Glotier tells you whether ChatGPT, Gemini and Perplexity name your product when buyers ask what to use in your category. It puts your buyers' real questions to all three as live web queries, records whether you were named and by which of them, which competitors were named instead, and which pages each answer was built from.

How does Glotier check AI visibility?

It runs one live web search per question. ChatGPT and Gemini read the identical results, and Perplexity runs its own retrieval because it does not accept results handed to it. Whether your product is named is decided by a literal presence check on the answer text rather than by asking a model for its opinion, so the number cannot be inflated by a model being agreeable. You can also connect your own ChatGPT or Claude key and have the questions run through your account instead.

Why ask three assistants instead of one?

Because they disagree. In our own study, across the 39 of 40 buying questions where all three answered, all three named the same product only 30% of the time and 47% of products were named by exactly one assistant. Checking a single assistant shows you 58% to 65% of the products named across the three, so a founder who checks ChatGPT, sees their name and relaxes has measured one engine on one phrasing.

Is Glotier free to use?

The check is free with no signup. Paid plans start at $39/month (Solo), $99 (Studio) and $249 (Agency), and cover weekly tracking and the content the agent writes.

Does Glotier make up data about my product?

No. Every result comes from observed data: the live search results, the answers the assistants actually returned, and the sources they cited. It does not invent metrics, ratings or engine claims, and when a measurement has not run it says so rather than printing a zero.

Anyone selling something buyers research before choosing, where the category has enough alternatives that an assistant has to pick a shortlist: software, tools, e-commerce, and services. Founders, marketers and agencies all use it, and the work is the same at any size. It is not useful for local or in-person businesses, where maps and reviews decide the outcome instead.

Is LLM SEO the same as GEO or AEO?

In practice, yes. GEO, AEO and LLM SEO all describe getting retrieved, cited and named by an AI assistant, and they differ mostly in which part of the pipeline the name emphasizes. Chasing them as three separate strategies spends effort on one job with three labels.

Does classic SEO still matter for LLMs?

It matters more, not less. A model answering a live buying question runs a search and reads what ranks, so a page that does not rank for the terms it searches is never retrieved and can never be named. Classic SEO is the entry ticket. LLM SEO adds the measurement of whether, once retrieved, the model actually named you.

How do I measure LLM visibility?

Ask the real buying questions in your category to ChatGPT, Gemini and Perplexity on a schedule, and record whether each named you and which sources each answer cited. Use all three, because they disagree often: in our own measurement a single model showed only 58% to 65% of the products named across the three. One model checked once is one opinion, not a measurement.

Guide

LLM SEO: getting named by ChatGPT, Gemini and Perplexity

By Oğuzhan Tüsen @oguzhanglotierPublished 2 August 2026Figures last checked 26 July 2026

LLM SEO, LLM visibility, large language model optimization: three names for one job, which is getting retrieved and named when someone asks ChatGPT, Gemini or Perplexity to recommend something in your category. It is the same work as GEO and AEO, and classic SEO still sits underneath all of it. Here is why, and what to measure.

What LLM SEO actually is

Strip the acronyms and LLM SEO is one sentence: when a buyer asks a large language model which product to use, get named instead of your rival. It is the same target as generative engine optimization and answer engine optimization, described from the model’s side rather than the answer’s. The vocabulary is new because the surface is new. The job is not.

What is genuinely different from classic search is the unit. You are not chasing a ranked position a user scrolls to. You are chasing a named place inside a written answer the model assembles, usually from pages it retrieved live at the moment of asking.

Why classic SEO still underpins it

A large language model answering a live buying question does not read your marketing from memory. It runs a search, retrieves what ranks for the terms it chose, and writes from those pages. If nothing of yours or about you ranks for what it searched, nothing of yours is retrieved, and a page that is not retrieved cannot be cited and cannot be named. Ranking is the entry ticket.

So LLM SEO does not replace the SEO you already do, it consumes it. Keep the rank tracker and the technical hygiene. Then add the measurement they do not make: whether the model, having retrieved what ranks, actually named you.

One model is one opinion

The trap in the phrase “LLM SEO” is optimizing for a single model as if it spoke for all of them. It does not. When we put 39 buying questions to ChatGPT, Gemini and Perplexity, a single assistant would have shown you only 58% to 65% of the products named across all three, and 47% of products were named by exactly one of the three. Checking one model and calling it your LLM visibility is measuring one opinion.

Even the sources do not force agreement. Handed an identical set of search results, ChatGPT and Gemini still agreed on only 64% of what they recommended, so the disagreement is partly in how each model reads the same pages, not only in which pages each one saw. Whatever you do to move the number has to be checked on all three.

What to measure

Three things, none of which a rank tracker produces. Whether each model named you, across ChatGPT, Gemini and Perplexity rather than one of them. The sources behind each answer, because that is the only part you can act on. And the movement over time, re-running a frozen set of your buyers’ real questions, because the answer is reassembled on every ask.

That last point is why a screenshot is worthless. LLM visibility is a weekly signal, not a one-time audit, and the honest version of the tooling is priced to be run continuously rather than presented once.

The proof, from our own site

We run this on ourselves and publish the numbers, including the ones that sting. Between 23 July 2026 and 29 July 2026, large language models referred 6 real human visitors to us (3 from ChatGPT, 2 from Bing, 1 from Perplexity), counted by a browser beacon no bot triggers, and 5 of the 6 landed on pages built from original data rather than on a sales page.

And the unflattering half, because a page that only flatters is not evidence. Put to the same buying questions in our own category, the models named us 0 of 78 times. And when we read 18 category answers in full, community pages fed them far more than vendor marketing did: Reddit alone was a source in 12 of them. We publish that zero on our own dashboard beside everything else, because a visibility number you cannot point back at its own maker is a slogan.

Get Glotier Solo

One model checked once is one opinion, and the three disagree more than they agree. The free check reads ChatGPT, Gemini and Perplexity together with every source behind each answer, and Solo re-runs it every day so you can see the trend for your own category.

The Citation Agent: which of your pages an assistant can actually cite, and the fix for each
A paragraph-quality read of your pages: what an assistant can lift whole, and where a claim is missing its source
An X agent and a Reddit agent: the live threads worth answering in your category, with a reply drafted for each
A daily check, and an article written from what it measured that day, ready to publish
The same buyer questions re-asked every day, with every source page behind each answer

Track 3 products and up to 150 buyer questions across ChatGPT, Gemini and Perplexity. $39/month.

Get Solo, $39/month Compare the plans

Cancel any time. Not for you? Email us within 7 days of a charge and we refund it in full. Refund policy

Not ready to pay? The check is free, with no card and no account. Run it on your own product.

Assistant referrals measured 23 July 2026 to 29 July 2026 from our own Cloudflare beacon. Model disagreement measured across 39 buying questions on one shared live web search per question. Category sources measured 26 July 2026 against ChatGPT, Gemini and Perplexity. Our own naming figure, 0 of 78, is from our daily category probe as of 31 July 2026. Full question sets and source lists are published in the category write-ups.