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Hariom Singh | GenAI Engineer • Cyber Security • Full Stack Developer — 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 Information And Network Security →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
avijitroy.com ↗	70 match 2 shared topics	Avijit Roy - Cybersecurity Researcher, AI Engineer & Educator	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
nathan-confavreux.com ↗	70 match 2 shared topics	Nathan Confavreux \| AI & Security Engineer	en	information-and-network-security	robotsllmsaihumans	emailphone	none
avinashanshu.com ↗	69 match 2 shared topics	Avinash Anshu \| Secure AI Automation Engineer \| Cybersecurity Expert \| LangChain \| Python \| FastAPI	IN~ en	artificial-intelligence	robotsllmsaihumans	emailphone	none
jonesdevlab.com ↗	68 match 2 shared topics	Jones Dev Lab — AI Systems, Security Engineering & Custom Infrastructure	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
2thrivellc.com ↗	68 match 2 shared topics	Cyber Security and AI Consulting	en	information-and-network-security	robotsllmsaihumans	emailphone	none
infosecservices.co.uk ↗	68 match 2 shared topics	Infosecservices \| Cyber Security & AI	GB en	information-and-network-security	robotsllmsaihumans	emailphone	none
cybrcon.com ↗	68 match 2 shared topics	Cybrcon — AI Governance & Cybersecurity Compliance Engineering	en	information-and-network-security	robotsllmsaihumans	emailphone	none
hannibyte.com ↗	68 match 2 shared topics	Hannibyte — Backend and AI engineering, with security at the core.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
acecyberconsult.com ↗	68 match 2 shared topics	Acelogic Cyber Consulting LLC \| Security Reviews, Reverse Engineering & AI Security	en	information-and-network-security	robotsllmsaihumans	emailphone	none
accendum.com ↗	68 match 2 shared topics	Accendum — Cybersecurity · AI / ML · IT Development	en	information-and-network-security	robotsllmsaihumans	emailphone	partial · 8
hashirsamikhan.com ↗	67 match 2 shared topics	Hashir Sami Khan — Pentester & AI Security Engineer	PK~ en	information-and-network-security	robots llmsaihumans	emailphone	none
narayananexus.com ↗	67 match 2 shared topics	Narayana Nexus — AI Security Audits, Compliance & Engineering	IN~ en	artificial-intelligence	robots llmsaihumans	emailphone	none
natecrisler.com ↗	67 match 2 shared topics	Nate Crisler \| Principal AI Security Architect \| Platform Engineering & AI Infrastructure	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
dependencies.com ↗	67 match 2 shared topics	Safety - The AI Development Security Platform	en	artificial-intelligence	robots llmsaihumans	emailphone	none
cyberlab.biz ↗	67 match 2 shared topics	CyberLab — AI Security Research Lab	en	information-and-network-security	robotsllmsaihumans	emailphone	none
cybermassive.tech ↗	67 match 2 shared topics	CyberMassive \| Cybersecurity Consulting for Startups & AI Companies	en	information-and-network-security	robotsllmsaihumans	emailphone	none
switchbackcyber.com ↗	67 match 2 shared topics	Switchback Cyber \| AI & Cloud Security Reviews	US~ en	information-and-network-security	robotsllmsaihumans	emailphone	partial · 8
svastitechsoln.com ↗	67 match 2 shared topics	Svasti Technology Solutions \| Advanced Cybersecurity & AI	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.