#AI Discoverability — Blog

SEO Tutorials Jun 6, 2026

Auditing Your Own Site for AI: robots.txt, llms.txt, JSON-LD, and the Four Gates of Citation

Most AI-visibility advice points outward — earn citations, get on Wikipedia, court the review platforms. All worthwhile. But there's a cheaper, faster lever sitting right under you: your own website. If a model can't retrieve your pages, can't rank them, can't extract clean claims from them, or can't attribute those claims back to you, no amount of off-site work fully compensates. This is a practitioner's walkthrough of the on-site AI audit — the files and signals that matter, organized around the four gates an answer has to pass through to cite you.

#GEO #Schema Markup #AI Discoverability

SEO Tutorials Apr 13, 2026

Schema Markup for LLMs: 7 Elements That Matter, 12 That Don't

Schema markup is the single most over-prescribed piece of tactical advice in GEO. Every checklist tells you to add it. Few tell you which parts actually affect how LLMs describe your brand, which parts only help Google's rich snippets, and which parts have become decorative. This post is the triage: the seven schema elements worth implementing properly in 2026 for AI visibility, the twelve you can safely deprioritize, and the one that matters more than all the rest combined.

#For SEO Managers #Schema Markup #AI Discoverability

SEO Tutorials Mar 10, 2026

The Entity-First Content Playbook: Structuring Pages for AI Retrieval

The content playbook that served SEO for a decade was keyword-first. Pick a target phrase, cluster supporting topics around it, match search intent, earn links. That playbook still works for Google — but it leaves a significant amount of AI visibility on the table. LLMs do not ingest pages as bags of keywords. They parse them as webs of entities and relationships. Restructuring content to match how the model actually parses is the difference between being retrieved in an answer and being skipped. This is the playbook.

#For SEO Managers #Schema Markup #Content Strategy