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82match
janax.com
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1 co-citations5 shared tech1 shared categoryweb-design-and-html
82match
corktownstudios.com
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76match
jennawakoluk.com
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1 co-citations4 shared tech1 shared categoryweb-design-and-html
76match
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76match
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1 co-citations4 shared tech1 shared categoryweb-design-and-html
76match
agcsinc.com
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1 co-citations4 shared tech1 shared categoryweb-design-and-html
76match
baileyconrad.com
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1 co-citations4 shared tech1 shared categoryweb-design-and-html
70match
jaredcarmichael.com
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1 co-citations5 shared tech1 shared categoryweb-design-and-html
69match
jacob-olson.com
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1 co-citations5 shared tech1 shared categoryweb-design-and-html
69match
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1 co-citations5 shared tech1 shared categoryweb-design-and-html
69match
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1 co-citations5 shared tech1 shared categoryweb-design-and-html
68match
festa-engineering.com
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68match
jaimeleiner.com
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1 co-citations5 shared tech1 shared categoryweb-design-and-html
68match
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68match
griffincgs.com
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68match
dfaustino.com
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1 co-citations5 shared tech1 shared categoryweb-design-and-html
66match
cccoretechnologies.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.