signal 1: Entity Clarity (Is Your Brand Unambiguously Identified?)
An "entity" in LLM terms is a named thing the model has learned to treat as a distinct, coherent object with known properties. Your brand is either an entity the model recognizes, a partial match it confuses with something else, or noise it ignores.
Entity clarity is weak when your brand name is a common word, shares a name with a different product or company, or has no consistent description across the pages that mention it. If your homepage says "the operating system for modern teams" and your G2 listing says "workflow automation software" and your ProductHunt entry says "a tool that helps you do more," the model gets three different signals and hedges. Low entity clarity means you might get mentioned under the wrong category, get confused with a competitor, or get omitted entirely in favor of a brand the model can place with confidence.
Signal 2: Citation Patterns (Who Links to You in AI-Relevant Sources?)
LLMs weight third-party mentions on authoritative domains far more than anything on your own site. The reason is simple: your site is not a reliable signal of your quality or category. What other sites say about you is.
Citation patterns cover which domains mention you, how they describe you, and whether those mentions agree. A brand mentioned in consistent terms across G2, a product review in a SaaS-focused publication, two podcast transcripts, and a comparison post on a DA-60 blog has strong citation patterns. A brand whose only off-site presence is a sparse ProductHunt listing from 18 months ago does not. In the LLMRadar Audit, citation pattern weakness is the most common finding for B2B SaaS products under $1M ARR, appearing in roughly 80% of audits.
Signal 3: Structured Data (Do You Have Schema Markup LLMs Can Parse?)
Schema markup (JSON-LD in your site's HTML) gives LLMs a machine-readable summary of what your product is, who it's for, what it costs, and what it does. Without it, the model has to infer all of this from unstructured prose, which means more guessing and more errors.
The specific schema type that matters for SaaS products is SoftwareApplication, ideally on both your homepage and your product page. The description field in that block is what gets pulled directly when a model answers "what does X do?" If that field says "the smarter way to work," you've wasted the slot. If it says "project management software for distributed teams, with async standup, automated status updates, and time-zone-aware scheduling," you've given the model exactly what it needs to describe you accurately. Fewer than 20% of B2B SaaS products have a complete, accurate SoftwareApplication block.
Signal 4: FAQ Coverage (Do You Answer the Questions Buyers Ask AI?)
When a buyer types "what's the best tool for [use case] under $100/month" into ChatGPT, the model looks for pages that directly answer that question or close variants. FAQ sections with specific question-answer pairs in your use case vocabulary map directly onto those queries. A product page with no FAQ, or a FAQ that answers "how do I reset my password" instead of "how does this compare to [Competitor] for [use case]," is invisible to most buyer-intent queries.
FAQ coverage matters especially for competitive queries. If someone asks "is [YourProduct] better than [Competitor] for [use case]" and you have a page that directly addresses that comparison, your citation likelihood is significantly higher than if the only comparison content lives on the competitor's site or on a third-party review roundup where your information is incomplete.
Signal 5: Review Signals (What Do Authoritative Sources Say About You?)
G2, Capterra, and Trustpilot are the review sources LLMs treat as authoritative for B2B SaaS. When a buyer asks "what do users say about [YourProduct]," the model pulls from these platforms first. If your G2 profile has 3 reviews, two of which are thin ("great product, would recommend"), the model either doesn't cite you or represents you vaguely.
Twelve or more reviews with substantive content (specific features mentioned, specific use cases described, specific outcomes named) changes this. The reviews also need to be recent: review platforms from 2022 with no 2025 or 2026 activity register as potentially inactive products in LLM training data. The threshold for meaningful review signal is lower than most founders think. Eight detailed reviews on G2 outperforms 40 shallow ones.
Signal 6: Social Proof Density (How Often Is Your Brand Mentioned in Relevant Contexts?)
Social proof density measures how frequently your brand name appears alongside your category terms, use case descriptions, and ICP identifiers across the internet. This is broader than formal citations: Reddit threads, Twitter/X discussions, LinkedIn posts, community forums, Slack group screenshots that got indexed, podcast show notes, YouTube video descriptions.
A brand that has zero Reddit presence in the subreddits where its buyers congregate is invisible to a large portion of the AI training data that covers buyer discussions. A brand that has been mentioned in r/SaaS, r/entrepreneur, or relevant vertical subreddits 20-30 times in the past year, in context, has social proof density that helps LLMs understand that real buyers use and discuss this product. This signal is slow to build but compounds: every authentic community mention is a data point LLMs absorb.
Signal 7: Competitor Share-of-Voice (How Often Do Competitors Appear When You Should?)
The final signal is comparative: in the queries where your product should appear, who is appearing instead? Share-of-voice in AI answers is zero-sum within a response. If a buyer asks "best [your category] tools" and gets a list of five products, your competitor showing up on that list means you didn't. Understanding which competitors are capturing the queries you should own tells you both the severity of your gap and the specific signals those competitors have that you don't.
In practice, the competitive share-of-voice audit involves running 15-20 category and use case queries across ChatGPT, Perplexity, and Claude, logging which products appear, and comparing the citation patterns, structured data, and review signals of those products against yours. The gaps are almost always traceable to signals 1 through 6 above.
How to Run a Basic Manual AI Brand Audit (3 Steps)
You can audit the most critical signals yourself in about an hour. Here's the minimum viable version.
Step 1: Run 8 baseline queries. In both ChatGPT (GPT-4o) and Perplexity, run: your product name alone, "best [your category] tools," "best [your category] for [your ICP]," and "[your product] vs [main competitor]." Log whether you appear, where, and whether the description is accurate. This establishes your current citation rate and accuracy rate across the two most important AI surfaces.
Step 2: Validate your structured data. Go to validator.schema.org, paste your homepage URL, and check whether a SoftwareApplication or Product schema block exists and is valid. Check the description field specifically. If it's missing, vague, or a marketing tagline, that's your highest-leverage fix: one afternoon of work that directly improves how LLMs describe you.
Step 3: Check your off-site footprint. Search your brand name in Perplexity (it shows inline citations). Search it in Google with site:g2.com, site:capterra.com, and site:reddit.com. Count the results and read the top 5 for each. This gives you a fast read on citation pattern strength and review signal quality. If any of these return zero results or thin content, you've found your citation pattern gap.
The manual audit tells you whether you have a problem and roughly where it lives. What it doesn't give you is the full signal-by-signal breakdown across 40 queries, a competitor share-of-voice comparison, and a ranked fix list with estimated impact. That's what the full LLMRadar Audit delivers.
The brands that are winning AI-driven discovery right now aren't the ones with the best products. They're the ones whose signals are clean. That gap is still closable for most B2B SaaS products, but it closes faster with a clear picture of which signals are weak and which fixes move the needle first.