Generative Engine Optimization, primary research only.
We test what other publications copy. Reproducible methodology, open datasets, editorial wall — for the brands and operators who refuse to guess.
Latest
All articles →- measurement · 7 Jun 2026Cornerstone
State of GEO Q2 2026: the AI engine you optimize for matters most
We probed 100 brands across ChatGPT, Claude, Gemini, and Perplexity — 4,000 prompts. Citation rate varies far more by which engine you ask than by which brand you are: Perplexity cites 42%, ChatGPT 7%, Gemini hides its sources, and legacy news publishers get 0%.
- measurement · 7 Jun 2026
Who LLMs cite for GEO: 927 sources, 417 domains, zero of them us
Across 50 GEO questions, four LLMs returned 1,105 source links. After setting aside Gemini's opaque redirects, the 927 attributable citations spread across 417 domains — a long tail led by YouTube and SEO-tool blogs. Here's the map, with the grounding caveat up front.
- technical · 7 Jun 2026
JSON-LD Recipes for Articles, Datasets, and FAQs
Copy-paste JSON-LD blocks that pass schema.org validators, render correctly in Google Rich Results, and surface in AI search engines. Annotated with what each field actually does.
- case-studies · 31 May 2026Cornerstone
This site is our GEO lab: the stack, the data, the experiments
GeoSalience measures its own AI-crawler traffic, tracks whether LLMs cite it, and runs controlled experiments on its own pages. Here are the first real numbers: an 898-request crawler footprint, a measured 0% citation baseline, and an honest account of where data is still thin.
- foundations · 31 May 2026
GEO vs AEO vs LLMO vs SGE: An Honest Taxonomy
Four acronyms, mostly the same thing — and a few clear distinctions worth keeping. We pulled the original definitions, checked who uses which term, and argue which one should win.
- foundations · 31 May 2026
Knowledge Cutoff, Web Access, and Why It Matters
An LLM that browses the web in 2026 still answers from a training anchor months — sometimes a year or more — in the past. Knowing which knowledge comes from where, and how the two interact, is the prerequisite for any GEO strategy.
Pillars
Foundations
Theory, mechanics, and history of AI search. How LLMs retrieve, rank, and cite.
Technical
Server-side, code-level, infra-level. llms.txt, robots, schema, structured data.
Content Engineering
Content format, structure, writing for LLM ingestion.
Measurement & Analytics
Tools, metrics, methodology. Reproducible benchmarks.
Brand & Behavior
User behavior in AI search. Brand strategy.
Case Studies
Brand-by-brand teardowns. Sector benchmarks.
Pulse
Newsroom: model releases, leaks, papers.
Cornerstone
- measurement
State of GEO Q2 2026: the AI engine you optimize for matters most
We probed 100 brands across ChatGPT, Claude, Gemini, and Perplexity — 4,000 prompts. Citation rate varies far more by which engine you ask than by which brand you are: Perplexity cites 42%, ChatGPT 7%, Gemini hides its sources, and legacy news publishers get 0%.
- case-studies
This site is our GEO lab: the stack, the data, the experiments
GeoSalience measures its own AI-crawler traffic, tracks whether LLMs cite it, and runs controlled experiments on its own pages. Here are the first real numbers: an 898-request crawler footprint, a measured 0% citation baseline, and an honest account of where data is still thin.
- technical
llms.txt: Spec, 100-Domain Adoption Audit, and Setup
We audited 100 top developer-tools and SaaS sites for an llms.txt file. Only 37 of them serve one at the apex — and the gap is concentrated in the places you might expect it not to be. The full spec, the audit, and a 10-minute setup.
- foundations
How to Get Cited by LLMs: The Complete Taxonomy of GEO Methods
Every method GEO practitioners use to surface in ChatGPT, Claude, Perplexity, and AI Overviews — grouped into five families and rated by evidence quality. A synthesis of the published literature, vendor docs, and our own audits. The map of the discipline as of June 2026.
- foundations
What is Generative Engine Optimization (GEO)?
An honest taxonomy of the discipline that is reshaping how the web is read — by both humans and machines.