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64match
apompili.com
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3 co-citations1 shared tech1 shared categorydesign
64match
apsworldedu.com
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3 co-citations1 shared tech2 shared categorydesign
63match
baharyarchitecture.com
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3 co-citations1 shared tech1 shared categorydesign
63match
elisabettaconter.com
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3 co-citations1 shared tech1 shared categorydesign
62match
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3 co-citations1 shared tech1 shared categorydesign
59match
4archs.com
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58match
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58match
jacobantoni.com
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3 co-citations1 shared tech2 shared categorydesign
58match
a-dtile.com
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3 co-citations1 shared tech2 shared categorydesign
57match
apopdesign.com
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3 co-citations1 shared tech2 shared categorydesign
57match
homamassiha.com
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3 co-citations1 shared tech2 shared categorydesign
56match
maisonssommet.com
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2 co-citations3 shared tech1 shared categorydesign
56match
graziamarcarini.com
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3 co-citations1 shared tech2 shared categorydesign
56match
dibaarchiventure.com
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56match
jasperchair.com
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3 co-citations0 shared tech2 shared categorydesign
56match
aakruthiabodes.com
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3 co-citations0 shared tech2 shared categorydesign
55match
kendeinternational.com
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2 co-citations1 shared tech2 shared categorydesign
55match
alixedging.com
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3 co-citations0 shared tech2 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.