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Sam Hind – lecturer in digital media and culture at the University of Manchester, UK. — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Artificial Intelligence →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
andi.ac ↗	68 match 1 shared topics	Andi Zhang • Assistant Professor at the University of Warwick	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
canto.global ↗	67 match 1 shared topics	Canto – The leader in digital asset management solutions	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
clulab.org ↗	66 match 1 shared topics	Computational Language Understanding (CLU) Lab at the University of Arizona	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
medicos.health ↗	65 match 1 shared topics	MedicOS - The Brain and the Heart of Your Digital Clinic	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
ekip-reply.com ↗	65 match 1 shared topics	The Future of Work: Hybrid Models and the Digital Workplace \| Reply	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
ekipreply.com ↗	65 match 1 shared topics	The Future of Work: Hybrid Models and the Digital Workplace \| Reply	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
abns.info ↗	65 match 1 shared topics	OGO World – AI Systems, Automation & Digital Infrastructure	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
allentownuniversity.com ↗	65 match 1 shared topics	Allentown University	United States~ en	artificial-intelligence	robotsllmsaihumans	email phone	none
nobandegani.com ↗	65 match 1 shared topics	Ardavan S. Nobandegani – Postdoctoral Researcher, Department of Psychology, McGill University	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
masterai4onehealth.eu ↗	65 match 1 shared topics	Master AI4ONEHEALTH - University Grenoble Alpes	European Union en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	emailphone	none
clinicalai.guide ↗	65 match 1 shared topics	MPHY 6120 - Artificial Intelligence for Medicine - University of Pennsylvania	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
aliceinailand.com ↗	65 match 1 shared topics	Alice in AI Land \| Exploring the Future of Kids and AI	en	artificial-intelligence WordPress	robots llmsaihumans	emailphone	partial · 8
alienfamiliarart.com ↗	65 match 1 shared topics	Alien Familiar Art – Learning with AI, Art, and the Uncanny	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
elephantventures.com ↗	65 match 1 shared topics	Elephant Ventures - Digital Innovation and Transformation	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
thehexfiles.com ↗	65 match 1 shared topics	The Hex Files — Dispatches from a Digital Mind	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
nmndigitalconsulting.com ↗	65 match 1 shared topics	NMN Digital \| Autonomous Digital Infrastructure	en	artificial-intelligence Spree Commerce	robotsllmsaihumans	emailphone	none
vovicx.com ↗	64 match 1 shared topics	Vovicx – Leading News and Media Outlets	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
rongyulin3.com ↗	64 match 1 shared topics	Ron(Rongyu) Lin \| Quinnipiac University	en	artificial-intelligence	robotsllmsaihumans	emailphone	none

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:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

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.