For two decades, being found online meant ranking on Google. Today a growing share of buyers never see a search results page at all — they ask ChatGPT, Gemini, Claude, or Perplexity a question and act on the answer. Generative Engine Optimization (GEO) is the discipline of making sure your brand is the one those engines name.
GEO vs. SEO
SEO optimizes for a ranked list of blue links. GEO optimizes for a single synthesized answer. The difference matters: in search, ranking #3 still gets clicks; in an AI answer, if you aren't mentioned, you are invisible. There is no second page.
- SEO rewards keywords and backlinks; GEO rewards entity clarity and citations the model trusts.
- SEO traffic is measurable in your analytics; GEO mentions happen off-site, inside the model's answer.
- SEO changes rank gradually; GEO mentions can shift the moment a model updates its training or retrieval sources.
How AI engines decide who to recommend
Answer engines pull from a mix of their training data and live retrieval (web search, indexed pages, and high-trust sources like Reddit, Quora, comparison sites, and news). When a model recommends a product, it is reflecting the consensus it has seen across those sources. Brands that appear consistently, with clear descriptions and credible citations, get named first.
If you aren't mentioned in the answer, you aren't in the consideration set — no matter how good your product is.
Where to start
- Measure your baseline: track which engines mention you for the prompts that matter, and which mention competitors instead.
- Find the gaps: identify prompts where competitors appear and you don't.
- Build citations: earn mentions on the high-authority sources models retrieve from.
- Strengthen your entity: add comparison pages, FAQ schema, and clear, factual descriptions of what you do.
Oras tracks all of this in one dashboard — your visibility score across each engine, competitor gaps, the citation sources behind every answer, and the specific actions that move you into the recommendation.