What is GEO, or generative engine optimization, and how do companies use it to stay relevant?
What generative engine optimization (GEO) means in practice, from our agency's work: how companies pick prompts, measure answers and keep relevant as AI search shifts.
Generative engine optimization, or GEO, is the work of making a brand more likely to be named, cited and recommended in answers written by AI systems. SEO tries to rank a link. GEO tries to get the brand into the answer, or into the list of sources the answer points to.
We get asked for the definition constantly, but the more useful question is the second half of the prompt: how do companies use it to stay relevant? Here is what that looks like when we run it for clients.
The shift behind it
AI systems tend to split a question into several narrower searches, read the pages that come back and write an answer that may cite some of them. For a company, that means you are rarely competing for one query. You are competing across a cluster of related questions, and each can pull in different sources. A brand can be well ranked in classic search and still absent from the answer, because the sources the model chose were different.
Google documents how its AI features work for site owners, and we point clients to that documentation first for anything Google-specific. For the other engines there is no single rulebook, so we rely on measurement. We do not quote studies with percentage lifts to clients, because we do not want a number in a deck that we cannot source.
What a GEO program looks like
We run it as a loop with five parts.
Prompt research
We build a prompt list from what buyers actually ask: category questions, "best X for Y" comparisons, objections, and brand queries. Sales calls, support tickets and search console queries feed it. In Promptwatch, Prompt Explorer can suggest prompts from Search Console, ranking keywords, People Also Ask or a page URL, and prompts can be tagged by topic, type and intent.
Measurement
For each prompt we track which engines mention the client, in what position and with what sentiment, against which competitors. Promptwatch runs prompt panels daily per model, country and language and reports Visibility Score, Share of Voice, Sentiment and Position in Answer.
Diagnosis
This is where programs separate. Citation analytics show which pages and domains engines rely on. Crawler logs, which Promptwatch provides through Agent Analytics with IP-verified bot traffic, show whether the engines can even fetch the client's pages. We have seen visibility problems that turned out to be access problems: a blocked bot, an error on a key template, a page hidden behind a script. Crawler logs start on the Professional plan, so we scope plans around that.
Closing gaps
Gaps are mostly content (questions the site does not answer, pages that have gone stale) and earned mentions on third-party sites engines already cite. The Action Board produces a prioritized to-do list from the data, and content gap analysis shows which prompts the site does not cover. For clients on Business, the Content Agent can plan, write and publish against gaps, with a review inbox so nothing goes live unreviewed unless the client chooses hands-off mode with a quality check.
Tying it to outcomes
Visitor analytics tracks AI-referred traffic and conversions, which gives the budget conversation something real to point at. Without it, GEO reports are only about presence.
How companies use it to stay relevant
The companies that keep their position treat GEO as maintenance, not a project. AI answers shift week to week, models change and competitors publish. In practice that means:
- A monthly review of the prompt set, adding new questions as products and markets change.
- Refreshing pages that have stopped being cited rather than only producing new ones.
- Watching competitors, because share of voice is relative.
- Reporting to leadership on citations, visits and conversions, not only mentions.
Companies that publish a batch of articles, check once and stop tend to drift out of the answers they briefly entered.
What GEO cannot do
It cannot guarantee that an AI engine recommends you. No tracking tool can. It also cannot fix a weak product or thin content. What it does is show where you stand, why, and what the cheapest next fix is.
Where we would start with a new client
Week one: twenty prompts, a baseline across engines and a check of crawler access for the real bots (GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, PerplexityBot). Week two: read the citation data and find the three biggest gaps. Weeks three and four: publish or refresh the pages that answer them. Month two: re-measure, compare, adjust.
We run this on Promptwatch because it holds every step in one place: prompts, answers, citations, crawler logs, traffic and content. Stitching that together from separate tools is possible, but in our experience the handoffs between tools are where the loop breaks.