What GEO is, and what success looks like
Generative engine optimization is the practice of getting AI engines, ChatGPT, Gemini, Claude, Perplexity and Google's AI Overviews, to cite and recommend you when they answer questions in your category. Success is not measured by classic organic rank. It is measured by whether the engines name you on the topics you want to own, because the goal is for Google and the LLMs to treat your brand, your site and your people as synonymous with the topic.
That reframing matters commercially. Even where AI Overviews eat the informational click, being the cited source still builds brand mentions and topical authority, which feed back into every other channel. When I started at Superpower, mentions of the brand in LLM answers were zero. A year later it was thousands, across biomarker testing and adjacent health topics, and that citation footprint moved before the traffic did.
How the engines choose who to cite
The engines reward additive, first-party expert content, validated by third parties. Not comprehensive content, additive content: things only you can say, said by a named person with a track record. Any citation has become an authority signal, not just links, because the models pull from social media, podcasts, press and forums as readily as from webpages. A brand mentioning your brand trains the model to connect you to the topic.
So the work splits in two. On your own site, hub-and-spoke pages built to rank in Google and be cited by AI answers at the same time, with a real expert's point of view on every page rather than a rewrite of what already ranks. Off your site, systematic placement of that expert's voice where the engines already look. The biggest jumps I saw at Superpower came from first-person authoritative content by our authors on third-party sites the engines trust. The on-site half is the same discipline as good SEO, and the question of what to publish is answered by how AI Overviews choose citations.
How to measure it
My measurement approach is citation share, not rankings. I run a fixed panel of 25 to 30 buyer queries monthly through the major assistants and AI Overviews, logging whether the client is cited and as whom. I built a tool that runs those prompts across OpenAI, Gemini, Claude and Perplexity and checks whether the client's domain appears. Citation share moves weeks before rankings do, which makes it the leading indicator this channel was missing.
Baseline before you spend anything, or nothing is attributable afterwards. The same panel also tells you what the engines currently believe about your category, which is often the most useful competitive research you will run this year. For the revenue stakes of getting this wrong, see how much traffic sites are losing to AI search, and for the buyer-side playbooks, how ChatGPT decides what to recommend and LLM SEO. If you want this run for you, it is part of every engagement I take.
FAQ
What is generative engine optimization?
The practice of getting AI engines like ChatGPT, Gemini and Google's AI Overviews to cite and recommend your brand when answering questions in your category. Success is measured by citation share on a fixed query panel, not by classic organic rank.
Does GEO actually work?
Yes, measurably. At Superpower, LLM mentions went from zero to thousands across target topics in about a year, driven by first-party expert content on-site and systematic third-party placement of the same experts. Citation share moved weeks before rankings did.
How is GEO different from SEO?
Roughly 70 to 80 percent of it is SEO done properly. The differences: the unit of success is a citation rather than a click, third-party mentions matter as much as links, and content must be additive and attributable to a named expert rather than comprehensive and anonymous.