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57match
autoinjuryadvisors.com
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1 co-citations1 shared tech1 shared categoryinsurance
56match
hamnesinsurance.com
1 co-citations1 shared tech1 shared categoryinsurance
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makiinsurancegroup.com
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1 co-citations1 shared tech1 shared categorylife-insurance
40match
getawayscouts.com
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1 co-citations1 shared tech0 shared categoryfinancial-planning
39match
coveredbyrosa.com
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1 co-citations1 shared tech1 shared categoryinsurance
39match
gerardmuhammad.com
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1 co-citations1 shared tech1 shared categoryinsurance
39match
hfnmedicare.com
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1 co-citations1 shared tech1 shared categoryhealth-insurance
38match
affinityhomerelief.com
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1 co-citations1 shared tech0 shared categoryhome-financing
36match
ivancanogroup.com
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1 co-citations1 shared tech0 shared categorygovernment-support-and-welfare
36match
healthfirstinsurancepros.com
1 co-citations1 shared tech0 shared categoryhealth-insurance
36match
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1 co-citations1 shared tech0 shared categorygas-and-electric
36match
elevatepuntacana.com
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1 co-citations1 shared tech0 shared categorypersonal-investing
36match
elite-mortgage-group.com
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1 co-citations1 shared tech0 shared categoryhome-financing
36match
genxto10k.com
1 co-citations1 shared tech0 shared categoryretirement-planning
36match
getmortgagecanada.com
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1 co-citations1 shared tech0 shared categoryhome-financing
36match
marcusturoinsurance.com
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36match
jmilliancredit.com
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1 co-citations1 shared tech0 shared categorypersonal-finance
36match
elevateretirementwealth.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.