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52match
jgc-indonesia.com
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2 co-citations2 shared tech1 shared categoryconstruction-industry
52match
janurteknik.com
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51match
bovis.com
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1 co-citations2 shared tech1 shared categoryconstruction-industry
51match
jasygroup.com
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2 co-citations2 shared tech1 shared categorycivil-engineering-industry
50match
advanceforminginc.com
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1 co-citations2 shared tech1 shared categoryconstruction-industry
50match
bonnevillebuilders.com
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49match
bpcdirectional.com
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1 co-citations2 shared tech1 shared categoryconstruction-industry
48match
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1 co-citations2 shared tech1 shared categorycivil-engineering-industry
48match
fentro.com
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2 co-citations2 shared tech1 shared categorymanufacturing-industry
48match
bahnuebergang.com
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48match
cercrete.com
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2 co-citations2 shared tech1 shared categorycivil-engineering-industry
47match
kenbay.com
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2 co-citations3 shared tech0 shared categoryenvironmental-services-industry
47match
corcon-usa.com
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1 co-citations3 shared tech1 shared categoryconstruction-industry
47match
jmige.com
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2 co-citations1 shared tech1 shared categoryconstruction-industry
47match
bracelinecalc.com
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2 co-citations2 shared tech1 shared categoryconstruction-industry
47match
kjfearless.com
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1 co-citations2 shared tech1 shared categoryconstruction-industry
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africaminingsupplies.com
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1 co-citations0 shared tech1 shared categoryconstruction-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.