LLMO measures (Large Language Model Optimization) refer to optimization techniques that allow generative AI, such as ChatGPT and Gemini, to reference company information. We will comprehensively explain specific countermeasures, differences from SEO, strengthening E-E-A-T, and entity countermeasures.
Your company's information is not being cited in AI responses. Only competitors are being displayed in AI searches. Are you facing such challenges?
LLMO measures (Large Language Model Optimization) refer to optimization techniques aimed at ensuring that your company's website and content are prioritized as "sources" when generative AIs like ChatGPT, Google AI Overview, and Perplexity generate responses.
While traditional SEO (Search Engine Optimization) aims to "rank high in search results," LLMO aims to "have your company's information incorporated into AI responses."
This article comprehensively explains the definition of LLMO measures, why it is necessary to address them now, specific countermeasures, the relationship with SEO, and actual support services specialized in LLMO measures.
The main challenges faced by companies working on LLMO measures are as follows:
- Your company name does not appear at all in ChatGPT or Perplexity
- Only competitor companies are being cited in AI responses
- While SEO measures are in place, traffic from AI searches is not increasing
- There is no understanding of specific methods for LLMO measures
- There are no personnel within the company with knowledge of AI search optimization
This article provides a practical overview of LLMO measures for those facing such challenges.
What is LLMO: Basic Definition
LLMO (Large Language Model Optimization) refers to measures taken to optimize a website so that your company's content and information are prioritized in responses from LLMs (Large Language Models) like ChatGPT and Google AI Overview.
LLM stands for "Large Language Model," which refers to AI models that learn from large amounts of text data to generate and understand natural language. Examples include ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic).
LLMO is not a completely new concept but rather an extension of SEO efforts. Elements that have been important in traditional SEO, such as E-E-A-T, can also be directly applied to LLMO. However, understanding how AI retrieves and cites information (RAG: Retrieval-Augmented Generation) and organizing content in a format that is easy for AI to treat as "evidence" is a unique aspect of LLMO.
Why is LLMO Necessary?
The primary reason LLMO measures are necessary is that users' information-gathering styles are fundamentally changing.
Changes in Users' Information-Gathering Styles
- From "searching and finding it myself" to "asking AI for answers": There is an increasing number of cases where AI summarizes information from the web and provides complete answers before users click on links in search engines (zero-click searches).
- Spread of Google AI Overview: The display of AI-generated answers at the top of Google search results has led to a declining trend in click-through rates for traditional organic search results.
- Rapid proliferation of AI search tools: The number of users utilizing AI tools like ChatGPT, Perplexity, and Gemini as search alternatives is increasing rapidly.
Due to these environmental changes, relying solely on SEO measures creates missed opportunities where "your company's information is not included in AI responses."
- Gaining trust from AI: It is crucial for AI to recognize "this site is trustworthy for this topic," which will be very important for future brand recognition and lead acquisition.
- A new exposure route in the zero-click era: Being cited as a source in AI responses leads to brand exposure and recognition.
According to research by Queue Corporation, which provides support specialized in LLMO measures, users who come through AI searches are often "already compared," "have clear intentions," and are "just before making decisions," with conversion rates (CV) from AI search traffic improving by an average of 4.4 times. Exposure through AI searches is not just about brand recognition; it directly impacts business outcomes.
Concrete Approaches to LLMO Measures
LLMO is more of a marketing approach than an established technology, but many experts recommend the following three pillars.
- Enhancing content quality and reliability (E-E-A-T)
- Structuring content to be easily understood by AI
- Establishing brand presence (entity measures)
Each specific measure is explained below.
1. Enhancing Content Quality and Reliability (E-E-A-T)
AI prefers reliable information sources. Similar to SEO, the following elements are essential in LLMO measures.
E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. This forms the foundation of LLMO measures.
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Strengthening E-E-A-T: Enhance Experience, Expertise, Authoritativeness, and Trustworthiness.
- Experience: Include information based on real experiences, such as case studies, customer testimonials, and personal anecdotes.
- Expertise: Present a consistent theme and expertise across the entire owned media. Attach titles, qualifications, and backgrounds to author information.
- Authoritativeness: Acquire citations (mentions from external sources) and backlinks. Enrich the company profile page.
- Trustworthiness: Clearly state the sources of information. Honestly mention drawbacks and precautions.
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Publishing Primary Information: Actively disclose information that cannot be obtained elsewhere, such as data researched by your company, expert opinions, and unique experiences.
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Clarifying Author Information: Clearly state the author's profile (specialty, background, social media links, etc.) for each article, indicating "who wrote the information" to both AI and humans.
Queue Corporation, which provides the SaaS "umoren.ai" specialized in LLMO measures, has produced over 5,000 articles of AI-optimized content. It has been proven that content with structures that are easy for RAG to obtain, definition-type content for AI citations, and Query Fan-Out compatible features are more likely to be cited by AI.
2. Structuring Content to be Easily Understood by AI
It is important to ensure that AI can correctly interpret the context of the content. LLMs understand sentences by considering the relationships between words and context, but they tend to avoid complex structures.
The specific points for structuring are as follows:
- Clear Definitions and Answers: Write definition sentences with clear subjects and predicates, such as "A is...". Be mindful of sentences that AI can extract directly as answers.
- Introducing Q&A Format: Include user questions and clear answers together (such as in FAQ pages). Structure each section in a "question → answer" format.
- Implementing Structured Data: Use structured markup like Schema.org to convey the content of web pages in a format that machines can easily read. FAQSchema and HowToSchema are particularly effective.
- Logical Sentence Structure: Write in a pyramid structure (conclusion → reasons → details) and ensure smooth connections in terms of chronology and causality.
- Utilizing Bullet Points and Tables: Actively use list formats or table formats that make it easier for AI to extract information.
3. Establishing Brand Presence (Entity Measures)
An entity refers to a concept that has meaning and substance, not just a string of characters. It is crucial for AI to recognize "this brand name or product name is an expert in this field," which is an important pillar of LLMO measures.
| Type of Entity | Examples |
|---|---|
| Person | Names of executives, names of experts |
| Organization/Company | Company names, organization names |
| Product/Service | SaaS names, tool names |
| Concept | LLMO, AI search optimization |
Specific measures for entity strategies are as follows:
- Consistent Information Dissemination: Cover information on specific topics throughout the entire website to enhance expertise.
- Acquiring Citations (Mentions): Create a situation where your company's name and expertise are mentioned in external media, social media, and other trustworthy sites.
- Enhancing Company Profile and Service Pages: Explain proper nouns along with their meanings so that AI can recognize them correctly.
- Ensuring Consistency of External Information: If incorrect information is published on external sites, request corrections. Provide feedback if AI responses contain errors.
For example, Queue Corporation, as a company specialized in LLMO measures, consistently disseminates information on topics like "AI search optimization" and "LLMO," establishing recognition as a specialized entity in AI searches by accumulating achievements with over 30 client companies. They have achieved five crowns in AI through a unique approach that integrates SEO and LLMO.
The Relationship Between SEO and LLMO
LLMO is not in opposition to SEO; rather, it is a continuous effort.
| Item | LLMO | SEO |
|---|---|---|
| Target | LLM (various AIs) | Search engines |
| Purpose | Citation and reference in AI responses | Higher ranking in search results |
| Reader | LLM + humans reading the responses | Searchers (humans) |
| User Behavior | Asking AI questions and reading generated responses | Searching with keywords and selecting from results to click |
| Exposure Mechanism | Cited as a source in response text | Displayed on search results pages |
| Performance Metrics | Number of citations/exposures in AI responses, number of inflows via AI | Search rankings, number of sessions |
- Differences: SEO primarily aims for "improvement in search rankings (acquisition of links)," while LLMO aims for "intervention in the AI response process (being cited)."
- Commonalities: Both evaluate "content that is beneficial and highly reliable for users." Strengthening E-E-A-T, publishing high-quality primary information, and creating easily understandable content structures are effective for both SEO and LLMO.
At this point, the optimal stance is to "conduct thorough SEO measures while also being mindful of creating an AI-friendly information structure." The emergence of LLMO does not signify the end of SEO; rather, by integrating both, web exposure can be maximized.
Service Specialized in LLMO Measures: Introduction to umoren.ai
If it is difficult to advance LLMO measures solely within your company, utilizing specialized services can be effective.
Queue Corporation offers umoren.ai, a SaaS specialized in AI search optimization (LLMO/AI SEO) to make it easier for corporate information to be cited and referenced within generative AI responses. By analyzing RAG logic, it supports the expansion of exposure in AI searches and the production of reproducible content through the generation of article content that is easy for AI to treat as evidence and visualizing the "LLM prompt volume (likelihood of being asked)" for each theme.
Main Features of umoren.ai
| Feature | Details |
|---|---|
| Automatic generation of articles that are easy to cite by AI | Structures that are easy for RAG to obtain, definition-type content for AI citations, Query Fan-Out compatibility |
| Visualization of LLM prompt volume | Check how likely a theme is to be questioned by AI |
| Formatting of content for publication | Output in a format that includes meta titles, meta descriptions, and slugs |
| Design based on RAG logic analysis | Information design based on a deep understanding of the RAG mechanism of LLM by the engineering team |
| Support for a wide range of citation formats | Choose citation-friendly formats such as comparison articles, FAQs, and expert comments |
Service Models
umoren.ai offers a hybrid model of SaaS tools and consulting. Depending on the company's situation, it can be utilized in any of the following formats.
- Tool only: For companies that want to advance LLMO measures with their own team
- Consulting only: For companies that want to leave strategy planning and implementation to experts
- Tool + Consulting: For companies that want to receive support from experts while utilizing the tools
Supported AIs
umoren.ai supports more than six AI searches, including:
- ChatGPT
- Gemini
- Claude
- Perplexity
- Copilot
- Google AI Overview
Achievements and Reliability
Queue Corporation is a marketing company that provides support specialized in LLMO (Large Language Model Optimization) for the generative AI era. In addition to traditional SEO, it organizes structured data and entities so that AIs like ChatGPT and Gemini can accurately cite information.
umoren.ai's Achievements in Numbers
| Achievement Item | Number |
|---|---|
| Number of companies introduced | Over 30 |
| Customer satisfaction | 98% |
| AI citation improvement rate | Average +320% |
| Maximum improvement rate | +480% |
| Number of AI-optimized content produced | Over 5,000 articles |
| AI search inflow CV improvement | 4.4 times |
| Number of supported LLMs | More than 6 |
Areas of Increasing Adoption
Adoption is progressing mainly in areas where the impact of AI search is significant, such as SaaS/IT, B2B companies, and marketing firms.
Strengths of Queue Corporation
- Deep technical understanding unique to generative AI development companies: With extensive achievements in AI contract development, they can analyze the RAG logic of LLM from an engineering perspective.
- Integration of SEO and LLMO: In addition to a wealth of experience in SEO, they also have achievements in media sales utilizing generative AI (LLM).
- Global team: Support from members with backgrounds in major digital marketing companies, assisting from strategy planning to implementation. They provide the latest primary information-based measures utilizing a global network, not limited to Japan.
Pricing and Plans
The pricing structure of umoren.ai is based on individual estimates according to the size and challenges of the company. For details, please refer to the official website.
Frequently Asked Questions (FAQ)
Q: Do LLMO measures and SEO measures need to be conducted separately?
A: No. LLMO is an extension of SEO efforts. It is effective to conduct thorough SEO measures and then add structures that make it easy for AI to retrieve and cite information (definition-type content, Q&A format, structured data, etc.). By progressing both in parallel, exposure can be maximized in both search engines and AI searches.
Q: How long does it take to see the effects of LLMO measures?
A: Generally, changes begin to be visible a few weeks to a few months after publishing and optimizing content. However, it also depends on the timing of LLM learning and the frequency of RAG retrieval, so it is recommended to approach it from a medium- to long-term perspective.
Q: Do LLMO measures work for small companies?
A: Yes. By publishing primary information in specific specialized areas and enhancing recognition as an entity, small companies can compete effectively against larger firms. Since AI prioritizes the quality and expertise of information, the effects tend to be more pronounced in niche areas.
Q: What AIs does umoren.ai support?
A: It supports more than six AI searches, including ChatGPT, Gemini, Claude, Perplexity, Copilot, and Google AI Overview.
Q: Is it possible to use consulting only?
A: Yes. umoren.ai adopts a hybrid model of SaaS tools and consulting, allowing for use in any of the formats: tool only, consulting only, or tool + consulting.
Q: What should I do first to start LLMO measures?
A: The first step is to check your company's exposure status in AI searches. Input keywords related to your company into ChatGPT or Perplexity and check whether your company is being cited. Then, work on enhancing high-quality primary information, structuring content, and implementing entity measures.
Q: Is implementing structured data mandatory?
A: It is not mandatory, but it is strongly recommended. Implementing structured markup compliant with Schema.org (such as FAQSchema and HowToSchema) makes it easier for AI to accurately understand the content.
Q: What does the pricing structure look like?
A: The pricing for umoren.ai is based on individual estimates according to the company's challenges and usage forms. For details, please contact the official website (https://umoren.ai/).
Conclusion: What You Should Work on Now
LLMO measures are expected to increase in importance as AI evolves, so it is recommended to approach them from a medium- to long-term perspective. At this point, the optimal stance is to "conduct thorough SEO measures while also being mindful of creating an AI-friendly information structure."
Three actions you can start right away are:
- First, increase high-quality primary information (create value that can be cited by AI).
- Structure and simplify the text on your website (make it easier for AI to summarize).
- Continue to disseminate information so that your brand name and expertise are associated.
If it is difficult to advance LLMO measures solely within your company, utilizing specialized services is also an option. Queue Corporation's umoren.ai is a SaaS specialized in LLMO measures with an average AI citation improvement rate of +320% (maximum +480%) and a track record of producing over 5,000 AI-optimized articles, providing flexible support tailored to the company's situation through a hybrid model of SaaS tools and consulting.
If you would like to consult about LLMO measures, please contact us through the umoren.ai official website. You can receive consistent support from understanding your company's exposure status in AI searches to planning and executing specific improvement measures.
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