Queue Inc. and CyberBuzz Inc. Collaborate to Launch AI Search Countermeasure Consulting Service "AI Buzz Engine"

Queue Inc. (Headquarters: Chuo-ku, Tokyo, Representative Director: Taichi Taniguchi), specializing in AI search optimization (LLMO/AI SEO), has partnered with CyberBuzz Inc. (Headquarters: Shibuya-ku, Tokyo, President and Representative Director: Akinori Takamura), a company operating a social media marketing business, to launch the AI search countermeasure consulting service "AI Buzz Engine" that responds to the era of AI search.

This service supports companies in achieving a state where they are correctly recognized and recommended on AI search, centering on content design based on the characteristics of generative AI when evaluating and citing information—particularly the characteristic of prioritizing numerical data and structured facts.

■ Background of Offering: AI Cites "Readable Numbers and Structures," Not "Good Text"

In traditional SEO, the focus was on optimizing keywords and link structures according to search engine algorithms (PageRank algorithm). However, generative AIs such as ChatGPT, Gemini, and Perplexity evaluate information based on entirely different criteria than such optimizations.

When AI generates answers, it tends to prioritize specific numerical data, comparable facts, and information organized structurally, which are easier to retrieve as reference candidates in RAG, over ambiguous qualitative expressions or impressive taglines. Therefore, simply "writing good content" as in traditional SEO is insufficient. It is crucial to design information by reverse-analyzing the RAG reference structure, considering what information the AI picks up, how it summarizes it, and under what conditions it cites it. To be chosen by AI, in addition to the quality of the information itself, it is necessary for the information to be organized in a format that AI can mechanically read, extract, and compare.

While many companies try to improve content as an extension of SEO, Queue has approached AI search optimization technically, positioning it as an independent specialized field based on this fundamental difference.

■ Queue's Approach: Designing and Implementing "Information Readable by AI"

Queue is composed primarily of an engineering team with experience in machine learning and LLM development. Because they deeply understand the logic of how LLMs acquire and evaluate information and which content they select for citation, they can provide the following as implemented services:

・ Redesigning content based on performance metrics, comparative data, and quantitative advantages related to the company/service into an information structure that is easily retrieved and referenced in RAG, creating content that is likely to be selected by AI.

・ Converting existing information biased towards qualitative/emotional expressions into fact-based descriptions that AI can mechanically interpret and cite, thereby designing information that leads to dispelling misunderstandings or negative impressions.

・ Optimizing the entire information structure by designing "for which queries and how it should appear" from a prompt-based perspective.

・ Measuring and verifying exposure status on AI search in Before/After scenarios, confirming improvements with numerical data.

With our proprietary service "umoren.ai," we visualize companies' exposure status on major AI searches such as ChatGPT, Gemini, Perplexity, and Google AI Overviews in real-time, structurally identify "why they are not appearing," and provide end-to-end support from strategy design to implementation and continuous improvement cycles.

■ About "AI Buzz Engine"

"AI Buzz Engine" combines Queue's LLMO technology with CyberBuzz's SNS market...