AI & GEO FAQ
Evidence-based answers to help CMOs, PR/Comms and agencies win references in AI search.
Frequently Asked Questions
What is GEO and how does it differ from SEO?
GEO prepares your site for LLM answer engines by optimizing machine-readable artifacts, entity clarity, and citation signals. SEO targets ranking in web search; GEO complements it to win citations in AI outputs.
Which metrics matter most for AI visibility?
AI Citation Share, Answer Influence Score, Schema Reliability Index, GRI/ACI, and brand mentions in LLM outputs.
How do LLMs decide what to cite?
Topical authority, structured signals, entity disambiguation, freshness, performance, and crawlability.
What are GRI and ACI?
0–100 readiness scores across six dimensions—Entity Clarity, Answerability, Freshness, Infra/Crawlability, Performance, Local/Brand Signals.
What is Answer Influence Score?
Likelihood your answer blocks get reused by LLMs.
What is Schema Reliability Index?
Completeness and consistency of JSON-LD across your site.
How does the GEO Engine work?
Crawl → Entity graph → Schema/structure → Performance → RAG Feed → LLM Testbed → Change Intelligence.
What is the RAG Feed?
A structured export of high-confidence facts with sources for retrieval systems.
How do we make PR pages AI-answer friendly?
Quotable blocks, media schema, compact FAQ per announcement, and clean URLs.
What are common JavaScript risks?
Hidden content/schema due to client rendering and delayed hydration—prefer server-rendered critical elements.
How often should we re-audit?
Monthly; and after major launches or IA changes.
How do we internationalize GEO correctly?
Canonical + hreflang clusters, locale Org markup, language-scoped sitemaps, localized answer blocks with mirrored @id.
Can GEO work with headless CMS?
Yes—add schema modules at model level and stable @id URIs.
Which artifacts are essential?
Organization, WebSite, WebPage/Article, FAQPage/HowTo, Product/Offer (if applicable), Dataset for public data.
How do you prove impact?
Show citation deltas, score improvements, before/after answer blocks, and LLM testbed logs.
How do we get started?
Begin with Starter/Pro, ship JSON-LD, then iterate monthly with Change Intelligence.