Do I need to install anything on my site?

Yes — a lightweight plugin or app depending on your platform. MeetGEO connects via a WordPress plugin or a Shopify app. Installation takes one credential step and requires no code changes. Once connected, MeetGEO handles all schema deployment and monitoring automatically.

How is MeetGEO different from tools like Semrush, Search Atlas, or Profound?

Semrush and Ahrefs are built for traditional SEO — keyword rankings, backlink analysis, and SERP positions. They do not diagnose AI citation gaps or deploy GEO signals. Search Atlas OTTO SEO deploys schema via a JavaScript pixel, which is invisible to AI crawlers that do not execute JavaScript. Profound tracks AI visibility but does not write or deploy fixes. MeetGEO is the only platform that diagnoses citation gaps across all three signals — semantic relevance, structural clarity, and entity validation — generates recommendations to close them, and deploys server-side schema automatically. All in one subscription.

What platforms does MeetGEO support?

MeetGEO supports WordPress via plugin and Shopify via app. For all other platforms, MeetGEO's Agency plan delivers a complete output package — optimized content files, JSON-LD schema blocks, and implementation instructions — ready for any technical team to deploy on any stack.

How long until I see results in AI search?

Schema and entity signals typically produce measurable citation movement within 2 to 4 weeks as AI crawlers re-index your pages. Content recommendations you publish produce citation movement within 4 to 8 weeks as AI systems discover and index the new content. MeetGEO's citation dashboard tracks your mention rate and citation rate weekly so you can see the delta from your baseline.

Can I use MeetGEO for client sites if I am an agency?

Yes. MeetGEO's Agency plan is available for teams managing multiple client sites. The Agency plan delivers a complete output package for any platform or stack. Contact MeetGEO for Agency pricing.

Does MeetGEO replace or rewrite my existing website content?

No. MeetGEO deploys schema markup and entity signals to existing pages without modifying your copy. When MeetGEO identifies a content gap, it generates recommendations and delivers them to your Content Library for review. You approve content and publish it on your own timeline. MeetGEO never deploys content changes without your explicit approval.

Is there a free trial?

Yes. MeetGEO offers a 7-day free trial with no credit card required. The trial runs the full agent suite against your connected site and produces a baseline GEO Health Score with a prioritized action queue.

Do you offer annual billing?

Monthly billing is available on all plans with no contracts and no cancellation fees. Annual billing options are available — contact MeetGEO for annual pricing.

Why doesn't ranking number one on Google guarantee visibility in AI search?

Citation overlap between organic top 10 results and AI Overview sources ranges from 17% to 54%. AI systems select sources based on semantic relevance, structural clarity, and entity validation — not traditional ranking signals. A page can rank well and still be invisible inside an AI-generated answer.

What is AI citation optimization?

AI citation optimization is the practice of structuring content so that AI systems — including ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews — select your brand as a cited source when generating answers. It requires semantic relevance in vector space, structural clarity for chunk-level extraction, and entity validation through cross-web consensus.

What did Google announce at I/O 2026 that changed SEO?

At Google I/O on May 19, 2026, Google declared Search is AI Search, making AI Mode the global default powered by Gemini 3.5 Flash. AI Mode now serves over one billion monthly users. The announcement confirmed that traditional ranking no longer guarantees visibility — being cited inside AI-generated answers is the new measure of search presence.

How many fanout queries does ChatGPT generate per question?

Simple questions generate two to three sub-queries. Comparison and recommendation questions generate ten or more. Google AI Mode's Gemini 3 averages 10.7 sub-queries per prompt.

Do fanout queries matter more than the original prompt for citation?

Ahrefs confirmed cited URLs show higher semantic similarity to fanout queries than to original prompts. The AI selects pages for sub-question alignment. The original prompt is the starting point, not the citation criterion.

How should fanout queries change content structure?

Convert H2 headings from generic labels to specific questions. Use the exact language from fanout queries in those headings. Each question heading becomes a fanout query answer that AI can match and cite.

What Are Fanout Queries? How ChatGPT's Hidden Sub-Searches Drive Citation Selection

ChatGPT does not search what users type. The AI generates internal sub-questions from every prompt. These sub-questions are called fanout queries. Fanout queries drive citation selection more than keyword targeting does.

What are fanout queries and how do they work?

Fanout queries are internal sub-searches ChatGPT generates to find specific facts. These queries determine which pages get cited.

A user enters a prompt called the seed query. ChatGPT expands the seed into multiple targeted sub-questions. Each sub-question hunts for a specific fact needed to answer the prompt. For a query about project management tools, ChatGPT might generate ten sub-questions. Sub-questions include specific integration queries, pricing queries, and use-case scenarios. Pages answering those sub-questions are the pages that get cited. AirOps found 95% of these sub-queries have zero traditional search volume. 32.9% of cited pages appear only in fan-out results, not in organic top-20.

How many fanout queries does ChatGPT generate per prompt?

ChatGPT generates multiple sub-queries from every user prompt. Complex prompts generate ten or more internal sub-questions.

Simple factual questions generate two to three sub-queries. Comparison and recommendation prompts generate ten or more. Google AI Mode's Gemini 3 generates 10.7 sub-queries per question on average. Each sub-query is searched independently. The AI cites pages that answer the sub-queries, not only the main prompt.

How does ChatGPT evaluate pages against its fanout queries?

ChatGPT evaluates pages using semantic scoring against fanout queries before opening any content. The title, snippet, and URL do the initial work.

This initial evaluation is called the gatekeeping layer. The AI measures semantic distance between page metadata and the sub-query text. Higher alignment scores produce higher citation probability. Ahrefs confirmed cited URLs show higher cosine similarity to fanout queries than non-cited URLs. Natural language URL slugs produce 89.78% citation rates. Opaque URLs produce 81.11% citation rates. The URL contributes to semantic scoring before the page is opened.

How do you find the fanout queries ChatGPT generates for your category?

Fanout queries are visible in ChatGPT's network traffic using browser developer tools. Ahrefs Brand Radar surfaces them directly for any prompt.

The manual method uses Chrome DevTools. Enter a relevant prompt into ChatGPT. Open developer tools and navigate to the Network tab. Filter by the conversation ID found in the browser URL. Search the response JSON for the word queries. The resulting array contains ChatGPT's exact internal search strings. Ahrefs Brand Radar shows fanout queries alongside cited URLs for any prompt.

How should fanout queries change how you write H2 headings?

H2 headings should mirror the specific language of fanout queries. Generic section labels do not align with AI sub-questions.

Vague headings like Our Features do not match any fanout query. Specific question headings like Which integrations support agency billing match directly. Cited pages show higher semantic similarity between titles and fanout queries than non-cited pages. Convert every vague section label into a specific question. Each converted heading is a fanout query your page now answers.

How does MeetGEO use fan-out query mapping?

MeetGEO maps each page against the probable fanout queries AI generates for that topic. Gaps appear as flagged headings in the weekly report.

The Content GEO agent identifies vague or label-format H2 headings each week. Each flagged heading includes the specific fanout query language it should reflect. Content updates addressing flagged headings are tracked across scan cycles. Semantic alignment scores improve over time as headings are updated. New pages receive fanout-aligned heading structures before content is written.

Frequently Asked Questions

What are fanout queries?: Fanout queries are the internal sub-searches ChatGPT generates from a user prompt. Each sub-query hunts for a specific fact. The AI cites pages answering the sub-queries, not just the original prompt.
How many fanout queries does ChatGPT generate per question?: Simple questions generate two to three sub-queries. Comparison and recommendation questions generate ten or more. Google AI Mode's Gemini 3 averages 10.7 sub-queries per prompt.
Do fanout queries matter more than the original prompt for citation?: Ahrefs confirmed cited URLs show higher semantic similarity to fanout queries than to original prompts. The AI selects pages for sub-question alignment. The original prompt is the starting point, not the citation criterion.
How do you find the fanout queries ChatGPT generates?: Enter your target prompt into ChatGPT and inspect network traffic using browser DevTools. Filter by conversation ID and search the response JSON for queries. The resulting array shows the exact sub-searches the AI performed.
How should fanout queries change content structure?: Convert H2 headings from generic labels to specific questions. Use the exact language from fanout queries in those headings. Each question heading becomes a fanout query answer that AI can match and cite.