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85match
maplesalmon.com
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3 co-citations3 shared tech1 shared categoryfood-industry
80match
havenwoodinvestments.com
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3 co-citations6 shared tech0 shared categoryreal-estate-industry
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hokhnos.com
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apexcater.com
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77match
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3 co-citations5 shared tech1 shared categoryfood-industry
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elbalor.com
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3 co-citations5 shared tech1 shared categoryfood-industry
76match
j5food.com
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76match
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3 co-citations3 shared tech1 shared categoryfood-industry
75match
jjsvending.com
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75match
hlcaribe.com
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3 co-citations5 shared tech0 shared categorymarketing-and-advertising
75match
aeriostechnologies.com
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3 co-citations5 shared tech0 shared categorymechanical-and-industrial-engineering-industry
75match
afieftrader.com
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3 co-citations5 shared tech0 shared categoryconstruction-industry
75match
aficontable.com
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3 co-citations5 shared tech0 shared categorybusiness-accounting-and-finance
75match
bandrcertification.com
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3 co-citations5 shared tech0 shared categorybusiness
75match
baraniam.com
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3 co-citations5 shared tech0 shared categorymechanical-and-industrial-engineering-industry
75match
boletnotary.com
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3 co-citations5 shared tech0 shared categorylegal-services-industry
75match
ccincorporated.com
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3 co-citations5 shared tech0 shared categoryconstruction-industry
75match
eksomaritimindonesia.com
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3 co-citations5 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.