AI Search & GEO

GEO is optimizing to be the source AI answers cite, not to rank in a list of links. Here is how it differs from SEO and AEO, why citation share is the metric, and what a GEO program involves.

GEO for a software company is owning the five questions a buyer asks an engine. The stack, the page types, and a 90-day program a small team can run.

Query fan-out is how AI engines rewrite one prompt into several hidden sub-queries and retrieve each separately. Here is how it works, how to reconstruct your own fan-out footprint, and why Ahrefs found over a third of AI Overview citations skip the top 100 organic results entirely.

ChatGPT launched ads in February 2026, Google and Microsoft serve them from campaigns you already run, and Perplexity walked away entirely. Here is how the surface works, how it differs from search, and the tier constraint that decides whether it fits B2B.

The full protocol for auditing AI search visibility: build a 60 to 150 prompt universe, test search on and off across engines, read the diagnosis matrix, and build a source ledger that becomes your roadmap.

Every AI lab ships a training bot, a search-indexing bot, and a live-fetch bot, each needing its own robots.txt directive. Here is the full configuration, the llms.txt verdict backed by four independent studies, and what to check beyond the file itself.

AI engines retrieve passages, not documents. Here are the sentence-, section-, and page-level rules that measurably move citation rate, backed by a 150,000-citation sentence-length study.

Campaign structure, objectives, budget, context hints, creative limits, and the measurement stack to configure before launch, plus the five failure patterns that account for most wasted first-campaign budget.

Context hints replaced keywords as the targeting control in ChatGPT Ads. Here is the four-part structure that matches well, worked examples at three intent stages, the six mistakes that waste budget, and how to source hints from real buyer language.