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80match
geraldineganun.com
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2 co-citations5 shared tech1 shared categoryfine-art
71match
kennydid.com
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3 co-citations5 shared tech0 shared categoryfine-art-photography
71match
janefaybaker.com
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70match
dgspiller.com
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65match
manuellahuerta.com
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65match
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62match
bouldercityartguild.com
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1 co-citations5 shared tech1 shared categoryfine-art
62match
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2 co-citations1 shared tech1 shared categoryfine-art
62match
georgeraab.com
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2 co-citations1 shared tech1 shared categoryfine-art
62match
barbaraamos.com
Barbara Amos :: Home
3 co-citations1 shared tech1 shared categoryfine-art
62match
barbararubensteinart.com
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2 co-citations1 shared tech1 shared categoryfine-art
62match
cesarsantos.com
cesarsantos.com |
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61match
corinsweenydeinhart.com
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3 co-citations4 shared tech1 shared categoryfine-art
61match
cosmicartbysue.com
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3 co-citations0 shared tech1 shared categoryfine-art
60match
manon-dumas.com
Manon Dumas
3 co-citations1 shared tech1 shared categoryfine-art
60match
jimstayton.com
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3 co-citations1 shared tech1 shared categoryfine-art
60match
mariajoseserrano.com
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2 co-citations1 shared tech1 shared categoryfine-art

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.