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71match
arangoarq.com
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62match
a-dtile.com
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52match
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51match
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50match
jacobantoni.com
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3 co-citations1 shared tech2 shared categorydesign
50match
arqene.com
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3 co-citations1 shared tech2 shared categorydesign
50match
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50match
hmvisual.com
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4 co-citations1 shared tech1 shared categorydesign
50match
hatadesign.com
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3 co-citations1 shared tech2 shared categorydesign
49match
4archs.com
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49match
apsworldedu.com
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3 co-citations1 shared tech2 shared categorydesign
48match
dibaarchiventure.com
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3 co-citations0 shared tech2 shared categorydesign
48match
dictallab.com
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4 co-citations1 shared tech1 shared categorydesign
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ivan-jimenez.com
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3 co-citations2 shared tech1 shared categorydesign
48match
bonetticasa.com
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3 co-citations1 shared tech2 shared categorydesign
48match
jasperchair.com
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3 co-citations0 shared tech2 shared categorydesign
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elbedesignstudio.com
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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.