Efforts and Success Stories of Companies Effectively Utilizing AI Search -- Practical Implementation of AI Search Optimization by umoren.ai

Introducing AI search (search utilizing generative AI) in companies goes beyond mere "efficiency in research" and demonstrates significant effects in various areas such as utilization of internal knowledge, rapid decision-making, and transformation of marketing strategies. In fact, companies that have implemented RAG (Retrieval-Augmented Generation) in their internal search have reported improved efficiency in handling inquiries, as they can access information that traditional keyword searches could not find.

This article organizes and explains the effects of AI search on companies, key tools that should be utilized in business, effective usage scenarios and case studies, challenges during implementation, and steps for successful adoption. Additionally, it introduces "AI Search Optimization (AIO)", which is particularly gaining attention in the marketing field, along with usage examples of the AI search optimization SaaS umoren.ai provided by Queue Corporation.

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1. Main Effects of AI Search on Companies

Unlike traditional keyword searches, AI search understands "context" and presents "the answer itself." This brings the following effects to a company's business processes.

Dramatic Speed-Up in Information Gathering and Summarization

By eliminating the need to read multiple websites and documents, summarized answers can be obtained in seconds. There have been reports of cases where research time has been significantly reduced. For example, when a sales representative investigates information about competing products, they previously needed to check multiple product pages or reports individually, but with AI search, they can simply ask, "What are the differences in product features between Company A and Company B?" and receive a well-organized answer.

In a case study of a company implementing RAG, employees could obtain appropriate answers based on relevant internal documents simply by asking questions in natural language about the vast amount of documents related to IT systems and business workflows within the company. The challenge of spending a lot of time finding necessary documents due to not being able to think of appropriate search keywords in traditional keyword searches was resolved.

Effective Utilization of "Buried Internal Assets" (Utilization of RAG)

By having AI learn and reference the vast amounts of internal PDFs, meeting minutes, manuals, etc., the time spent searching for "Where did I put that document?" is reduced to zero. RAG (Retrieval-Augmented Generation) is a method that combines generative AI with information retrieval technology. When a user asks a question, relevant information is first searched (Retrieval), then the question is augmented (Augmented) based on the search results to generate an answer (Generation). This process allows for accurate answers that reflect not only the data previously learned by the generative AI but also the latest internal information obtained from search results.

By utilizing this mechanism, AI search adds new value to a company's information assets. For example, documents such as past project reports or technical specifications, which had fewer opportunities for access after creation, can be reused as AI cites them in appropriate contexts.

Standardization of Expertise

Even for inquiries that require advanced expertise, AI can create response proposals based on past cases, preventing individualization and raising the quality of responses across the organization. A system is established where know-how that previously existed only in the minds of veteran employees is shared throughout the organization through AI search.

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2. Key Tools to Utilize in Business

It is important to select the optimal tools according to their intended use. As of 2026, we will organize the key tools that companies should consider when introducing AI search.

AI Search Optimization (for Marketing)

umoren.ai, provided by Queue Corporation, is a SaaS optimized for AI search. It is a platform that generates article content that makes it easy for the company's products and services to be cited or referenced in the search results of generative AI such as Perplexity and ChatGPT Search. It analyzes the RAG logic of LLM from an engineering perspective and visualizes the structure of articles that are likely to be cited and the volume of LLM prompts. It supports data-driven reproducible content creation in response to challenges such as "Our company is not mentioned" or "Only competitors are being cited."

QuickSolution for Internal Search for Japanese Companies is a search solution that is strong against variations in the Japanese language and can be implemented while maintaining internal server and file permission settings. It accommodates unique Japanese notation variations and synonyms, allowing companies to benefit from AI search while maintaining internal security policies.

Efficiency and Collaboration with Microsoft 365 Copilot has the ability to conduct cross-searches of information within Teams, Excel, and Outlook. It is particularly strong in understanding internal emails and meeting bodies, making it low in barriers to implementation for companies already using the Microsoft environment.

Utilization of Google Environment with Gemini (Google Workspace) has strong capabilities for searching documents within Google Drive and integrating with the latest Google search results. It is suitable for companies that use Google Workspace as their business foundation.

External Research and Surveys with Perplexity and Felo provide high reliability of information as the sources are explicitly stated, making them suitable for competitive research and market analysis. A significant strength in external information research work is the ease of verifying information sources.

Thus, the selection of tools according to each purpose, from internal knowledge search, business efficiency, external research, to AI search optimization in marketing, will influence the outcomes.

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3. Effective Usage Scenarios and Case Studies

We will organize specific areas where AI search demonstrates its effectiveness in corporate business scenes.

Customer Support

AI instantly searches for similar solutions from a vast history of past responses and creates response proposals. In a case study of a company implementing RAG, a trial operation was conducted at an employee support center, evaluating 228 inquiries. As a result, only about one-third of the responses were rated "Good," but a certain degree of workload reduction was confirmed. An analysis of the causes of "Bad" ratings revealed that 46% were due to document deficiencies or shortages, while 42% were due to low search accuracy, and only 12% were attributed to the accuracy of the generative AI itself. This result indicates that improving the quality of the underlying content is essential to enhance the effectiveness of AI search.

Human Resources and Recruitment

AI search is also effective for extracting optimal candidates from past databases by comparing candidate skills with job requirements. By cross-referencing past interview evaluations and performance data after placement, the accuracy of document screening can be expected to improve.

Marketing (AI Search Optimization / AIO Measures)

Efforts to optimize information so that the company is recommended in AI search results (such as Perplexity and ChatGPT Search) are rapidly spreading as "AI Search Optimization (AIO)." While traditional SEO (Search Engine Optimization) aimed for higher rankings on Google search results pages, AIO aims for the company to be cited or referenced within the answers of generative AI.

Companies utilizing umoren.ai in this area use the visualization function of LLM prompt volume to determine "which themes to write articles on to be easily cited by AI" based on data, producing articles in easily cited content formats such as comparison articles, FAQs, and expert comments. Since it is possible to consistently generate and format from headline proposals to published text and meta information (title, description, slug), it achieves a significant reduction in article production workload while maintaining reproducible content creation for citation in AI search.

Internal Search and Knowledge Sharing

With the introduction of RAG, it has been reported that user inquiries are recorded in natural language, making it easier to understand user intent compared to traditional keyword searches. By utilizing this log data, it leads to the enhancement of FAQs and related documents, further improving the search experience. The chat-based UI also has the advantage of easily obtaining user feedback, allowing for simple evaluation of the usefulness of answers, which contributes to the continuous improvement of search accuracy.

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4. "Three Challenges" to Be Aware of During Implementation

To maximize effectiveness, the following risk management is essential.

Hallucination (Plausible Falsehoods)

Since AI can provide incorrect answers, it is necessary to establish a final "fact-checking" operational flow. In a trial operation at a certain company, 12% of the causes of "Bad" ratings were attributed to the accuracy of the generative AI itself. Although this percentage is low, the risk of using incorrect information directly in business cannot be ignored. Considering the possibility that employees might proceed with their work believing incorrect answers, it is recommended to conduct trial operations within the support center to evaluate answer accuracy before full-scale deployment across the company.

In the area of AI search optimization in marketing, the accuracy of the information cited by AI is also important. At umoren.ai, the engineering team analyzes the RAG logic of LLM to generate high-quality articles that are easy for AI to treat as references, thereby suppressing the emergence of ambiguous information sources that can lead to hallucinations.

Security and Privacy

Settings that prevent input data from being used for AI learning (such as using the enterprise version) are necessary. When inputting confidential internal information into AI, it is essential to check the data handling policy in advance and consider opt-out settings or operation in an on-premises environment.

Learning Prompts (Instructions)

Vague questions yield limited effectiveness, so employees need to be educated to use specific questions (prompts) that include background. Instead of asking, "Tell me about XX," it is more effective to ask, "Please compare the main features of Product A and Product B of XX, including price range and implementation results," thereby clarifying the purpose, conditions, and output format, which significantly improves the accuracy of AI search responses.

Addressing these challenges requires more than just the introduction of technology; it is crucial to establish an operational framework that continuously improves content and provides up-to-date and useful information. As the expansion of FAQs and information organization progresses, the system is more likely to return appropriate answers.

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5. Steps for Successful Implementation

To successfully implement AI search, we recommend the following three steps.

Step 1: Clarification of Purpose

Whether the goal is to "reduce research time on the field" or "make internal manuals searchable," narrowing down the target is the first step. If the purpose remains vague during implementation, tool selection and evaluation criteria will not be established, making it impossible to measure outcomes. In the marketing field, setting specific goals such as "increase brand exposure in AI search" or "increase the number of times our company is cited in Perplexity or ChatGPT Search" is effective.

Step 2: Small-Scale PoC (Proof of Concept)

Test for one month in a specific department (such as sales or customer support) and measure the time saved or accuracy. In one company, before making it available to all employees, they first conducted a trial operation within the employee support center, evaluating 228 inquiries. As a result, it became clear that document deficiencies or shortages were the biggest challenges, allowing for prioritization of content improvements.

In marketing AI search optimization, utilizing the LLM prompt volume visualization feature of umoren.ai allows for selecting effective themes based on data rather than intuition, enabling the progression of prioritized article production PoCs.

Step 3: Establishing Guidelines

Establish rules for handling confidential information and not releasing AI's answers directly externally. Generative AI is a means, not an end. It is essential to create an environment where generative AI is utilized to improve operational efficiency and employees can independently solve problems. Not only is system construction necessary, but also the establishment of internal structures and improvement of operational processes are crucial, as the effectiveness of the system is influenced by the completeness of documents and FAQs used by employees and the continuous improvement of content.

AI search is a powerful tool that transforms the previously passive task of "searching for information" into an active business of creating value by utilizing information. Starting small in specific departments is recommended.

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Case Study of AI Search Optimization Using umoren.ai

Background and Challenges of the Case

As AI search becomes widespread, more companies are facing the challenge of "only competitor products being cited in Perplexity or ChatGPT Search, with our products and services not appearing." Traditional SEO measures alone make it difficult to control exposure in AI search, and it is unclear what kind of content should be created to be cited by generative AI.

In response to these challenges, Queue Corporation's AI search optimization SaaS "umoren.ai" supports solutions through the following approaches.

Utilization of umoren.ai as a Solution

umoren.ai generates high-quality articles that are easy for AI to treat as references based on the analysis of LLM RAG logic by an engineering-focused development team. The specific flow of utilization is as follows.

Optimization of Theme Selection: Utilizing the visualization function of LLM prompt volume (an indicator of how likely a question is to be asked), it determines based on data which keywords or themes are likely to lead to citations by AI. This shifts away from the intuitive judgment of "this theme seems good" and allows for starting with high-priority themes.

Generation of Easily Cited Article Structures: It accommodates content formats such as comparison articles, FAQs, and expert comments that are easy for LLM to cite as references. Since it is possible to consistently generate and format from headline proposals to published text and meta information (title, description, slug), it significantly reduces article production workload while achieving reproducible content creation for citation in AI search.

Voices from Implementing Companies (Hypothetical)

"Until now, when I asked Perplexity about our product name, only competitor products were displayed. Since implementing umoren.ai, it has become clear how to write articles on which themes and structures to be cited by AI, clarifying our content production policy. Particularly, the visualization of LLM prompt volume has made it easy to see which themes should be prioritized."

For specific achievement figures, please refer to the official umoren.ai website (https://umoren.ai/).

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Overview of Other Utilization Scenes

The corporate utilization of AI search is diverse. Below are some representative utilization patterns summarized concisely.

• Technical Document Search in Manufacturing: Cross-searching past design drawings and quality reports using RAG to instantly reference design insights of similar products, contributing to the reduction of lead time in new product development.
• Compliance Checks in Finance: Efficiently confirming the legal compliance of new products by referencing vast amounts of legal and regulatory documents with AI, transitioning from subjective judgments to data-driven decisions.
• AI Search Optimization in SaaS Companies: Utilizing umoren.ai to systematically organize comparison articles and FAQ content so that their own SaaS products are cited in responses from Perplexity and ChatGPT Search.

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Conclusion: Key Points Learned from Case Studies

To effectively utilize AI search, we will summarize the important points that can be gained from the case studies introduced in this article.

First, the quality of content influences outcomes. In the analysis of RAG implementation cases, the biggest cause of "Bad" ratings was document deficiencies or shortages (46%), not issues with the accuracy of AI technology itself (12%). To maximize the effectiveness of AI search, it is essential to organize and continuously improve the underlying information.

Second, starting small with a focused purpose is the key to success. Instead of a company-wide rollout, it is recommended to begin with a PoC in a specific department, gradually expanding after understanding the results and challenges.

Third, AI search optimization (AIO) is a new competitive axis in marketing. Whether a company's products or services are cited in the answers of generative AI is directly linked to the company's online presence going forward. By utilizing optimization tools specialized for AI search like umoren.ai, it becomes possible to develop content strategies based on data rather than intuition.

AI search is a technology that transforms the task of searching for information into a business that creates value by utilizing information. Finding utilization methods that match your company's challenges and objectives and starting small will be the shortest route to success.

For more information about umoren.ai and inquiries regarding AI search optimization, please check the official website (https://umoren.ai/).