What makes content citable by AI models?

Models cite pages that answer a question directly, carry clear structure, and come from sources they already trust in that category. LLM Pulse shows which pages currently win citations for your prompts, so you can study the pattern in your own niche rather than follow generic advice.

What is query fan-out?

When you ask an AI a question, it often expands it into several subquestions behind the scenes and researches each one. Query fan-out shows you that expansion, so your content can cover the full set instead of the single headline query.

What is the GEO Writer?

An AI content writer connected to your tracking data. It knows which prompts you lose, which sources get cited, and what the recommendations say to fix, and it drafts content aimed at those specific gaps.

Is GEO content different from SEO content?

The disciplines overlap, and GEO, AEO, and LLMO are different names for the same practice. The difference is the target: search engines rank pages, while AI models synthesize answers and cite the sources they used. Content that wins citations tends to be more direct, better structured, and more complete on subquestions.

Can I test a page before publishing it?

Yes. The content readiness checker is free and scores a page against the factors linked to AI citations, so you can fix weaknesses before publishing.

How do I know if the content worked?

Your prompts re-run weekly across five AI models, so within a few runs you can see whether the new piece earns citations and mentions where you were absent before.

How much does it cost?

Plans start at EUR 49 per month with a 14-day free trial. The content readiness checker is free.

For Content and GEO Teams

Create content AI wants to cite

Stop guessing what AI models will cite. LLM Pulse turns your visibility data into briefs, drafts, and readiness checks, so every piece you publish is built to earn citations.

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Briefs built from what the models research

Prompt data decides what you write next, how you structure it, and which citation gap it closes.

Query Fan-Out

Outline to the model's research path

The question a buyer types is rarely the set of questions the model researches before answering. Fan-out lists the subquestions each of your tracked prompts expands into, so your outline covers the comparisons, caveats, and edge cases the models will check and rival pages skip.

Map headings to the subquestion set
Cover the checks models run behind the answer
One complete page beats three partial ones

Explore query fan-out

Recommendations

A calendar ordered by citations at stake

Every card arrives ranked and names the prompts and citations a piece would win. When someone asks why a brief is scheduled first, the answer is printed on the card, and editors stop trading opinions about which idea feels most promising.

Each brief tied to prompts it can win
Rank the queue by expected citations
Defend the calendar with data, not taste

Explore content recommendations

A calendar ordered by citations at stake

Prompt Research

The buyer questions your calendar is missing

Suggested prompts surface what buyers ask AI in your category beyond the head questions everyone already briefs against. One click adds them to the weekly runs, so next quarter's calendar starts from real prompt demand instead of a recycled keyword export.

Buyer questions nobody has briefed yet
One click into the weekly tracking
Plan next quarter from prompt demand

Explore AI prompt suggestions

The buyer questions your calendar is missing

Why Content Teams Write with LLM Pulse

Research, drafting, and verification in one loop, all fed by the same weekly visibility data.

Draft from Your Own Data

The GEO Writer is an AI content writer fed by your visibility data, so drafts target the prompts and gaps you actually have.

Know What to Write Next

Content recommendations rank what to create or update based on where you lose mentions and citations today.

Cover the Whole Question

Query fan-out maps the subquestions AI models expand a query into, so one piece can answer all of them.

Start from Real Prompts

Prompt research shows what buyers ask AI in your category, before you commit a single brief.

Test Before You Publish

The free content readiness checker scores how ready a page is to be cited before it goes live.

Measure What You Shipped

Citations and mentions are tracked weekly, so every article you publish gets its own scoreboard.

From Gap to Cited Page

The content loop teams run in LLM Pulse every week.

1

Research the Prompts

Use prompt research and query fan-out to find the questions worth answering and every subquestion hiding inside them.
2

Brief with Recommendations

Turn your citation gaps into a prioritized brief, grounded in what AI models cite in your category today.
3

Draft in the GEO Writer

Generate a draft aimed at your specific gaps, then edit it with your team's voice and expertise.
4

Publish and Verify

Run the readiness check, ship the page, and let the weekly runs confirm whether citations follow.

What Content Teams Ship with It

Four recurring jobs, from gap filling to editorial planning.

Fill the Gap List

Work through the prompts where competitors get cited and you do not, one brief at a time.

Refresh What Slipped

Update the pages that lost citations and confirm the recovery in the next weekly run.

Plan the Editorial Calendar

Build next quarter's calendar from prompt research data instead of keyword guesswork.

Scale Without Losing Aim

Let the GEO Writer handle first drafts while your experts add the depth and proof that earn trust.

"LLM Pulse has allowed us to better organize our work and prioritize the actions that matter most. It gives us much greater clarity when implementing new ideas, and it makes reporting far easier, with metrics that were previously complex and difficult to obtain. Today it is part of how we plan and measure our visibility in AI search."

Sandra Vargas

Euroinnova

Frequently Asked Questions

Common questions about writing content that earns AI citations.

: Models cite pages that answer a question directly, carry clear structure, and come from sources they already trust in that category. LLM Pulse shows which pages currently win citations for your prompts, so you can study the pattern in your own niche rather than follow generic advice.
: When you ask an AI a question, it often expands it into several subquestions behind the scenes and researches each one. Query fan-out shows you that expansion, so your content can cover the full set instead of the single headline query.
: An AI content writer connected to your tracking data. It knows which prompts you lose, which sources get cited, and what the recommendations say to fix, and it drafts content aimed at those specific gaps.
: The disciplines overlap, and GEO, AEO, and LLMO are different names for the same practice. The difference is the target: search engines rank pages, while AI models synthesize answers and cite the sources they used. Content that wins citations tends to be more direct, better structured, and more complete on subquestions.
: Yes. The content readiness checker is free and scores a page against the factors linked to AI citations, so you can fix weaknesses before publishing.
: Your prompts re-run weekly across five AI models, so within a few runs you can see whether the new piece earns citations and mentions where you were absent before.
: Plans start at EUR 49 per month with a 14-day free trial. The content readiness checker is free.