Tag

#generative-engine-optimization

3 posts

What Is Generative Engine Optimization (GEO)? The Measured Guide

Generative engine optimization (GEO) is making your content likely to be retrieved, used and cited when AI systems generate answers. The KDD 2024 paper that coined the term measured what works across 10,000 queries: quotations +41%, statistics +40%, cited sources +30–40%, keyword stuffing ~nothing — and rank-5 sites gained +115% while top-ranked sites lost 30%. What the paper found, what we've measured running a six-engine monitor (ChatGPT grounded only ~74% of calls), and the two-halves playbook: liftable content on your site, retrievable coverage everywhere else.

Aug 1, 2026Read article→

Comparisons

Best AI Visibility Tools in 2026 (AEO/GEO) — With the Numbers Vendors Don't Show

AEO, GEO and 'AI visibility' are three names for one category, and the tools all promise the same dashboard. From running our own six-engine monitor in production: measuring one question across six engines costs 24.5¢ at vendor-billed rates, ChatGPT's API only browses the web on ~74% of calls, and corpus mention counts invert against live answers at both ends. The tier map, the measured cost table, and the five questions that separate real monitors from vibes.

Aug 1, 2026Read article→

AI Research

How AI Decides Which Brands to Recommend (and How to Get Cited)

AI recommends brands with two engines: training-data recall and live web retrieval — and neither takes payment. Both reward the same thing: a consistent, credible, well-corroborated web presence. Here's how the machinery works, which sources AI cites most (Reddit, Wikipedia, review sites, press), what Princeton's GEO research found makes content citable, and the honest playbook to get named.

Jul 1, 2026Read article→

← Stork.AI Blog