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Activités et Compétences: Traducteur langage école-maison à langage DEM — 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 Language Learning →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
mlinternationalcenter.com ↗	64 match 1 shared topics	cours de langues antibes, bilan de compétences, aide administrative	fr	language-learning	robotsllmsaihumans	emailphone	none
diane-langues.fr ↗	64 match 1 shared topics	Dianelangues.fr : Cours et traduction à Paris et Fontainebleau	France fr	language-learning	robotsllmsaihumans	emailphone	none
dianelangues.fr ↗	64 match 1 shared topics	Dianelangues.fr : Cours et traduction à Paris et Fontainebleau	France fr	language-learning	robotsllmsaihumans	emailphone	none
mister-lingo.com ↗	64 match 1 shared topics	Mister Lingo – Traducteur vocal sans internet, compact & autonome	fr	language-learning Shopify	robots llmsaihumans	emailphone	none
crsc.fr ↗	64 match 1 shared topics	Accueil - Maison russe des sciences et de la culture à Paris	France fr	language-learning WordPress	robots llmsaihumans	emailphone	none
a-fourcade.com ↗	64 match 1 shared topics	Amélie Fourcade, traduction et correction	fr	language-learning	robotsllmsaihumans	emailphone	none
dicobourguignon.fr ↗	64 match 1 shared topics	Dictionnaire & Traducteur : Français - Patois Bourguignon	France fr	language-learning	robotsllmsaihumans	emailphone	none
danielle-leeman.fr ↗	63 match 1 shared topics	Enseignement en Sciences du langage - Danielle Leeman	France fr	language-learning WordPress WooCommerce	robotsllmsaihumans	emailphone	none
mcguinness-school.com ↗	63 match 1 shared topics	McGuinness Language School : école de langue, soutien scolaire et formation pour apprendre l'anglais	fr	language-learning	robotsllmsaihumans	emailphone	none
kemelang.com ↗	63 match 1 shared topics	Traduction entre langues africaines \| Kemelang	fr	language-learning	robotsllmsaihumans	emailphone	none
emdl.fr ↗	63 match 1 shared topics	EMDL - Éditions Maison des Langues	France fr	language-learning WordPress WooCommerce	robots llmsaihumans	emailphone	none
a2b-immersions.com ↗	63 match 1 shared topics	A2B Immersions, école de français pour les étrangers à Montpellier	fr	language-learning WordPress	robotsllmsaihumans	emailphone	none
cecilekellertraduction.com ↗	63 match 1 shared topics	Cécile Keller \| Traduction et cours particuliers d'allemand et de français	fr	language-learning Wix	robots llmsaihumans	emailphone	none
applelanguages.be ↗	63 match 1 shared topics	Apple Language Courses \| Apprenez une langue à l'étranger	Belgium fr	language-learning	robotsllmsaihumans	emailphone	none
enfin-bilingue.fr ↗	62 match 1 shared topics	Guide pratique sur les langues et leur apprentissage	France fr	language-learning WordPress	robotsllmsaihumans	emailphone	none
centre-conseils-langues.com ↗	62 match 1 shared topics	C.E.C.A.L. - Centre d’expertise et de conseil sur l’apprentissage des langues	fr	language-learning	robotsllmsaihumans	emailphone	none
omili.org ↗	62 match 1 shared topics	A future beyond language borders – A future beyond language borders	fr	language-learning WordPress WooCommerce	robotsllmsaihumans	emailphone	none
culturesourde.fr ↗	62 match 1 shared topics	Culture Sourde - Découvrez la langue des signes et la culture sourde	France fr	language-learning WordPress WooCommerce	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.