An AI visibility report tells you whether AI platforms name your brand when buyers ask category questions, who they name instead, and which gaps are most likely behind it. Read it as a diagnosis first, then fix things in this order: entity signals, schema, then content structure.

Most people open their first report and go straight to the biggest number. That’s understandable, but the number alone doesn’t tell you what to do. The sections around it do. This guide walks through the sections of the Fix My AI Rank report, what each one means, and how to turn them into a short to-do list.

One note on scope: the report is a point-in-time audit. Where we give example numbers below, they are illustrative, not benchmarks or real client results.

What does an AI visibility report measure?

The report looks at how AI platforms respond to a set of buyer-intent questions for your market, then checks the likely reasons behind the pattern. We describe how the question set is built in how the AI Visibility Report works. The core measures are:

Measure What it means Illustrative example
Citation rate The share of queries where your brand name appears in the answer A brand named in 3 of 10 queries has a 30% citation rate
Position Where in the answer your brand appears when it is named First mention vs. third mention in a list
Accuracy Whether the description of your brand is correct and current The answer describes an old product line
Share of AI Voice Your mentions as a share of all mentions across you and your tracked competitors 6 of 26 total mentions is about 23%

Citation rate here means brand mentions, not linked sources. If you want the distinction in full, see what an AI citation is, and for the competitive metric, Share of AI Voice. The same three core metrics are laid out in how to track AI visibility.

The diagnostic half of the report checks entity signals, schema markup, content freshness, and content gaps. Those are the “why” behind the numbers.

How do I read each section of the report?

Here is the report in the order we’d read it, using the sections listed on the report page.

AI citation rate and competitive query breakdown

This is the headline. It shows, query by query, whether ChatGPT, Perplexity, and Claude mention your brand, and which competitors are named instead.

Look at three things:

  • The platform split. A brand that shows up on one platform but not another has a different problem from a brand that’s absent everywhere. In our experience, ChatGPT leans more on training data and established entity signals, while Perplexity often leans on live retrieval of recent content. These are tendencies we’ve observed, not fixed rules.
  • The query pattern. Which questions name you and which don’t? Gaps that cluster around one topic usually point at missing content on that topic.
  • The competitor names. If the same two competitors appear wherever you don’t, those are the brands to study.

Entity signal assessment

This section checks whether the places AI systems use to verify a brand exist and agree with each other: Wikipedia, G2, Capterra, Crunchbase, Google Business Profile, and similar sources. A missing or inconsistent profile is a gap. If your citation rate is low, this is usually the first place to look. What entity signals are explains the full list.

Schema markup gap analysis

This lists missing or incomplete JSON-LD types on your site, such as Organization, FAQPage, Article, and BreadcrumbList. Schema helps machines read what a page is about. It is a supporting signal, not a guarantee of citation, and we don’t claim it is proven to change AI answers. In our audits, missing Organization schema is one of the most common gaps, and it’s a developer task rather than a content project. See schema markup for the types and examples.

Content freshness and content gap review

Freshness flags key pages that haven’t been updated in a while. The content gap review compares what you have against formats AI platforms commonly draw on: FAQs, how-to guides, comparison pages, case studies, and original research. These are the longer-lead items, since they take real writing time.

Prioritized quick wins list

This ties it together: a ranked list of actions by impact and effort, with the target platform for each. If you only have an hour, start here.

What should I fix first?

Our order, consistent with how to track AI visibility:

  1. Entity signals. If AI systems can’t verify who you are from independent sources, nothing else carries much weight. Claim and complete the profiles the report flags, and make the company name, description, and category consistent everywhere.
  2. Schema. Add the missing JSON-LD types the report lists. This is usually fast, and it’s the clearest hand-off to a developer.
  3. Content structure. Add or restructure FAQs, comparison pages, and how-to content for the queries where competitors are named and you aren’t. Lead each page with a direct answer.

This is our prioritization from the audits we’ve run, and your situation may differ. If the report shows you’re named often but described inaccurately, fixing the description on your own pages and profiles may come before anything else. For a broader checklist, see why your website isn’t showing up in AI answers.

What are the common ways to misread the report?

  • Treating one run as the truth. AI outputs vary between sessions, and query wording matters. A 30% rate this month and 40% next month may be noise. Look for direction across several runs.
  • Treating mentions as sourced citations. Being named in an answer is not the same as a platform linking your page. Don’t assume a good mention rate means your pages are being used as sources.
  • Chasing the aggregate. A blended score hides the platform split, which is where the specific fixes are.
  • Expecting a guarantee. Fixing the listed gaps targets what’s most consistently associated with better results in our audits. It doesn’t guarantee a result.

What should I do in the first 30 days?

A simple plan:

  • Week 1: Read the report end to end. Write down your citation rate per platform, the top three competitors named, and the top three entity or schema gaps. Save the report as your baseline.
  • Week 2: Complete the entity profiles flagged and fix inconsistencies in your company description.
  • Week 3: Implement the missing schema types and validate them.
  • Week 4: Draft or restructure one or two pages for the queries where you’re absent. Then wait a full cycle before re-testing the same query set.

Change things in batches and log what you changed. That’s the only way to connect a later shift to a cause.

If you’re new to the term, what AI visibility is has the plain definition.