“Ranking” in AI search is less about position and more about being recommended and cited. The engines differ in detail, but the underlying levers are consistent. Here is the framework we use.
1. Establish entity authority
Before a model can recommend you, it must clearly understand who you are. That means a well-defined, consistent entity across your site, structured data, and the broader web — so the model can disambiguate you from similarly named organizations.
2. Earn citations from sources AI trusts
Models lean on sources they regard as credible. A steady presence in authoritative publications, reference sites, and industry directories increases the odds of being surfaced and cited.
3. Structure content to be retrieved and quoted
- Answer real questions directly and early in the page.
- Use clear headings, summaries, and structured data.
- Make key facts explicit and easy to extract.
4. Build cross-web consensus
When multiple credible sources describe you consistently, models gain confidence in recommending you. Consistency of your entity data across the web matters as much as any single page.
5. Do not neglect Bing
Microsoft Copilot is built on Bing's index, and Bing data feeds AI answers more broadly. Bing organic visibility is a leading indicator of AI visibility — track it.
Being recommended by AI is earned the way trust is earned: clarity, credibility, and consistency.
For definitions of the terms used here, see the GEO glossary. To understand the formatting side in depth, read our piece on LLM optimization.