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94match
gentedelibros.com
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2 co-citations4 shared tech1 shared categoryhouses
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2 co-citations4 shared tech1 shared categoryhouses
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2 co-citations4 shared tech1 shared categoryhouses
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bookbutler.ai
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2 co-citations4 shared tech1 shared categoryhouses
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2 co-citations4 shared tech1 shared categoryhouses
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ferrane.com
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2 co-citations4 shared tech0 shared categorydevelopmental-sites
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ceahsw.com
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80match
haviland-homes.com
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79match
fernandoimoveis.com
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2 co-citations4 shared tech1 shared categoryhouses
76match
kanana-jakarta.com
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1 co-citations4 shared tech1 shared categorydevelopmental-sites
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diclassehomes.com
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2 co-citations4 shared tech1 shared categoryhouses
73match
ixtlilitibu.com
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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.