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55match
jasminepowers.com
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3 co-citations5 shared tech0 shared categorymarketing-and-advertising
52match
bpozim.com
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2 co-citations5 shared tech1 shared categoryhuman-resources
51match
kyawtkhine.com
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1 co-citations5 shared tech0 shared categoryadvertising-industry
51match
grdbooks.com
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1 co-citations5 shared tech0 shared categorybusiness-accounting-and-finance
50match
corerewards.com
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1 co-citations5 shared tech1 shared categorybusiness
49match
apte.com
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49match
healthcarefacilitiesnetwork.com
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3 co-citations5 shared tech0 shared categoryhealthcare-industry
48match
elitecpastaffing.com
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3 co-citations3 shared tech1 shared categoryhuman-resources
48match
breinchild.com
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3 co-citations3 shared tech1 shared categoryhuman-resources
47match
hobrothers.com
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3 co-citations5 shared tech0 shared categorymanufacturing-industry
47match
mantx.com
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3 co-citations5 shared tech0 shared categorybusiness-operations
47match
corcon-usa.com
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3 co-citations5 shared tech0 shared categoryconstruction-industry
46match
1hrsolutions.com
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3 co-citations3 shared tech1 shared categoryhuman-resources
46match
majolix.com
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3 co-citations1 shared tech1 shared categoryhuman-resources
46match
jillrobb.com
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1 co-citations5 shared tech0 shared categorymarketing-and-advertising
46match
hdrecruit.com
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1 co-citations3 shared tech1 shared categoryhuman-resources
46match
healthcarestaffworks.com
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3 co-citations1 shared tech1 shared categoryhealthcare-industry
46match
3lcsolutions.com
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1 co-citations3 shared tech1 shared categoryhuman-resources

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.