Entity analysis
What answer engines believe your brand is — led by the entity-accuracy gate per engine, with the facts they get wrong beneath it.
Overview
Entity analysis answers one question first: do answer engines describe you correctly, and as you — not as your parent company? The page leads with the entity-accuracy gate, per engine, then the facts the engines get wrong, then how engines relate and sometimes confuse your brand. The older composite scores (Entity Clarity, Topic Authority, Coherence) are kept as a closed appendix under the gate — they are indices of our own making, not the entity figure.
The page works on your existing answers — no configuration beyond captured answers and the facts you record in the Claim library.
The entity-accuracy gate
For every engine, one figure: correct and about you, in n of N checked brand answers.
- The denominator is the brand-question answers an accuracy check actually ran on. An answer nobody checked is evidence of neither correctness nor error, so it counts for neither side.
- "Correct and about you" means the check found nothing wrong and the answer described your brand rather than your curated parent company or legal entity. An answer describing your parent has not described you.
- An engine is judged once at least five of its questions have been checked. Below that it is listed as "too few questions checked", never as failing. An engine with no checked answer reads "not checked".
- The gate is met when at least four engines are at 90% or better. With fewer than four judgeable engines the gate is reported as not enough evidence to judge, not as failed. With no recorded facts it says so and points at the Claim library — the check has nothing to compare answers against.
The gate counts every brand question, whatever lens the page is filtered to — accuracy checks are not stored per question — and its card says so.
What engines get wrong
One table, one row per recorded fact a checked answer contradicted in the window: the fact, the truth you recorded, one dated example of what an engine said, the engines that said it, how many answers, and the fact's sign-off status. Each row opens the same fact in the Fact check, where a correction is drafted.
Relationships and confusions
- Related entities — other brands, companies or concepts engines mention alongside you, with mention counts and the tone of the association.
- Entity confusions — cases where an engine mixes you up with another entity. Detection is brand-anchored: a confusion phrase ("not to be confused with…", "sometimes confused with…") only counts when it appears next to your brand name in the same sentence.
Establishment checklist
The page also shows a five-step establishment checklist — the foundational moves that make your brand resolvable as a distinct entity:
- Website URL set on the project — the anchor every other signal joins on.
- Facts recorded in the Claim library — your ground truth for accuracy checking.
- Schema markup detected on your site — read from your latest website readiness check.
- Wikidata / Wikipedia entity established — the knowledge-graph identity engines resolve against.
- Name, address & phone recorded consistently — the consistency signal directories and engines cross-check.
Every verdict comes from a signal the platform has already stored, with three honest states:
| Status | Meaning |
|---|---|
| Done | A stored signal positively confirms the step. |
| To do | A stored signal shows the step is missing or thin. |
| Unknown | No stored signal can answer the question — never a guess. |
Appendix — the indices
Under the gate, closed by default, sit three composite scores kept for continuity:
- Entity Clarity (0–100) — a weighted composite of attribute richness, accuracy, cross-model consistency and relationship depth, minus a confusion penalty. The breakdown chart shows each component's contribution.
- Topic Authority (ETAS) — how strongly engines associate you with specific topics, with the topics they never associate you with.
- Coherence (BNCI) — how faithfully engines tell your brand story against your stated value propositions and differentiators.
They are appendix material because a composite of our own weights is not something a reader can act on directly; the gate and the wrong-facts table are.
How to use
- Read the gate: which engines are below 90%, and why (below the line, or too thin to judge).
- Open a wrong fact in the Fact check and draft the correction.
- Work the establishment checklist for any step that reads To do.
- Record facts in the Claim library — the check cannot run without them.
Plan requirements
Entity analysis requires the Advanced AI Insights feature, available on Pro-Individual plans and above.