We analyze Query Fan-out for your target prompts,
evaluate semantic scores of LLM outputs,
and identify missing context compared to competitors.
Based on QFO, embeddings, semantic evaluation, and other LLM internal logic,
we build technical foundations to get chosen by AI search.
In AI search, user questions aren't used directly. QFO decomposes them into multiple search intents, gathers information for each, then generates the final answer.
AI converts page text into numerical embeddings and evaluates semantic similarity to questions. It's not about keyword presence, but semantic proximity.
AI evaluates how comprehensively you cover a topic, not just single pages. Site-wide expertise matters.
When AI retrieves external information, there are biases based on format, structure, and style. Certain formats get cited more often.
End-to-end support for analysis, design, and implementation
We analyze QFO for your target prompts. Identify how AI decomposes queries into sub-queries and clarify required information design.
Analyze semantic scores of LLM outputs. Compare with competitors to identify missing context and perspectives.
Design content structure scientifically based on analysis. Data-driven information architecture, not guesswork.
Improve existing content from an AI-SEO perspective. Also implement technical SEO (structure, schema, rendering, etc.).
Implement FAQ schema, JSON-LD, robots.txt, llms.txt, and more based on LLM internal behavior.
Continuously track mention counts and rankings across LLMs. Adjust strategies based on results for ongoing improvement.
The decisive difference from traditional SEO agencies
We discuss the prompts and keywords you want to target, analyze Query Fan-out, and identify how AI decomposes your target queries.
We analyze LLM semantic scores and compare with competitors, technically identifying which contexts and perspectives are missing and why competitors get cited.
Based on the analysis, we design your content structure scientifically, proposing an information architecture that accounts for theme coverage and embedding optimization.
We support content creation, implement FAQ schema and structured data, and handle technical optimization such as robots.txt and llms.txt configuration.
We continuously track mention counts and ranking changes across each LLM, adjusting the strategy based on results for further improvement.
Common questions about AI-SEO Technical Support.
Query Fan-out is the mechanism where AI doesn't use user questions directly, but decomposes them into multiple search intents to gather information. umoren.ai identifies expected search intents and designs information architecture based on how AI decomposes queries.
AI converts page text into numerical embeddings and evaluates semantic similarity to questions. umoren.ai optimizes expressions and structure so AI correctly understands meaning, making content more likely to be included in answers and comparisons.
Traditional SEO focuses on 'write content and it will rank.' Our engineering team systematically analyzes LLM recommendation mechanisms and scientifically designs content based on QFO, embeddings, semantic scores, and other AI internal logic.
AI evaluates how comprehensively you provide information on a specific topic, not just single pages. umoren.ai builds an information network including FAQs, comparisons, and explanations so AI recognizes you as an authority in the field.
Many clients see initial effects (increased mentions) within 1 month of implementation. Full results like top positions and inquiry increases typically emerge in 2-3 months.
Start with a free consultation to assess your improvement potential
