See why AI search cites everyone but you.

AI Search Optimization is the practice of improving how AI-powered search engines — ChatGPT, Google AI Overviews, Gemini, Claude, and Perplexity — find, interpret, and cite your website when they generate answers. It combines content structure, entity clarity, structured data, and monitoring.

AI SEO applies search optimization techniques to AI-driven results. Instead of optimizing only for ranked links, AI SEO makes your content easy for language models to understand, quote, and attribute: direct answers, clear entities, schema markup, and verifiable proof.

Generative Engine Optimization (GEO) is the discipline of optimizing content for generative engines — AI systems that synthesize answers rather than list links. GEO focuses on making pages easy to summarize and cite inside AI-generated responses.

Answer Engine Optimization (AEO) structures content so answer engines can extract direct responses to specific questions. Core AEO techniques include question-led headings, concise answer blocks, FAQ schema, and comparison tables.

LLM SEO is a common name for optimizing content so large language models select and cite it when composing answers. In practice it overlaps heavily with AI SEO, GEO, and AEO: the goal is to be the source a model trusts enough to mention.

AI visibility measures how often, and how favorably, your brand appears in AI-generated answers for the prompts your buyers ask. As more product research happens inside AI assistants, a brand that never gets cited loses consideration before a traditional search even happens.

Traditional SEO competes for positions on a results page; AI SEO competes to be mentioned inside a synthesized answer. Rankings and clicks are replaced by citations and brand mentions, and signals shift toward entity clarity, direct answers, structured data, and provable trustworthiness. The two complement each other — strong traditional SEO remains a foundation for AI visibility.

CyrumRank is an AI search optimization platform. It audits how AI answer engines describe your category, identifies citation gaps against competitors, generates content and schema fixes, and monitors your AI visibility over time.

You enter your domain, brand, and keywords. CyrumRank scans buyer-intent prompts, compares what AI answers cite against what your site exposes, and ranks the gaps by impact. It then generates publish-ready fixes — answer blocks, FAQ and entity schema, technical actions, and llms.txt guidance — and can schedule recurring scans to catch regressions.

CyrumRank analyzes visibility across ChatGPT, Google AI Overviews, Gemini, Claude, and Perplexity, and its recommendations follow structural best practices that generalize to other LLM-powered search experiences.

Yes. CyrumRank audits how ChatGPT answers questions in your category, which competitors it mentions, and what content or structural gaps keep your brand out of those answers.

Yes. Perplexity cites its sources explicitly, which makes citation gaps especially measurable. CyrumRank tracks which domains Perplexity references for your target prompts and what your pages are missing.

Yes. CyrumRank checks when AI Overviews appear for your category queries, whether your pages are referenced, and which schema, content, or authority gaps explain missing citations.

A citation gap is a prompt where AI answers cite competitors or third parties but not you. Each gap has identifiable causes — missing direct answers, thin entity markup, weak proof, absent comparison content — that CyrumRank turns into concrete fixes.

Yes, by systematically querying AI engines with the prompts your buyers use and recording which brands and sources appear. CyrumRank scores visibility per prompt, tracks trends over time, and compares your citation share against competitors.

The reliable levers are: publish direct answers to buyer questions, expose clear entities with schema markup (organization, product, FAQ), add comparison and proof content, keep technical crawlability healthy, provide an llms.txt file, and monitor AI answers so regressions are caught early.

Structured data tells machines unambiguously who you are, what you offer, and how your content is organized. Organization, Product, FAQ, and Article schema make pages easier for both search engines and AI systems to parse, ground, and cite. CyrumRank generates and validates these schema types from your existing pages.

llms.txt is a proposed convention: a plain-text file at your domain root that gives AI systems a concise, reliable summary of your site — what it is, key pages, and how you prefer to be cited. CyrumRank helps you generate and maintain one.

It varies by engine. Retrieval-based engines like Perplexity can reflect improved pages within days or weeks of recrawling; answers grounded in periodically refreshed indexes take longer. Treat AI visibility as an ongoing program: ship fixes, monitor, and iterate.

Yes. Most AI engines retrieve from the same web index that powers traditional search, so crawlability, quality content, and authority still matter. AI SEO builds on that foundation and adds the structure and entity clarity that answer engines need.

Marketing, SEO, and content teams at SaaS companies, agencies, and service businesses that want their brand mentioned in AI answers. Fixes are written so marketers can review them; publishing and technical actions may involve someone with CMS or engineering access.

Each analysis of a website URL consumes credits based on its scope — a single-keyword domain scan costs less than a 10x run, for example. Scheduled scans draw from the same credit balance as manual ones.

Most content and schema fixes can be reviewed by marketers or editors. Publishing and technical audit actions may require someone with CMS or engineering access.

Credit-gated actions pause until you buy a one-time credit pack, upgrade to a plan with a larger monthly allotment, or your next billing cycle adds fresh credits — monthly credits accumulate rather than reset, so unused credits carry forward.

Yes. The Free plan includes 10 one-time credits — enough for a domain scan and an AI Readiness audit — plus 1 workspace and 1 seat, so you can validate your first AI visibility gaps before upgrading.