A short primer on why AI search is different from Google, and what that means for your visibility.
The shift: from links to answers
Traditional search engines return a list of links. AI assistants return a single synthesized answer, often with a handful of sources cited beneath it. This changes everything about how brands get discovered.
On Google, ranking #1 gets you clicks.
On ChatGPT or Perplexity, being mentioned in the answer or cited as a source is what matters.
How AI assistants build answers
When a user asks an AI assistant a question, the model typically:
Interprets the query and may expand it into multiple sub-questions (called query fan-out).
Retrieves information from its training data, real-time web searches, or both.
Synthesizes an answer that combines multiple sources into a single response.
Cites sources (sometimes) to back up specific claims.
Your brand can appear in two places: named directly in the answer text (a brand mention) or linked as a source (a URL citation).
What AI models look for
AI models favor content that is:
Clear and factual: Direct answers beat marketing fluff.
Well-structured: Headings, lists, and clean formatting help models extract information.
Authoritative: Sources with topical depth and trust signals get cited more often.
Present across the web: Mentions on Reddit, Wikipedia, review sites, and industry publications all feed into how models understand your brand.
What is LLMO / GEO / AEO?
These acronyms all describe the same emerging discipline:
AEO: Answer Engine Optimization
GEO: Generative Engine Optimization
LLMO: Large Language Model Optimization
ALLMO: Applied Large Language Model Optimization
The goal is the same: make sure AI models understand your brand accurately and surface you in relevant answers.
Why this matters now
AI search traffic is growing fast, and the companies optimizing today are building a durable visibility advantage. ALLMO.ai gives you the data to do this systematically.
Next up: How to set up your first report →