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80match
jackfrostironworks.com
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1 co-citations8 shared tech0 shared categorymetals-industry
76match
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74match
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1 co-citations8 shared tech1 shared categoryhuman-resources
73match
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72match
marequipmentco.com
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71match
bookerbrokerage.com
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1 co-citations7 shared tech0 shared categorylogistics-and-transportation-industry
70match
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1 co-citations7 shared tech1 shared categoryhuman-resources
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1 co-citations7 shared tech1 shared categoryhuman-resources
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1 co-citations0 shared tech1 shared categoryhuman-resources
68match
ballpointlearning.com
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1 co-citations7 shared tech1 shared categoryhuman-resources
67match
3s-team.com
3S Team Home Page
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65match
kenwego.com
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62match
januslease.com
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1 co-citations8 shared tech0 shared categoryreal-estate-industry
62match
greenmountainaptus.com
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1 co-citations5 shared tech1 shared categoryhuman-resources
62match
copyandgo1.com
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1 co-citations8 shared tech0 shared categorymarketing-and-advertising
62match
diamond-freight.com
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1 co-citations8 shared tech0 shared categorylogistics-and-transportation-industry
61match
kelseysinternationalpayroll.com
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0 co-citations7 shared tech1 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.