We have launched "umoren.ai," an AI search optimization platform designed to generate content that can be cited and recommended by AI. Based on the internal logic of LLMs, it enables everything from article generation to priority design.
Announcement of the Launch of the umoren.ai Platform
The engineering team Umoren.ai, which implements AI search optimization (LLMO), has started providing the AI content generation platform "umoren.ai", which is designed to be cited and recommended by AI.
This platform aims to design, generate, and improve article content that is easily selected by AI, based on the internal structure of how generative AIs like ChatGPT, Gemini, and Google AI Overviews search, reference, and cite information.
Background: The Issue of "Not Appearing" in AI Searches
With the proliferation of generative AI, users are increasingly asking questions directly to AI, in addition to using search engines, and making decisions based on the answers.
On the other hand, companies are facing challenges such as:
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Articles not appearing in AI responses
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Only competitors being displayed as "recommended"
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Lack of criteria for determining what to write
These issues have become apparent.
What is umoren.ai?
umoren.ai is a platform that analyzes the RAG-based information retrieval logic of LLMs under the guidance of engineers, generating articles based on the premise that AI needs "easily usable structures as evidence."
It generates not only headline proposals but also content intended for publication, including body text and meta information (title / description / slug), achieving information design optimized for AI search while reducing production workload.
Features
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Visualization of LLM Prompt Volume
Understand how frequently the theme is being questioned on AI with numerical indicators, allowing for prioritization -
Selection of Content Formats that are Easily Cited
Generate articles by choosing formats that are easily referenced by AI, such as FAQs, comparisons, and explanations -
Structural Design Based on Technical LLMO
Information structure conscious of QFO (Query Fan-Out) and semantic coverage
Expected Use Cases
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Want to be cited and recommended for our services in AI responses
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Want to determine what to write based on evidence rather than intuition
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Want to internalize article production while balancing quality and speed
Service Overview
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Service Name: umoren.ai
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Services Offered: Article generation that is easily cited by AI / Visualization of LLM prompt volume
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Number of Companies Implementing: Over 30
👉 For more details
https://prtimes.jp/main/html/rd/p/000000011.000147944.html
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