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79match
aidtechnologies.co.uk 🇬🇧
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1 co-citations7 shared tech2 shared categorymechanical-and-industrial-engineering-industry
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altusgroup.co.uk 🇬🇧
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62match
gregorycc.com
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1 co-citations8 shared tech0 shared categorymanagement-consulting-industry
62match
aeg-us.com
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1 co-citations8 shared tech0 shared categorycivil-engineering-industry
61match
corexind.com
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0 co-citations7 shared tech2 shared categorymechanical-and-industrial-engineering-industry
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cambourneengineering.co.uk 🇬🇧
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0 co-citations7 shared tech2 shared categorymanufacturing-industry
60match
greensquareconsulting.com
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1 co-citations7 shared tech1 shared categorymanufacturing-industry
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jimmcauslan.com
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1 co-citations7 shared tech0 shared categoryexecutive-leadership-and-management
59match
bbandtconsulting.com
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1 co-citations7 shared tech0 shared categorymanagement-consulting-industry
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breightrecruitment.com
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1 co-citations7 shared tech0 shared categoryhuman-resources
59match
3pvalueandaccess.com
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1 co-citations7 shared tech0 shared categoryhealthcare-industry
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bordinienterprises.com
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1 co-citations7 shared tech0 shared categorymanagement-consulting-industry
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cdifinancialgroup.com
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1 co-citations7 shared tech0 shared categorybusiness-and-finance
59match
cedrusbioadvisors.com
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1 co-citations7 shared tech0 shared categorybiotech-and-biomedical-industry

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.