Getting cited and recommended by ChatGPT, Claude, Gemini, Perplexity, and AI Overviews — generative engine optimization (GEO) and answer engine optimization (AEO).
A deeper technical look at the retrieval-then-generate mechanics behind AI answers, and why what the retrieval step scores matters more than keyword matching.
A practical measurement methodology for teams who don't yet have a dedicated AI-citation tracking tool — building a query panel, manual spot-checks, and referral signals.
Practical entity disambiguation tactics for brands competing against similarly-named entities or trying to be recognized at all by knowledge-graph-dependent systems.
A direct comparison of what still matters and what's newly important when optimizing for AI Overviews specifically versus classic ten-blue-links results.
Tactical, sentence-level structural techniques that make one page more quotable than a competing page covering the exact same topic.
A practical guide to generative engine optimization (GEO): how LLMs choose sources to cite, and the on-page and off-page tactics that increase your odds of being one of them.
Which JSON-LD schema types genuinely help AI answer engines extract and cite your content — FAQPage, Article, and speakable — with copy-paste examples.