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60match
hcollman.com
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7 co-citations1 shared tech1 shared categorydesign
60match
bonopros.com
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7 co-citations1 shared tech1 shared categorydesign
60match
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6 co-citations2 shared tech1 shared categorydesign
52match
jffdesign.com
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4 co-citations1 shared tech1 shared categorydesign
52match
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52match
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4 co-citations1 shared tech1 shared categorydesign
51match
care-services.co.uk 🇬🇧
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4 co-citations1 shared tech1 shared categorydesign
51match
coullarchitecture.com
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3 co-citations1 shared tech1 shared categorydesign
50match
baidakov.com
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2 co-citations1 shared tech1 shared categorydesign
50match
camrosemedia.com
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5 co-citations0 shared tech1 shared categorydesign
49match
kendeinternational.com
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3 co-citations1 shared tech1 shared categorydesign
49match
arqerstudio.com
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3 co-citations1 shared tech1 shared categorydesign
49match
afaati.com
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3 co-citations1 shared tech1 shared categorydesign
48match
apsworldedu.com
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3 co-citations1 shared tech1 shared categorydesign
48match
julianajara.com
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1 co-citations1 shared tech1 shared categorydesign
48match
jaclynalili.com
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1 co-citations1 shared tech1 shared categorydesign
48match
haymancollective.com
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1 co-citations1 shared tech1 shared categorydesign

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.