Before AI can name you, it has to resolve you.
Directories, profile pages, and knowledge graphs are how AI engines decide which entity "your brand" refers to. A clean entity layer — canonical name, sameAs markup, Crunchbase and Wikidata consistency — makes every off-site corpus move compound instead of conflict.
The Layer Under the Off-Site Corpus
The companion piece — The Off-Site Corpus Playbook — covered the four surfaces AI engines retrieve from to assemble a B2B shortlist: review sites, analyst coverage, community, earned media. This piece is about the layer underneath those four — the layer that decides whether the model can recognize your brand at all.
Before an answer engine can name you, it has to resolve you. That means linking the brand mentioned on G2 to the brand discussed on Reddit to the brand quoted in an analyst note to the brand on your website, and concluding they are all the same entity. That resolution happens against a small set of structured sources — business directories, profile pages, knowledge graphs — that act as the connective tissue between every other retrieval surface. Get the entity layer wrong and the corroboration plan from the off-site corpus piece dissolves into noise.
Why Directories Show Up in Almost Half of AI Citations
Directories and profile sources punch above their weight in AI citations because of what they are, not what they say. A directory page is structured: a canonical name, a founding year, a category, a location, named leadership, links to other surfaces. The model can lift those fields and use them to deduplicate the same brand across the rest of the corpus.
A recent tracking study by Jasmine Directory across a few thousand commercial AI queries found business directories appearing in 47% of AI-cited sources across ChatGPT, Grok, and Gemini (October–November 2025). The headline number deserves a caveat — the study is published by a directory, and the source authority is medium — but the directional point is consistent with what is already understood about retrieval: structured, verifiable sources are weighted heavily, and directory pages are some of the most structured sources on the web.
What Entity Resolution Actually Means
Three behaviors sit underneath the concept, and missing any one of them is how brands lose AI citations they should have won:
- Deduplication. The model has to decide that "Trendscoded," "Trendscoded Inc.," and "trendscoded.com" are one entity, not three. It uses directory fields — exact name, URL, founders — to merge them.
- Disambiguation. If two real companies share a name, the model has to pick the right one. Directory metadata — category, location, founding year — decides which one gets named.
- Confidence. The more independent structured sources agree on the same set of facts about a brand, the higher the model's confidence in naming it. Conflicting facts across sources reduce confidence and, with it, citation likelihood.
If your LinkedIn page says you were founded in 2024, Crunchbase says 2023, and your homepage footer says "© 2022," the model has three sources disagreeing on one of the simplest facts about the company. That confusion does not get the brand cited; it gets a rival with three sources agreeing cited instead.
The Directory and Profile Stack a B2B Brand Should Control
Most B2B teams treat directory presence as an afterthought — a Crunchbase profile set up at the seed round and never touched again, a LinkedIn page someone in marketing nominally owns. For AEO, this stack is operating infrastructure. The model retrieves from it constantly.
- Crunchbase. The most important single profile for B2B brands. AI engines retrieve Crunchbase aggressively because of its structured fields — funding stage, founders, headcount, category, locations. A complete Crunchbase profile with current funding, an up-to-date description, and named founders is the single highest-leverage entity move for a venture-backed B2B brand.
- LinkedIn Company page. The model uses LinkedIn for headcount, leadership, and category descriptors. The "About" section is retrieved as canonical company self-description. Underused: pinning a recent post that names the buyer, the job, and the positioning gives the model an extra retrievable chunk per visit.
- Wikipedia and Wikidata. The gold standard for entity resolution. If a brand has a Wikidata entry, the major models treat it as the canonical reference and pull the other directory facts into agreement with it. Wikipedia eligibility is high (notability bar, third-party sourcing), but a Wikidata entry is more accessible and worth pursuing actively.
- Google Business Profile. Often dismissed by B2B teams as a local-search artifact. It is also a structured entity source the major models retrieve from for company facts. Worth setting up and keeping consistent with the rest of the stack.
- Industry directories — Crunchbase-adjacent and vertical. Owler, BuiltWith, StackShare for technical buyers, Glassdoor for the employer entity (yes, the model retrieves this), CB Insights mentions, and any vertical-specific directory in your category.
- G2 / Capterra as entity sources, not just review sources. The off-site corpus piece covered the review velocity angle. The entity-resolution angle is separate: the structured fields on those pages — pricing, founded, headquarters, employee count, category — feed the model's brand entity, not just its review summary.
The Five Consistency Rules
Across every directory and profile in the stack above, five fields must match exactly. They are the fields the model uses to deduplicate and disambiguate, and conflicts on any of them is where citation confidence leaks.
- Canonical name. Pick one. "Trendscoded," not "Trendscoded" on one surface and "Trends Coded Inc." on another.
- Founding year. Pick one. The model treats inconsistency here as a strong signal of low-quality data.
- Founders and leadership. Same names, same titles, same spellings.
- Category descriptor. Pick a single primary category descriptor and use it on every surface. "AI Answer Intelligence Platform" everywhere, not "AEO platform" on G2 and "marketing intelligence software" on Crunchbase.
- Canonical URL. One URL, no www / non-www confusion, no trailing slash inconsistency, no http leftovers.
None of these are hard. They are tedious. Most B2B teams have not done a directory consistency audit in years and would find at least three of the five misaligned across their stack.
SameAs: Telling the Model the Surfaces Are You
The most underused entity move is structured data on the owned site. A schema.org Organization block in the head of the homepage, with a sameAs array listing every profile URL — Crunchbase, LinkedIn, Wikidata, G2, Capterra — explicitly tells the model that those surfaces are the same entity. The model does not have to infer the connection; the markup states it.
Search Atlas's research on schema for AEO directly validates this mechanism: AI agents check for consistent sameAs data across sources before citing a brand. The @graph pattern — a schema.org structure that links multiple entities into a single traversable network — makes that verification cheap for the model to perform, and makes the brand more retrievable in the process (Search Atlas).
This is the single fastest entity-layer move a team can ship. One markup block, one merge, one declaration of "all of these surfaces are us." Most B2B sites do not have it. The ones that do reduce entity-resolution conflicts to near zero overnight.
Common Failure Modes
Four failure modes dominate:
- Stale Crunchbase after a pivot. The product changed; Crunchbase still describes the old category. The model retrieves the old category and treats the brand as off-target for the new buyer query. Fix: quarterly Crunchbase review on the AEO operating cadence.
- Slightly different brand name on LinkedIn vs Crunchbase. One says "Trendscoded," the other "Trendscoded, Inc." The model can usually merge — but it down-weights confidence, and the citation goes to the rival whose entity is cleaner.
- No Wikidata entry. A surface that costs nothing to create and that the major models treat as canonical. Brands that ignore it are leaving the strongest single entity-resolution signal on the table.
- No sameAs markup on the owned site. The brand has eight profile surfaces and never tells the model they are all the same entity. The model infers, imperfectly, what could have been declared.
| Fragmented entity | Resolved entity |
|---|---|
| Name varies across LinkedIn, Crunchbase, G2 | Single canonical name, identical everywhere |
| Founding year disagrees across surfaces | One founding year, one source of truth |
| Different category descriptor on each profile | One primary category descriptor used everywhere |
| No Wikidata entry | Wikidata entry linked from sameAs markup |
| No schema.org Organization block on the homepage | Organization block with sameAs listing every profile URL |
| Stale Crunchbase reflecting the pre-pivot brand | Crunchbase audited quarterly on the AEO cadence |
How This Pairs With the Off-Site Corpus
The off-site corpus piece said the model retrieves from review sites, analyst coverage, community, and earned media. This piece adds the layer beneath it: the model has to know which entity those retrievals belong to before it can name the brand in an answer. A great G2 profile with strong reviews does no work for a brand whose entity is fragmented across the web. Conversely, a brand with a clean entity layer compounds every off-site corpus move, because every retrieval lands on the same unified entity.
The sequence inside a quarterly AEO program: audit the entity layer first (a one-week fix that compounds permanently), then run the off-site corpus motion against it. A team that runs the corpus motion first builds noise into the model's picture of the brand and then spends quarters trying to undo it.
And once the entity layer is clean, the highest-leverage corpus move is reviews. Codal's research on AI search visibility puts it directly: review content is the most powerful, and most underutilized, AI visibility signal a brand has. The entity layer makes the model recognize you; review content gives the model something specific and verifiable to retrieve about you. Together they are the foundation of every other off-site move (Codal).
The Standard to Hold
A B2B marketing team running AEO seriously should be able to answer four questions about its entity layer at any moment: is our canonical name identical across our top five profile surfaces, is our Crunchbase profile current as of this quarter, is there a Wikidata entry, and is the sameAs markup on the homepage pointing to every profile we control. If the answer to any of those is "we would have to check," the entity layer is leaking citations the off-site corpus work is paying to earn.
Directories used to be a checkbox in a marketing onboarding doc. They are now the layer that decides whether the model recognizes the brand. The teams that treat them that way get cited; the teams that treat them as administrative do not.
