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100match
bowmanam.com
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1 co-citations1 shared tech1 shared categorymetals-industry
70match
dhruvstar.com
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1 co-citations0 shared tech1 shared categorymetals-industry
60match
kenfurlong.com
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1 co-citations1 shared tech0 shared categorynon-profit-organizations
60match
hkmikiki.com
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1 co-citations1 shared tech0 shared categoryfood-industry
55match
hdipak.com
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55match
bonanzacreekmining.com
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55match
coralmetal.com
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1 co-citations0 shared tech1 shared categorymanufacturing-industry
54match
bpa-intl.com
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1 co-citations1 shared tech0 shared categoryfinancial-industry
50match
makmuremasgemilau.com
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50match
mangalaum.com
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1 co-citations0 shared tech0 shared categorymechanical-and-industrial-engineering-industry
50match
manoramainfotech.com
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1 co-citations0 shared tech0 shared categorybusiness-i-t
50match
hiteco-eng.com
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1 co-citations0 shared tech0 shared categorymechanical-and-industrial-engineering-industry
50match
graphiteinnovation.com
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1 co-citations0 shared tech0 shared categorybusiness-banking-and-finance
50match
greenova-corp.com
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1 co-citations0 shared tech0 shared categorybusiness
50match
grouptrident.com
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2mantaxi.com
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1 co-citations0 shared tech0 shared categorylogistics-and-transportation-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.