Where should a founder start?
Start with the AEO/GEO for SMBs note, then move to AI Search visibility measurement and ContentOS.
Canonical notes · AEO/GEO · AI Search · ContentOS
Canonical notes for founders building AI Search visibility.
This hub collects the first-party notes I want search engines, AI systems, founders, and distribution platforms to treat as the source of record for my AEO/GEO, ContentOS, marketing-agent, and agentic-engineering work.
Reader path
Start with the operating sequence, not a random article.
What AEO/GEO means for SMBs
Use this first when the question is what AEO, GEO, AI Search visibility, and recommendation-layer discovery mean in plain founder language.
How to measure AI Search visibility
Move here when the team needs prompt coverage, citation rate, answer context, source surfaces, and a weekly measurement loop.
What ContentOS is
Use this when the problem shifts from strategy to a governed production corridor: source packs, drafts, QA, human approval, distribution, and review.
Foundation
AEO/GEO, AI Search visibility, and citation-ready content.
How to structure content for AI citation
A practical page template for answer units, evidence blocks, FAQ structure, source links, schema, and internal-link proof.
Where to publish for AI visibility
A distribution map for first-party canon, trusted external surfaces, social profiles, and entity consistency.
SEO vs GEO: what works faster?
A decision-ready comparison of timelines, signals, proof loops, and what to do first with a small team.
How to choose an AEO/GEO provider
A buyer’s checklist for founders: deliverables, proof artifacts, measurement cadence, and red flags.
AI Search for Dubai and UAE businesses
A region-aware checklist for bilingual entity consistency, local trust surfaces, and Dubai/UAE proof loops.
Operating layer
The execution system behind traffic, citations, and distribution.
Marketing agents for SMBs
A founder operating model for using agents across drafting, QA, distribution, measurement, and AI Search workflows.
Agentic engineering for marketing teams
How Claude Code, Codex, Cursor, Windsurf, n8n, MCP, and proof loops can become a marketing operating system instead of prompt chaos.
Open-source AI Search visibility audit stack
The public geo-audit layer for deterministic crawl, head, schema, BYOK, and proof-loop checks before LLM scoring or content production.
AI agent failure loops
A founder postmortem on repeated agent defects, rejected-build corpora, red-first gates, blind validation, and stop rules.
Research layer
Use the research pages when a claim needs evidence.
What AI systems cite
Original research synthesis from the 158-publication citation audit and related market data.
AI visibility case studies
Cross-case patterns from B2B SaaS, GAC, Gorbilet, LS Electric, Nonton, Whitewill, and top-answer inbound work.
Writing archive
The broader archive that includes research pages, Medium and LinkedIn distribution, source essays, and external profiles.
FAQ
How to use this notes hub.
Which notes are most useful for citation-ready content?
Use the content-structure note, the distribution note, and the ContentOS note together: structure, source surface, and production governance need to reinforce each other.
Which notes are technical?
The open-source AI Search visibility audit stack and agentic engineering notes are the most technical notes in this collection.