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50match
maneseinteriors.com
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0 co-citations1 shared tech2 shared categoryinterior-decorating
50match
marcusandmarion.com
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0 co-citations1 shared tech2 shared categoryinterior-decorating
45match
0dots.com
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45match
fenestradesign.com
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0 co-citations1 shared tech2 shared categoryinterior-decorating
45match
fidesigngroup.com
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0 co-citations1 shared tech2 shared categoryinterior-decorating
42match
boostlocalservices.com
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0 co-citations1 shared tech2 shared categoryinterior-decorating
42match
gen5ai.com
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0 co-citations1 shared tech2 shared categoryinterior-decorating
41match
kelliesmith.com
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0 co-citations1 shared tech2 shared categoryinterior-decorating
41match
barbaraquinlanstyle.com
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41match
ffdmiami.com
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40match
heterogentech.com
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0 co-citations1 shared tech1 shared categoryinterior-decorating
40match
heyuchi.com
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0 co-citations2 shared tech1 shared categoryinterior-decorating
40match
hibbardandrosa.com
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40match
arquitecturavalor.com
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0 co-citations2 shared tech2 shared categoryinterior-decorating
40match
automatednetworkingsolutions.com
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0 co-citations1 shared tech1 shared categoryinterior-decorating
40match
adivasimart.com
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0 co-citations1 shared tech1 shared categoryhome-and-garden
40match
appalachianbarnquilts.com
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0 co-citations1 shared tech1 shared categoryinterior-decorating
40match
dhdeco.com
DHdeco pte ltd
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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.