Tools / AI Search & GEO

AI Search Optimization Tools

The playbook for improving citation readiness in ChatGPT, Perplexity, and Google AI Overviews is different from traditional SEO. These free tools measure your AI visibility, score content extractability, audit technical foundations, and find the gaps where competitors appear but you do not.

Below you will find every tool organized by purpose, plus clear explanations of what GEO, AEO, and AI visibility actually mean — and why they matter right now.

SEO, AEO, GEO, AI visibility — what is the difference?

Traditional SEO

Optimizing pages to rank higher on Google and other search engines. Focuses on keywords, backlinks, page speed, and technical crawlability. The metric is page position.

Answer Engine Optimization (AEO)

Structuring content so AI-powered answer engines can extract and cite it as a direct answer. Focuses on schema markup, FAQ structure, and concise answer blocks. Teams often track whether relevant answers cite or mention their pages.

Generative Engine Optimization (GEO)

The broader strategy for visibility in all AI-generated responses — not just answers but recommendations, comparisons, and summaries. GEO combines technical foundations, content structure, brand authority, and entity optimization. Teams often track mention share, citation quality, and how AI answers frame the brand.

AI Visibility & Extractability

LLM visibility measures whether AI models mention your brand and how they describe it. AI extractability measures how easily AI can pull accurate facts from your content. Both are inputs to GEO. Visibility is the output. Extractability can make content easier to interpret accurately.

Think of it as a stack: traditional SEO builds the foundation. AEO layers answer-ready content on top. GEO wraps everything into a full strategy for improving AI answer visibility. AI visibility is what you measure to know if it is working. Use the AI Search Visibility Guide for a step-by-step walkthrough of the full strategy.

GEO tools by category

Ten free tools organized by purpose. Start at the top if you are new to AI search optimization, or jump to the category you need.

Structured Data & Schema

Schema markup is how you tell AI systems exactly what each page contains. It is a practical GEO lever that many sites still underuse.

Authority & Architecture

AI search visibility can improve when your brand authority signals are easier to understand. Internal linking and brand footprint both matter.

Frequently asked questions about AI search optimization

What is generative engine optimization (GEO)?

Generative engine optimization (GEO) is the practice of making your online presence easier for AI systems like ChatGPT, Perplexity, and Google AI Overviews to understand, cite, or mention accurately. It includes technical work (robots.txt, llms.txt, schema markup), authority building (consistent brand mentions, structured data), and content work (AI-extractable writing, answer-first structure). GEO is distinct from traditional SEO because AI answers can draw on different retrieval and trust signals, including clear claims, structured data, and authoritative sources.

How is GEO different from SEO?

SEO optimizes for search engine ranking. GEO optimizes for visibility in AI-generated answers. SEO cares about page position, click-through rates, and keyword rankings. GEO cares about whether an AI model mentions your brand, how prominently, and in what context. SEO traffic comes through the blue links on Google. GEO visibility can happen inside AI-generated answers where your brand is cited or discussed. The two overlap — strong technical SEO helps GEO — but GEO adds work around structured data, AI-readable content, brand authority signals, and AI crawler access.

What is AEO (answer engine optimization)?

Answer engine optimization (AEO) is a subset of GEO focused specifically on answer engines — systems that generate direct answers with citations rather than ranked lists of links. Perplexity, Google AI Overviews, and ChatGPT with search are all answer engines. AEO prioritizes question-answer content structures, FAQ schema markup, and concise answer blocks that AI models can parse and cite when relevant. If GEO is the broad strategy for AI visibility, AEO is the specific tactic for making answers easier to extract and attribute.

What is AI extractability and why does it matter?

AI extractability measures how easily AI models can pull accurate facts and quotes from your content. Clear headings, short paragraphs, explicit claims, and simple formatting can improve extractability. When AI assistants answer questions, they are more likely to use content they can parse cleanly. If your page is dense or unclear, clearer competitor content may be easier to cite. Improving extractability means structuring content so AI models can more accurately pull, attribute, and summarize your claims.

Which AI search optimization tools should I start with?

Start with the LLM Visibility Checker to measure your current AI search presence across ChatGPT, Claude, and Perplexity. Then run the AI Extractability Scorer on your key pages to check whether your content is structured for citation readiness. After that, use the AEO/GEO Readiness Checker for a technical audit covering robots.txt, structured data, and content gaps. Together, these tools give you a practical baseline: where you stand, what may be holding you back, and what to fix first.

Do I need an llms.txt file for my website?

An llms.txt file helps AI systems understand your website structure and which pages are most important. It is a simple text file, similar to robots.txt but for AI readability rather than crawler instructions. While not yet as widely adopted as robots.txt, it is gaining attention as a lightweight way to document AI-readable site priorities. Adding one takes minutes using our LLMs.txt Generator and gives AI systems a clearer map of your important pages.

How often should I check my AI search visibility?

Check monthly at minimum. AI systems and search indexes change over time, and new content from competitors can affect which brands get cited or mentioned. Run the LLM Visibility Checker after publishing major content, after a PR push or brand mention wave, and whenever a competitor launches something new. Unlike traditional SEO, AI visibility can change unevenly across platforms, so recurring checks are more useful than one-off snapshots.