The answer layer, explained.
How AI engines crawl, score, and cite your site — written from the code that measures it.
Generic AI Content Tools All Sound the Same: Here's What a Real GEO Platform Does Differently
Generic tools produce text AI engines have no reason to cite. A before/after example — and the parts of GEO most tools skip entirely.
Which AI Crawlers Matter for Your Visibility — and How to Set robots.txt Without Hurting Yourself
Training, search-index, and user-fetch bots have completely different consequences. Block the wrong one and you disappear from AI answers.
Discoverability for AI Search: Beyond the First Page — and Why Brand Searches Lie to You
LLM search retrieves past the visible top 10 — so presence is graded by depth, and prompts stay brandless so recall can't masquerade as reach.
Simulation: Testing Whether AI Answers Actually Cite You
Your page and its search-result rivals enter one arena; an LLM answers the prompt. Citation share and position decide who wins each question.
From Brief to Citable Draft: What AISEOP's Content Generator Does for Your AI Visibility
Brief in, publish-ready draft out — grounded in your own sources and already shaped for the way AI engines read and cite content.
llms.txt Explained: The File That Tells AI Assistants What Your Site Is About
A curated, token-efficient map of your site for AI assistants — what the emerging spec looks like and how AISEOP generates it automatically.
Rewriting Pages for AI Citation Without Losing the Plot: Inside AISEOP's Optimizer
Rewrites held to the same standards that evaluate them — with built-in guards so a better score never comes from invented facts.
One Brand, Many Domains: How Audience and Scope Keep Multi-Domain Visibility Honest
Your .com and .com.tr are one brand. Proof-based owned-domain sets keep sister sites from showing up as competitors in your own reports.