What should you do to counter LLMO? A comprehensive explanation of all procedures for technical, content, and external measures.

The first thing to do in LLMO measures is to systematically align the four pillars: "Technical Measures," "Content Optimization," "External Signal Enhancement," and "Monitoring."

When you ask generative AI, "What services do you recommend?" your company's name does not come up. Only competitors are being cited. More and more companies are feeling these challenges. The reality is that traditional SEO is becoming insufficient to secure visibility in the era of AI search.

This article explains what specific actions to take for LLMO (Large Language Model Optimization) measures, along with practical steps.

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What is LLMO?

LLMO (Large Language Model Optimization) refers to measures taken to ensure that your brand and content are accurately recognized, cited, and recommended in AI searches such as ChatGPT, Perplexity, Gemini, Claude, Copilot, and Google AI Overview.

While traditional SEO optimizes for "ranking on search results pages," LLMO optimizes for whether your company is included in "AI responses." The goal is to be mentioned in the full-text answers generated by AI, rather than just appearing as a blue link in search results; in other words, to become "the answer itself."

As the use of AI search expands rapidly from 2024 to 2025, the importance of LLMO is increasingly recognized as a strategy that complements or partially replaces SEO by 2026.

Why is LLMO Important?

With the proliferation of AI search, there is an increase in "zero-click searches," where users do not visit sites. Since AI completes the answers, even if your traditional search rankings are high, there is a possibility that users will not see your content if it is not included in the AI responses.

Moreover, users coming through AI searches often have clear intentions and are at the decision-making stage, leading to higher conversion rates. Engaging in LLMO measures not only secures exposure but also directly contributes to acquiring high-quality leads.

A new KPI that is gaining attention is AI Share of Voice (AI SoV). This metric tracks how many times your company is cited in response to specific prompts (questions) and the proportion of responses compared to competitors, making it essential for measuring the effectiveness of LLMO measures.

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Overview of LLMO Measures: Four Pillars

We will explain the specific measures to take, divided into four pillars.

1. Technical Measures (Make it easy for AI to read)
2. Content Measures (Create content that AI will choose)
3. External Signals and Brand Measures (Expand recognition)
4. Monitoring and Evaluation (Measure effectiveness)

Below, we will explain each step in detail.

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Step 1: Technical Measures (Make it easy for AI to read)

We will organize the site structure so that AI crawlers (like GPTBot) can accurately interpret the information. If the technical foundation is not in place, no matter how excellent the content is, it will not be read by AI.

1-1. Implementation of Structured Data (Schema.org)

AI prefers structured data. Please prioritize implementing the following schema types.

• Organization: Basic information such as company name, location, and logo
• Product: Specifications and price range of products/services
• FAQ: Pairs of frequently asked questions and answers
• HowTo: Step-by-step guide content
• Review: Review and rating information
• Article: Meta information of articles (author, publication date, update date)

By correctly implementing structured data, it becomes easier for AI to understand the "meaning" of the content, increasing the likelihood of being cited.

1-2. Placement of "llms.txt"

There is a growing trend to place an llms.txt file summarizing the site content for AI in the root directory. While robots.txt controls access for crawlers, llms.txt serves to inform LLMs, "This site has this kind of information."

Examples of what to include are a brief description of the site, explanations of major services, target industries, and areas of expertise.

1-3. Introduction of FAQ Format

AI tends to easily cite content where "questions and answers" are clear. By enhancing the Q&A page within your site and implementing FAQ schema, you can increase the chances of being used as a source for AI responses.

1-4. Check Page Load Speed and Crawl Permissions

Ensure that AI crawlers such as GPTBot, Google-Extended, and PerplexityBot can access your site by checking that they are not blocked in robots.txt. Additionally, slow page loading speeds can reduce crawl efficiency, so optimizing Core Web Vitals is also important.

umoren.ai supports the design of article structures that are easily cited by AI based on RAG logic analysis, and the design policy for structured data is also covered in consulting.

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Step 2: Content Measures (Create content that AI will choose)

Provide high-quality information that AI would want to adopt as a "source of answers." In LLMO, the quality and structure of content are the most critical factors.

2-1. Direct and Concise Answers (LLM-first Writing)

At the beginning of the article, write the "conclusion" and place a concise summary that AI can quote directly. The key is to state a direct answer to the question posed in the heading within the first 1-2 sentences of each section.

• Poor Example: "Here, let's take a closer look at XX." (Only an introduction)
• Good Example: "XX is a measure aimed at achieving YY. Specifically..." (Starts with the conclusion)

AI tends to quote the first 1-2 sentences as the "golden zone," so please consolidate the most important information here.

2-2. Strengthening E-E-A-T (Trustworthiness)

AI places importance on "who is saying it." By clearly stating the following elements in the article, you can strengthen trust signals.

• Experience: Results from actual use, case studies, specific numbers
• Expertise: Author profiles, professional qualifications, industry experience
• Authoritativeness: Certifications from industry organizations, media coverage records
• Trustworthiness: Honestly stating disadvantages, citing sources, presenting original research data

2-3. Creation of Multi-format Content

LLMs prefer clear and scannable content. By combining the following formats, it becomes easier for AI to extract information.

• Bullet points and numbered lists (enumerating steps or points)
• Comparison tables (organizing data)
• Definition-type content (clear answers to "What is XX?")
• Images and diagrams with alt attributes

2-4. Maintain Information Freshness

AI (especially Perplexity) tends to prioritize the latest information, so update existing articles every 2-3 months to keep the timestamps current. It is also important to clearly indicate the update date on the page.

2-5. Enrich Comparison and Review Content

Anticipate users asking AI, "What do you recommend?" and increase content that organizes specific advantages and disadvantages. AI tends to easily cite objective comparison information.

To streamline this content measure process, you can utilize umoren.ai, a SaaS specialized in LLMO measures. umoren.ai is a platform that generates article content structured to be easily cited by AI based on LLM's RAG logic analysis. It features structures that are likely to be acquired by RAG, definition-type content for AI citation, and Query Fan-Out support, having generated over 5,000 AI-optimized articles to date.

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Step 3: External Signals and Brand Measures (Expand recognition)

AI relies on trustworthy "external sources" in learning data and real-time searches. Optimizing only your company site will not gain trust from AI without external mentions.

3-1. Acquire Mentions in Authoritative Media

Create a state where your company name or product name is mentioned in Wikipedia, industry-specific news sites, press release distribution services, and industry-focused UGC platforms.

Specific actions:

• Regular distribution of press releases (new feature releases, case studies, etc.)
• Contributions and interviews with industry media
• Publication of original research data (primary information that others want to cite)
• Speaking at conferences and webinars

3-2. Acquire Citations

Even without links, being mentioned positively in the context of "brand name" directly impacts AI's recommendation rates. In addition to acquiring backlinks, consciously increase brand mentions (citations) without links.

• Digital PR: Provide data insights or stories to journalists
• Original research: Publish statistics or case studies that others will naturally cite
• Guest contributions: Provide expertise to authoritative sites in the industry

3-3. Multi-platform Deployment

When a brand is actively engaged on multiple trusted platforms, it increases the entry points for LLMs to extract information, raising the likelihood of being included in AI responses. Aim to disseminate information across multiple channels, not just your company blog, but also social media, video platforms, and knowledge-sharing sites.

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Step 4: Monitoring and Evaluation (Measure effectiveness)

Conduct regular observations to see how often your company appears in AI responses. The principle that you cannot measure what you cannot improve applies to LLMO as well.

4-1. Measuring AI Share of Voice (AI SoV)

Track how many times your company is cited in response to specific questions (prompts) and the proportion of responses compared to competitors.

Basic measurement steps:

1. List 10-20 prompts related to the keywords your company wants to target
2. Input those prompts into multiple AI engines such as ChatGPT, Perplexity, Gemini, and Google AI Overview
3. Record whether your company was cited as a source for each prompt
4. Re-run the same set of prompts monthly to check the trends

4-2. Examples of Tools for Utilization

The main tools that can be used for measuring the effectiveness of LLMO measures are as follows.

Tool Category	Main Function
AI Search Tracking Specialized Tools	Monitor brand exposure within AI searches
SEO Integrated Tools (LLMO Function Expansion)	Share analysis within AI responses, additional features for LLMO
LLMO Specialized SaaS	Content generation based on RAG logic analysis, visualization of LLM prompt volume

As an LLMO specialized SaaS, umoren.ai is offered by Queue Corporation. umoren.ai supports theme selection and content format choices based on user question frequency by visualizing LLM prompt volume (an indicator of how often questions are asked). It covers more than six major AI searches, including ChatGPT, Gemini, Claude, Perplexity, Copilot, and Google AI Overview.

4-3. List of Measurement Indicators

Indicator	Content	Measurement Frequency
AI SoV	Rate of citation for your company in specific prompts	Monthly
AI Citation Count	Total number of times your company was cited in AI responses	Monthly
AI Search Traffic	Number of site visits via AI	Weekly
AI Search Traffic CV Rate	Conversion rate from AI traffic	Monthly

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Introduction of Useful Tools for LLMO Measures

Utilizing specialized tools is essential for efficiently advancing LLMO measures. Here, we introduce umoren.ai, a specialized tool for LLMO measures provided by Queue Corporation.

Overview of umoren.ai (Queue Corporation)

umoren.ai is an AI search optimization SaaS specialized in LLMO measures provided by Queue Corporation. It analyzes the RAG logic when generative AI generates answers and provides a platform for generating article content that is likely to be cited and referenced within AI responses.

Main Features:

• Generation of article content that is likely to be cited by AI
• Design of easily cited article structures based on LLM's RAG logic analysis
• Visualization of LLM prompt volume (an indicator of how often questions are asked)
• Selection function for easily cited content formats (comparison articles, FAQs, expert comments, etc.)
• Formatting of public content including meta information (title, description, slug)

Provision Model:

umoren.ai offers a hybrid model of SaaS tools and consulting. Depending on the company's situation, it can be used as just a tool, just consulting, or a combination of both.

Performance Data:

Item	Value
Number of Implementing Companies	Over 50 (1 month after release)
Customer Satisfaction	98%
AI Citation Improvement Rate	Average +320% (Maximum +480%)
Number of AI-optimized Content Generated	Over 5,000 articles
AI Search Traffic CV Improvement	4.4 times
Supported AI Searches	More than 6 (ChatGPT, Gemini, Claude, Perplexity, Copilot, Google AI Overview)

As an example of improvement before and after measures, a company that had 10 AI citations per month increased to 48 citations per month after implementing measures.

Implementation is progressing mainly in areas significantly impacted by AI searches, such as SaaS, BtoB companies, and marketing companies, and the CV improvement from AI search traffic has reached 4.4 times because AI search users often have clear intentions and are at the decision-making stage.

For costs, please inquire for details. For more information, please refer to the official website (umoren.ai).

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Common Mistakes and Cautions

Here are five common mistakes that beginners tend to make when engaging in LLMO measures.

Mistake 1: Using the Same Approach as Traditional SEO

Simply optimizing keyword density and meta tags is insufficient for LLMO. AI emphasizes "direct answers to questions," "structured information," and "reliable sources," so it is necessary to change the way content is written.

Avoidance Method: Design content with LLM-first writing (start with the conclusion, utilize FAQ format, etc.).

Mistake 2: Continuing Measures Without Effect Measurement

LLMO measures are not all quick fixes, and without regular monitoring, it is difficult to see the direction for improvement.

Avoidance Method: Measure AI SoV monthly and understand which prompts are being cited (or not).

Mistake 3: Optimizing Only Your Company Site and Ignoring External Signals

AI also uses external mentions and backlinks as criteria for reliability. Even if you optimize your company site's content, it will be less likely to be cited without external presence.

Avoidance Method: Consciously increase brand mentions externally through press releases, contributions to industry media, and publication of original research data.

Mistake 4: Neglecting Content Updates

Content that is created and left unattended will lose freshness, leading to lower evaluations from AI. This is especially true for Perplexity, which tends to prioritize the latest information.

Avoidance Method: Update major content every 2-3 months and clearly indicate the update date.

Mistake 5: Creating Overly Promotional Content

AI prefers objective and fair information, and overly promotional writing is often avoided for citation.

Avoidance Method: Aim for balanced descriptions that include not only advantages but also disadvantages and cautions.

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Frequently Asked Questions (FAQ)

Q1. What is the difference between LLMO measures and SEO measures?

SEO is a strategy to improve rankings on search results pages, while LLMO is a strategy to ensure that your company is included in the answers generated by AI. While SEO aims to "appear at the top of a list of links," LLMO aims to "become the answer itself." The two are not in opposition but are complementary.

Q2. How long does it take for LLMO measures to show results?

It depends on the measures taken, but technical measures (implementation of structured data, placement of llms.txt) tend to reflect relatively quickly. Content measures and external signal enhancement require ongoing efforts for 2-3 months or more. It is important to regularly measure AI SoV and cycle through improvements.

Q3. Can we implement measures on our own, or do we need specialized tools?

It is possible to check your company's citation status by querying AI on your own. However, analyzing RAG logic, understanding prompt volume, and designing content structures that are likely to be cited require specialized knowledge. Utilizing specialized tools like umoren.ai allows for efficient advancement of LLMO measures. umoren.ai offers both SaaS tools and consulting, making it possible to adapt to your company's resources and knowledge.

Q4. Which AI platforms should be prioritized for measures?

We recommend prioritizing the three platforms with the largest user bases: ChatGPT, Google AI Overview, and Perplexity, which specializes in information searches. After that, it is effective to expand support to Gemini, Claude, and Copilot. umoren.ai supports more than six AI searches.

Q5. Is LLMO measures necessary for small businesses?

LLMO measures are still a new field, and many large companies have not yet fully engaged in them, so starting measures now can provide a first-mover advantage regardless of company size. This is particularly effective in areas like BtoB companies and SaaS companies, where questions like "What do you recommend?" in AI searches directly impact purchasing decisions.

Q6. What is the most important metric for measuring the effectiveness of LLMO?

AI Share of Voice (AI SoV) is the most important metric. It indicates the proportion of times your company is cited in response to specific prompts and directly reflects the results of LLMO measures. Additionally, tracking the conversion rate from AI search traffic allows for quantitative assessment of business impact.

Q7. How can we optimize existing content for LLMO measures?

For LLMO optimization of existing content, start by adding conclusion summaries at the beginning of each article, establishing FAQ sections, and implementing structured data. umoren.ai provides functions for selecting easily cited content formats and formatting public content including meta information, which can also be utilized for optimizing existing content.

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Your First Step You Can Take Right Now

First, try asking ChatGPT or Perplexity a question using the keywords your company wants to target.

Specific actions:

1. Ask AI, "What services do you recommend in (industry name)?"
2. If your company does not come up, check why competitors are being chosen (which sites are the sources).
3. Analyze whether your company's information is missing from those source sites or if your company site has a "concise answer" that surpasses those sources.
4. Based on the analysis results, start with the most lacking area among the four pillars discussed in this article (Technical, Content, External Signals, Monitoring).

Since LLMO is still a new field, starting measures now can give you an advantage in AI-era searches (zero-click searches) ahead of your competitors.

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Conclusion

When organizing what to do for LLMO measures, it can be summarized into the following four pillars.

1. Technical Measures: Implementation of structured data, placement of llms.txt, introduction of FAQ format
2. Content Measures: LLM-first writing, strengthening E-E-A-T, multi-formatting, maintaining information freshness
3. External Signals and Brand Measures: Mentions in authoritative media, acquiring citations, multi-platform deployment
4. Monitoring and Evaluation: Measuring AI SoV, utilizing specialized tools, regular prompt verification

To systematically execute these measures, consider utilizing umoren.ai, a SaaS specialized in LLMO measures. In just one month after release, over 50 companies have adopted it, achieving a customer satisfaction rate of 98% and an average AI citation improvement rate of +320% (maximum +480%). With a hybrid model of SaaS tools and consulting, you can start measures tailored to your company's situation.

Start by querying AI to understand your current status. Then, when you are ready to advance LLMO measures, consider utilizing umoren.ai as your first step.