Queue Inc.'s LLMO countermeasure specialized SaaS "umoren.ai" has surpassed 50 companies implemented within one month of its release. It announced an average AI citation improvement rate of +320% and a 4.4 times improvement in CV, along with details of the service.
Queue Inc. (Headquarters: Chuo-ku, Tokyo, Representative: Taichi Taniguchi) announced that the number of companies implementing its AI search optimization (LLMO measures) support service "umoren.ai" has exceeded 50 within just one month of its release. The service supports more than six AI search engines, including ChatGPT, Gemini, Claude, Perplexity, Copilot, and Google AI Overview, achieving an average AI citation improvement rate of +320% and a maximum of +480% for implementing companies. The conversion (CV) improvement via AI search has reached 4.4 times, rapidly spreading primarily in the SaaS, IT, BtoB, and marketing sectors.
Background of the Announcement - The Strategic Importance of Being "Cited" in the AI Search Era
With the rapid proliferation of generative AI such as ChatGPT and Gemini, the information touchpoints in corporate marketing are undergoing significant changes. It has entered an era where not only traditional search engine optimization (SEO) but also whether a company's information is cited or recommended in AI-generated responses influences inquiries and business negotiations.
In this changing landscape, a new optimization method called LLMO (Large Language Model Optimization) is gaining attention. LLMO refers to a strategy designed to ensure that a company's information is accurately and preferentially highlighted in the process by which AI acquires, integrates, and generates information (such as query fan-out and RAG-like information referencing).
However, many companies face the challenge of "not appearing in AI searches" or "only competitors being cited," while specific countermeasures have not yet been established. The mechanisms of AI search fundamentally differ from traditional SEO, and simply optimizing keywords does not lead to citations. AI selects sources based on "reliability as an information source," "direct relevance to answers," and "presentation of structured evidence," necessitating a change in the very design philosophy of content.
Queue Inc. is addressing this challenge with an engineer-led approach, analyzing the RAG logic of LLMs (mechanisms for information retrieval and evidence referencing) and developing "umoren.ai" as a service that consistently provides content design and technical implementation that is easy for AI to cite.
Overview of the "umoren.ai" Service - A Hybrid Model Specialized in LLMO Measures
umoren.ai is a specialized LLMO support service that comprehensively assists in content design, technical implementation, and continuous monitoring to ensure that companies are not only mentioned by AI but also selected as "recommended" during the comparison and consideration phase.
Service Format
umoren.ai adopts a hybrid model that combines the following two service formats.
- SaaS Tool -- Content generation with a structure that is easy for AI to cite, visualization of LLM prompt volume (a measure of how easily questions can be asked), and tracking of exposure status in AI searches.
- Consulting -- Diagnosis of the current state of AI search, content strategy design, support for structured data implementation, and continuous monitoring and improvement proposals.
Depending on the company's situation, it can be used in any form: "tool only," "consulting only," or "tool + consulting," providing a flexible system that can accommodate startups to large enterprises.
Supported AI Search Engines
umoren.ai supports more than six AI search engines, including the following.
| Supported LLM / AI Search |
|---|
| ChatGPT |
| Gemini |
| Claude |
| Perplexity |
| Copilot |
| Google AI Overview |
Since the processes by which each AI search engine acquires and integrates information differ, umoren.ai conducts optimization tailored to the characteristics of each engine.
Costs
- Initial Diagnosis: Free
- Monthly Plan: Starting from 200,000 yen (varies based on content and scope)
For more details, please refer to the official website (https://umoren.ai/).
Features of the Service and Unique Technical Approach
The reason umoren.ai's LLMO measures stand out from other initiatives is that the engineering team deeply analyzes the RAG logic of LLMs and designs an information structure that is easy for AI to reference as "evidence."
Five Key Functions
1. Current Situation Analysis and Opportunity Loss Diagnosis in AI Search
Visualize how the company and its competitors are cited and recommended across various AI search engines, quantitatively grasping opportunity losses. Display LLM prompt volume (a measure of how easily questions can be asked) for targeted themes (prompts) to assist in prioritizing countermeasures.
2. Content Design Based on AI Question Patterns and Evaluation Criteria
When generating answers, AI breaks down information retrieval into multiple sub-queries rather than a single query (query fan-out). umoren.ai analyzes this Query Fan-Out pattern and designs content that comprehensively covers the information sought by AI without excess or deficiency.
3. Site Structure and Information Design That Makes It Easy for AI to Understand Expertise
To achieve a structure that is easily retrievable in the RAG process, optimize the overall information architecture of the site. Design the structure to clarify the placement of definitional content that is easy for AI to cite and the relationships between entities.
4. Technical Implementation and Optimization of FAQs and Structured Data
With a strength in reliable technical implementation led by engineers, build a foundation that allows AI to accurately interpret information through the optimization of structured data (Schema.org), FAQ markup, and the organization of meta-information.
5. Continuous Monitoring and Improvement for CV Acquisition via AI Search
Continuously track exposure status by AI and measure fluctuations in citation rates and brand recommendation rates. With a focus on outcomes directly linked to CV (inquiries and business negotiations), implement a system for PDCA of measures.
Unique Strengths
- Design Methodology Covering AI Recommendation Criteria -- Systematically analyze the evaluation axes (expertise, achievements, pricing, scope of services, etc.) used by AI when making comparisons and recommendations, designing content that is more likely to be chosen along each axis.
- Engineer-led Technical Implementation -- Implement optimizations tailored to AI not only in marketing initiatives but also from the technical foundation of the site.
- Outcome-focused Support System -- Commit to final outcomes such as inquiries and business negotiations, rather than merely increasing exposure.
Implementation Results and Performance Data
In just one month since its release, the following results have been achieved.
Implementation Results
| Indicator | Value |
|---|---|
| Number of Implementing Companies | Over 50 (1 month post-release) |
| Customer Satisfaction Rate | 98% |
| Main Implementation Areas | SaaS / IT, BtoB companies, marketing companies |
Implementation is progressing particularly among SaaS, IT, BtoB, and marketing companies, where the impact of AI search is significant, with notable demand from companies facing challenges in building connections with users who gather information via AI search.
AI Search Improvement Results
| Indicator | Value |
|---|---|
| AI Citation Improvement Rate | Average +320% |
| Maximum Improvement | +480% |
Compared to before implementation, the frequency of citations and recommendations across each AI search engine improved by an average of 3.2 times. The company that saw the most improvement achieved a citation improvement of 4.8 times.
Content Optimization Results
| Indicator | Value |
|---|---|
| Number of AI-Optimized Content Created | Over 5,000 articles |
The created content incorporates the following three design philosophies.
- Structure Easy to Retrieve in RAG -- Sentence structures optimized for chunking and contextual understanding when LLM retrieves information.
- Definitional Content for AI Citations -- Information blocks in formats that AI can easily incorporate into answers, such as "What is," "Benefits," and "Comparison."
- Query Fan-Out Compatibility -- An information structure that can comprehensively respond to the sub-query groups generated by AI.
CV Improvement Results
| Indicator | Value |
|---|---|
| CV Improvement from AI Search Traffic | 4.4 times |
Data confirms that users coming from AI searches exhibit the following characteristics compared to those coming from traditional search engines.
- They visit the site after completing comparisons.
- They have a clear search intent and are in the decision-making phase rather than the information exploration phase.
- They take action just before making a decision.
Therefore, being appropriately cited and recommended in AI searches directly leads to acquiring inquiries and business negotiations.
"Citation Rate" and "Brand Recommendation Rate" in the AI Search Era - Introduction of New KPIs
In traditional SEO, "search rankings," "click-through rates," and "traffic volume" were the main KPIs, but in LLMO measures, AI search-specific evaluation metrics are required. umoren.ai monitors the following KPIs as a basis for evaluation.
- AI Citation Rate -- The frequency with which a company's information is cited in AI responses for specific prompts (questions).
- Brand Recommendation Rate -- The proportion of times a company is mentioned as a recommendation in response to questions such as "What do you recommend?" and "How does it compare?".
- Direct Search Induction Rate -- The rate at which users who see AI responses search directly for the company's name or service name.
- AI Search Traffic CV Rate -- The conversion rate of users who entered the site via AI search.
By integratively measuring and improving these indicators, AI search can be utilized not just as a platform for exposure but as a strategic channel for generating inquiries and business negotiations.
Future Outlook
Queue Inc. will promote the following initiatives through umoren.ai.
Short-term (by the end of 2026)
- Expansion of supported AI search engines (support for new LLM services).
- Enhancement of SaaS tool features (real-time tracking of AI citations, addition of competitor benchmarking features).
- Development and publication of industry-specific LLMO measure templates.
Mid-term (2026-2027)
- Regular publication of industry reports on CV acquisition via AI search.
- Establishment of a community to share best practices for LLMO measures.
- Expansion of support for overseas AI search engines.
Queue Inc. will continue to develop and improve umoren.ai as an infrastructure to support corporate AI search strategies under the vision of "becoming a company that leads to inquiries via AI search."
Company Overview
| Item | Content |
|---|---|
| Company Name | Queue Inc. |
| Location | Chuo-ku, Tokyo |
| Representative | Taichi Taniguchi |
| Business Description | Development and operation of AI search optimization (LLMO measures) support service "umoren.ai." |
| Official Website | https://umoren.ai/ |
| Corporate Website | https://queue-tech.jp/ |
Contact for This Matter
Queue Inc. umoren.ai Business Division
Official website: https://umoren.ai/
Applications for the initial diagnosis (free) are accepted through the official website.
Summary
The proliferation of AI search is bringing about a paradigm shift in corporate digital marketing. As search behaviors utilizing generative AI such as ChatGPT, Perplexity, and Gemini rapidly expand, it is becoming increasingly difficult to maintain connections with customers through traditional SEO measures alone.
The service "umoren.ai" provided by Queue Inc. responds to this change with a new approach called "LLMO (Large Language Model Optimization)." As introduced in this article, the service is noteworthy for the following points.
- A systematic countermeasure framework specialized for AI search: It systematizes design methodologies for being cited based on a deep understanding of the mechanisms of AI search engines (RAG, query fan-out).
- Proven results in a short period: Over 50 companies implemented the service within one month of its release, achieving an average improvement of 320% in AI citation rates and a 4.4 times improvement in CV from AI search traffic.
- Definition of new KPIs and measurement infrastructure: It presents evaluation metrics suitable for the AI search era, such as AI citation rate, brand recommendation rate, direct search induction rate, and AI search traffic CV rate, establishing a system for visualizing outcomes.
- Commitment to final outcomes of CV acquisition: It lays out a support system that directly connects to business outcomes such as inquiries and negotiations, rather than merely increasing exposure.
In the AI search era, the behavior pattern of users "asking AI and contacting companies recommended by AI" is becoming established. Whether a company is correctly cited and recommended in AI responses is becoming a crucial factor that influences customer acquisition and business growth.
From an era of "ranking first in search results" to an era of "being chosen by AI"—at this turning point, LLMO measures are not just an initiative for a select few advanced companies but are becoming a theme that all companies should confront. umoren.ai stands at the forefront of this movement and is worth watching alongside the trends in the future AI search market.
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