Entity coverage maps: a working template

Before you can improve a brand's AI search visibility, you need to understand what the model currently thinks about it. That sounds obvious. In practice, almost no one does it. They go straight to producing new content, fixing schema, or chasing editorial links without knowing what the gap is or whether those interventions address it.

An entity coverage map is the diagnostic that precedes the treatment. Here's the exact template I use, with scoring, and what the output tells you about where to focus.

What an entity coverage map is.

An entity is any distinct, nameable thing a model can hold a representation of: a company, a product, a person, a concept, a place. A B2B brand has multiple entities to manage: the organisation itself, its primary products, its key people, and the category concepts it wants to be associated with.

An entity coverage map is a structured inventory of what a model currently knows about each of those entities: how it describes them, how confidently, and where the description comes from. It is not a keyword gap analysis. It is not a backlink audit. It is specifically about the model's representation, which is built from training data and (for some engines) live retrieval rather than from your sitemap or your link profile.

The map tells you three things: what the model gets right, what it gets wrong, and what it doesn't know at all. Each has a different fix.

The five entity dimensions to map.

For a B2B company, I map across five dimensions. Each dimension gets a score of 0–2: 0 = absent or wrong, 1 = present but incomplete or inconsistent, 2 = accurate and consistent across sources.

Dimension 1: Category placement

Ask a model: "What category does [brand] operate in?" and "What problem does [brand] solve?" Compare the answers to your own positioning. Are you placed in the right category? If you build observability software but the model consistently calls you a "DevOps tool," that's a category placement gap, which means you're being surfaced (or not surfaced) for a different set of buyer prompts than the ones that should trigger you.

Dimension 2: Customer description

Ask: "Who is [brand] designed for?" and "What size of company typically uses [brand]?" Mismatches here (a model that describes your enterprise product as SMB-focused, or vice versa) affect whether you appear in prompts from the right buyer segment. This gap is almost always traceable to an inconsistency in how you're described across external sources.

Dimension 3: Differentiation accuracy

Ask: "How is [brand] different from [primary competitor]?" The model's answer should reflect your actual differentiation, not a generic feature list or an outdated characterisation. If the model's differentiation narrative contradicts your current go-to-market positioning, it's usually because the older positioning is more strongly represented in the training data: it appeared in more documents, more consistently, for longer.

Dimension 4: Key people

Does the model know who founded the company? Who leads it? Are those people described accurately? For B2B brands, executive credibility is part of the entity footprint. A model that doesn't know the CEO's background or confuses them with someone at a different company has a person-entity gap that affects the brand's overall source-worthiness perception.

Dimension 5: Factual accuracy

Founding date, headquarters, funding stage, key partnerships, major product releases. Errors here (particularly a wrong founding date or a description that reflects a product pivot from three years ago) are usually fixed fastest via Wikidata and Wikipedia corrections, since those are the reference sources models weight most heavily for factual claims about organisations.

/ The template

Five dimensions. Five questions per dimension. Score each 0–2. Total possible: 50. A score below 30 means the model has material gaps or inaccuracies about your brand that will directly suppress citation rates regardless of your content programme. A score of 40+ means the entity foundation is solid and the work shifts to source-surface expansion.

How to run the map in practice.

Run the five dimensions across three engines: ChatGPT, Perplexity, and Claude. That's fifteen question sets. Take notes verbatim: don't paraphrase what the model says, because the exact language it uses is the data. You're looking for the frequency of specific descriptions, not just whether the model gets it right or wrong.

Then compare what each model says against three sources: your own homepage copy, your Wikidata entry, and your most-cited Wikipedia text (if a page exists). The gaps between what the model says and what your authoritative sources say are your intervention targets.

One important nuance: not all gaps are fixable by changing your content. If a model consistently describes you in a way that reflects how you were positioned two years ago, that description is likely dominant in the training data, and the only way to shift it is to produce enough consistent, authoritative new signals that the newer description eventually becomes the more-cited one. That takes time. The short-term fix is Wikidata and Wikipedia; the medium-term fix is the editorial and reference source work described in building source-worthiness for AI search.

What to do with the gaps you find.

The output of an entity coverage map is a prioritised list of interventions, not a content plan. The interventions fall into three buckets:

Bucket 1: Reference source corrections (fast, high leverage)

Wrong or missing Wikidata entries. Outdated Wikipedia descriptions. Crunchbase entries with the wrong industry classification. These can be fixed in hours and affect the model's entity profile within weeks as training data refreshes and retrieval indexes update. Always fix these first.

Bucket 2: Description convergence work (medium-term)

If the model's description of your category placement or customer type is inconsistent across sources, the fix is getting your current, accurate description into as many authoritative sources as possible: consistently, specifically, and repeatedly. This means editorial outreach that targets the right characterisation, conference talks where you describe yourself clearly, and named-author content on your own site that uses the exact language you want models to learn. This is the core of what entity coherence work looks like in practice.

Bucket 3: Factual gaps (variable speed)

Missing information (the model doesn't know your founding story, your key partnerships, your flagship customer logos) is slower to fix than inaccurate information, because you're building net-new model knowledge rather than correcting existing knowledge. The mechanism is the same (reference sources, editorial, community) but the timeline is longer and the results are less predictable.

Entity coverage map · scoring sheetTemplate
Dimension              | Question asked                          | ChatGPT | Perplexity | Claude | Notes
-----------------------|-----------------------------------------|---------|------------|--------|------
Category placement     | What category does [brand] operate in?  |   /2    |    /2      |  /2    |
Customer description   | Who is [brand] designed for?            |   /2    |    /2      |  /2    |
Differentiation        | How is [brand] different from [comp]?   |   /2    |    /2      |  /2    |
Key people             | Who founded/leads [brand]?              |   /2    |    /2      |  /2    |
Factual accuracy       | When was [brand] founded? HQ? Stage?   |   /2    |    /2      |  /2    |
-----------------------|-----------------------------------------|---------|------------|--------|------
Per-engine total       |                                         |  /10    |   /10      | /10    |
Overall total          |                                         |         |   /30      |        |
                                                                                                 
Score guide:  0–15 = significant gaps, entity work is the first priority before any other GEO    
              16–22 = moderate gaps, targeted reference-source fixes will move citation rate      
              23–28 = solid foundation, focus shifts to source-surface expansion                 
              29–30 = strong entity foundation, marginal gains from ongoing editorial work

The map is a starting point, not a complete audit. For a full picture of AI search visibility (including citation share measurement, source-surface analysis, and a prioritised 90-day roadmap), that's the GEO audit service. But the five-dimension map will tell you in two to three hours whether you have an entity problem, and that's the question to answer before anything else.

NOTES

The scoring rubric is a practical working tool, not a statistically validated instrument. The 0–2 scale is deliberately coarse: precision on a subjective assessment creates false confidence.
Models are non-deterministic: the same question asked twice may produce different answers. Run each question at least twice and score based on the consistent response, not an outlier.
Entity representations in models change over time as training data is refreshed. Run the map quarterly rather than once, particularly in categories where competitive positioning shifts rapidly.

/ Frequently asked

Should I do this for my competitors too?

Yes, at least for your two or three closest competitors. Running the same map on competitors tells you where they have stronger entity foundations than you, and usefully where they have gaps you can exploit by building stronger coverage in the same areas.

What if a model gives completely different answers every time I ask?

That inconsistency is itself a finding. It means the model has weak or conflicting signals about your brand and is effectively guessing. Score that dimension as 0 or 1 and treat it as a high-priority gap: a model that can't give a consistent answer is unlikely to cite you confidently in response to buyer prompts.

Does this work for personal brands as well as company brands?

Yes. The five dimensions apply to people-entities as well as organisation-entities: the category becomes "area of expertise", the customer description becomes "who reads or follows this person", and so on. The reference sources shift slightly: LinkedIn profile, speaking page, Wikipedia if applicable, and editorial bylines in relevant publications.

Entity coverage maps: a working template.