For years, the first page of Google decided which brands buyers discovered. That habit is starting to shift: buying research can now begin with a question typed into an AI assistant: "What are the best async video tools for a remote sales team?" or "Which CRM should a ten-person agency use?" The assistant answers directly, names a handful of brands, and often cites its sources. There is no page two.
Generative engine optimization - GEO - is the practice of measuring and improving how AI answers talk about your brand. Where SEO asks "where do we rank on the results page?", GEO asks three different questions: are we mentioned at all, is our website cited as a source, and how do we compare to the competitors named alongside us?
Why the answer box changed the game
A search results page offers ten links and lets the buyer choose. An AI answer makes the choice for them: it synthesizes whatever the model knows and whatever it can retrieve, then presents two to five brands as the shortlist. If your brand is not in that shortlist, you are not losing a click - you are absent from the consideration set entirely.
The mechanics are also different. Rankings on a results page are comparatively stable and observable. AI answers vary by provider, by phrasing, and over time as models are updated and their retrieval sources change. ChatGPT, Claude and Gemini can give three different shortlists to the same question on the same day. That variability is exactly why measurement has to come first.
The four numbers that define AI visibility
- Mention rate: the share of buyer questions where the AI answer names your brand. This is the GEO equivalent of ranking at all.
- Citation share: how often the answer cites your domain as a source. Citations are the closest thing AI answers have to backlinks - they signal that the model treats your content as evidence.
- Average position: when several brands are named, where yours appears in the answer. Being the first brand named is very different from being the fourth.
- Share of voice: your mentions compared to every competitor named across the same set of questions. This is the number that tells you whether you are winning the category conversation.
Threadly computes all four from real answers. You track the questions your buyers actually ask, and each scan sends them to ChatGPT, Claude and Gemini, reads every answer, and records which brands were mentioned, which domains were cited, and in what order.
GEO does not replace SEO
The two disciplines feed each other. AI assistants lean heavily on the open web: comparison articles, documentation, community threads, review sites. A brand with thin, outdated content gives the models nothing to work with. But classic SEO tools will not tell you whether ChatGPT recommends you, because they were built to watch a results page, not to read an answer. That gap is what GEO tooling exists to close.
Where to start
- Write down the ten to twenty questions a buyer would ask an AI assistant before choosing a product like yours. Be honest and category-level: "best X for Y", "X vs Y", "affordable X alternatives".
- Run those questions through the major AI providers and record the answers. Note every brand named and every source cited.
- Compare against your competitors question by question. The questions where they appear and you do not are your content roadmap.
- Repeat on a schedule. AI answers drift as models and sources change, so a one-off check goes stale quickly.
You can do all of this by hand, and many teams start that way. Threadly exists to make the loop automatic: track the questions once, scan on a schedule, and watch mention rate, citation share, position and share of voice move over time - across all three providers in one place.