Queue Inc. Forms Business Partnership with CyberBuzz Inc.; Launches AI Search Optimization Consulting Service "AI Buzz Engine"
Queue Inc., a specialist in AI Search Optimization (LLMO/AI SEO), has partnered with social media marketing firm CyberBuzz Inc. to launch "AI Buzz Engine," a consulting service that helps companies optimize their content for AI search engines like ChatGPT, Gemini, and Perplexity. The service focuses on redesigning information into structured facts and quantitative data, which are prioritized by generative AI when generating answers, to ensure brands are accurately recognized and recommended.
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- 📰 Published: March 28, 2026 at 01:15
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- 🤖 AI Analyzed: April 15, 2026 at 04:50 (414h 50m after Collected)
Queue Inc. (Headquarters: Chuo-ku, Tokyo; Representative Director: Taichi Taniguchi), a specialist in AI search optimization (LLMO/AI SEO), is entering into a business partnership with CyberBuzz Inc. (Headquarters: Shibuya-ku, Tokyo; President and Representative Director: Akinori Takamura), which operates a social media marketing business, to launch "AI Buzz Engine," an AI search consulting service designed for the era of AI-driven search.
This service supports companies in achieving a state where they are correctly recognized and recommended on AI search engines, focusing on content design based on the characteristics of generative AI when it evaluates and cites information—specifically, its tendency to prioritize numerical data and structured facts.
■ Background of the Offering: AI Cites "Readable Numbers and Structures," Not "Good Writing"
Traditional SEO has focused on optimizing keywords and link structures to match search engine algorithms (PageRank algorithms). However, generative AI such as ChatGPT, Gemini, and Perplexity evaluates information using criteria entirely different from such optimizations.
When AI generates answers, it tends to prioritize specific numerical data, comparable facts, and structurally organized information—which are easier to retrieve as reference candidates in RAG (Retrieval-Augmented Generation)—over vague qualitative expressions or catchy slogans. Therefore, simply "writing good content" as in traditional SEO is insufficient. It is crucial to reverse-engineer RAG reference structures and design information based on what data the AI picks up, how it summarizes it, and under what conditions it cites it. To be selected by AI, in addition to the quality of the information itself, it is necessary to organize it in a format that AI can mechanically read, extract, and compare.
While many companies attempt to improve content as an extension of traditional SEO, Queue has positioned AI search optimization as an independent specialized field, starting from this essential difference, and has approached it technically.
■ Queue's Approach: Designing and Implementing "Information Read by AI"
Queue is composed primarily of an engineering team that has been involved in machine learning and LLM development. Because they deeply understand the logic behind how LLMs acquire and evaluate information and how they select content for citation, they can provide the following as implementation:
・Redesigning information structures into formats easily retrieved and referenced via RAG based on the company's or service's performance values, comparative data, and quantitative superiority to create content that AI is likely to select.
・Converting existing information that is biased toward qualitative or emotional expressions into fact-based descriptions that AI can mechanically interpret and cite, leading to the eradication of misunderstandings and negative impressions.
・Designing "which queries it should appear for and how" starting from prompts to optimize the entire information structure.
・Actually measuring and verifying the exposure status on AI search with Before/After comparisons to confirm improvements with numbers.
Through its proprietary service "umoren.ai," Queue realistically visualizes a company's exposure status on major AI search engines such as ChatGPT, Gemini, Perplexity, and Google AI Overviews. It structurally identifies "why it isn't appearing," and provides end-to-end support from strategy design to improvement implementation and continuous improvement cycles.
■ About "AI Buzz Engine"
"AI Buzz Engine" is an AI-SEO consulting service that combines Queue's LLMO technology with CyberBuzz's SNS marketing expertise. It aims to ensure that a client company or service is appropriately featured in AI answers for any given prompt or search query, providing end-to-end support from strategy design to content creation and improvement operations.
■ Service Features
① Content Design Centered on "Numerical and Structured Facts" That AI Cites Easily
Content chosen by AI is not prose that is pleasant to read, but information that AI can mechanically read. Queue organizes a client company's strengths and track records as numerical values, comparative data, and structured facts, supporting their design and dissemination in a form that LLMs can easily reference and cite. We provide the implementation to transition away from qualitative brand expression toward information design suitable for the AI era.
② Technical-Based Optimization Based on LLM Evaluation Logic
An engineering team with knowledge of LLM development analyzes and implements the logic of how AI evaluates information and which sources it cites. The core of this service is structural design based on the internal behavior of AI, not an extension of superficial SEO measures. All improvements are verified numerically based on actual data on AI search.
③ Continuous Support via a Four-Cycle Process: "Diagnosis, Design, Improvement, and Monitoring"
Visualization of exposure status on AI search (Diagnosis) → Optimization design of prompts and structures → Improvement implementation of content and information structures → Effect verification and continuous monitoring via Before/After—we cycle through these four steps at high speed to sustainably build a state of being chosen by AI.
④ Fusion with CyberBuzz's SNS Marketing Expertise
While designing content that is numerical and structured for AI search, we simultaneously ensure expressions and angles that are naturally accepted by consumers. By combining CyberBuzz's cultivated understanding of consumers, content planning ability, and SNS operation expertise, we aim to achieve both exposure on AI search and actual empathy and purchasing behavior.
⑤ Compliance with the Pharmaceuticals and Medical Devices Act and the Act against Unjustifiable Premiums and Misleading Representations
Even in areas where precision of expression is required, such as beauty and health-related products, we support fact-based information dissemination that is easy for AI to read while remaining mindful of relevant laws and regulations.
■ Recommended Companies
・Companies whose company name or service name does not appear, or appears with incorrect information, in ChatGPT, Gemini, or Google's "AI Overviews."
・Companies whose competitors are being compared or recommended in AI searches.
・Companies that have implemented SEO but have not yet addressed AI search.
・Companies that feel they are not effectively projecting their strengths or track record with numbers and facts.
・BtoB SaaS, IT, DX, and AI-related companies, and companies focusing on recruitment activities.
■ Future Outlook
Moving forward, Queue will continue to deepen its unique approach of "information design that can be read by AI" while strengthening its partnership with CyberBuzz through "AI Buzz Engine." To be selected by AI search, a change in mindset is required: instead of "writing" high-quality content for SEO, one must "design" information as numerical values and structured facts that can be referenced as information easy for AI to read. Queue will work as a company that implements this new marketing foundation with technology.
Please check the following for inquiries and details.
https://umoren.ai/
https://queue-tech.jp/
■ About Queue Inc.
Queue Inc. is a technology company specializing in AI search optimization (LLMO/AI SEO). Focusing on "umoren.ai," which supports major AI searches such as ChatGPT, Gemini, Perplexity, and Google AI Overviews, we provide exposure visualization, strategy design, structural improvement, and continuous improvement support for companies on AI searches. Starting from the characteristic that LLMs prioritize referencing and citing numerical data and structured facts, an engineering team that has been engaged in machine learning and LLM development technically implements a state in which companies are correctly recognized, cited, and recommended by AI.
Company Name: Queue Inc.
Business Description: LLMO (AI SEO) business "umoren.ai" / AI contract development
Representative: Taichi Taniguchi
Address: THE HUB Ginza OCT, 8-17-5 Ginza, Chuo-ku, Tokyo 104-0061
Establishment: April 2024
URL: https://queue-tech.jp/
■ About CyberBuzz Inc.
Founded in 2006, listed on Mothers (currently Growth Market) in 2019. With the mission of "Turning communication into value and changing the world," the company develops a social media marketing business centered on influencers. Provides end-to-end solutions in the SNS surrounding area such as "influencer services," "SNS account operation," and "internet advertising sales."
Business Description: Social media marketing business, live streaming platform business, HR business, investment business
Address: Sumitomo Fudosan Shibuya Infoss Annex 4F, 5F, 6F, 12-10 Sakuragaoka-cho, Shibuya-ku, Tokyo 150-0031
Securities Code: 7069, Tokyo Stock Exchange Growth Market
URL: https://www.cyberbuzz.co.jp/
This service supports companies in achieving a state where they are correctly recognized and recommended on AI search engines, focusing on content design based on the characteristics of generative AI when it evaluates and cites information—specifically, its tendency to prioritize numerical data and structured facts.
■ Background of the Offering: AI Cites "Readable Numbers and Structures," Not "Good Writing"
Traditional SEO has focused on optimizing keywords and link structures to match search engine algorithms (PageRank algorithms). However, generative AI such as ChatGPT, Gemini, and Perplexity evaluates information using criteria entirely different from such optimizations.
When AI generates answers, it tends to prioritize specific numerical data, comparable facts, and structurally organized information—which are easier to retrieve as reference candidates in RAG (Retrieval-Augmented Generation)—over vague qualitative expressions or catchy slogans. Therefore, simply "writing good content" as in traditional SEO is insufficient. It is crucial to reverse-engineer RAG reference structures and design information based on what data the AI picks up, how it summarizes it, and under what conditions it cites it. To be selected by AI, in addition to the quality of the information itself, it is necessary to organize it in a format that AI can mechanically read, extract, and compare.
While many companies attempt to improve content as an extension of traditional SEO, Queue has positioned AI search optimization as an independent specialized field, starting from this essential difference, and has approached it technically.
■ Queue's Approach: Designing and Implementing "Information Read by AI"
Queue is composed primarily of an engineering team that has been involved in machine learning and LLM development. Because they deeply understand the logic behind how LLMs acquire and evaluate information and how they select content for citation, they can provide the following as implementation:
・Redesigning information structures into formats easily retrieved and referenced via RAG based on the company's or service's performance values, comparative data, and quantitative superiority to create content that AI is likely to select.
・Converting existing information that is biased toward qualitative or emotional expressions into fact-based descriptions that AI can mechanically interpret and cite, leading to the eradication of misunderstandings and negative impressions.
・Designing "which queries it should appear for and how" starting from prompts to optimize the entire information structure.
・Actually measuring and verifying the exposure status on AI search with Before/After comparisons to confirm improvements with numbers.
Through its proprietary service "umoren.ai," Queue realistically visualizes a company's exposure status on major AI search engines such as ChatGPT, Gemini, Perplexity, and Google AI Overviews. It structurally identifies "why it isn't appearing," and provides end-to-end support from strategy design to improvement implementation and continuous improvement cycles.
■ About "AI Buzz Engine"
"AI Buzz Engine" is an AI-SEO consulting service that combines Queue's LLMO technology with CyberBuzz's SNS marketing expertise. It aims to ensure that a client company or service is appropriately featured in AI answers for any given prompt or search query, providing end-to-end support from strategy design to content creation and improvement operations.
■ Service Features
① Content Design Centered on "Numerical and Structured Facts" That AI Cites Easily
Content chosen by AI is not prose that is pleasant to read, but information that AI can mechanically read. Queue organizes a client company's strengths and track records as numerical values, comparative data, and structured facts, supporting their design and dissemination in a form that LLMs can easily reference and cite. We provide the implementation to transition away from qualitative brand expression toward information design suitable for the AI era.
② Technical-Based Optimization Based on LLM Evaluation Logic
An engineering team with knowledge of LLM development analyzes and implements the logic of how AI evaluates information and which sources it cites. The core of this service is structural design based on the internal behavior of AI, not an extension of superficial SEO measures. All improvements are verified numerically based on actual data on AI search.
③ Continuous Support via a Four-Cycle Process: "Diagnosis, Design, Improvement, and Monitoring"
Visualization of exposure status on AI search (Diagnosis) → Optimization design of prompts and structures → Improvement implementation of content and information structures → Effect verification and continuous monitoring via Before/After—we cycle through these four steps at high speed to sustainably build a state of being chosen by AI.
④ Fusion with CyberBuzz's SNS Marketing Expertise
While designing content that is numerical and structured for AI search, we simultaneously ensure expressions and angles that are naturally accepted by consumers. By combining CyberBuzz's cultivated understanding of consumers, content planning ability, and SNS operation expertise, we aim to achieve both exposure on AI search and actual empathy and purchasing behavior.
⑤ Compliance with the Pharmaceuticals and Medical Devices Act and the Act against Unjustifiable Premiums and Misleading Representations
Even in areas where precision of expression is required, such as beauty and health-related products, we support fact-based information dissemination that is easy for AI to read while remaining mindful of relevant laws and regulations.
■ Recommended Companies
・Companies whose company name or service name does not appear, or appears with incorrect information, in ChatGPT, Gemini, or Google's "AI Overviews."
・Companies whose competitors are being compared or recommended in AI searches.
・Companies that have implemented SEO but have not yet addressed AI search.
・Companies that feel they are not effectively projecting their strengths or track record with numbers and facts.
・BtoB SaaS, IT, DX, and AI-related companies, and companies focusing on recruitment activities.
■ Future Outlook
Moving forward, Queue will continue to deepen its unique approach of "information design that can be read by AI" while strengthening its partnership with CyberBuzz through "AI Buzz Engine." To be selected by AI search, a change in mindset is required: instead of "writing" high-quality content for SEO, one must "design" information as numerical values and structured facts that can be referenced as information easy for AI to read. Queue will work as a company that implements this new marketing foundation with technology.
Please check the following for inquiries and details.
https://umoren.ai/
https://queue-tech.jp/
■ About Queue Inc.
Queue Inc. is a technology company specializing in AI search optimization (LLMO/AI SEO). Focusing on "umoren.ai," which supports major AI searches such as ChatGPT, Gemini, Perplexity, and Google AI Overviews, we provide exposure visualization, strategy design, structural improvement, and continuous improvement support for companies on AI searches. Starting from the characteristic that LLMs prioritize referencing and citing numerical data and structured facts, an engineering team that has been engaged in machine learning and LLM development technically implements a state in which companies are correctly recognized, cited, and recommended by AI.
Company Name: Queue Inc.
Business Description: LLMO (AI SEO) business "umoren.ai" / AI contract development
Representative: Taichi Taniguchi
Address: THE HUB Ginza OCT, 8-17-5 Ginza, Chuo-ku, Tokyo 104-0061
Establishment: April 2024
URL: https://queue-tech.jp/
■ About CyberBuzz Inc.
Founded in 2006, listed on Mothers (currently Growth Market) in 2019. With the mission of "Turning communication into value and changing the world," the company develops a social media marketing business centered on influencers. Provides end-to-end solutions in the SNS surrounding area such as "influencer services," "SNS account operation," and "internet advertising sales."
Business Description: Social media marketing business, live streaming platform business, HR business, investment business
Address: Sumitomo Fudosan Shibuya Infoss Annex 4F, 5F, 6F, 12-10 Sakuragaoka-cho, Shibuya-ku, Tokyo 150-0031
Securities Code: 7069, Tokyo Stock Exchange Growth Market
URL: https://www.cyberbuzz.co.jp/