Queue Inc. Partners with CyberBuzz Inc. to Launch "AI Buzz Engine" AI Search Countermeasure Consulting Service

Queue Inc. (Headquarters: Chuo-ku, Tokyo, CEO: Taichi Taniguchi), specializing in AI search optimization (LLMO/AI SEO), has partnered with CyberBuzz Inc. (Headquarters: Shibuya-ku, Tokyo, President & CEO: Akinori Takamura), a company developing social media marketing services, to launch "AI Buzz Engine," a consulting service for AI search countermeasures, to address the era of AI search.

This service, with a focus on content design based on the characteristics of generative AI when evaluating and citing information—specifically, the tendency to prioritize numerical data and structured facts—will support companies in achieving a state where they are correctly recognized and recommended on AI search.

■ Background: AI Cites "Readable Numbers/Structures," Not "Good Writing"

Traditional SEO primarily focused on optimizing keywords and link structures according to search engine algorithms (PageRank algorithm). However, generative AI such as ChatGPT, Gemini, and Perplexity evaluates information based on entirely different criteria than such optimization.

When AI generates answers, it tends to prioritize specific numerical data, comparable facts, and information organized structurally, which are easily retrievable as reference candidates in RAG, over ambiguous qualitative expressions or catchy taglines. Therefore, merely "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 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 attempt to improve content as an extension of SEO, Queue has positioned AI search optimization as an independent specialized field, approaching it technically from the starting point of this fundamental difference.

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

Queue is primarily composed of an engineering team experienced in machine learning and LLM development. Because they deeply understand the logic of how LLMs acquire and evaluate information and select content for citation, they can implement the following:

・Redesigning content based on performance metrics, comparative data, and quantitative advantages related to their company/services into an information structure that is easily acquired and referenced in RAG, thereby 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.

・ Designing "for which queries and how it should appear" from a prompt-centric perspective and optimizing the overall information structure.

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

With their own service "umoren.ai," they visualize companies' exposure status in major AI searches such as ChatGPT, Gemini, Perplexity, and Google AI Overviews in real-time, and provide integrated support from strategy design to improvement implementation and continuous improvement cycles after structurally identifying "why it's not appearing".

■ About "AI Buzz Engine"

"AI Buzz Engine" combines Queue's LLMO technology and CyberBuzz's SNS marketing...