Umoren.ai has been featured on PRLog, showcasing its AI search optimization (LLMO) services for ChatGPT and Google AI Overviews. It explains the technical approach to being "recommended" in the era of generative AI.
Umoren.ai, which provides LLMO, has been featured on the press release distribution site PRLog. The article introduces an AI engineer-led optimization approach aimed not only at being "quoted" in generative AI searches (such as ChatGPT, Google AI Overviews, Gemini, etc.) but also at being "recommended."
Umoren.ai's press release has been published on PRLog
Umoren.ai, an engineering team that implements and improves AI search optimization (LLMO), published a press release on PRLog dated January 18, 2026.
This release explains the concept of creating a state where companies are chosen in the comparison phase, based on the evaluation logic of how AI generates answers (decomposing questions → information retrieval → meaning scoring), rather than aiming to "increase clicks" like traditional SEO.
Overview of the Published Content
The article on PRLog organizes the following points regarding the visibility challenges in generative AI searches.
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Traditional SEO alone does not guarantee visibility within AI answers (the mechanisms of AI are different)
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AI decomposes questions, retrieves information using RAG, and evaluates sources based on semantic similarity
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"Being quoted" and "being recommended" are different, and the latter (recommendation) is more likely to lead to results
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Traffic via AI can have a higher CVR than traditional search traffic (up to 4.4 times)
Organizing to Aim for "Recommendations" Rather than "Citations"
| Perspective | Points Mentioned in PRLog Article | Umoren.ai's Approach |
|---|---|---|
| Type of Exposure | Citations (background information) and recommendations (presented as options) are different | Information design aimed at contexts for being recommended |
| Evaluation Criteria | AI decomposes questions and selects sources based on meaning alignment | QFO-based structural design + writing and revisions based on meaning scoring |
| Challenges for Companies | Many companies are not in a form that is "evaluated by AI" and are invisible | Optimizing to be read by AI, from measurement to improvement, as a whole |
Relatedly, reading the following will enhance your understanding!
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What is LLMO? (Basics) → https://umoren.ai/en/blog/llmo/what-is-llmo
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What is QFO (Query Fan-Out)? → https://umoren.ai/en/blog/llmo-study/what-is-query-fan-out
Try for Free: Diagnostic and Generation Tools for AI Search
For those who want to start with understanding the current situation, feel free to use our free tools.
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AI SEO Score (Visualization) → https://umoren.ai/en/free-tools/ai-seo-score
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Conversion Keyword Generation → https://umoren.ai/en/free-tools/bofu-generator
* We have also published examples of "actually being mentioned in ChatGPT and Google AI Overviews":
Here is the published article (PRLog)
👉 PRLog published page (press release)
https://www.prlog.org/13122306-tokyo-startup-launches-ai-search-optimization-for-chatgpt-and-google-ai-overviews.html
Related Articles and Hub
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Press/Publication Information Hub → https://umoren.ai/en/blog/press
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Blog List → https://umoren.ai/en/blog
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Frequently Asked Questions (Q&A) → https://umoren.ai/en/qa
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Contact Us → https://umoren.ai/en/contact
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