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69match
kjordanconsultingllc.com
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2 co-citations4 shared tech1 shared categorymanagement-consulting-industry
67match
mangalamfamily.com
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1 co-citations4 shared tech1 shared categorybusiness
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majolix.com
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66match
banexusglobal.com
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2 co-citations4 shared tech1 shared categorybusiness
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borealinnovate.com
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2 co-citations4 shared tech1 shared categorybusiness
66match
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2 co-citations4 shared tech1 shared categorybusiness
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cmadintl.com
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1 co-citations4 shared tech2 shared categorymanagement-consulting-industry
63match
4coreent.com
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2 co-citations4 shared tech1 shared categorymanagement-consulting-industry
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aaworldwidesupplies.com
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2 co-citations4 shared tech1 shared categorybusiness
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eleven23management.com
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2 co-citations4 shared tech1 shared categorybusiness
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elevateopspk.com
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2 co-citations4 shared tech1 shared categorymanagement-consulting-industry
62match
eliogiornoalimentos.com
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2 co-citations4 shared tech0 shared categoryfood-industry
62match
jacmahoedesigns.com
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1 co-citations4 shared tech1 shared categorymanagement-consulting-industry
62match
hkelite-supply.com
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1 co-citations4 shared tech1 shared categorybusiness
62match
5-h-consulting.com
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1 co-citations4 shared tech1 shared categorymanagement-consulting-industry
62match
adinsitu.com
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1 co-citations4 shared tech1 shared categorybusiness
62match
caylorconsulting.com
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1 co-citations4 shared tech1 shared categorymanagement-consulting-industry
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marcomilanovic.com
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1 co-citations4 shared tech1 shared categorymanagement-consulting-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.