The question founders ask most often before buying the audit is some version of: "what will it actually find?" That is a fair question. You are spending $197 for a report, and you want to know whether it will show you something actionable or just confirm you are invisible.

The short answer: almost every brand has fixable gaps, and the audit surfaces the specific ones. The longer answer is that the findings cluster into three patterns that appear across SaaS brands at different stages. Understanding which pattern fits your situation tells you what kind of fix list to expect.

What the 40-query scan tests

Before getting into the patterns, it helps to understand what the audit measures. The 40 queries cover four categories:

Each query runs across four models: ChatGPT-4o, Claude 3.5, Gemini 1.5 Pro, and Perplexity. The results are scored by citation depth, accuracy, and position (cited first vs. mentioned in passing vs. absent). That produces the overall visibility score and the per-category breakdown the fix list is built from.

Pattern 1: Strong SEO, zero LLM presence

Pattern 1
The brand that ranks well but does not appear in LLM answers
Common in: B2B SaaS, 3-7 years old, SEO-invested, solid organic traffic, 50-200 employees
What the scan finds
Recommendation queries return no citation (the brand is not in any list LLMs build for buyers). Definitional queries return a generic summary pulled from the homepage, with no specifics on use cases, integrations, or differentiators. Comparison queries mention the brand by name but describe it inaccurately. Branded problem queries return zero citations across all four models.
Root cause
LLMs train on patterns, not page authority. A brand can have thousands of inbound links and still be invisible to LLMs if its content never answers the specific questions LLMs use to build recommendations. The typical finding here: the site has many SEO-optimized pages but no pages that explicitly place the brand inside a category, compare it to named alternatives, or answer "what is X used for" in structured prose.
Typical fix list (numbered, as delivered)
  • 1Write a 2,000-word "What is [Brand]?" page that explicitly names your category, ICP, top 3 use cases, and 3 named competitors. Publish under /about/ or /what-is-[brand]/.
  • 2Add a "Compared to alternatives" page naming at least 3 competitors with an honest feature table. LLMs cite comparison content more reliably than feature lists.
  • 3Add structured FAQ markup to your top 3 landing pages with questions that match the branded problem queries that returned zero results.
  • 4Publish one "tools for [your ICP's job title]" list post that includes your brand alongside 4-6 complementary tools. Third-party list inclusion is the single strongest LLM citation signal.
Score before and after (typical range)
Score: 18-28 → Score: 55-70 (90 days post-fix)

This pattern is the most common one the audit surfaces. Brands in this position feel confused: they are investing in content, rankings are improving, but none of that appears to translate into LLM mentions. The explanation is consistent across every case: LLMs do not cite page authority, they cite content patterns. The fixes are almost always structural and require no new features or product changes, just different content types.

Not sure which pattern fits your brand? The free AI visibility self-audit takes 5 minutes and scores your brand across the same four query categories. If you score below 70, the audit delivers the specific fix list.

Pattern 2: Present, but for the wrong buyers

Pattern 2
The brand LLMs mention in the wrong context
Common in: horizontal tools, developer-focused products, tools that pivoted ICP in the last 18 months
What the scan finds
Recommendation queries do return the brand, but in categories that do not match current positioning. A tool that pivoted from SMB to enterprise still appears in SMB recommendation lists. A developer tool that added a no-code tier still appears only in developer contexts. Definitional queries produce an accurate summary of the old positioning, not the current one. Branded problem queries for the target ICP return zero results while queries for the old ICP return citations.
Root cause
LLMs form associations from the content patterns they trained on, and those associations are sticky. A brand that published 3 years of developer-focused content and then added enterprise positioning in the last 6 months will still read as a developer tool to most LLMs. The audit identifies which associations are dominant and which query types are producing the misalignment.
Typical fix list (numbered, as delivered)
  • 1Publish 4-6 posts explicitly targeting the new ICP's job titles and problem language. Use the exact phrases your new buyers use (sourced from sales calls, support tickets, or community posts), not the phrases your old buyers used.
  • 2Update your homepage H1 and meta description to name the new ICP explicitly. LLMs weight homepage content heavily for definitional queries.
  • 3Add a "Who this is for" and "Who this is not for" section to your main product page. LLMs use explicit exclusion language to update their category associations faster than implicit pivots.
  • 4Get cited in 2-3 external lists or roundups targeting the new ICP's context. External citations are the fastest signal to shift LLM associations away from older positioning.
Score before and after (typical range)
Score: 35-50 (technically visible, wrong context) → Score: 60-75 in target context (60-90 days)

This pattern is harder to spot without the audit because the brand does appear in LLM outputs. The problem only surfaces when you look at which queries produce citations and which do not. Founders who have done a basic brand mention check will see their name appearing and assume visibility is fine. The audit separates total citation volume from contextually accurate citation, and for brands that have pivoted, the gap is usually significant.

Pattern 3: Invisible in the category even though the product is strong

Pattern 3
The brand absent from "tools for X" lists despite category leadership
Common in: newer SaaS (under 3 years), bootstrapped brands, products that grew via word of mouth with little content investment
What the scan finds
All four query categories return near-zero results. Recommendation queries produce lists of 5-7 tools that do not include the brand. Definitional queries produce a vague one-sentence summary or nothing. Comparison queries return results about competitors without mentioning the brand. Branded problem queries return no citations. The brand may have strong product reviews on G2 or Capterra but those signals are not consistently indexed by LLMs.
Root cause
LLMs need to encounter a brand name in multiple authoritative contexts to form reliable associations. A brand that has primarily grown via direct sales, referrals, or community mentions has a thin content footprint that LLMs cannot pattern-match reliably. Review site presence helps but is insufficient on its own. The audit identifies exactly which query types are producing zero results and what content types would address each gap.
Typical fix list (numbered, as delivered)
  • 1Write the definitive "tools for [your category]" post on your own site and include yourself. This sounds self-serving but it is the fastest way to establish the association between your brand name and your category in LLM training contexts.
  • 2Publish a "how it works" page with specific technical or process details. LLMs cite sources that contain specific, verifiable claims (numbers, steps, integrations) more reliably than sources with only marketing language.
  • 3Create or claim your entry on at least 3 software directories that LLMs are known to index (Capterra, Product Hunt, and G2 at minimum). Structured directory presence is a citation shortcut.
  • 4Write one 1,500-word post framing your brand as the answer to the most common buyer objection in your category. Objection-handling content matches the "what should I do" query pattern that produces recommendation citations.
  • 5Target one "alternatives to [dominant competitor]" keyword. This is the single highest-conversion LLM query type for brands entering an established category.
Score before and after (typical range)
Score: 5-22 → Score: 45-60 (90-120 days post-fix)

Brands in this pattern often have high conviction that their product is better than the alternatives. The audit is useful not because it reveals this (you already know your product), but because it gives you the specific content actions that translate product quality into LLM recognition. "Better product" is not a content signal. "Used for X, compared favorably to Y, recommended by practitioners for Z" is.

What the fix list actually looks like

The three patterns above share a common structure in the deliverable: the audit report is a numbered list ordered by impact, with each item specifying what to create, where to publish it, and what query type it addresses. The typical report is 4-8 items. Some items are 30-minute content tasks. Some are engineering changes (structured data, FAQ markup). The report separates them so you can route each item to the right person.

The report does not include vague recommendations like "create more content" or "improve your SEO." Every item in the fix list maps back to a specific query category that produced a zero or near-zero result in the scan.

The goal is that you can hand the report to whoever owns content or engineering and they know exactly what to build next, in what order, and why each item matters for LLM visibility specifically.

What to do before you order

Run the free 5-minute self-audit first. It covers the same four query categories as the full scan but at a surface level. If your score is above 70, you probably do not have a structural visibility problem and the audit is unlikely to surface high-priority fixes. If your score is below 70, the audit gives you the specific numbered list that the self-audit cannot.

The audit requires your domain and your top 3 competitors. No setup, no account, no calls. The report arrives by email within 2 hours.