Entity & Knowledge Graph
We make your brand resolvable, so a model knows exactly who you are before it decides whether to recommend you.
- Pillar
- AI Visibility
- Engagement
- Retained, monthly
- Ownership
- One senior strategist
- Reporting
- Weekly digest, monthly review
- Pairs with
- Generative Engine Optimisation, Technical Audit & Schema
Entity and knowledge graph work is the practice of making a brand unambiguously identifiable to machines through structured claims, schema and disambiguation against similarly named organisations.
Why entity resolution matters now
A model that cannot tell you apart from a similarly named company will default to the one it can resolve. Ambiguity is the most common and most fixable cause of AI invisibility.
Resolution happens before generation
Answer engines identify the entities in a question before they compose prose. An unresolvable brand is filtered out before the answer is written.
Descriptions drift
Directories, aggregators and old press describe you as you were three years ago. Models average those descriptions, so stale claims quietly become the official version.
It is the cheapest layer to fix
Schema, claim consistency and disambiguation are structural work with a short cycle time. They also make every other visibility investment attributable to the right brand.
How does Entity & Knowledge Graph work?
Audit the footprint
Wikidata status, schema coverage, disambiguation risks and identity drift.
Build the claim set
Property-level plan for what a model needs to know and where it will read it.
Deploy
Schema, claims and consistent identity across owned and earned surfaces.
Re-audit quarterly
Catch and correct claim erosion before it affects recommendation.
What is included?
Canonical definition
A single sentence describing what you are and who you serve, deployed consistently everywhere.
Wikidata entity work
Entity creation or claim expansion: inception, headquarters, industry, products.
Schema deployment
Full JSON-LD coverage across every page type, validated.
Disambiguation
Collision resolution against look-alike brands by segment, geography and use case.
Knowledge graph submission
Google and Bing entity surfaces where applicable.
Drift audit
Quarterly review of how third-party sources describe your entity.
How is entity work measured?
Whether models and knowledge bases resolve you correctly, describe you accurately, and stop confusing you with anyone else.
- Resolution accuracy: whether a model returns you, and only you, for your brand name
- Description accuracy: category, audience, geography and offering stated correctly
- Claim coverage: how much of your structured record is present and verifiable
- Collision rate: how often a similarly named entity is returned instead
- Schema validity: coverage and error rate across priority page types
BASELINE FIGURES · TO SUPPLY PER CLIENT
Where does it apply?
How does this differ from structured-data SEO?
| Dimension | Structured-data SEO | RankingBite |
|---|---|---|
| Goal | Rich results in search listings | Correct resolution inside AI retrieval |
| Scope | Markup on your own pages | Owned markup plus third-party records |
| Disambiguation | Not addressed | Core of the work |
| Maintenance | Set once | Audited for drift on a schedule |
| Success measure | Rich snippet appears | A model describes you correctly and consistently |
Is entity work right for you?
A good fit when
- Your brand name collides with another company, product or common phrase.
- Models describe your category, pricing or market incorrectly.
- You have rebranded, merged or changed your offering recently.
Not the right service when
- Your entity already resolves cleanly and descriptions are accurate.
- You have no third-party presence at all yet, coverage has to come first.
- You want a one-off schema deployment with no follow-up.
Entity work is included in every AI visibility engagement we run. It is available standalone where identity is the only broken layer.
Proof
Four documented engagements. Figures are confirmed with each client before publication rather than estimated.
Frequently asked questions
Do I need a Wikidata entity?
For enterprise brands competing in AI surfaces, almost always. Wikidata is the most-cited knowledge base across frontier models.
What schema types matter?
Organization and Product form the spine. FAQPage, HowTo, Article, Author, Service and BreadcrumbList cover the rest.
Will entity work alone lift citations?
It is necessary but not always sufficient. Brands with good content and weak entities often see the largest single gain from this work.
How do you handle sub-brands?
Each gets its own canonical entity linked via parentOrganization and subOrganization properties.
Do we need a Wikidata entry?
It helps, because it is one of the most widely reused structured records on the web. It is not sufficient on its own, and it has to be supported by verifiable third-party sources rather than created in isolation.
How long does entity remediation take?
Owned-surface work, schema, consistent descriptions, disambiguation copy, ships in weeks. Third-party record corrections depend on each platform, and knowledge base acceptance can take a couple of months.
What about sub-brands and product lines?
Each gets its own record and description, linked to the parent organisation. Collapsing them into one entity is a common cause of models attributing your product features to the wrong name.
Will fixing entities alone increase citations?
Sometimes substantially, where identity was the only broken layer. Where content is thin or third-party coverage is absent, entity work makes those deficits visible rather than solving them.
This depends on the authority pillar.
Corroboration is what turns a citation into a recommendation, and it is earned the same way ranking authority is, through independent editorial coverage.
Related services
Is EKG the right place to start?
Send your domain and what you are trying to fix. We will tell you whether entity & knowledge graph is the constraint, and, candidly, whether we are the right firm for it.