Queue Inc. Partners with Cyber Buzz Inc. to Launch AI Search Optimization Consulting Service "AI Buzz Engine"

Queue Inc. (headquartered in Chuo-ku, Tokyo; Representative Director: Taichi Taniguchi), a specialist in AI search optimization (LLMO/AI SEO), has partnered with Cyber Buzz Inc. (headquartered in Shibuya-ku, Tokyo; Representative Director & President: Akinori Takamura), a social media marketing company, to launch "AI Buzz Engine," an AI search optimization consulting service designed for the age of AI-driven search.

This service centers on content design based on the characteristics of how generative AI evaluates and cites information—particularly its tendency to prioritize numerical data and structured facts—helping companies achieve a state where they are correctly recognized and recommended in AI search results.

■ Background: AI Cites "Machine-Readable Numbers and Structure," Not "Well-Written Text"

Traditional SEO focused on optimizing keywords and link structures to align with search engine algorithms (PageRank). However, generative AI systems such as ChatGPT, Gemini, and Perplexity evaluate information by entirely different criteria.

When generating answers, AI tends to prioritize concrete numerical data, comparable facts, and structurally organized information that is easy to retrieve as RAG reference candidates—over vague qualitative expressions or catchy copy. Simply "writing good content" as in traditional SEO is therefore insufficient. It is essential to reverse-engineer RAG reference structures and design information with an understanding of what data AI picks up, how it summarizes it, and under what conditions it cites it. To be chosen by AI, information must not only be high quality but also 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 a distinct specialized field, approaching it technically from the standpoint of this fundamental difference.

■ Queue's Approach: Designing and Implementing "Information That AI Can Read"

Queue's team is composed primarily of engineers with backgrounds in machine learning and LLM development. Their deep understanding of how LLMs retrieve and evaluate information and select content for citation enables them to deliver the following implementations:

· Redesign information structures based on actual performance metrics, comparative data, and quantitative advantages of a company or service into formats that are easily retrieved and referenced via RAG, creating content that AI is more likely to select.

· Convert existing information skewed toward qualitative or emotional expressions into fact-based descriptions that AI can mechanically interpret and cite, designing information that corrects misperceptions and dispels negative impressions.

· Design from a prompt-first perspective—defining "for which queries and in what way" a brand should appear—and optimize the entire information structure accordingly.

· Measure and verify AI search visibility on a Before/After basis, confirming improvements with quantitative data.

The company's own service, "umoren.ai," visualizes in real time a company's visibility across major AI search platforms—including ChatGPT, Gemini, Perplexity, and Google AI Overviews—structurally identifies "why they're not appearing," and provides end-to-end support from strategy design through improvement implementation and ongoing optimization cycles.

■ About "AI Buzz Engine"

"AI Buzz Engine" combines Queue's LLMO technology with Cyber Buzz's social media marketing...