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0 co-citations5 shared tech1 shared categorybusiness-accounting-and-finance

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studiosardellaalessandra.it 🇮🇹

0 co-citations4 shared tech1 shared categorybusiness-accounting-and-finance

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0 co-citations4 shared tech1 shared categorybusiness-accounting-and-finance

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0 co-citations4 shared tech1 shared categoryfinancial-industry

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0 co-citations4 shared tech1 shared categorymanagement-consulting-industry

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0 co-citations5 shared tech0 shared categorybusiness-administration

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0 co-citations2 shared tech1 shared categorybusiness-accounting-and-finance

consensus-software.it 🇮🇹

0 co-citations4 shared tech1 shared categoryfinancial-industry

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0 co-citations3 shared tech1 shared categorybusiness-accounting-and-finance

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0 co-citations2 shared tech1 shared categorybusiness-accounting-and-finance

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0 co-citations2 shared tech1 shared categorybusiness-accounting-and-finance

studiodesimoneonline.it 🇮🇹

0 co-citations3 shared tech1 shared categorybusiness-accounting-and-finance

studiobfb.it 🇮🇹

0 co-citations1 shared tech1 shared categorybusiness-accounting-and-finance

contabilitatorino.it 🇮🇹

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studiocodeluppi.it 🇮🇹

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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:

co-citations how often the same third-party websites link out to both — sites referenced together tend to serve the same world.
shared tech how much of their technology stack (CMS, e‑commerce, analytics, frameworks…) overlaps.
shared category whether they sit in the same business / industry category.
positioning how closely their titles and on‑page wording line up.

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.