What is GEO? Generative engine optimization, explained

GEO, short for generative engine optimization, is the practice of making AI assistants recommend your brand when buyers ask them questions. It's also called AEO (answer engine optimization) or AI search optimization. Whatever the acronym, the work is the same: be quotable, be cited, and be mentioned when someone asks ChatGPT, Claude, Gemini or Perplexity who to hire.

How AI answers choose what to say

Assistant answers are synthesized from sources: search results, publisher pages, reviews, comparison articles, and the web at large. When a model recommends a shortlist, it's echoing a pattern it found in those sources. Which means visibility is earned upstream: in the press coverage, the comparison pages, and the clear answers on your own domain.

The three levers

• Earned media: being named in the articles and shows models read. Podcast appearances, newsletter mentions, and journalist coverage feed the pattern.
• Answer-shaped content: pages on your domain that directly answer buyer questions ("X vs Y", "best X for Z"), written plainly enough to be quoted.
• Measurement: asking the models the buyer questions daily, tracking mention rate and share of voice, and fixing what's weak.

Why PR teams own it

Because lever one is the biggest. A PR team that lands podcast after podcast is manufacturing the citations AI reads, which is why pitching platforms like PodPitch, and AI-visibility products built on top of them like VisibilityOS, sit naturally in the PR stack. Monitoring-only tools like Muck Rack's Generative Pulse show the effect without doing the cause.

What doesn't work

Keyword stuffing your way into AI answers (they paraphrase, not scrape), buying fake reviews at scale (models discount obvious patterns), and treating GEO as separate from PR (it's the same work with a new scoreboard).