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85match
coopscookiecompany.com
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2 co-citations2 shared tech2 shared categorydesserts-and-baking
82match
dgelatomalta.com
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1 co-citations2 shared tech2 shared categorydesserts-and-baking
75match
breezewaybakes.com
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72match
mamasusies.com
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1 co-citations2 shared tech1 shared categoryfood-and-drink
72match
manztou.com
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1 co-citations2 shared tech1 shared categoryfood-and-drink
72match
izideli.com
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1 co-citations2 shared tech1 shared categoryfood-and-drink
72match
jimies.com
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1 co-citations2 shared tech1 shared categorydesserts-and-baking
72match
jimmyspoke.com
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bakebugs.com
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1 co-citations2 shared tech1 shared categorydesserts-and-baking
72match
bakery4you.com
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1 co-citations2 shared tech1 shared categorydesserts-and-baking
72match
bananacaliper.com
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1 co-citations2 shared tech1 shared categoryfood-and-drink
72match
banarasichatkara.com
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1 co-citations2 shared tech1 shared categorycooking
72match
barnamayi.com
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1 co-citations2 shared tech1 shared categoryfood-and-drink
72match
bonappetistas.com
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1 co-citations2 shared tech1 shared categoryfood-and-drink
72match
coreyfiske.com
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1 co-citations2 shared tech1 shared categoryfood-and-drink
72match
crapeating.com
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1 co-citations2 shared tech1 shared categoryfood-and-drink
68match
kanbake.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.