AI Search & GEO Visibility Checker
Audit your website citability and crawlability for generative answer engines including ChatGPT Search, Perplexity AI, Google Gemini, and Claude. Track 12+ AI crawlers, validate /llms.txt, and inspect Knowledge Graph entity schemas.
AI Bot Permissions
Verify which AI crawlers have access to your live pages in robots.txt. Distinguish real-time search agents (ChatGPT-User, PerplexityBot) from foundation model training scrapers.
Knowledge Graph Anchoring
Inspect Organization schema and external sameAs links (Wikidata, Crunchbase, LinkedIn, GitHub) to ensure LLMs correctly disambiguate your brand from homonyms.
llms.txt Generator
Instantly audit or generate a valid /llms.txt manifest following the official open specification to provide AI models clean markdown navigation context.
From SEO to GEO: How Generative Answer Engines Change the Web
For over two decades, search engine optimization was centered on winning a blue link on a ten-result search engine results page (SERP). Today, the search landscape is undergoing its largest structural paradigm shift with the rapid ascent of Generative Engine Optimization (GEO) and AI-synthesized answer engines: ChatGPT Search, Perplexity AI, Google Gemini AI Overviews, and Claude Artifacts.
In generative search, users rarely click ten different links to compare fragmented answers. Instead, the AI agent performs autonomous real-time web retrieval, parses multiple authoritative candidate pages, resolves factual discrepancies, and synthesizes a direct, cohesive answer—citing the most authoritative primary sources with interactive attribution cards.
Inside the RAG Pipeline: How AI Engines Decide What to Cite
To optimize your website for AI citations, you must understand the underlying mechanics of Retrieval-Augmented Generation (RAG) utilized by answer engines:
1. Query Decomposition & Vector Retrieval
When a user enters a complex prompt, the AI decomposes it into sub-queries and fetches candidate URLs from live web indexes using both lexical BM25 search and semantic dense vector embeddings.
2. Content Extraction & Chunk Re-ranking
The engine extracts clean text from fetched HTML pages, segments them into semantically coherent chunks, and applies a cross-encoder re-ranking model to score chunks by factual relevance.
3. Information Gain & Fact Density Evaluation
Chunks containing concrete empirical figures, comparison tables, and direct definition answers receive higher citation weights than generic, adjective-heavy marketing fluff.
4. Knowledge Graph Trust & Synthesis
Before finalizing citations, the LLM cross-references the source against its Knowledge Graph entities (Wikidata, Wikipedia, LinkedIn) to filter out unverified domains and hallucinations.
The Modern robots.txt Playbook for AI Crawlers
One of the most catastrophic mistakes webmasters make is adding a blanket User-agent: * Disallow: / or indiscriminately blocking all AI user-agents. This inadvertently blocks real-time search agents, cutting off your business from ChatGPT Search and Perplexity organic traffic.
A sophisticated AI crawler strategy differentiates between Real-Time Search Agents and Model Training Scrapers:
- • Allow ChatGPT-User & PerplexityBot: Guarantees your newest product pages and blogs appear in live search citations.
- • Selectively Govern GPTBot & Google-Extended: If you wish to protect your intellectual property from training future foundation models while still ranking in search.
- • Disallow Internal Sensitive Endpoints: Keep
/api/,/checkout/, and/admin/closed to all crawlers.
What Is /llms.txt and Why You Should Adopt It Today
Standard web pages are cluttered with HTML wrappers, CSS layouts, client-side JavaScript bundles, and advertising scripts that waste LLM context window tokens. The /llms.txt standard proposes a clean, structured Markdown file hosted at your domain root that acts as an executive briefing document for AI systems.
An optimal /llms.txt contains a concise H1 project title, an executive blockquote summary, curated links to key documentation pages, and API specifications. By adopting this standard, you dramatically lower the compute overhead for AI tools (like Cursor, Anthropic, and OpenAI) analyzing your services.
Frequently Asked Questions
Everything technical leaders, SEO directors, and developers need to know about AI search and GEO.
What is Generative Engine Optimization (GEO)?
Generative Engine Optimization (GEO) is the next-generation practice of structuring, formatting, and architecting website content so generative AI search engines—such as ChatGPT Search, Perplexity AI, Google Gemini, and Claude—cite your brand as an authoritative primary source when synthesizing answers to user queries.
Does this tool predict exact rankings inside ChatGPT or Perplexity?
No. Any tool claiming to predict exact rankings inside private AI models is misleading you. Generative answer engines use non-deterministic generation and contextual query embeddings. Our tool evaluates the objective, verifiable technical criteria that determine whether AI engines can crawl, understand, and cite your site: robots.txt crawler permissions, /llms.txt manifests, Knowledge Graph entity disambiguation, tabular structures, and empirical statistics density.
What is the difference between GPTBot and ChatGPT-User in robots.txt?
GPTBot is OpenAI’s background web crawler used to scrape data for training future foundation models (GPT-4o, o1, o3). ChatGPT-User is the real-time search agent that fetches live web pages when an active user performs a search in ChatGPT. You can disallow GPTBot while allowing ChatGPT-User if you wish to earn organic search citations without contributing your proprietary content to foundation model training.
What is an /llms.txt file and why should my website have one?
/llms.txt is an emerging web standard (similar to robots.txt and sitemap.xml) that provides clean, human-readable markdown summaries, API documentation links, and key page overviews specifically structured for consumption by LLMs and developer AI assistants (such as Cursor, Claude, and OpenAI). Our tool includes an automated generator that writes an /llms.txt file for your domain.
Why are comparison tables so important for AI search citations?
Generative answer engines (especially Perplexity and ChatGPT Search) preferentially extract and cite tabular data when answering comparison, pricing, and feature questions. Tables format information with high semantic density, structured headers, and clear relationships, making them prime targets for direct LLM extraction.
How does Knowledge Graph disambiguation (sameAs) help AI visibility?
Large Language Models rely on Knowledge Graph entities to disambiguate your brand name from common words or competitors. By adding "sameAs" links in your Organization JSON-LD pointing to Wikidata, Wikipedia, LinkedIn, GitHub, or Crunchbase, you allow AI retrieval systems to cross-verify your company history, leadership, and domain authenticity.
Will blocking AI crawlers in robots.txt harm my Google rankings?
No. Disallowing AI-specific bots like GPTBot, ClaudeBot, or PerplexityBot does not affect your traditional Google Search rankings, which are crawled by Googlebot. However, blocking Google-Extended prevents your content from training Gemini and Vertex AI models. If you block ChatGPT-User or PerplexityBot, you forfeit traffic from those AI search platforms.
Is this AI Search / GEO audit completely free to use?
Yes, 100% free with zero registration, login, or gated credit cards required. You can audit any public website, inspect all 12 tracked AI bots, generate a tailored /llms.txt file, copy recommended code snippets, and share public audit reports with your engineering and marketing teams.