AI search vs SEO: what changes, what doesn't, and what teams get wrong
A practical guide to the differences between AI search and SEO, what still matters, and how teams using Claude Code and OpenClaw can build for both without duplicating work.
AI visibility playbooks, without the fluff.
Practical workflows for getting cited and measured across major AI answer engines.
- Runbook-first: fast experiments you can ship this week
- API-ready: citation + share-of-voice reporting workflows
- Decision support: tool and implementation scorecards
Who this is for + what we cover
If you lead growth, SEO, or product marketing and need a clear AI visibility system, start here. We focus on signal quality, reproducible tests, and compounding distribution loops.
Every post includes a short scan-first summary at the top, followed by long-form implementation depth underneath so teams can move quickly without losing the full SEO and AEO context.
Latest insights
Scan the summary first, then open each guide for the full long-form playbook.
AI search ranking signals: what helps agent documentation show up in AI answers
Learn which ranking signals matter when you want Claude Code docs, OpenClaw skill libraries, and agent runbooks to show up in AI answers.
AI Search Optimization: how brands get found in LLMs
A practical guide to AI search optimization for teams that want to show up in ChatGPT, Claude, Gemini, and Perplexity without turning content into fluff.
How to Track AI Visibility by Country and Language
A practical workflow for measuring how AI answers change across markets, languages, and buyer contexts before you make the wrong expansion decisions.
How to Write Content Briefs That Get Cited by AI Answer Engines
Most content briefs are still optimized for Google's blue links. This guide rewrites the brief format for 2026, where ChatGPT, Claude, Gemini, and Perplexity decide which sources to surface in zero-click answers.
How to keep Claude Code and OpenClaw docs fresh for AI citations
Keep Claude Code and OpenClaw docs current enough for AI answer engines to cite by combining static-first docs structure, release-linked updates, and evidence-based monitoring.