AI Search Optimization case study | Specialist recruitment, Japan
How a Major Japanese Recruitment Company Improved Its Visibility in AI Search
A well-established Japanese specialist recruitment company had a strong reputation offline but little presence in AI search. When users asked generative AI tools to recommend recruitment firms in its field, the company’s name often did not appear. This case study describes how that gap was addressed, and how the company’s recommendation status and the way AI answers described it changed over time.
The company has a long history and has built trust by supporting both job seekers and hiring companies in its specialist field. Its reputation in the real world, however, was not matched by its online presence.
The approach was not to publish large volumes of articles. The consultant first researched the competitive landscape and AI answers, identified the company’s genuine competitive strengths, and built an SEO and AI Search Optimization strategy that connected those strengths to what job seekers actually need.
After content and service pages were improved and AI answers were checked on an ongoing basis, answers to related questions began to recommend the company as a leading recruitment option.
01 · Background
A respected company that struggled online
The client is a long-established specialist recruitment company operating in a major Japanese metropolitan area.
It has extensive experience placing candidates in its specialist field, and it is known for careful support of both job seekers and hiring companies. Its service is built on face-to-face relationships: direct communication with candidates, detailed sharing of job information, and individual support and development tailored to each person’s situation.
Over the years, this approach earned the company lasting trust in its field.
Online, the picture was different. Compared with offline channels such as job-seeker events, acquisition through the web was growing slowly. Meanwhile, competitors that publish actively online, including web-based services that had entered the Japanese market from overseas, were becoming more visible.
The company had deep industry experience and high service quality, but neither came across clearly online.
02 · The challenge
Generative AI was not recommending the company
The concern raised most strongly by senior management was the company’s absence in generative AI tools such as ChatGPT.
Consider a user who asks an AI tool which company to use for specialist recruitment in a major Japanese city. A company with a long history and a strong track record in the field would reasonably be expected to appear as one of the options.
When the AI answers were actually reviewed, competitors were named in some cases while the client was not. Management raised the issue directly: the company was not being recommended by AI.
A second challenge sat behind the first. The company did not have in-house staff with specialist web marketing knowledge and hands-on experience, which made it difficult to respond effectively to online competition.
To address this, the engagement began by examining how generative AI recognized and recommended the company. The goal was for the company to be introduced as a strong option in answers to relevant questions.
03 · Research
Clarifying competitive strengths before producing content
The client had initially considered producing blog articles quickly to increase its online output.
The consultant judged that the competitive environment needed to be understood first. Two questions came before any writing: why was a company with a strong track record not being recommended by AI, and on what basis should it compete against competitors that were strong online?
To answer them, competitor research was carried out in both web search results and AI answers. The research looked at which companies appeared for relevant search intents and what information was presented about them.
The client’s services and track record were then compared with those of competitors to identify where the company held a real advantage.
What emerged was the quality of support offered to each individual job seeker.
Competitors were stronger in some areas of online job listing visibility. The client, however, had direct communication with job seekers, careful information sharing, and individual support and development. These services had been provided over many years and were a key source of the company’s credibility.
The problem, in other words, was not simply a shortage of content compared with competitors. The advantage the company held in its real-world service was not reflected in its online information or in AI answers.
04 · Strategy
Focus on where user needs and company strengths overlap
Based on the research, the consultant did not recommend a strategy centered on publishing large volumes of job listings or general career-advice articles.
Competing on the same ground as companies whose strength is the volume of online listings and information was not considered the best use of the client’s position.
The recommendation was instead to concentrate on themes where job seekers’ needs overlap with the client’s service strengths.
Job seekers considering a move within a specialist field do not all want the same thing. Some want to compare a wide range of openings quickly. Others want a company that will properly assess their experience and expertise, advice from someone who knows the industry well, or support in building their career through the job search.
The needs in the latter group fit closely with the face-to-face support and specialist expertise the client had built over many years.
Priority themes were therefore narrowed down based on search intent and the kinds of questions people put to AI tools. The content strategy explained which job seekers the client’s services suit, and why.
The aim was not to put the company’s name in more places online. It was to connect the specific problems users have with the characteristics of a company that can address them.
The same principle applied to AI search. Rather than promoting the company’s strengths in abstract terms, the emphasis was on preparing information that explains which questions the company is a suitable answer to, and why.
05 · Execution
Improving content and service pages, and checking AI answers over time
Following the strategy, content work prioritized the themes where the client’s strengths met job seekers’ needs.
Existing service pages were also addressed, not only new content. Improvements were proposed based on the analysis of user needs. The direction was to make clear how what job seekers want to know corresponds to the value the company provides, and to review page structure and navigation so that visitors could move naturally to the relevant services.
Content and service pages were not treated as separate initiatives. The work was designed around the full journey: a user searches or asks an AI tool, learns about the company, and considers using its services.
After the work began, web search results and AI answers continued to be checked. The consultant observed which companies were recommended for relevant questions and, where the client appeared, how it was described, and used those observations to inform the next round of improvements.
The cycle of research, implementation, verification and refinement turned the work into continuous SEO and AI Search Optimization improvement rather than a one-time content project.
06 · Results
The company appeared in AI answers and was recommended as a leading option
During the ongoing work, a clear change was observed in how AI answers presented the client.
At the start, asking AI tools to recommend specialist recruitment companies in the client’s metropolitan area sometimes returned answers that did not include the company.
After content and service page improvements and continued verification, answers to related questions began to introduce the company as a leading recruitment option.
In some answers, the company was not merely listed among others. It was recommended with reference to its services and reputation in its specialist field.
This marked an important improvement against the original management concern that the company was not being recommended by AI.
The characteristics AI answers attributed to the company also shifted toward the strengths identified during the research phase, which was another meaningful change.
The shift from a company that did not appear in AI answers to one recommended as a leading option for related questions is a significant outcome of this AI Search Optimization work.
07 · Key takeaways
AI Search Optimization starts with a strategy that communicates a company’s strengths
This case shows that AI Search Optimization is not complete with simple content production or technical SEO alone.
A strong real-world reputation does not automatically carry over to AI
The client had a long history and clear strengths in its specialist field. Even so, AI answers recommended competitors and sometimes did not mention the company.
A gap can exist between the value a company actually delivers, the information available about it on the web, and how AI describes it. Understanding that gap is the first step in AI Search Optimization.
Decide what to communicate before producing more content
Even when the goal is more visibility in AI search, increasing the volume of content is not always the priority.
Had the client produced large numbers of job listings and general advice articles in the same way as its competitors, it might not have communicated its own advantage effectively. That is why competitor research and user needs analysis, which establish why the company is chosen, came first.
Before deciding what to write, an AI Search Optimization strategy should define what kind of company this is and which users should understand it.
Keep improving while checking how AI answers change
Identifying a company’s strengths and publishing them once does not guarantee that AI answers will change as expected.
AI answers to relevant questions need to be reviewed, noting whether the company appears and what characteristics are described, and content and information structure refined as needed. In this case too, changes in how AI recommended the company were seen through continued implementation and verification.
This points to AI Search Optimization as an ongoing marketing activity, not a one-off measure.
The key to improving AI visibility was not producing more content. It was identifying the company’s real competitive strengths and aligning its online presence with the needs of prospective customers.
Related reading: More SEO case studies | AI Search Optimization Hub
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