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87match
greatkc.com
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1 co-citations4 shared tech1 shared categoryremodeling-and-construction
87match
1wayconstructionservices.com
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87match
bostoncornice.com
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1 co-citations4 shared tech1 shared categoryremodeling-and-construction
87match
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87match
georgiafloorrestore.com
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1 co-citations4 shared tech1 shared categoryhome-improvement
80match
mapletontree.com
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80match
jamiekoenig.com
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80match
greenappleorganizers.com
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1 co-citations4 shared tech0 shared categoryinterior-decorating
80match
greendiamondelectric.com
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1 co-citations4 shared tech0 shared categoryhome-improvement
80match
greenseasonaz.com
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1 co-citations4 shared tech0 shared categorylandscaping
80match
gregarlotta.com
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1 co-citations4 shared tech0 shared categoryinterior-decorating
80match
appliancejoe.com
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80match
botanicaaustralis.com
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1 co-citations4 shared tech0 shared categorygardening
79match
bpcrane.com
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1 co-citations4 shared tech2 shared categoryremodeling-and-construction
78match
jmsdesigngroup.com
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1 co-citations4 shared tech2 shared categoryremodeling-and-construction
74match
jarhomeservices.com
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1 co-citations4 shared tech0 shared categoryhome-and-garden
74match
greenalawn.com
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1 co-citations4 shared tech0 shared categorylandscaping
74match
greentouchlincoln.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.