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65match
jitech.io
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0 co-citations2 shared tech1 shared categoryinformation-and-network-security
65match
digimet.com
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elsholz.com
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62match
guardverge.com
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1 co-citations1 shared tech1 shared categoryinformation-and-network-security
55match
3r-ent.com
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53match
jfhackett.com
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1 co-citations1 shared tech1 shared categoryinformation-and-network-security
53match
hidden-hat.com
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1 co-citations1 shared tech1 shared categoryinformation-and-network-security
53match
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1 co-citations1 shared tech1 shared categoryinformation-and-network-security
50match
jacobeconsulting.com
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50match
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0 co-citations1 shared tech1 shared categoryinformation-and-network-security
50match
aeontechnic.com
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0 co-citations1 shared tech1 shared categoryinformation-and-network-security
50match
2blefactor.com
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0 co-citations1 shared tech1 shared categoryinformation-and-network-security
50match
baltimoredigitaltrust.com
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0 co-citations1 shared tech1 shared categoryinformation-and-network-security
50match
bosskeyit.com
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0 co-citations1 shared tech1 shared categoryinformation-and-network-security
50match
costeffectiveit.com
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0 co-citations1 shared tech1 shared categoryit-and-internet-support
49match
kentmayer.com
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1 co-citations1 shared tech1 shared categoryartificial-intelligence
49match
hattonsec.com
Home | hattonsec
1 co-citations1 shared tech1 shared categoryinformation-and-network-security
47match
kelvincancio.com
Kelvin Charles Cancio
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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.