How DeepSearch matches people with the same name

A shared name is a search clue, not proof that two records describe the same person. When DeepSearch finds several people with the name you searched, it ranks them as separate candidates so you choose a person, rather than fusing their records into one confident profile. If the public evidence cannot tell two namesakes apart, the honest result is an incomplete one.

Our privacy policy calls these candidate matches and says "a candidate match is not a verified identity". Our terms explain that a match score is not identity verification either.

How many namesakes a particular name is likely to have is a separate question, answered from Census data in How many people have the last name Smith? This post is about reading the candidates DeepSearch shows you.

What evidence supports a DeepSearch match?

Agreement between independent sources supports a match. The name never does. Use these rules when comparing the candidates:

• a name alone never establishes a match
• a single overlapping detail is a hypothesis, not a conclusion
• confidence rises only when independent sources agree
• results are ranked so you choose a person, rather than being handed one

Independent means a separate evidence path. Two directories that copied the same listing are one source, repeated. A LinkedIn URL that appears on a personal site, plus the same uncommon handle on a second platform, plus a matching employer on a third page, is the beginning of a case.

Example: two people with the same name

Illustrative example, not a real search result. Both people and every page below are fictional.

Two women share the name Laura Bennett. Laura A is a physical therapist who has treated patients at a clinic in Columbus, Ohio, since 2011. Laura B is a 29-year-old graphic designer who grew up in Columbus and moved to Denver in 2022. A product that merges on the name writes one card:

Every row has a source, so the card looks well evidenced. Two of its rows cannot both be true of one person: the staff page says its Laura Bennett has treated patients there since 2011, and a subject aged 29 today was about 14 that year. That conflict should have stopped the merge before the photo or the court case was attached.

Call it the timeline test. Before two records are treated as one person, check that every dated fact on both could belong to a single life: ages, start dates, moves and graduations. One impossible pair is enough to keep them apart.

Keeping the records separate produces two supported candidates and some unresolved records:

• Candidate 1: the physical therapist in Columbus, supported by the staff page and a license listing that names the same clinic. No photo.
• Candidate 2: the designer in Denver, supported by her portfolio and the professional profile it links to, which links back.
• Unresolved: the forum username and the court entry, because no anchor ties either one to a candidate.

The corrected result is less complete: neither card has every field, and the court entry sits on no one's card. A mixed identity looks complete, which is what makes it harder to check. The incomplete version keeps the uncertainty visible instead of assigning an unsupported record to someone.

Fictional worked example

Keep each evidence trail in its own lane

Both candidates are named Laura Bennett. The shared name does not connect their records.

Laura A

Physical therapist · Columbus

• Clinic staff page

  Treating patients since 2011

• State licence listing

  Names the same clinic

A supported candidate. No photo assigned.

Laura B

Graphic designer · Denver

• Design portfolio

  Her work and headshot

• Professional profile

  Links to and from the portfolio

A separate supported candidate.

Unresolved

No corroborating anchor

• Forum username

  No link to either candidate

• Court index entry

  A name alone is not enough

Attached to neither person.

The conflict stops the merge.

One source says practising since 2011; the other person is 29 today. Do not borrow her age or photo to complete the first candidate.

Which details help distinguish people with the same name?

No single identifier proves who someone is. Each one can narrow a list, rule a candidate out or anchor a comparison, and each can be shared, copied or out of date. Our terms say so: "Names can be shared, accounts can be impersonated, phone numbers can be recycled, images can be altered, and records can be stale."

What if the sources disagree?

A conflict stops the merge. Different cities in the same year, two ages that cannot both be true, a photo history that belongs to someone else: those are not details to average away. They are the reason to keep the records apart and say so.

The common-names guide adds overlapping full-time jobs in different countries and different middle names. A conflict can mean one source is stale, but until a dated source shows that, the records stay apart, labelled as different people or as unresolved.

What does a DeepSearch match score mean?

A match score ranks candidates against each other. It does not verify anyone. The product shows the score so that several possible people can be compared, and our terms say what the number means: only that "the system found signals consistent with a candidate", which is not identity verification or a factual finding.

So a high score does not mean every field on the selected card is true, and it does not mean the person you had in mind is the person on the page.

The AI layer that writes the summary is under the same limit. Its job is to describe the evidence that was collected, not to supply the missing link that would make two namesakes into one person. If the sources do not support a merge, the honest output is "possible match" or "no public record ties these together", not a fluent biography.

Models can still get this wrong. Our terms say AI output may "fabricate, omit, misattribute, or overstate information".

Why can DeepSearch show several candidates or no match?

Public sources may not contain enough information to separate people who share a name. When they don't, the matching rules keep an ambiguous candidate separate rather than pick one for you, which can leave several candidates or none. Our privacy policy lists the usual causes: "Common names, stale sources, recycled phone numbers, parody accounts, and conflicting records can produce an incorrect or incomplete result."

Adding a city, employer, username or relevant date can help narrow the search, but does not guarantee a match. Keep unresolved records separate.

The product coverage guide explains other reasons a search may return little information. Under our terms, a completed search can use a credit even when it finds no useful match, because the requested work was performed.

How to check a DeepSearch result yourself

Check the fields that matter to you one at a time, against the pages they cite. How to verify a DeepSearch result using its sources splits that into four questions: does the source exist, is it about the same person, does it say what the summary says, and is it current?

For a namesake problem the second question does most of the work. Find the exact passage behind the field and a stable anchor beyond the name, such as an official profile link, a distinctive handle or an employer page. Then check that two agreeing sources have separate origins: several broker pages can inherit one commercial dataset.

Until you have done that, treat the result as a lead. Our terms ask you to verify important information at the original source and not to "confront, publish about, accuse, or take action against someone based only on a DeepSearch result."

How do you correct a wrong DeepSearch match?

If a page mixes you with someone else, request a correction. You can challenge a wrong association without asking us to remove every reference to you.

Email the result URL, the field that is wrong, why it is wrong and a reliable public source that supports the correction. The route is on the contact page, and the steps are in How to correct or remove information from DeepSearch. Call it a namesake mix-up, and add the anchor that identifies the right person, such as your own profile or employer page.

Depending on the evidence, we may update, remove, label, or disconnect a candidate match. Correct the original source too, because fixing one source may not fix another.

To stop DeepSearch showing you at all, use the free forms on Remove my info. Search-results removal asks for an email address you can open as well as your name, because "a name alone can't prove the request is yours."

A takedown that gives only a name goes to review, since a name-wide block could remove a different person's page. Add a DeepSearch URL or non-sensitive context, such as a general location or a public profile link, so the right page is found. Do not send government identification.

If a removal matched the wrong page, Remove my info gives an appeal route: email the confirmation and the URL with the subject "Removal appeal".

If you find us merging on a name, or speaking more confidently than the sources allow, report it through the contact page too. We would like to hear about it.