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72match
mara-moore.com
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3 co-citations7 shared tech1 shared categorybusiness
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2 co-citations7 shared tech1 shared categorymarketing-and-advertising
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66match
aegis-enterprises.com
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3 co-citations7 shared tech0 shared categorymechanical-and-industrial-engineering-industry
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3 co-citations7 shared tech0 shared categorybusiness-accounting-and-finance
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cpandco.com
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3 co-citations7 shared tech0 shared categorybusiness-accounting-and-finance
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elevate-business.com
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4 co-citations4 shared tech1 shared categorymarketing-and-advertising
65match
adspuff.com
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2 co-citations7 shared tech1 shared categorymarketing-and-advertising
65match
advancedpartners.com
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2 co-citations7 shared tech1 shared categorymarketing-and-advertising
65match
cqwebsolutions.com
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2 co-citations7 shared tech1 shared categorymarketing-and-advertising
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diasporamindslab.com
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4 co-citations7 shared tech0 shared categorystartups
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kemptonsolutions.com
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3 co-citations7 shared tech0 shared categoryhuman-resources

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.