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60match
kellyeenterprises.com
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7 co-citations0 shared tech1 shared categorybusiness
60match
larainfovision.com
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7 co-citations0 shared tech1 shared categorybusiness
60match
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7 co-citations0 shared tech1 shared categorylogistics-and-transportation-industry
60match
jkwhitelogistics.com
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7 co-citations0 shared tech1 shared categorylogistics-and-transportation-industry
60match
hmm-group.com
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7 co-citations0 shared tech1 shared categorybusiness
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aecitgrp.com
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7 co-citations0 shared tech1 shared categorybusiness
60match
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7 co-citations0 shared tech1 shared categorybusiness
60match
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60match
genxoutsourcing.com
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7 co-citations0 shared tech1 shared categorybusiness
56match
kennedyinnovations.com
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7 co-citations0 shared tech1 shared categorybusiness
56match
ferndale-tech.com
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7 co-citations0 shared tech1 shared categorylogistics
56match
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7 co-citations0 shared tech1 shared categorybusiness
55match
mandllogistics.com
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6 co-citations0 shared tech1 shared categorylogistics-and-transportation-industry
51match
kenjivue.com
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6 co-citations0 shared tech1 shared categorybusiness
51match
hgvfinance.com
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6 co-citations0 shared tech1 shared categorylogistics-and-transportation-industry
51match
apex-courierservices.com
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6 co-citations0 shared tech1 shared categorylogistics
50match
juliancrane.com
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5 co-citations0 shared tech1 shared categorybusiness
50match
majorcapitalgroup.com
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7 co-citations0 shared tech0 shared categoryventure-capital

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.