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Moltis alternatives & similar sites

Moltis — A secure persistent personal agent server in Rust — 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
auth0.com ↗	65 match shared topic	Secure AI Agent & User Authentication \| Auth0	United States~ en	artificial-intelligence	robots llmsaihumans	emailphone	none
snyk.io ↗	65 match shared topic	Snyk AI Security Fabric \| Secure Code, Models & Agents \| Snyk	en	information-and-network-security	robotsllmsaihumans	emailphone	none
alice.io ↗	65 match shared topic	Alice \| Secure, Safe, & Trustworthy AI	en	information-and-network-security	robots llmsaihumans	emailphone	none
snyk.co ↗	65 match shared topic	Snyk AI Security Fabric \| Secure Code, Models & Agents \| Snyk	Colombia en	information-and-network-security	robotsllmsaihumans	emailphone	none
schemapin.org ↗	65 match shared topic	SchemaPin — Cryptographic Tool Verification for AI Agents & MCP Servers	en	information-and-network-security	robotsllmsaihumans	emailphone	none
onetrust.com ↗	60 match shared topic	Responsible AI Governance & Compliance Solutions \| OneTrust	United States~ en	artificial-intelligence Adobe Experience Manager	robotsllmsaihumans	emailphone	none
okta.com ↗	60 match shared topic	Secure Identity for Employees, Customers, and AI \| Okta	en	information-and-network-security Adobe Experience Manager	robotsllmsaihumans	emailphone	none
hackthebox.com ↗	60 match shared topic	Cyber Mastery: Community Inspired. Enterprise Trusted. \| Hack The Box	en	artificial-intelligence HubSpot	robots llmsaihumans	emailphone	partial · 8
trstd.com ↗	60 match shared topic	#trstd – Know What's Real \| Trust Infrastructure for People, Businesses & AI	en	information-and-network-security HubSpot	robotsllmsaihumans	emailphone	none
wald.ai ↗	60 match shared topic	Wald: AI Data Loss Prevention (AI DLP) and Secure LLM Access for Enterprises	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
yoti.com ↗	60 match shared topic	Building the world's trusted identity platform • Yoti	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	partial · 8
ipswitch.com ↗	60 match shared topic	Transfer Data the Secure & Compliant Way \| Progress	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
hackthebox.eu ↗	60 match shared topic	Cyber Mastery: Community Inspired. Enterprise Trusted. \| Hack The Box	European Union en	artificial-intelligence HubSpot	robotsllmsaihumans	emailphone	none
signicat.com ↗	60 match shared topic	Identity solutions for a trusted digital world - Signicat	en	information-and-network-security	robotsllmsaihumans	emailphone	none
endorlabs.com ↗	60 match shared topic	Endor Labs \| Agentic Application Security Platform	United States~ en	information-and-network-security	robotsllmsaihumans	emailphone	none
hoop.dev ↗	60 match shared topic	hoop.dev \| Accelerate your AI agents. One gateway to every data source.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
trmlabs.com ↗	60 match shared topic	TRM Labs \| Agents and intelligence to fight crime	United States~ en	information-and-network-security	robotsllmsaihumans	emailphone	none
securityaudit.dev ↗	60 match shared topic	Security Audit AI Agent	en	information-and-network-security	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.