Be the AI’s pick.
Get qualified inquiries.
Designed for AI recommendations.
Built for qualified inbound.
When people ask AI for recommendations,
does your company show up?
People are shifting from "searching" to learning via generative AI
(ChatGPT / Gemini / Claude / Perplexity / Grok).
Point: the "entry" quality is different
- Users coming from AI answers are often already in the evaluation stage
- That's why they convert better into inquiries and demo requests
This isn't just a feeling. Industry data supports it too.
Some reports show AI-referred traffic reaching ~4.4x higher CVR than traditional SEO traffic (Search Engine Land).
That's why the goal isn't just "showing up." It's showing up as a named option when people compare and evaluate.
Even if you have a great product or service,
if AI can't find you, it's as if you don't exist.
umoren.ai doesn't treat "getting cited" as the goal.
We help you build a state where AI search drives inquiries and sales conversations.
We partner with you on strategy, content, and technical implementation.
AI-first discovery
"Recommendation" beats clicks
User
"Which companies should I consider for this?"
AI
Be the company AI can cite, compare,
and finally recommend.
We improve structure, context, and credibility across content and implementation.
The "AI search trap"
As search traffic declines, many teams hope:
Expectation
"If AI cites us, traffic will increase."
Reality
Even if you're cited, inquiries don't automatically increase.
The reason is simple.
AI doesn't just pick "information."
It picks who to recommend.
Being cited alone rarely turns into outcomes.
You need to become a named option.
Outcomes mean: inquiries, demo requests, sales calls, and signed deals.
Being recommended by AI is like passing the first sales screening.
Once you're in the comparison set, user behavior becomes conversion-oriented.
Two outcomes in AI answers
Cited = part of the info / Recommended = a choice
Case A
Cited only
(used as an info snippet)
Result: low user action
- Used as partial information
- No clicks
- Not included in comparisons
Case B
Recommended
(becomes a choice)
Result: enters evaluation
- Named explicitly ("Choose this company")
- Included in comparison candidates
- Leads to inquiries and demos
- Higher inquiry rate
- Lower sales explanation cost
- More branded, comparison-intent inbound
What drives outcomes today is being recommended.
Marketing Challenges in the AI Search Era
3 Key Pain Points
First of all, Your company doesn't appear in LLM results
Despite increasing content, strengthening SEO, and spending on ads, your company's name doesn't appear anywhere when searching on ChatGPT or Perplexity...
Uncertain how you're being presented
Even if your company is mentioned, it's unclear if your strengths, target audience, and value proposition are being communicated correctly. There's a risk of being introduced in the wrong context or with outdated information...
Only competitors are being "recommended"
Only competitors in the same space are being recommended by LLMs, and your company isn't even on the comparison list. Your achievements and strengths are essentially "non-existent" in AI search...
This happens because your company/service information isn’t structured in a way AI can evaluate quickly and accurately.
umoren.ai strengths
4 approaches to win in AI search
01
Question patterns analysis
Map how prospects ask and compare
AI answers are shaped by the questions users ask ("best," "recommended," "for X use case," "vs," "how to choose"). We identify the common question patterns and comparison criteria in your space, then design what needs to be communicated so you're chosen in evaluation moments.
→ More likely to be recommended for comparison/evaluation queries
02
Meaning-first content
Make your value unmistakable to AI
AI doesn't reward keyword stuffing. It rewards clarity: who it's for, what it solves, how it's different, and why it's credible. We design and produce first-party information so AI can explain your offer correctly, especially in "recommendation" contexts.
→ Introduced in contexts that lead to inquiries, not just visibility
03
Authority structure
Be recognized as "strong in this domain"
AI doesn't judge one page in isolation. It looks for consistent, trustworthy coverage across your site. We organize FAQs, comparisons, case studies, and definitions so AI can confidently treat you as a legitimate option.
→ More "shortlisted" inbound (brand + comparison intent)
AI search optimization is
Traditional SEO often focuses on rankings. AI search is different: your brand is either recommended as a choice, or not. We align messaging, evidence, and site structure so AI can confidently describe you in comparison and "best for" answers.
Why an engineering approach?
Because "recommendation" depends on whether AI can parse, trust, and compare your information. We handle the technical foundation (JSON-LD/structured data, page structure, internal linking, rendering) so your content is usable as evidence in AI answers.
04
Technical implementation
Make your site easy for AI to reference
Great content won't convert if AI can't reliably reference it, or if users land on pages that don't support decision-making. We implement FAQ schema, comparison tables, internal links, and structured data so AI recommendations lead to ready-to-buy inbound.
→ Users arrive pre-educated, reducing sales explanation cost
Diagnose how many conversion opportunities you’re missing from AI search
Reveal why AI search isn’t turning intorevenue
Get the opportunity-loss diagnosis
Case Study
Recommended by ChatGPT in 2 weeks
after launching the site
And inbound inquiries from AI search started to follow.
* Verified in Dec 2025.
This result came from designing the company/service information around how LLMs work, not content spam.
Visibility on comparison/evaluation queries became an entry point into the inquiry funnel. AI recommendation turned into sales opportunities.
We can apply the same approach to your company or service.
ChatGPT (Japanese)
Service Comparison
Why AI Citation Technical Support?
Compare umoren.ai with traditional SEO and marketing solutions
| Comparison | umoren.ai AI Citation Technical Support | Traditional SEO | Content Marketing | Ad Agencies |
|---|---|---|---|---|
| AI Search (LLMO) Support | ◎ Specialized | △ Not supported | △ Indirect | × Out of scope |
| LLM Internal Logic Analysis | ◎ QFO & Embedding | × Not supported | × Not supported | × Not supported |
| Technical SEO Setup | ◎ JSON-LD & Schema | ○ Basic | △ Limited | × Out of scope |
| Results Visualization | ◎ Monthly tracking | ○ Rank reports | △ PV focused | ○ Ad metrics |
| Engineering Team | ◎ Tech-driven | △ Outsourced | △ Writer-focused | △ Operators |
| ChatGPT Visibility | ◎ Direct targeting | × Out of scope | △ Side effect | × Out of scope |
AI Search (LLMO) Support
LLM Internal Logic Analysis
Technical SEO Setup
Results Visualization
Engineering Team
ChatGPT Visibility
With AI Citation Technical Support for the AI search era,
get recommended by ChatGPT, Claude, and Gemini
Service Flow
Service Flow
- 1
STEP 01
AI search diagnosis (conversion-focused)
Across ChatGPT / Gemini / Claude / Perplexity, verify whether your appearance leads to inquiries, not just mentions
- 2
STEP 02
Competitive gaps & evaluation factors
Clarify why competitors get chosen, and which contexts trigger conversion-ready behavior
- 3
STEP 03
Design & build for comparison/evaluation queries
Create first-party information AI can use as reasons, and turn it into inquiry-driving messaging and paths
- 4
STEP 04
Implementation & structural optimization
Make it easy for AI to reference, and ensure recommendations convert once users arrive
- 5
STEP 05
Continuous monitoring & improvement
Stay chosen over time, and keep inquiries coming
Not just "showing up once."
Turn AI search into a stable conversion channel.
Pricing & Plans
Pricing & Plans
We tailor the scope to your goals and target surfaces. Start with a free initial diagnosis.
Pricing Guide
From a free diagnosis to a monthly retainer
Initial diagnosis
Free
We assess your current AI search visibility and outline the highest-impact opportunities.
Monthly plan
From ¥150,000 (varies by scope)
We support strategy, content, technical implementation, structure optimization, and monitoring.
*Final pricing depends on the number of sites/pages, coverage, and production volume.
Q&A
Frequently Asked Questions
Common questions about LLMO (AI Search Optimization), pricing, process, and measurement.
Can we start even if we're not mentioned or recommended in AI search yet?
Yes, absolutely. Starting from zero visibility can actually be ideal because we can build the foundation correctly from the ground up. We support you from diagnosis and competitive analysis, through first-party information design and production, to technical implementation so AI search can drive inquiries.
What is LLMO (AI Search Optimization), and how is it different from SEO?
LLMO (Large Language Model Optimization) focuses on getting your company or product recommended and cited in AI search experiences like ChatGPT, Claude, Gemini, and Perplexity. Traditional SEO mainly targets rankings in web search, while LLMO emphasizes trustworthy first-party information, clear explanations, and machine-readable structure so LLMs can confidently use your content in answers.
Which AI search products do you optimize for?
We optimize for the major LLM-based search experiences including ChatGPT, Claude, Gemini, and Perplexity. Because behavior differs across models and products, we validate visibility and messaging across multiple AI surfaces.
Where should we start? (What does the initial diagnosis cover?)
We start by checking current brand/product mentions in AI answers, the competitive gap, and the evaluation factors behind why competitors get recommended. We also review first-party information gaps, structured data (JSON-LD), and page structure opportunities, then prioritize the fastest, highest-impact actions.
What's the pricing?
The initial diagnosis is free. Monthly plans start from ¥150,000 (pricing varies by scope and coverage). We provide a quote based on your goals (mentions/citations, winning comparisons, etc.), page volume, and production/implementation needs.
What do you actually do (deliverables and actions)?
We analyze competitors and evaluation factors, then design and produce first-party information LLMs can reliably understand and cite (facts, definitions, specs, evidence, FAQs). We also implement technical improvements like structured data, page structure and internal linking, and content formatting that increases citation and recommendation likelihood.
How long does it take to see changes?
It depends on your current site state and the competitive landscape. We implement high-priority improvements first, then monitor changes in AI mentions/citations and how your brand is described. The goal is both short-term lift and long-term, consistent recommendation over time.
How do you measure success?
We track mentions and citations in AI answers, the context you appear in (comparison criteria and stated reasons), message accuracy, and relative visibility versus competitors. We iterate on query sets and evaluation rubrics as needed.
Who is this best for?
It's especially effective for B2B, SaaS, and specialized services where users compare options ("best," "comparison," "how to choose"). If your first-party information is fragmented or unclear, LLMO tends to create outsized gains.
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