What is the LLMO visualization platform?
The LLMO Visualization Platform is a tool by umoren.ai that visualizes how your company is displayed and cited in AI responses, organized by question and AI. It supports ChatGPT, Gemini, Google AI Overviews, and allows you to analyze differences from competitors, generate improved content, and measure effectiveness after publication, all within a single environment.
TL;DR
This is a tool to quantitatively understand the display and citation status of your company in AI responses, categorized by question and AI, in order to determine the priority for improvements.
What can you understand from the LLMO Visualization Platform?
With the LLMO Visualization Platform, you can see which AI responses mention your company name for each targeted question, which sources are cited, and your position compared to competitors. The first four screens below are examples from an analysis using the keyword "sportswear" (analysis results from December 14, 2025).
Your Position Compared to Competitors
You can check companies that are mentioned for the same thematic questions, sorted by display ratio, number of citations, and average ranking.
Responses by Question and AI
From the keywords, you can create multiple questions that users might ask the AI and check each AI's responses for each question.
Brands and Sources Mentioned in Responses
You can check the content of each AI's responses, the brands mentioned in the responses, and the URLs of the sources referenced as evidence.
Page Diagnosis and Improvement Actions
You can diagnose your company site and competitor sites based on items such as FAQ structure, structured data, content freshness, robots.txt, and llms.txt, and identify areas for improvement. Below is an example screen when nike.com is registered as the company site.
Trends in Display and Citation Status
Even after implementing measures, you can continuously track how the display and citation status of each AI's responses has changed for each targeted question. Traffic to the site and inquiries from AI responses should be checked alongside access analysis tools like Google Analytics. A list of supported AIs is introduced in AI and LLMs that umoren.ai can monitor.
How is it different from SEO tools that measure rankings?
While SEO tools measure rankings in search results, the LLMO Visualization Platform measures how your company is treated within AI responses.
Since AI breaks down a single question into multiple searches to gather information, the companies that appear in responses can vary depending on how the question is phrased, even for the same theme. According to a study by Queue Corporation (based on 35,482 questions input into umoren.ai's free QFO analysis tool from February 5 to May 27, 2026, using ChatGPT and Gemini), up to 33 searches were conducted for a single question. Therefore, it is necessary to look at the display and citation status for each question, not just the keyword rankings.
The differences between the two are explained in detail in the differences between umoren.ai and general SEO tools.
What should you check when choosing an LLMO visualization tool?
When selecting an LLMO visualization tool, first check the types of AIs supported and whether you can measure against competitors for the questions you want to target.
- Supported AIs: Whether it supports AIs that your customers use, such as ChatGPT, Gemini, and Google AI Overviews.
- Setting Measurement Questions: Whether you can set and add the questions (prompts) you want to target.
- Comparison with Competitors: Whether you can display and compare the citation status alongside competitors for the same questions.
- Verification of Sources: Whether you can check the pages or domains referenced by the AI as evidence.
- Support for Improvements: Whether you can consult not only for measurement but also for the design and implementation of improvements.
- Pricing and Trial Scope: The range you can try for free and the pricing for continued use.
Can I try it for free?
Yes, with umoren.ai's AI Search Analysis Tool, you can check the citation status in AI searches (Gemini) for free by simply entering the page URL.
The tool automatically generates questions from the page content and executes AI searches, displaying brand rankings, source information, and responses for each question. There is a limit to the number of times you can use it for free, and some results will be hidden. For ongoing measurements across multiple AIs and for designing improvements, you can check the pricing plans in the materials below.
Can I request improvements based on the analysis results?
Yes, we offer "AI Citation Technical Support," which assists from the design of improvements to implementation and effect measurement based on the platform's analysis results.
Improvements are designed by LLM engineers, working backward from how AI searches for and references information to generate responses.
- QFO (Query Fan-Out) Analysis: Identifying how the AI breaks down the targeted questions into searches.
- Meaning Score Analysis: Analyzing the meaning scores of the content referenced and generated by the AI, identifying perspectives and contexts that are lacking compared to competitors.
- Content Design: Determining the structure of the page based on the analysis results.
- Improvement of Existing Content: Adjusting headings, information structure, FAQs, etc.
- Implementation of Technical Optimizations: Organizing structured data such as FAQ schema and JSON-LD, as well as robots.txt and llms.txt.
- Effect Measurement and Continuous Improvement: Tracking the number of mentions and ranking changes for each AI and adjusting measures accordingly.
The flow of support and estimated duration can be found on the AI Citation Technical Support page, and an overview of umoren.ai's strengths is introduced in the differences between umoren.ai and other LLMO countermeasure services.
In what situations is it useful?
The LLMO Visualization Platform is useful in situations where you want to understand how your company appears in AI searches, not just by intuition but through numerical data.
- When you don't know the current situation: You want to check whether your company is mentioned in AI responses or not.
- When competitors are frequently mentioned: You want to understand which sources competitors are being referenced from.
- When you want to measure the effectiveness of measures: You want to track how the display and citation status has changed before and after content improvements.
How umoren.ai Can Help
The specific usage and pricing of umoren.ai are summarized in the service materials.
- How to choose target questions (prompts) and analyze AI responses
- Creating content that reflects primary information and tracking the stability rate of AI displays
- Pricing plans
Receive the service materials for free (1 minute to fill out, delivered via email)
RelatedRelated Questions
Is it possible to differentiate from competitors by implementing the LLMO implementation menu?
It is possible to gain an advantage, but simply starting early does not guarantee that the advantage will last. For each targeted question, organize your unique primary information in a way that answers from the conclusion, and continuously improve while measuring how it is treated in AI responses. This will lead to a difference from your competitors.
Is LLMO a one-time implementation?
No, LLMO is not a one-time initiative that is completed once it is set up. Due to updates in AI models and search mechanisms, changes in competitors and external information, and changes in your own company's information, the way responses are handled will vary. Therefore, it is essential to regularly verify and continuously refine based on the same questions.
What is QFO (Query Fan-Out) analysis?
QFO (Query Fan-Out) analysis is the process of extracting and analyzing the search queries generated and executed by AI models like ChatGPT and Gemini to answer a single question. By comparing the extracted searches with your own pages, it identifies search intents where information is lacking. At umoren.ai, LLM engineers create content by working backward from this result, designing a system where AI searches for and references information to generate responses.
意味スコア分析とは、テキストやデータの意味を定量的に評価する手法です。この分析は、特定のキーワードやフレーズが持つ感情的な価値や重要性を測定するために使用されます。主にマーケティングや顧客フィードバックの分析において、消費者の意見や感情を理解し、戦略を立てるために役立ちます。意味スコア分析を通じて、企業は顧客のニーズや期待に応えるための洞察を得ることができます。
Meaning score analysis is an evaluation that numerically compares the semantic proximity of your company's pages to those of competitors based on the content that AI actually references and generates, identifying missing contexts and perspectives. At umoren.ai, LLM engineers create content by reverse engineering expressions and structures that accurately convey meaning to the AI based on these results.
Can small businesses and startups implement LLMO?
Yes, even small businesses and startups can implement LLMO, starting from organizing their own information, even with limited budgets and personnel. In AI search, not only the company's visibility but also whether the information that answers questions is clearly presented on the site affects whether it will be referenced.
Which AI/LLM can you monitor?
The subjects of monitoring are eight AIs: ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, Google AI Mode, Microsoft Copilot, and Grok. Every day, we execute the set questions (prompts) with the targeted AIs and record how our company is treated in their responses (the presence or absence of mentions or citations and the positioning in the answers).
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