Frequently Asked Questions
Common questions about Generative Engine Optimization, the 13-signal framework (8 binary infrastructure + 5 metric thresholds), AI citations, and GEO infrastructure.
What is Generative Engine Optimization (GEO)?
Generative Engine Optimization (GEO) is the practice of structuring a website so that AI-powered systems — such as ChatGPT, Claude, Gemini, and Perplexity — can understand, trust, and cite its content. Unlike traditional SEO, which optimizes for search engine result page rankings, GEO optimizes for inclusion in AI-generated answers and recommendations.
How does GEO differ from SEO?
SEO focuses on ranking in traditional search engine result pages (blue links on Google). GEO focuses on being cited in AI-generated responses. SEO relies heavily on backlinks and keyword optimization; GEO relies on structured data, clean-room HTML, content authority, and AI surface files. Both are valuable — they address different discovery channels.
What are AI citations?
AI citations occur when an AI system like ChatGPT, Claude, or Perplexity references your website or business by name in a generated response. When a user asks an AI assistant a question and it recommends your business or links to your content, that is an AI citation. GEO infrastructure increases the likelihood and frequency of these citations.
What is clean-room HTML?
Clean-room HTML is server-rendered, framework-free HTML served specifically to AI crawlers. Most modern websites use JavaScript frameworks (React, Vue, Angular) that render content in the browser. AI crawlers do not execute JavaScript, so they cannot see this content. Clean-room HTML provides the same content in a format AI systems can parse directly.
What is the GEO signal framework?
The 13-signal audit framework evaluates whether a digital property exposes the infrastructure AI systems need to understand, verify, and cite public content. It combines 8 binary signals (presence/absence of foundational machine-readable surfaces) with 5 quantitative signals measuring how well a site delivers content to AI retrieval. The 8 binary signals span structured data markup, crawlable server-rendered HTML, actual bot crawl activity, content authority and depth, AI citation patterns, server performance, AI surface files (llms.txt, ai-content-index.json, etc.), and protocol support. The 5 quantitative signals measure render fidelity, source grounding, retrieval efficiency, page delivery throughput, and content freshness. Detailed scoring logic and threshold values are GEOlocus proprietary IP, available through controlled diligence.
What is a GEO score?
A GEO score reflects how well a property exposes the infrastructure AI systems need to understand, verify, and cite it. GEOlocus uses the 13-signal audit framework to evaluate readiness across accessibility, structure, currency, attribution, machine-readability, and delivery performance. Detailed scoring methodology, weights, and target values are proprietary and discussed under controlled diligence.
What are AI surface files?
AI surface files are specific files that AI systems look for when indexing a website. These include llms.txt (a human- and AI-readable description of your site for LLMs), robots.txt (with explicit Allow directives for AI crawlers), sitemap.xml (with accurate lastmod dates), ai-content-index.json (a structured index of your content), and MCP manifests (.well-known/mcp.json).
Which AI crawlers does GEO target?
GEO infrastructure targets all major AI crawlers: GPTBot and ChatGPT-User (OpenAI), ClaudeBot and Claude-Web (Anthropic), Google-Extended (Google AI), PerplexityBot (Perplexity), Bingbot, Applebot, Amazonbot, Meta-ExternalAgent, YouBot, DuckAssistBot, and cohere-ai. Robots.txt is configured to explicitly allow all of these crawlers.
How long does it take to see GEO results?
Initial infrastructure changes (clean-room HTML, structured data, AI surface files) can be implemented in days. AI crawler activity typically increases within one to two weeks as bots discover the improved content. Measurable citation improvements depend on crawl frequency and content authority, but significant GEO score improvements are often visible within the first week of implementation.
Does GEO work for any industry?
Yes. GEO infrastructure is industry-agnostic because it addresses the technical layer of AI visibility. The 13-signal framework (8 binary infrastructure signals + 5 metric thresholds) applies universally — every website benefits from structured data, clean-room HTML, and proper AI surface files. The content authority signal is the most industry-dependent, as it requires domain-specific expertise in the published content.
How is GEOlocus.ai different from an SEO firm?
SEO firms optimize for search engine ranking — keyword placement, backlink acquisition, meta-tag tuning. GEOlocus.ai engineers the infrastructure layer that makes content machine-readable to AI systems: clean-room HTML, Schema.org structured data, AI surface files, and the 13 signals that determine whether an AI cites you or ignores you. These are distinct disciplines addressing distinct discovery channels. In our 100-site benchmarking cohort, every major SEO firm we measured failed their own GEO readiness criteria — achieving a median of 3 out of 13 signals.
What does GEOlocus.ai pricing look like?
GEOlocus.ai offers two engagement types. Translation is a one-time engineering engagement that installs the full GEO infrastructure layer — clean-room HTML, structured data, AI surface files, bot routing — on your existing site. Maintenance is an ongoing retainer for continuous GEO optimization, monitoring, and quarterly reporting. Pricing depends on site complexity and current signal baseline. The initial GEO assessment is complimentary. Book a discovery call at geolocus.ai/book to get a scope and estimate.
How long until we see AI citation improvements after the Translation engagement?
The translation infrastructure is typically deployed within two to four weeks. AI crawler activity tends to increase within one to two weeks of deployment as bots discover the improved signal surface. GEO score gains are often measurable within the first 30 days. AI citation frequency (Source Grounding Ratio — SGR) can take 60 to 90 days to show material improvement, as AI systems need time to re-index and incorporate the new content quality into their response patterns.
Can we implement GEO ourselves without GEOlocus.ai?
The individual components are documented and implementable by an experienced engineering team: clean-room HTML rendering, Schema.org JSON-LD, llms.txt, robots.txt bot allowlisting. The challenge is the integration, the scoring methodology, and the judgment layer. In our 100-site cohort covering 31 industries, only one site (Top10Lists.us) achieved all 13 signals — and that site was purpose-built as a GEO reference implementation. Most teams that attempt self-implementation get the surface signals right (robots.txt, a sitemap) but miss the structural signals (RR, SGR, RTC) entirely.
Who owns the IP and who owns the content?
GEOlocus.ai retains ownership of the translation-layer IP — the methodology, scoring rubrics, structured-data templates, retrieval optimizations, and bot-routing patterns we use to render your content into AI-citable surfaces. You retain ownership of your content — every word, image, and editorial decision on your human-facing site stays yours. What gets deployed to your infrastructure is the OUTPUT of our translation layer running against your content; the IP that produces and refreshes it stays with us. The optional Maintenance retainer is month-to-month and fully cancellable; on cancellation the deployed snapshot keeps working as long as your infrastructure does, but stops being recalibrated as AI ingestion standards evolve.