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55match
jasonheidema.com
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0 co-citations5 shared tech1 shared categorybusiness
55match
elitronicmusic.com
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0 co-citations5 shared tech1 shared categorybusiness
53match
hctjanitorial.com
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1 co-citations1 shared tech0 shared categoryconstruction-industry
49match
kylegoffinet.com
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0 co-citations4 shared tech1 shared categorybusiness
49match
malcarnw.com
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0 co-citations4 shared tech1 shared categorybusiness
49match
jmltesting.com
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0 co-citations4 shared tech1 shared categorybusiness
49match
cbadeiresources.com
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0 co-citations4 shared tech1 shared categorybusiness
45match
kjonesaccountant.com
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0 co-citations5 shared tech0 shared categorybusiness-accounting-and-finance
45match
maloafrica.com
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0 co-citations2 shared tech2 shared categorysales
45match
jacksonnunesconsulting.com
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jasteel.com
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0 co-citations5 shared tech0 shared categoryentertainment-industry
45match
jayoakes.com
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0 co-citations5 shared tech0 shared categorymarketing-and-advertising
45match
jintongecon.com
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0 co-citations5 shared tech0 shared categoryeconomy
45match
hastriter.com
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0 co-citations5 shared tech0 shared categoryconstruction-industry
45match
hitny.com
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0 co-citations4 shared tech1 shared categorysales
45match
hoeraufcontracting.com
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0 co-citations5 shared tech0 shared categoryconstruction-industry
45match
breathe-water.com
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0 co-citations5 shared tech0 shared categorymanagement-consulting-industry
45match
greatlakescadcam.com
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0 co-citations5 shared tech0 shared categorymanufacturing-industry

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.