Field notes on AI visibility.
Working research, published as we go: how generative engines choose brands, protocols you can copy, and honest guides with named sources. No gated PDFs, no fabricated case studies — specimens are labeled specimens.
What is generative engine optimization (GEO)?
The 2026 field guide: definition, why 94% of B2B buyers using LLMs changes distribution, how engines pick brands, and the five-part workflow.
How ChatGPT chooses which brands to cite
Two memories — training data and Bing-backed retrieval — and the ranked signals: consensus, authority, extractability, access, entity clarity.
The 27-point AI visibility audit checklist
Our paid-audit checklist, published in full: baseline, crawler access, schema, extractable content, third-party footprint — with scoring.
GEO vs SEO: what actually changes
Same inputs, different scoreboard. The honest comparison table, the 2026 evidence, and how to split a small budget.
llms.txt: an honest guide, with a working example
What the file is, what engines actually do with it (insurance, not a hack), and a complete worked example for a B2B SaaS.
How to measure AI search visibility
The share-of-voice protocol behind our audits: frozen prompt panels, weighted scoring, framing logs — free to copy.
New entries are logged as the research happens. Every statistic carries a named source; every claim about engine behavior is dated, because engines drift.