Make every page that describes your agency agree.

AI search does not just rank pages; it resolves entities. Before an engine cites or recommends your agency, it tries to decide whether the many mentions of your name across the web point to one real, distinct organization or to several fuzzy ones. It does this by cross-referencing the facts about you — name, location, markets, people, links — across every source it has read. When those facts line up, you read as a confident entity worth surfacing. When they conflict, you read as a reliability problem, and the safest thing a model can do is pick someone whose story is consistent.

This matters more for an agency than for most businesses, because roughly 85% of AI brand mentions come from third-party pages rather than your own domain. The version of your agency an engine describes is assembled mostly from what other sites say about you: directories, marketplaces, press, social profiles, industry lists. If those third-party facts disagree with your homepage — or with each other — the engine has no clean signal to anchor on. Entity consistency is how you make the off-site evidence agree with the on-site evidence, so the assembled picture is the one you intend.

It is the same discipline behind knowledge-graph readiness and entity disambiguation, applied to your own footprint. The goal is not to game a ranking; it is to remove the contradictions that make a model hesitate. This guide walks the three layers where your facts live, the fields that must match across all of them, and a checklist to run when they drift apart.

Your agency's identity is spread across three layers, and an AI engine may draw from any of them. The first is your owned site: the About page, footer, contact block and, critically, your Organization structured data. Google's own documentation notes that properties like sameAs are used behind the scenes to disambiguate your organization from others, so the machine-readable facts on your site are the anchor the rest hang from. If your schema disagrees with your visible copy, you have contradicted yourself before anyone else gets involved.

The second layer is your third-party profiles: independent directories, review marketplaces such as Clutch and DesignRush, professional networks, and any industry list you appear in. These are the highest-volume source of what engines read about you, and they drift the fastest — an old address, a former company name, a market you have dropped, a specialty you have added but never updated. A criteria-based directory entry records only publicly evidenced facts, but it can still lag reality if you never confirm it; that is exactly what confirming or correcting a directory listing fixes.

The third layer is the knowledge-graph layer: Wikidata, Wikipedia where it applies, and the entity records engines build internally by reconciling everything they have read. You do not edit this layer directly, but you shape it. It reflects the consensus of the first two layers, so it becomes accurate only when your site and your profiles already agree. Getting all three to tell one story is the whole job; the checklist below is how you run it.