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53match
gerosir.com
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0 co-citations3 shared tech1 shared categoryshopping
50match
bakebutik.com
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0 co-citations4 shared tech1 shared categoryshopping
50match
makepod.ai
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0 co-citations4 shared tech1 shared categoryshopping
50match
3ndi.com
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0 co-citations4 shared tech1 shared categoryshopping
50match
accsbay.com
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0 co-citations4 shared tech1 shared categoryshopping
50match
baharshop.com
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0 co-citations4 shared tech1 shared categoryshopping
50match
bargainbinge.com
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0 co-citations3 shared tech1 shared categoryshopping
45match
dibadeals.com
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0 co-citations2 shared tech1 shared categoryshopping
44match
laplxus.com
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0 co-citations4 shared tech1 shared categoryshopping
44match
heybiblee.com
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0 co-citations4 shared tech1 shared categoryshopping
44match
hobbyshe.com
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0 co-citations4 shared tech1 shared categoryshopping
44match
hobbyshi.com
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0 co-citations4 shared tech1 shared categoryshopping
44match
grasshi.com
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0 co-citations4 shared tech1 shared categoryshopping
44match
guntexist.com
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0 co-citations4 shared tech1 shared categoryshopping
44match
gustrena.com
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0 co-citations4 shared tech1 shared categoryshopping
44match
bakisshop.com
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0 co-citations4 shared tech1 shared categoryshopping
44match
balraens.com
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0 co-citations4 shared tech1 shared categoryshopping
44match
barkshi.com
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0 co-citations4 shared tech1 shared categoryshopping

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.