Generative Engine Optimization, AI search, LLM crawlers, citation strategy.
13 articles
Citation rate is the GEO equivalent of organic CTR — and your dashboard does not show it. Here is how to define it, instrument it, and report it without lying.
llms.txt is a proposed standard, not a ratified one — but the practical case for shipping it in 2026 is stronger than the chatter suggests. Here is what to put in yours.
GEO is not a replacement for SEO. It is a sibling discipline that shares technical foundations, diverges on what counts as a win, and forces a different team workflow.
LLMs don't read your site the way Googlebot did. They read entities. If your brand isn't a node in their knowledge graph, you don't exist in the answer.
Whether to block AI training crawlers is a strategic question, not a technical one. The right answer depends on your business model, your IP, and your appetite for licensing leverage.
GEO is not SEO with a coat of paint. It's a different game played on the same technical board. Here's the four-leverage framework, the metrics that matter, and how to put it on your team's roadmap.
Perplexity citations don't follow Google's logic. Older domains, .edu and .gov bias, deeper retrieval, and a freshness signal that punishes thin update cycles. Here's the playbook for the second-largest answer engine.
Zero-click search is not a future risk; it is current state. The strategies that capture revenue when users never visit your site look different from classical SEO.
Eight LLM crawlers now hit your site. Some train, some retrieve, some do both. Blocking the wrong one costs you AI-channel visibility for nothing. Here's the matrix and the robots.txt that maps to it.
AI Overviews launched US-wide May 14 2024 and now show on roughly 18% of US queries. Citations don't go to the top-ranked page — they go to the most retrievable one. Here's how to engineer for that.
OpenAI's search layer is opaque, but parts of it are testable. The ChatGPT-User vs GPTBot split, freshness sensitivity, and brand entity strength all leave fingerprints — here's how to read them.
Most schema audits inflate the number of types deployed. LLM retrieval layers reward correctness over coverage. Three well-formed schemas beat twelve sloppy ones — here's which three.