A clear identity for the brand

Entity Inventory & Disambiguation

One canonical identity separates the brand from each namesake, former name and product overlap found in search and AI-answer mix-ups.

Observed search and AI-answer mix-ups provide the case list. We trace each wrong fact to the page publishing it, and a named owner reviews the proposed correction before anything changes. Each confirmed mix-up receives a corrected source record. A fixed measure then shows whether search and chat answers begin resolving to the intended entity. Brand and legal leads dealing with a namesake, former name, or acquired product that AI answers keep confusing with the current entity.

Figure sorting identical name tags onto two distinct entity figures, separating a brand from its namesake

Some of the 500+ brands we've worked with

See all references
  • Mustela
  • GAP
  • Madame Coco
  • Yandex
  • Capital Dergisi
  • Gedik Yatırım
  • Eureko Sigorta
  • Amazon
  • BMW
  • Shell
  • Hyundai
  • PepsiCo
  • Red Bull
  • Decathlon
  • MediaMarkt
  • Bayer
  • Sanofi
  • EY
  • KPMG
  • GE
  • 3M
  • Domino’s
  • Lexus
  • Trendyol

Five steps to one identity

From observed mix-up to approved correction

The investigation moves from observed collisions to approved corrections. We assemble and compare the evidence, while named people decide which identity facts are true.

How we hold ourselves to it

  • One canonical identity
  • Every fact sourced
  • Owner approves changes
  • No guaranteed panel

  1. Build the collision set

    Observed mix-ups become a set of branded, abbreviated, category, location, and product-overlap scenarios. Brand and legal confirm which cases are genuine collisions and which are similar wording.

    Collision scenario set with the intended entity, the competing entity, and a harm rating for each.

  2. Compare identities side by side

    Stable identifiers and distinguishing attributes place every collision candidate beside the intended entity for review. The accountable fact owner picks the canonical value and marks anything genuinely disputed.

    A canonical identifier register with every candidate entity's name, ID, and aliases in one table.

  3. Trace every wrong fact to its source

    An incorrect founding date, address, or leadership fact is traced to the specific directory, profile, or article publishing it. The source or domain owner confirms the contradiction and whether the surface can be changed.

    A conflicting-source ledger. The exact page, its control status, and who can fix it.

  4. Fix what you control, in the right order

    User harm and the number of surfaces repeating the error determine correction priority. Ease of implementation does not. Brand, legal, and delivery owners approve the order and any escalation to an outside publisher.

    A sequenced correction roadmap with rollback notes for anything sensitive.

  5. Retest with queries nobody's seen

    The retest covers the original collision set and fresh ambiguous queries the team never optimized for. A correction that only resolves known cases therefore remains incomplete. An independent reviewer applies the frozen test set and reopens anything that shifted or stayed broken.

    A post-correction report showing what's resolved, what's still open, and what's new.

The register and the number

The collision register and resolution measure

The handoff combines a reusable identity register with a fixed measure of how often ambiguous queries resolve to the intended entity.

  • Disambiguation audit

    Focused on priority contexts (the searches and answers your buyers and press encounter).

  • Canonical identifier register

    Built to survive a rebrand or a new competitor entering with a similar name.

  • Owned-surface correction spec

    One document content and engineering both work from, with effective dates attached.

  • Wrong-entity resolution rate

    The scope, the exact set of queries counted, and the period we watch are all fixed before the comparison runs, so the number can't be quietly redefined afterward.

  • Post-correction report

    What's resolved, what's still open, and what's newly broken once the corrections ship. You get a fresh one at every rebrand, acquisition, location change, or identifier update, plus a quarterly check on the highest-harm cases. Rankings, citations, and referrals stay separate, ongoing measurements. This report only tracks entity resolution.

Spotting a documented collision

A repeatable name collision has a traceable source

This method fits a repeatable mix-up where search or chat answers borrow facts from the wrong company and the source of the confusion has not yet been mapped.

A good fit when

  • An unrelated business shares your name — A knowledge panel, a chat answer, or a review site cites the founding date or leadership of a different "Acme Robotics".
  • A rebrand or acquisition keeps two identities live — A former name, an acquired product, or a legacy domain still resolves as the current entity.
  • The team has examples but no full picture — Someone has screenshotted three wrong answers, but nobody holds the list of namesakes, aliases, and repeating sources.
  • A canonical fact already exists somewhere — Legal name, former names, effective dates, and boundaries are approved, even if only in a contract or brand doc.
  • Every collision scenario gets a record — Namesakes, abbreviations, former names, product overlaps, and geographic variants each name the entity they wrongly reach.
  • The identifier has to be one you can maintain — A canonical URL, a stable ID, or a Wikidata item works, and we never invent one to look thorough.
  • Current facts stay separate from history — A former subsidiary, a retired product line, or a departed founder keeps its own timestamped fact.

Better handled as other work when

  • A knowledge panel is the only goal — The work resolves documented identity confusion, and panel inclusion stays an external decision.
  • You want an unflattering mention removed — Correcting a wrong-entity mix-up never suppresses an accurate mention or claims a generic term as yours.
  • The named owner controls publication — We draft the comparison and the fix, and brand, legal, or engineering signs off before a live page changes.

People who watch how AI cites a brand

GEO work starts with recording what AI answers actually say about a brand today, then moves to the parts you can influence. The consultants below work on the specific capability this page covers.

Content we've produced on this topic

Articleby Elif Naz Akan KarakoçStructured Data for AI: Tips for Supporting AI Visibility with SchemaArticleby Samet ÖzsüleymanHow to Manage Product Variants with isVariantOf Schema?

Tools we use

Tools behind this work

  • Google Knowledge Graph Search API

    a quick diagnostic check on what Google's index currently shows for a name

  • Wikidata

    the claimable public entity record several AI systems draw from directly

  • OpenRefine

    clusters near-identical name values so a spelling variant is not filed as a collision

  • InLinks

    publishes the corrected identity as entity markup on the surfaces the brand controls

  • Google Business Profile

    the one outside record in this method the brand can edit directly instead of requesting

  • Airtable

    the collision register, one row per mix-up with its own named-owner review

A panel is never guaranteed, a correction is

Bring us the identity mix-ups you've seen

Those examples become the starting cases for a wider collision map. Each wrong fact is traced to its publisher, and the correction waits for the accountable brand or legal owner.
Fix the entity mix-up

Questions we hear

Does fixing a collision guarantee that Google or ChatGPT will stop citing the wrong entity?

Correcting a collision cannot guarantee that Google or ChatGPT changes a particular answer. We verify the public record on surfaces we can inspect. We also measure the resolution rate. Each outside system still controls the timing and method of re-ingestion.

When is this the right task instead of something else?

Use it when search results or AI answers repeatedly merge your brand with a namesake, former name, or unrelated company. One odd answer is not enough. The useful signal is a pattern visible across more than one query or mention.

Which correction gets made first?

Two things set the order. How much the wrong answer hurts a real reader, and how many surfaces are repeating it. Ease of implementation doesn't move a case up the list. Brand, legal, and delivery owners approve the sequence before anything ships, including any escalation to an outside publisher.

How do you know it worked?

We track the wrong-entity resolution rate across a frozen query set plus a set we held back that the correction never touched, so a fix that only worked on known cases shows up as incomplete.