Disambiguation assessment
A review of every place your name, your founders' names, and your product names could be confused with something else, and what additional signal would resolve the ambiguity.
AEO service
Entity optimization resolves every mention of you into one trusted identity
Before a model cites you, it has to decide that the ten scattered mentions of your name across the web all point to the same organization or person. Inconsistency at that step quietly costs citations no page-level fix can recover.
An entity, in this context, is a specific, identifiable thing a machine can reason about: an organization, a person, a product, a location. Entity optimization is the work of making sure that when your name, your founder's name, your address, or your product appears anywhere on the web, a model can confidently resolve it back to one consistent identity rather than treating it as an ambiguous string of text or, worse, confusing it with a similarly named but unrelated entity.
Disambiguation is the core problem. Search and language systems do not process names, they process entities, and any name that could plausibly refer to more than one thing needs enough surrounding signal to resolve correctly. A firm named after a common word, a founder who shares a name with someone more famous, a product name reused across categories: all of these create ambiguity a model has to resolve before it can safely attribute a claim to you, and ambiguity it cannot resolve confidently tends to get treated cautiously or ignored.
Consistency across identifiers does the resolving. The same organization name spelled the same way everywhere, the same address and phone number on every directory and profile, sameAs links in structured data pointing to your verified social and knowledge graph profiles, and author bylines tied to real, consistently described people all give a model corroborating evidence that the mentions belong together. Get one of these wrong, list a different suite number on one directory, spell the company name differently on one profile, and you have handed the model a reason to treat that mention as a separate, unverified entity.
This work sits upstream of citation, not downstream of it. A model that cannot confidently resolve who you are will rarely cite you as an authority on anything, no matter how well the content on your own site is written, because the risk of misattribution is exactly the kind of error these systems are tuned to avoid.
What it is
What resolves an entity, and what breaks it
Knowledge graph presence is the clearest external signal that an entity has been resolved. When a search engine has enough corroborating evidence about an organization, it builds a panel or graph entry for it, drawing on structured data, third-party corroboration, and consistent identifiers. Getting listed there is not something you request directly, it is an outcome of the consistency work done everywhere else, and its presence is a strong indicator that other systems can resolve you with similar confidence.
sameAs links, a property in schema.org markup, are a direct, machine-readable statement that a given URL and a given external profile, such as a verified social account or a knowledge base entry, refer to the same entity. They are cheap to implement and easy to get wrong: pointing sameAs at a stale, unclaimed, or inconsistent profile actively damages confidence rather than helping it, so an audit of what those links actually point to matters as much as adding them.
NAP consistency, name, address, and phone number held identical across every place they appear, remains one of the highest-leverage and most overlooked signals, especially for any organization with a physical presence. A single inconsistent listing rarely causes damage on its own, but a pattern of small inconsistencies across directories, profiles, and citations adds up to genuine ambiguity a model has no efficient way to resolve in your favor.
Author and organization identity connect content to a real, describable source. A byline with a named person, a consistent bio, and a trail of other content under that same name gives a model something to corroborate. Unattributed content, or content attributed inconsistently across a site, forces the model to evaluate the claim on its own merits with no identity behind it to lend it weight, which is a harder bar to clear.
Fit
Who this is for, and who it is not for
We would rather say no early than sell a program that cannot work.
Good fit
- Organizations with a name, founder, or product that could plausibly be confused with something else.
- Multi-location businesses where NAP data has drifted across directories over the years.
- Sites publishing expert content without consistent, corroborated author identities behind it.
Not a fit
- Brand-new organizations with no third-party mentions yet to corroborate against.
- Anyone expecting a knowledge graph panel to appear on a fixed timeline. It does not work that way.
- Sites unwilling to attribute content to real, named people.
Deliverables
What you get
A full accounting of where your identity is inconsistent, and the fixes that resolve it.
NAP consistency audit
Every directory, profile, and citation checked against your canonical name, address, and phone number, with a corrected list of every discrepancy found.
sameAs implementation
Structured data linking your site to verified external profiles, added only after confirming each target profile is active, claimed, and actually consistent with your identity.
Author identity system
Consistent bylines, bios, and author schema across your content, so real people accumulate a corroborated identity instead of publishing under inconsistent or anonymous attribution.
Corroboration gap map
A list of facts about your organization that currently exist on only one domain, prioritized by how much a second independent mention would help resolution.
Knowledge graph readiness review
An honest assessment of how close your current signal set is to what tends to precede a knowledge panel appearing, without promising a date it will show up.
How we run it
How we run it
We map what exists before we change anything, since fixing consistency requires knowing every inconsistent instance first.
Step 1: Inventory every mention
We collect every directory, profile, citation, and structured data instance of your organization's name, address, and phone number currently live on the web.
Step 2: Identify disambiguation risk
We check whether your name, founders, or products could plausibly resolve to something else, and what specific signal is missing to prevent that.
Step 3: Correct and consolidate
Inconsistent listings get corrected to a single canonical format, and sameAs links get added or fixed to point at verified, active profiles.
Step 4: Build author identity
Bylines, bios, and author schema get standardized across content so named contributors accumulate a consistent, corroborated identity over time.
Step 5: Monitor for drift
New directory listings, partner mentions, and press coverage get periodically checked against the canonical identity so consistency does not quietly decay.
What is entity optimization in AEO?
Entity optimization is making sure every mention of your organization, name, address, and key people resolves to one consistent identity a machine can trust. It relies on disambiguation, sameAs links, NAP consistency, and author identity, and it matters because unresolved entities rarely get cited even when their content is accurate.
It is upstream work. A model has to trust who you are before it will trust what you say, and that trust is built from corroboration across many independent sources, not from any single page.
How do machines decide two mentions of a brand are the same entity?
They weigh corroborating signals: identical or near-identical names, matching address and contact data, structured data sameAs links pointing to verified profiles, and consistent context such as industry, location, or associated people. Agreement across several independent sources resolves the match with confidence, while conflicting or sparse signal leaves it ambiguous.
- A single inconsistency rarely breaks resolution, but a pattern of them does.
- sameAs links only help when they point at active, verified, consistent profiles.
- Third-party corroboration carries more weight than anything stated on your own site alone.
Where this connects
Where this connects
Entity work is foundational to the rest of the pillar, not a parallel track.
This program sits alongside five others inside the AEO pillar and it is usually addressed early, since citation and structure work both depend on a resolved identity underneath them.
sameAs links and organization data are implemented as part of schema markup so entity data and structured data get built together rather than as separate efforts.
Corroboration gaps identified here directly affect whether a claim gets grounded and cited, the mechanics covered under AI search visibility so the two programs share findings.
Whether entity fixes actually move citation behavior gets measured over time through AI citation tracking rather than assumed from the fixes alone.
Third-party corroboration often comes from an active, claimed presence across platforms, which is why consistent social profiles feed directly into how confidently a model resolves your identity.
Questions
Entity optimization questions we get asked
Find out whether machines can actually tell it's you
We audit every mention of your name before recommending a single fix.