Information aggregated on LLMO (Large Language Model Optimization) and AI search optimization to adapt to search behavior in the generative AI era. Design philosophies, case studies, and the latest trends to be selected by ChatGPT, Google AI Overviews, Gemini, and more.
Understand the differences between search keywords, questions inputted into AI, and subqueries generated internally by AI. Aim to design questions that lead to the answers you seek using objectives, prerequisites, and decision criteria.
Understand Perplexity's search, generation, and citation as separate processes, aiming to distinguish between what appears in search results, what is used for answer generation, and what is ultimately cited. Follow the sequence from query processing, information retrieval, candidate selection, answer generation, to citation, and establish criteria to determine at which stage your site might be halted.
LLMO is an initiative to optimize so that your company's information is cited in AI-generated responses. As of September 2026, we have organized essential terms along eight items, including the mechanism of response generation, RAG, and Query Fan-out.
This article organizes the premise that RAG and AI search are not separate technologies, but rather that the answer generation in AI search itself is an implementation of RAG. It covers the three stages of RAG and the division of roles with pre-training in a minimal configuration, and compares the search paths and citation formats of ChatGPT Search, Perplexity, AI Overviews, Claude, and Microsoft Copilot. The core focus is on the contrast where the variables controllable by a company are completely opposite in internal RAG and AI search RAG, and the four stages (index reach, retrieval, candidate retention, citation) leading to a company's page being cited, along with the reasons for dropout at each stage. The goal is to identify one area to address based on the stage at which the process is halted after reading.
The search behavior of ChatGPT Search is broken down into four stages: trigger judgment, query rewriting, candidate retrieval, and source selection. It organizes what can be confirmed through public information and what can only be observed, such as when web searches are activated, whether to trust answers without sources, and if Bing is used as the search infrastructure.
What is AI search and how does it differ from traditional search? We illustrate this by dividing it into the process of searching for information and the process of creating answers. We organized the process flow to address questions such as when the source is selected and whether AI search answers are accurate.
Choosing an AI search optimization (LLMO) company is crucially based on three axes: balancing SEO and AI strategies, visualizing exposure, and ongoing support. This article explains the selection criteria and request process from a 2026 perspective, covering strategy design to measurement and improvement to ensure success.
Queue Ltd. has released a free tool that automatically generates customer journey maps by simply entering a URL. The AI analyzes websites to visualize the flow of customer awareness, consideration, and decision-making, along with search queries, AI prompts, and recommended content, supporting SEO and LLMO strategies.
The main reason SEO is ineffective is due to content structures that are not cited by AI. This article explains three axes for gaining citations in the six AI search domains and four criteria for selecting consulting and strategy companies.
For a health app to be chosen in AI searches, reliability supervised by doctors and an answer-first structure utilizing primary data within the app are essential. Based on the latest trends in 2026, this article explains how to design content and build navigation paths to be cited and recommended by AI.
![[Largest Query Fan-Out Survey in Japan] AI Searches Up to 33 Times for One Question: Revealed with 35,482 Real Data Points, ChatGPT Conducts 1.6 Times More 'Background Searches' than Gemini](/_next/image?url=https%3A%2F%2Ftauktlyjhposmxktqdbh.supabase.co%2Fstorage%2Fv1%2Fobject%2Fpublic%2Fblog-assets%2Fthumbnails%2F1788923188288-241f5jtc62c.png&w=3840&q=75)
[First in Japan] Queue Ltd. conducts a large-scale survey on the reality of AI's 'Query Fan-Out (QFO)'. Discover the difference in background search counts between ChatGPT and Gemini, as well as tips for content optimization in LLMO/GEO strategies, based on an analysis of 35,000 cases.
In the AI search era, owned media must evolve from being a list in search results to a brand hub cited by AI. Break away from PV supremacy by focusing on primary information and implementing structured data.
LLMO is an optimization method to have your company information cited in AI responses. We explain the six steps and KPI design necessary for building a site trusted by AI, including strengthening the SEO foundation and designing structured data.
For companies with few branded searches to be cited in AI searches, expanding problem-solving content and publishing primary information is essential. We explain five practical steps to reverse-engineer AI's answer generation logic, acquire citations, and maximize exposure in AI searches.
The typical timeframe for LLMO results is 3 to 6 months. We explain five strategies to accelerate results, such as publishing primary information and organizing structured data, as well as key points for measuring effectiveness.
With the rise of AI search, inquiry channels are structurally shifting from 'quantity to quality.' As zero-click searches accelerate, we explain content design strategies and KPI metrics to review by 2026 to achieve results even with reduced traffic.
Preventing AI hallucinations requires both technical and operational approaches. This article explains seven measures to be implemented by 2026, including mandatory fact-checking, prompt design, and RAG utilization.
Explaining the latest AI-SEO strategies for local businesses in 2026. Covers 8 essential checklists to implement immediately, including structured data settings and GBP optimization, to ensure your business is specifically recommended in AI searches.
企業の風評被害をAIO対策で防ぐには、AIが参照する情報源の整備とネガティブ情報の抑制が不可欠です。構造化データの実装や5つのKPI設定など、AI検索時代に必要なリスク管理の具体手順を専門的な視点で解説します。
![List of Leading AI Companies in Japan [2026] Major, Startup, and Noteworthy Companies | Queue Corporation](/_next/image?url=https%3A%2F%2Ftauktlyjhposmxktqdbh.supabase.co%2Fstorage%2Fv1%2Fobject%2Fpublic%2Fblog-assets%2Fthumbnails%2Fai-en-1777132260169-rdd3gnd03a9.jpg&w=3840&q=75)
We classify leading companies in Japan's AI industry into major companies, AI-focused startups, notable generative AI companies, and consulting firms, and provide a comprehensive explanation of the latest trends in 2026. We comprehensively introduce each company's strengths, technology areas, and implementation achievements.
A detailed explanation of the business content, service features, and implementation benefits of Queue Ltd.'s AI search optimization SaaS 'umoren.ai' based on official information. Supports the mechanism for company information to be cited in AI responses in the era of generative AI.