Representative example
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A sample GEO Visibility Report
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This is the monthly deliverable every Resonate Labs engagement produces: a full read on where a company stands inside AI answers, and a ranked plan to change it. Walk through it section by section below. The company, competitors, and numbers are invented to show the shape. Real reports run on your own category, your own buyers, and your own data.
This page walks through the report a section at a time. To read the whole thing the way a client does, in the account it is delivered in, open the live sample → It is the same interface we deliver in, running on the same invented data.
A real report runs on your own category, competitors, and numbers.
A GEO Visibility Report answers one question: when your buyers ask AI assistants about your category, do you show up, and if not, who does instead? Everything below is built to demonstrate the deliverable. None of it is real client data.
What this sample run covers
This sample report tests 150 buyer-intent queries on four AI engines: ChatGPT, Claude, Gemini, and Perplexity. Resonate Labs scores each engine separately, so a win on one never hides a gap on another. Every query is written for one of five buyer personas and tagged with one of seven buying stages.
The scorecard
The top-line read across the 150 queries this company's buyers actually use.
What this is and why it matters
Win Rate is scored across all 150 queries: how often [Client] is named the single top pick, whether or not it appears. That's different from its hit rate when visible (62% — see below), which only counts the queries it shows up in. A strong hit rate on a low win rate is the honest, common pattern: competitive when present, but present in a small slice of buyer questions and invisible almost everywhere else. This is a reach problem, not a persuasion problem, and it's exactly what the plan is built to move.
Visibility analysis
Visibility is not one number. The report breaks it down three ways: by buying signal, by where the buyer is in their journey, and by who's asking.
By funnel stage
What this is and why it matters
This company shows the shape we see most often: a little present near the finish, where buyers compare a shortlist they have already built, and close to absent at the start, where they first describe the problem. Late-funnel presence feels reassuring, but by then the shortlist was assembled without them. The visibility that changes pipeline is the visibility at the beginning, and that is exactly where the gap sits.
By buyer persona
The same run, split by who's asking. Every real report also breaks this out by buying job, product feature, and pain point.
| Persona | Visibility | Hit rate | Seen / total |
|---|---|---|---|
| VP / Head of Engineering | 67% | 8 / 36 | |
| Delivery / PMO Lead | 60% | 5 / 31 | |
| Ops / RevOps Manager | 50% | 3 / 30 | |
| Individual Contributor / IC | 67% | 3 / 33 | |
| Founder / Exec Buyer | 50% | 2 / 20 |
What this is and why it matters
Visibility is uneven across the buying committee. Here the brand reaches engineering leaders reasonably well but is nearly absent for the ops and IC personas who often start the search. Knowing which persona can't find you tells you which content to build first, and for whom.
Share of voice
Who owns the AI answer when your buyers ask, and where you land against them.
Competitive Share of Voice
| Vendor | Mentions | Share of Voice |
|---|---|---|
| Competitor A | 64 | |
| Competitor B | 52 | |
| Competitor C | 47 | |
| Competitor D | 38 | |
| Competitor E | 31 | |
| [Client] | 18 | |
| Competitor F | 15 | |
| Competitor G | 11 |
What this is and why it matters
Share of voice counts every brand mention across the run and ranks the field. Sitting sixth of eight, behind five competitors, means the engines reach for someone else first when your buyers ask. The brands above the line are not necessarily better products. They are more legible to AI, because they have published the content that answers the question being asked. This is the scoreboard a 30-day plan is built to move.
Head-to-head: 11W–8L when you meet a competitor in the same answer
On the queries where both brands appear, who does the engine rank first.
vs Competitor A
3W – 2L
22 shared answers
vs Competitor B
4W – 2L
18 shared answers
vs Competitor C
2W – 3L
15 shared answers
vs Competitor D
2W – 1L
11 shared answers
What this is and why it matters
A winning head-to-head record confirms the product competes once it's in the room. That's the reassuring half of the story, and the trap: it's easy to read a positive record as "we're fine." The share-of-voice rank above is the reality check. You win most match-ups but enter far too few of them.
Resonate vs. Evertune for AI brand monitoring
Resonate Labs and Evertune both measure how AI answers treat a brand, but they count different things. Resonate Labs measures buyer-journey share of voice: when your buyers ask the questions that build a shortlist, which competitors does AI name, and how often are you the pick. Evertune measures brand mentions at the model level: how AI models describe and mention your brand across responses, at scale. One tells you who wins your buyers' questions. The other tells you how the models see your brand.
Buyer-journey share of voice vs. brand mention tracking
| Resonate Labs | Evertune | |
|---|---|---|
| The question it answers | When my buyers ask AI about my category, which competitors does it name instead of us? | How often, and how, do AI models mention my brand? |
| What it counts | Mentions, top picks, and head-to-head wins across 150 buyer-intent queries, split by persona and buying stage | Brand mentions and model behavior across AI responses |
| Engines | ChatGPT, Claude, Gemini, and Perplexity, scored individually | Model-level analysis across AI models |
| What you get | A scorecard, citation landscape, and ranked 30-day action plan | Brand analytics; acting on them is up to your team |
| Built for | B2B marketing and demand gen leaders deciding what to publish next | Brand and insights teams |
| Better fit when | You need to know which buyer questions you lose, to whom, and what to fix first | You want deeper model-behavior analysis at scale and have a team to act on it |
When to choose which
Evertune goes deeper on model behavior than any tool in the set, which makes it the stronger choice for a brand or insights team studying how AI models represent a brand. Resonate Labs is built for the mid-market B2B team that needs a different answer: which competitors AI recommends when your buyers ask, at which stage of the journey, and which pages to ship to change it. If your question starts with "who is AI naming instead of us," start with buyer-journey share of voice.
Where you're invisible
A sample of the early-funnel queries where [Client] never appears and a competitor wins the answer.
Invisibility Gaps
| Buyer query (early funnel) | Who wins it |
|---|---|
| best project management software for distributed teams | Competitor A |
| how to track dependencies across multiple teams | Competitor C |
| agile project management for fully remote teams | Competitor B |
| project management software with built-in time tracking | Competitor A |
| how to run an effective sprint retrospective | Competitor D |
| resource planning tools for growing agencies | Competitor E |
What this is and why it matters
A gap is a buyer query where the brand never appears and a competitor does. These are high-intent, early-funnel questions, the moments a buyer is choosing the vocabulary that frames everything after. Closing them is pipeline, not vanity. Each row is a buying conversation happening right now without you in it, and the brand that answers it shapes the requirements the buyer carries into every later stage.
Where AI looks for answers
Visibility is downstream of citations. The engines answer by pulling from pages they trust, so the report maps exactly which pages get cited, yours and your competitors'.
Your cited pages
The pages doing the work, ranked by how many answers quote them.
High-authority sources citing competitors, not you
The third-party pages the engines trust for this category, and who they currently name.
What the citation source analysis reveals
The citation source analysis in every Resonate Labs Visibility Report turns "AI doesn't cite us" into a list of specific URLs. In this sample, it surfaces five data points:
- Top citing domains. The third-party sites the engines quote most for the category, with a count for each: g2.com leads with 14 citations naming Competitors A and C.
- Client presence gap. 8 high-authority sources cite competitors and never the sample company.
- Pages already earning citations. 9 distinct URLs on the company's own site get quoted, led by the Jira integrations page at 11 citations.
- Domain citation rank. The company's domain ranks #5 for how often the engines cite it against the field.
- Remediation priority. Each gap becomes a ranked item in the 30-day action plan, with the third-party sources handled in Layer 3.
What this is and why it matters
Citations are the raw material of every AI answer. Your own cited pages show which content already earns trust, so you double down on what works. The third-party gaps are the bigger prize: when G2, Capterra, and the category's go-to review sites name your competitors and never you, the engines inherit that bias on every related question. Closing citation gaps, on your site and on the sources AI leans on, is what moves visibility in the next crawl.
Every query, every engine
The numbers above roll up from the raw run: all 150 buyer queries, scored on ChatGPT, Claude, Gemini, and Perplexity individually. The report lets you open any query and read exactly what each engine said, who it picked, and which sources it cited.
Win rate isn't uniform. You're strongest on ChatGPT and weakest on Claude, so the plan can prioritize the engines where you're closest to breaking through.
Two queries, opened up
"best project management software for distributed teams"
"[Client] vs Competitor A for engineering teams"
What this is and why it matters
Every headline number traces back to queries like these. The first is a high-intent, early-funnel question the brand loses on all four engines, a clear content gap. The second shows it winning the comparison it's built for, everywhere except Perplexity. Seeing the actual answers, picks, and citations is how you turn a score into a to-do list, and how you verify the fix worked on the next crawl.
How we build the query set
Resonate Labs builds every query set from the people who buy from you. The 150 queries in a Visibility Report come out of four steps, and each query carries a persona and a buying stage before it is sent to a single engine. That is what lets the report say who can't find you, and when, instead of handing you one blended score.
Identify the personas. Your AI Visibility Foundation maps who buys and who you're compared against, and you approve it before anything is measured. This sample uses five personas, from VP / Head of Engineering to Founder / Exec Buyer.
Map the buying jobs. Each persona is mapped across seven buying stages: problem identification, solution exploration, requirements building, comparison, shortlisting, validation, and artifact creation.
Write the queries for each stage. Every query is phrased the way that persona asks at that stage. "How to track dependencies across multiple teams" is an early-funnel question; a VP of Engineering asking how the company compares with Competitor A is comparing a shortlist. The full process is documented in how queries are generated.
Run every query on every engine. Each query goes to ChatGPT, Claude, Gemini, and Perplexity, and each engine is scored on its own, down to what it said, who it picked, and which sources it cited.
What this is and why it matters
The persona-to-query mapping is visible in the numbers. In this sample, the 150 queries split 36 for the VP / Head of Engineering, 31 for the Delivery / PMO Lead, 30 for the Ops / RevOps Manager, 33 for the Individual Contributor, and 20 for the Founder / Exec Buyer. Because every query belongs to one persona and one stage, a 4% visibility score at problem identification narrows to the buyers asking those questions and the pages that would answer them.
Keyword list or buyer-persona query model?
A keyword-list approach turns category terms into prompts and tracks whether AI names you. A buyer-persona query model starts from who buys and what they're trying to get done, then writes the questions they actually ask. Both are honest ways to measure AI visibility. They answer different questions, and the gap shows up in the queries themselves.
| Keyword-list approach (generic tools such as Peec AI) | Buyer-persona query model (Resonate Labs) | |
|---|---|---|
| Starts from | Category keywords, turned into prompts | The personas and buying stages you approve in your Foundation |
| Example query | "project management software" | "best project management software for distributed teams" (Ops / RevOps Manager, solution exploration) |
| What a gap tells you | Your brand is missing for a term | Which buyer, at which stage, on which engine, gets an answer without you |
| How results break out | By keyword or prompt | By persona, buying stage, engine, buying job, product feature, and pain point |
| How often | Always on, checked any day | A full crawl every 30 days |
| Better fit when | You want continuous trend data at a low monthly price and have a team to act on it | You need to know which content to build first, and for whom |
A tracker such as Peec AI is the better choice when you want always-on trend data, starting around $95 a month, and your team already has the capacity to act on what it surfaces. Choose a buyer-persona query model when the question is which of your buyers can't find you, at which stage, and what to publish to change it.
The 30-day action plan
Every report ends with a ranked plan, not just a diagnosis. The full plan here runs 28 items across three layers; see the sample action plan for how the deliverable is structured, across the three layers, on and off your own site. A representative sample follows.
Unblock AI crawlers on the docs subdomain, where robots.txt and a noindex tag are currently hiding the most-cited pages in the category.
Add Organization and SoftwareApplication schema to the homepage so engines can resolve what the company is and which category it competes in.
Rewrite the integrations page to answer "does it connect to [tool]" directly, in extractable language, for the integrations buyers name most.
Add a plan-comparison table to the pricing page so engines can lift tiers and limits cleanly into a comparison answer.
New page targeting the invisible early-funnel cluster: project management for distributed teams, written to match how buyers phrase the problem before they know the category.
New comparison hub: the category's workflow tools compared, structured so each row is a clean, citable answer to a head-to-head query.
What this is and why it matters
Layer 1 is technical fixes that gate whether AI can read the site at all. Layer 2 optimizes pages that already rank so they answer the question being asked. Layer 3 builds net-new content aimed at the invisible clusters, on your own site and on the third-party sources AI engines cite. The layers are ordered on purpose: a new page cannot win an answer if a crawler cannot reach it, so the gating fixes come first. The plan is sized to a team's real deployment capacity, not an idealized roadmap.
The strategic read
How the numbers add up to a single story, and what to do about it.
[Synthesis] [Client] reads as wins-when-present, absent-by-default. The brand appears in just 14% of the 150 buyer queries, yet it wins 62% of the ones it does enter and holds a positive 11–8 head-to-head record. The problem is reach, not persuasion. Visibility concentrates at the late funnel, near 28% at shortlisting, and collapses at the start where buyers first describe their problem, at 4%. The citation map shows why: G2, Capterra, and the category's go-to review sites cite Competitors A through C and never [Client], so the engines inherit that bias on every related question. By the time [Client] enters the conversation, the requirements have already been framed by someone else. The 28 prioritized actions attack that gap in three layers: 4 technical fixes that gate crawlability, 15 optimizations to pages that already rank, and 9 net-new builds aimed at the invisible early-funnel clusters and the third-party sources AI leans on.
Frequently asked questions
What people ask most when they first see a sample report.
Is this a real client's report?
No. It is a representative example built with illustrative data to show the shape of the deliverable. The company, competitors, queries, and numbers are all invented. A real report runs on your own category, your own buyers, your own competitors, and your own numbers.
How do I get a report for my company?
Start with your AI Visibility Foundation: the review of who buys, who you're compared against, and what AI can read on your site. A full AI Visibility Crawl then runs 150 buyer-intent queries across ChatGPT, Claude, Gemini, and Perplexity, scores where you appear per engine, maps the citation landscape, and returns a ranked 30-day action plan like the one shown here.
Which AI engines does the report cover, and is it broken out per engine?
Every report scores visibility across the four engines buyers actually use: ChatGPT, Claude, Gemini, and Perplexity, and it breaks the numbers out per engine, down to what each one said on each query and which sources it cited. The methodology is documented in How We Measure GEO.
What do the metrics mean?
Visibility is how often you appear at all. Win Rate is how often you're named the single top pick, scored across all 150 queries (including the ones you're absent from). Hit rate when visible is how often you win only among the queries you appear in, so it's always higher. Share of voice is who owns the answer across all brand mentions. Each term is defined with examples in the GEO Metrics Glossary.
What a buyer-persona query model includes
How many queries are tested?
A full AI Visibility Crawl tests 150 buyer-intent queries, and Resonate Labs runs each one on ChatGPT, Claude, Gemini, and Perplexity and scores every engine separately. In this sample, the 150 queries split across five personas, from 36 for the VP / Head of Engineering to 20 for the Founder / Exec Buyer.
How are personas defined?
Resonate Labs defines personas in your AI Visibility Foundation, the review of who buys, who you're compared against, and what AI can read on your site. You approve the personas, buying stages, and competitive set before anything is measured, and every query is written for one of those personas.
What buying stages are covered?
Seven: problem identification, solution exploration, requirements building, comparison, shortlisting, validation, and artifact creation. Every query is tagged with one stage, so the report shows where in the buying journey you appear and where you drop out.
No card required
See this on your own data
Start with your AI Visibility Foundation: the map of your buyers, your competitors, and what AI can read on your site. The full Visibility Crawl is the real report this sample previews, run on your category, your buyers, and your competitors, scored across every engine:
- Where you appear today across the four engines
- Who is winning the answers you are absent from
- The first moves that would change it