Can AI Explain What Your Company Does? The 6-Axis Entity Footprint Audit (2026)

TL;DR: Ask an AI to "explain what this company does" and the holes in its answer map directly onto the holes in the evidence you have left behind. Across 231,347 LLM responses, entities holding entity authority, third-party validation and community discussion together averaged about 12,174 mentions, versus roughly 1,565 for those holding none — a 7.8x gap (Seer Interactive). Visibility is not built by amplifying one signal but by having several kinds of evidence exist at the same time. This article lays out how to score the evidence AI uses to understand your organization across six axes, mapped to the evidence channels that actually exist in the Korean market (as of 2026-07-28).

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Three-line summary

• The next step in AI visibility diagnostics is not "were we mentioned" but "were we described correctly." Checking whether your brand name appears tells you nothing about why it doesn't. You have to make AI describe you, then dissect that description.
• Stacking one kind of evidence does little on its own. Entities holding authority, third-party validation and community discussion simultaneously showed a 7.8x mention gap over those holding none.
• What evidence you need depends on your vertical and the question type. Factual questions pull institutional sources; judgment questions pull expert and community voices. That is what decides which axis to fix first.

What is an entity footprint?

An entity footprint is the full body of digital evidence AI systems draw on to understand an organization. The Search Engine Land article that framed the concept spans owned signals (website, profiles), customer signals (reviews, testimonials), third-party signals (press, awards) and ecosystem signals (partnerships, associations).

"the body of digital evidence that contributes to how AI systems understand an organization" — Rich Sanger, Search Engine Land, 2026-07-27

The essential point is that this is a sum of scattered evidence, not a single page. Where search-era SEO was about making one page understood, the center of gravity has moved to making one organization understood. That is why polishing your own site alone leaves a stretch of the description unfilled.

Why one axis of evidence is not enough

The most direct quantitative basis comes from Seer Interactive's large-scale observation during the 2026 Winter Olympics: 231,347 responses collected across 7 AI platforms over 52 days, tested against five pre-registered hypotheses.

Signal combination	Average AI mentions
Entity authority + third-party validation + community discussion — all three	~12,174
None of the three	~1,565
Gap	7.8x

What matters here is not the multiple itself but the structure. The three signals answer different questions. Entity authority establishes what the entity is; third-party validation establishes that the claim holds up outside your own site; community discussion establishes that real people engage with it. Grow only one and the other two questions stay unanswered.

In the same study, Wikipedia recency (7-day pageviews) was the strongest single predictor at rho=0.810. That is a correlation, not a cause. Creating an article does not create views — views follow entities that are genuinely discussed. Invert the order and you only spend money.

One caveat should be explicit. This study measured athletes and events, not corporate brands. From a knowledge-graph perspective people and organizations are both entities, so the structure transfers; the multiple itself cannot be asserted to hold for brands. Whether the same structure holds in your category is something to confirm by measuring it.

Different question types demand different evidence

The more operationally useful finding from the same study is that the types of sources cited shift with the nature of the query.

Query type	Sources typically cited
Factual ("when, where, how much")	Institutional sources, 76.4%
Judgment ("which is better, and why")	Expert voice +873%, social/UGC +165%, prestige editorial +157%

Read practically: when a customer asks about facts concerning your company, AI looks for official records — business registration, certifications, public registries. When they ask for a judgment, it looks for what people said — expert commentary, community reviews, editorial assessment.

So collapsing everything into "we don't show up in AI" leads you astray. Being omitted from factual questions and being omitted from judgment questions have different causes and different remedies. That is why the audit splits into six axes.

The six axes — what you are scoring

The framework proposed in the Search Engine Land article divides an organization into six dimensions scored 0–5 (0 = no meaningful evidence, 5 = exceptional understanding). Each axis has a question it must be able to answer.

Axis	The question AI must be able to answer	Symptom when it is empty
Identity	What does this organization do?	Descriptions vary run to run, or the vertical is defined vaguely
Differentiation	What makes it different?	Listed only as "one of several vendors"
Evidence	Is that claim independently confirmed?	Your own wording is echoed back, or omitted entirely
Consistency	Do platforms reinforce the same understanding?	Different platforms state different things
Relationships	Where does it sit in the industry ecosystem?	Never surfaces in the candidate set for category questions
Specialization	What is it genuinely known for?	Mentioned, but framed against strengths you don't have

The method is simple. Ask several AI systems to describe your company from public information only, then decompose the answers along the six axes. Whichever axis the answer hedges on is the axis where evidence is missing. Looking at one model alone cannot separate that model's bias from a genuine gap, so read at least several together.

Mapping the six axes to Korean evidence channels

The original framework assumes U.S. channels — Google Business Profile, domestic press coverage, speaking engagements. Many of those do not carry over, so here are the channels that actually fill each axis in Korea.

Axis	Evidence channels that fill this axis in Korea
Identity	About and business-registration pages, Naver Smart Place, company profiles on recruiting platforms (Saramin, JobKorea), Wikidata entries
Differentiation	Methodology and feature documentation, patent and trademark gazette records (KIPRIS), technical certifications, self-published data
Evidence	Korean press coverage, government and institutional certifications (Venture Business, Inno-Biz, GS certification), public-sector project records, awards
Consistency	Company name, vertical, address and representative matching across site ↔ Smart Place ↔ job postings ↔ social profiles
Relationships	Association and trade-body member lists, partner pages, exhibition participation records, government support program selection lists
Specialization	The context of community mentions (Naver Cafés, open chats, developer forums), YouTube reviews, recurring themes in blog write-ups

The axis where Korean brands most often show a gap is Relationships. Documents stating "we belong to this field" in a machine-readable form — association rosters, program selection lists — are surprisingly often absent. Failing to surface as a candidate for category questions is usually not a content shortage but the absence of this connection.

Evidence runs the opposite way: often already earned, but unusable. Certifications and awards buried inside PDFs or images are not read. Moving them into text fills that axis on its own.

Which axis to fix first depends on your vertical

Your vertical largely decides where to start. In Similarweb's May 2026 data (U.S. desktop) as compiled by Search Engine Journal, the mix of sources AI cites differs sharply by industry.

Vertical	Citation source mix
Beauty	Retail and e-commerce sites 54.7%
Travel	Reviews and UGC 54.1%
Finance	Finance-specific sites 36.6%, news publishers 28.0%

The same "improve AI visibility" goal therefore means product-data consistency across retail channels for a beauty brand, accumulated reviews for travel, and listings in trade media and industry sites for finance. This is why lifting a playbook from another vertical fails. Note that these figures come from a U.S. desktop sample and may not match the Korean industry mix — treat them as directional and confirm the actual weighting in your own category.

Discourse volume, not performance, drives visibility

The most telling number in the study is its geographic bias.

In neutrally worded prompts the United States appeared 36.8% of the time, while Norway — the actual medal leader — appeared 16%. — Seer Interactive

The side that was talked about more online, not the side that performed better, showed up more often. That structure works squarely against Korean brands: a better product with thin English-language discourse loses ground in the model's default candidate set. Put the other way, claiming Korean-language discourse in your own category is work that has to happen separately from accumulating results.

One more finding. Three weeks after the facts had reversed, 1 in 5 factually correct responses still repeated the earlier narrative, and ambiguous outcomes were 3x harder to correct than clean reversals. This is why answers do not change the moment you fill a gap — and why this runs as a cycle of audit → remediate → re-measure rather than a one-time fix.

Turning audit results into execution

1. Make AI describe you — ask several AI systems to explain your company from public information only, and record the answers verbatim.
2. Decompose into six axes — mark how well each axis is filled, 0 to 5. The deliverable is not the score but which axis is empty.
3. Check the channels for the empty axes — use the mapping table above, separating what exists but isn't readable from what doesn't exist at all.
4. Fix what isn't being read first — surfacing certifications and track record you already hold as text is faster and cheaper than manufacturing new mentions.
5. Re-measure to confirm change — repeat the same questions and see whether the description actually moved.

For the measurement steps, RanketAI's AI brand visibility analysis tracks how your brand is described across questions over repeated runs, and competitor comparison places rival brands side by side to show what evidence accompanies them in the same questions. Single answers fluctuate, so read the trend.

Choices that split by situation

• Fix the site first, or build outside mentions first? — If Identity and Consistency score low, start with your own properties. If Evidence and Specialization score low, adding pages to your site will not fill them. The axes answer the question for you.
• Already hold plenty of certifications but see no effect? — Usually a format problem. Information inside images and PDFs is not read. Moving it into body text outranks earning another certification.
• A new brand with all six axes empty? — Start with Relationships. Making the fact that you belong to a field machine-readable — association membership, partner page listings — buys entry into the candidate set at the lowest cost.
• Where should a limited budget go? — Into consistency of existing information before buying new mentions. If your naming is inconsistent, newly created mentions won't resolve to the same entity anyway.

Frequently asked questions

AI does describe our company, but the description is thin. Is that also a problem?

Yes, though it is a good state to diagnose from. Getting a description means entity resolution worked, so Identity at least holds. Thinness usually points to gaps in Evidence and Specialization. When externally verifiable grounds and human discussion are scarce, AI fills the space with generalities.

Do all six axes have to be filled at once?

No. The finding that entities holding all three signals showed a large gap means the effect is strongest when they coexist — not that you must start everything simultaneously. Working through the empty axes in order of what is cheapest to fill is the realistic path.

Is the audit score an absolute standard?

No. Treat the 0–5 scale as a relative measure for prioritizing between axes. What matters is not the total but which axis is low, and the operational metric is whether AI answers changed after you acted.

Will creating a Wikipedia article solve it?

Whether it is actually read and discussed matters more than whether it exists. The strong predictor in the study was recent pageviews, not the presence of an article. Creating one before you meet notability requirements invites deletion and does not function as evidence either.

How long until AI answers change after we act?

Not immediately. In the same study, one in five correct responses still held the earlier narrative three weeks after the facts changed. Model refresh and index cycles overlap, so periodic re-measurement beats expecting a single pass to settle it.

Do we need English content as well?

It depends on your target market. If your customers are domestic only, claiming Korean-language discourse comes first. That said, the geographic-bias figures above show AI defaults lean toward English-language discourse, so English evidence has to be built as a separate asset if you are targeting customers abroad.

Further reading

• The Source Gap — Finding the Third-Party Sites That Cite Only Your Competitors — how to find the specific placements that fill the Evidence axis. This article asks what is empty; that one asks where to publish.
• Entity SEO Returns — Why Wikidata and Knowledge Graph Matter Again in the LLM Era — building the entity assets that underpin the Identity axis.
• AI Visibility Isn't an SEO Problem — It's Organizational Alignment — why the Consistency axis goes empty, viewed from the organization side.
• When AI Recommends Competitors but Omits Your Brand — the signature symptom of an empty Relationships axis, and how to respond.
• AI Search Cannot Verify Your Business — a field audit of how Identity and Evidence go empty for physical businesses.