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50match
cow-knife.com
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0 co-citations3 shared tech2 shared categoryshopping
42match
hb-wares.com
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0 co-citations3 shared tech2 shared categoryhousehold-supplies
42match
bolderriver.com
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0 co-citations3 shared tech2 shared categoryhousehold-supplies
42match
cityhomesolutions.co.uk 🇬🇧
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0 co-citations3 shared tech1 shared categoryhousehold-supplies
42match
handycamrepair.com
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0 co-citations3 shared tech1 shared categoryshopping
42match
diamondtouchco.com
diamond touch cleaning
0 co-citations3 shared tech1 shared categoryhousehold-supplies
42match
automagicalhomesolutions.com
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0 co-citations3 shared tech1 shared categoryshopping
41match
botanneca.com
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0 co-citations3 shared tech1 shared categoryshopping
41match
hanakahimarine.com
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0 co-citations3 shared tech1 shared categoryshopping
41match
botanistart.com
Botanist Art
0 co-citations3 shared tech1 shared categoryshopping
41match
dewinterdesign.com
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0 co-citations3 shared tech1 shared categoryshopping
41match
dianacleveland.com
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0 co-citations3 shared tech1 shared categoryshopping
41match
advocateclothing.com
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0 co-citations3 shared tech1 shared categoryshopping
41match
centipedesuction.com
Centipede Suction
0 co-citations3 shared tech1 shared categoryshopping
41match
centreblockgroup.com
CENTRE BLOCK GROUP
0 co-citations3 shared tech1 shared categoryshopping
41match
3wksnotice.com
Three Weeks Notice Collective
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41match
baganhamasonry.com
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40match
jenkins2d.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.