No black boxes. Below: the peer-reviewed paper, the official guidelines, and the primary docs behind every pillar — plus an honest label for what's measured, what's documented, and what's our judgment.
Princeton researchers coined “Generative Engine Optimization” and benchmarked 9 methods on Perplexity.ai and GPT-4 with search across 10,000 queries. Statistics, quotations, and cited sources won decisively (up to ~40% gains); style tweaks did almost nothing. Our Citability pillar and weighting philosophy come straight from these results.
arxiv.org/abs/2311.09735No paper measures wedge-vs-generic directly, but the strategy follows from retrieval economics: a new brand can only be the defensible answer in a narrow buying situation (customer × geography × size × constraint). We score 0–4 detected dimensions.
Practitioner playbooks + RAG retrieval literatureRetrieval systems prefer self-contained, directly-answering chunks; the GEO paper's best methods all reward quotable blocks with question structure. Selection pages (pricing, vs, GDPR, integrations) mirror measured buyer-query clusters.
GEO paper (ibid.) + RAG retrieval literatureStatistics Addition, Quotation Addition and Cite Sources were the top-performing GEO methods — up to ~30–40% visibility lift. Fluency/style rewrites barely moved results. Methodology disclosure and limitation honesty are our synthesis on top.
GEO: Generative Engine Optimization — Aggarwal et al., Princeton, Nov 2023Google's rater guidelines define E-E-A-T — third-party validation beats self-claims — and Google explicitly warns against manufactured mentions. AI Overviews/Gemini inherit these signals.
Google Search Quality Rater Guidelines + AI-optimization guideBot operators publish exactly what they need: allowed user-agents (GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot), renderable HTML, sitemaps, IndexNow. Google states its AI features rely on the normal search index — no special markup shortcuts.
OpenAI bots docs · schema.org · Google AI-optimization guide