When AI confuses your brand with a look-alike

Entity Clarify shows you when AI assistants confuse your brand with similarly-named ones — and generates the exact assets to set the record straight.

paste-ready asset types per case
6
mismatch lookback on monitored answers
~90d
generations per site / period by plan
1–8

How it works

  1. Flag mismatches and look-alike hosts

    From monitored answers, we surface brand mismatches over a recent window (about the last 90 days) and cases where citations or grounding point at sibling-family hosts instead of your canonical host.

  2. Open a disambiguation case

    Each case shows the confused-with name, how many monitored answers it appeared in, and example quotes — so you see the confusion, not just a score.

  3. Generate paste-ready assets

    On demand: a differentiation brief, Organization JSON-LD, About-page copy, FAQ, an llms.txt line, and an interactive implementation checklist — plus a read-only Wikidata search to help you anchor a canonical entity (you create the Wikidata item yourself).

  4. Track the case to verified

    Cases move Suggested → In progress → Verified (or Dismissed). Plan limits per website and period: Launch 1, Growth 3, Scale 8 generations.

Honest about what it does

What you get per case

Paste-ready assets to strengthen entity signals — not a promise that every model will stop confusing you overnight.

  • It detects confusion and equips you with deployable assets — it does not guarantee AI stops confusing your brand or that citations will appear.
  • Adoption of files such as llms.txt is voluntary; stronger signals help, they do not force every assistant.
  • Wikidata lookup is a read-only helper in-product — establishing the canonical item remains your browser action.

Frequently Asked Questions

Similarly-named products and look-alike domains cause models to attribute mentions elsewhere or blend you with another entity. GetCited.me flags mismatches and sibling-family citation hosts from your monitored answers.
Per case, on demand: a differentiation brief, Organization JSON-LD, About-page copy, FAQ, an llms.txt line, an implementation checklist with progress tracking, and a Wikidata search to help establish a canonical entity.
No. It strengthens the signals models use. You deploy the assets and verify over time — there is no guarantee of citation or disambiguation outcomes.
Per website and billing period: Launch 1, Growth 3, Scale 8 Entity Clarify generations.

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