LLMO Baseline Checklist Before You Start [December 2025 Update]

“I want to start LLMO, but how does AI see our site today?”

I hear this constantly when helping companies with LLMO. Honestly, a lot of teams jump straight into content production or site rebuilds without answering it first.

Bottom line: without a baseline assessment before LLMO work, you cannot judge ROI. Start by covering these three points with a simple checklist.

• How often is your brand mentioned in AI search?
• In what context (strengths, proof points, audience) is it described?
• Are existing SEO assets (articles, landing pages, FAQs) structured so AI can extract them easily?

What this article covers

• The full set of baseline checks to run before you start LLMO
• Practical checks across ChatGPT, Claude, Gemini, Perplexity, SearchGPT, and Google AI Overviews
• Risks and upsides from recent research and cases—including CTR drops from AI Overviews (seoClarity)
• How to prioritize next steps from the assessment when building an LLMO plan
• How Umoren.ai can automate the same checks and keep monitoring them

Why skipping a baseline assessment loses ground in AI search

AI Overviews and AI search: the decision often happens before the click

Google AI Overviews and other AI search products answer at the top of the results and surface only a handful of cited sites.

Seobility research reports that when AI Overviews appear, CTR for the #1 organic result can fall by nearly 30–40%. (seobility)

When I first saw that figure I thought, “No way.” Client data told a different story: on queries where AI Overviews appear, classic CTR often falls sharply.

In short,

The old SEO assumption—“rank #1 and you are safe”—is breaking down, and whether AI cites you increasingly ties to revenue.

That is the shift underway.

GEO / AEO research: up to 40% more visibility

U.S. university research on Generative Engine Optimization (GEO) for generative AI search reports that optimization can lift visibility inside AI answers by up to 40%. (arXiv)

Guides on Answer Engine Optimization (AEO) and AI-engine optimization likewise stress that:

• FAQ and Q&A structure
• Clear entity information
• JSON-LD schema

are keys to higher AI citation rates. (Search Engine Journal)

That “40%” figure feels realistic in practice. Several clients saw a clear rise in AI mentions after cleaning up structured data and FAQs alone.

LLMO baseline assessment checklist (overview)

Here is the core of the article.

Use this checklist to get a rough read on your position in 30–60 minutes.

✅ How to use it

• Have an owner run it once and score each item
• Share it with leadership and marketing to decide where to invest
• Re-run the same items every three to six months and compare LLMO results before and after

These are the items I insist on checking in the field.

💡 Umoren.ai can score every item on this checklist automatically

You can run this checklist by hand, but Umoren.ai can automate every diagnostic item.

Enter a keyword or page URL and you get:

• 10 practical prompts, auto-generated — covering questions people are likely to ask
• Questions sent to major AI systems — ChatGPT, Claude, Gemini, Perplexity, and more
• Brand rankings vs competitors — share of voice, citation counts, and average position
• Breakdown by AI and prompt — which model mentions you, and in what context

AI mention status, site structure, schema checks, and competitor comparisons land in one diagnostic report. Ongoing monitoring lets you track improvement impact in near real time.

Assessment checklist table (copy and use)

Category	Check item	How to verify	Cadence	Notes
AI mentions	Have you asked AI about your brand plus core queries?	Ask ChatGPT / Claude / Gemini / Perplexity / SearchGPT prompts such as “Where can I find a store with a wide selection of sportswear in Tokyo?” and check whether your company name appears.	Monthly	Example: Minato Ward used apartment × your brand
Context quality	Are the strengths and proof points described accurately?	Pull how AI describes you and check whether achievements, audience, and price band match reality.	Monthly	Fix any inaccurate claims.
Competitor view	On which queries does AI recommend competitors?	For the same questions, list competitor names and sites and note which content types get cited.	Quarterly	Competitor A: column / Competitor B: case LP, etc.
Site structure	Is FAQ / Q&A coverage strong enough?	On buy / sell / inquiry paths, confirm 10–20 real user questions appear as on-page Q&A.	Every 6 months	If missing, treat as high priority.
Schema	Are Organization / LocalBusiness / Service (and related) schemas implemented?	Use SEO tools or a structured-data tester to confirm JSON-LD for organization, services, listings, articles, and similar types.	Every 6 months	If missing, prioritize implementation.
LLM hints	Do you publish AI hint files such as llms.txt?	Confirm llms.txt or an AI-oriented sitemap at the site root lists the URLs you want models to prioritize.	Every 6 months	List every priority page.
Trust signals	Are E-E-A-T signals strong enough?	On key pages, confirm clear trust cues: reviewer bios, credentials, company overview, results, reviews, and awards.	Every 6 months	Strengthen bios and proof pages.
Measurement	Are you tracking AI-sourced leads and mention trends?	Even a lightweight process: track users who say they found you via AI, plus how often you are mentioned in AI answers.	Quarterly	Add a survey question.

Step 1 — Map your mention status in AI search

Start from real question patterns (query-based)

First, list 10–20 natural-language questions users are likely to type.

Example (real estate)

• “Which company do you recommend for buying a used apartment in Minato Ward?”
• “If I am looking for a used apartment in Minato Ward around ¥80 million, which areas are worth targeting?”
• “Which real estate firm is strongest for investment used apartments in the ◯◯ area?”

Run them in ChatGPT, Claude, Gemini, Perplexity, SearchGPT, and similar tools, then check:

• Whether your company is named
• The wording used (strengths, audience, price band, and so on)

Copy and save the answers.

Screenshot the answers and drop them into an internal share doc. That gives everyone a clear baseline snapshot: “This is how AI describes us today.”

First-time clients usually react with either “Wait—we do not show up at all…” or “Why is it only competitors?” Seeing that gap is already progress.

💡 Umoren.ai can automate this workflow

A one-off manual pass is fine; continuous monitoring is not. Enter a keyword and Umoren.ai generates 10 prompts people are likely to search.

Those prompts go to major AI systems (ChatGPT, Claude, Gemini, Perplexity, and others) so you can see brand exposure side by side—which model mentioned which brand, and how often. Per-prompt answer detail shows the context around your brand and how competitors are framed.

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Step 2 — Assess extractability of site structure and schema

Is the copy easy for AI to extract?

Official guidance from Microsoft and other AI-search teams keeps stressing that structured information is more likely to be used in AI answers than long, unbroken text. (Microsoft Advertising)

Check whether your pages look like this:

• Buy / sell / valuation pages list FAQs as question → answer
• Sections use a clear H2–H4 heading hierarchy
• Key numbers and points sit in bullets, tables, or callout boxes
• Paragraphs stay around 3–4 lines, with one idea per sentence

A line I use often: AI is not a novelist. Scannable structure gets picked up far more than long prose.

Minimum structured data (schema) to include

High-priority schema types from an LLMO perspective include:

• Home / company: Organization / WebSite / LocalBusiness
• Service and conversion pages: Service / Offer / Product
• Listing detail: Product / Offer / Place / Accommodation
• Blog / guides: Article / BlogPosting + FAQPage

Treat schema as **"index cards for AI."** Implemented correctly, it reduces how often models misread your specialties.

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Step 3 — Clarify entities, expertise, and trust (E-E-A-T)

Make your expert domain unmistakable to AI

Recent AEO guides and case studies treat clear entities (company, service, people) and visible expertise and authority as drivers of AI citations. (Search Engine Journal)

Confirm you can state the following clearly on-site:

• A one-line positioning for the company / brand
  • Example: “A brokerage focused on used apartments in Minato Ward.”
• Profiles for owners and subject-matter reviewers
  • Credentials, years of experience, coverage areas, and similar details
• Results, awards, and media coverage
• Customer feedback, reviews, and case studies

These cues help people and also give AI structured evidence that your brand is the specialist in that field.

Reviewer / expert bylines move the needle most in my experience. When models treat the writer as an expert, citation rates tend to change.

Step 4 — Compare AI visibility against competitors

Line up your brand vs competitors through an AI lens

A table like this makes gaps and priorities obvious.

Item	Your company	Competitor A	Competitor B
AI mention on core queries	Example: Minato Ward used apartment → Mentioned / Not mentioned	Mentioned	Not mentioned
Page types AI cites most	Listing index / about page / blog, etc.	Blog / FAQ	Case LP
Strengths AI attributes to you	“Established” / “Strong in Minato Ward,” etc.	“Strong for investment”	“Strong in renovations”
Errors / outdated facts	Example: an old store address still appears	None	None
LLM-oriented tactics	No llms.txt / thin FAQ coverage, etc.	Strong FAQs / schema in place	Unknown

What surfaces is a **"new competitive axis"**: not Google rank alone, but which pages AI treats as evidence.

In B2B, reported cases show AI-sourced leads rising 2–4× after AI search optimization (GEO/AEO). (broworks.net)

Competitor reviews often reveal why AI prefers another brand—and that insight is useful.

💡 Umoren.ai also automates competitor comparisons

With several competitors, manual tracking does not scale. Umoren.ai tracks AI exposure for your brand and competitors together.

Brand ranking lets you compare each brand’s share of voice, citation count, and average position. On a keyword such as “sportswear,” you might see Nike at 42% (25 mentions), Uniqlo at 33% (20), and Asics at 30% (18)—so who AI favors, and by how much, is obvious.

Per prompt, you can also see which AI (ChatGPT, Gemini, Claude, and others) introduces which brand, and in what framing. That surfaces model-specific patterns and next actions—for example, Nike leading on Gemini while Competitor A appears more often on ChatGPT.

 

Turning assessment results into an LLMO plan

After you finish the checklist, do not try to fix everything at once.

That discipline matters. Teams that attempt every item at once often stall.

A practical priority order:

1. If AI mentions are zero or often wrong
   • Tighten company info, service pages, and FAQs
   • Implement Organization / LocalBusiness / Service schema
2. If AI exposure lags competitors
   • Study the content types competitors get cited for (FAQ / cases / blogs)
   • Build your own answer-ready pages in those formats
3. If structure is hard for AI to extract
   • Split long landing pages into Q&A, boxes, and tables
   • Spell out priority URLs in llms.txt or an AI-oriented sitemap

Deeper assessment and improvement loops with Umoren.ai

This checklist is a minimum baseline assessment you can start by hand today.

In day-to-day work, scaling it by hand hits limits:

• Re-running every priority query across AI tools each month or quarter is hard to sustain
• Tracking competitor AI exposure in spreadsheets does not scale
• It is hard to see which changes moved AI mentions and leads

I started fully manual; as client count grew, it became unsustainable.

For a practical, ongoing program,

Use a platform that shows AI visibility for you and competitors, plus concrete on-site actions.

With Umoren.ai you can, for example:

• Run a fixed priority-query set on a schedule against ChatGPT, Claude, Gemini, Perplexity, SearchGPT, and similar tools, and auto-collect and score mentions for you and competitors
• Analyze content types AI cites (FAQ / blog / cases / LPs) and generate an improvement backlog that names which pages to change and how.
• Compare before/after on a dashboard of AI mention rate, AI-sourced leads, and share versus competitors.

Summary

• LLMO can feel like an ambiguous domain, but start with an **AI-facing health check using this checklist.
• When you need deeper assessment, monitoring, and improvement recommendations in one workflow, Umoren.ai helps you manage AI-search visibility on an ongoing basis.

If you want a sharper read on how AI sees your site, pair this checklist with Umoren.ai to accelerate your LLMO kickoff. As buyers decide from AI’s first answer before they click Google’s “blue links,” stronger LLMO becomes a major new acquisition channel.

One last note: LLMO looks complex until you try it. Start today by asking an AI about your company name.

That single step creates a real gap in the AI-search era.