similar sites

Browse

Pricing AI Report

Tools

🚀Submit Your Site to AIPush your URL to AI search engines 🛰️AI Bot ObservatoryEvery AI crawler & who blocks it 📇AI Crawler Database900+ AI user agents + robots.txt gen ⚖️AI Crawler ComparisonCompare two AI crawlers head-to-head 💰AI Licensing / Pay-Per-CrawlWho can charge AI crawlers — Pay-Per-Crawl 🎛️AI Robots.txt Policy BuilderGoal-based robots.txt (cite me, don't train)🤖AI Crawler CheckerSee which AI bots your site blocks 🎯AI Readiness CheckerScore your site 0–100 for AI 🔎Similar Site SearchFind websites like any domain 🔗Backlink CheckerWho links to any site — free All tools →

Browse free →

Sites similar to stephanlemmer.com

Stephanlemmer alternatives & similar sites

Stephan J. Lemmer, Ph.D. — 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
stephanhoyer.com ↗	68 match 1 shared topics	Stephan Hoyer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
kevinstorer.com ↗	68 match 1 shared topics	Kevin M. Storer, Ph.D.	en	artificial-intelligence Ghost	robotsllmsaihumans	emailphone	none
stephanw.net ↗	67 match 1 shared topics	Stephan T. Wäldchen	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
stepheckman.com ↗	66 match 1 shared topics	Stephanie Eckman	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
stephanieschoch.com ↗	66 match 1 shared topics	Stephanie Schoch	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
stephaniemuxfeld.com ↗	66 match 1 shared topics	Stephanie Muxfeld	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
namitadube.com ↗	66 match 1 shared topics	Namita Dube, Ph.D.	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
neuroneural.net ↗	65 match 1 shared topics	neuroneural \| Sergey Plis, Ph.D.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
3mrullah.com ↗	65 match 1 shared topics	Emrullah Sonuç, Ph.D.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
garrettgreeleyphd.com ↗	65 match 1 shared topics	Garrett D. Greeley, Ph.D.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
gfairchild.com ↗	65 match 1 shared topics	Geoffrey Fairchild, Ph.D.	en	artificial-intelligence	robots llmsaihumans	emailphone	none
mebassett.info ↗	64 match 1 shared topics	Matthew Eric Bassett, Ph.D.	en	artificial-intelligence Drupal	robotsllmsaihumans	emailphone	none
stephankaras.com ↗	64 match 1 shared topics	Stephan Karas — AI Engineer, Researcher & Visionary	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
mcknightresearch.com ↗	64 match 1 shared topics	Oscar McKnight Ph.D.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
stephanzheng.com ↗	64 match 1 shared topics	Stephan Tao Zheng, Machine Learning Researcher.	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
stephanielterry.com ↗	64 match 1 shared topics	Stephanie Terry — Product Leader	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
dianmuzhang.com ↗	64 match 1 shared topics	Dianmu Zhang, Ph.D. — ML Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
stephanclaxton.com ↗	64 match 1 shared topics	Stephan Claxton: Systems Engineering for AI & Autonomy	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.