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59match
federalbakeshop.com
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4 co-citations2 shared tech2 shared categorydesserts-and-baking
59match
elainestoffee.com
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59match
havecakeeatcake.com
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4 co-citations0 shared tech2 shared categorydesserts-and-baking
57match
baraahchocolate.com
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4 co-citations1 shared tech2 shared categorydesserts-and-baking
56match
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54match
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2 co-citations2 shared tech2 shared categorydesserts-and-baking
54match
alldayrecipes.com
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3 co-citations1 shared tech2 shared categorydesserts-and-baking
54match
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54match
4elephantsfood.com
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54match
1bakeshop.com
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4 co-citations2 shared tech2 shared categorydesserts-and-baking
52match
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4 co-citations1 shared tech2 shared categorydesserts-and-baking
52match
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4 co-citations2 shared tech1 shared categoryfood-and-drink
52match
agapebakeryhouse.com
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4 co-citations1 shared tech2 shared categorydesserts-and-baking
52match
bakingtogether.com
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4 co-citations2 shared tech2 shared categorydesserts-and-baking
52match
countertoppizzaoven.com
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52match
cebubestmango.com
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4 co-citations1 shared tech2 shared categoryfood-and-drink
51match
mamericansweets.com
mamerican - Tasty American Sweets
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51match
bakingcareofbusiness.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.