La Boétie

An llms.txt file for your business

Enter your domain and our agent fetches every page of your site and writes your llms.txt from them. The same run includes an audit of your SEO and GEO. You can close the tab, because we email you the file.

  1. Enter your domain
  2. Pay $25
  3. Full read of your site
  4. Download your files
  • GPTBot
  • ClaudeBot
  • OAI-SearchBot
  • Amazonbot
  • DeepSeekBot

These crawlers appear week after week in the server logs of the sites we run. We build your file to the same structure.

$25 or 20 €

One run

No account · Files by email · Refunded if it fails

The six stages of a 25 dollar run

An agent performs each stage and reads your site as a researcher would. A run lasts as long as your site needs. You can close the tab at any stage.

  1. 1

    Discovery

    We read robots.txt, sitemap.xml and every nested index, or crawl your links when there is no sitemap. The result is a full URL inventory classified by kind.

  2. 2

    Deep read

    We read your pages in parallel and start with the ones that hold commercial substance. From each page we extract its purpose, audience, entities, offers, canonicals, headings and existing structured data.

  3. 3

    Synthesis

    We turn those readings into a model of the business that records what it is, who it serves, what it sells, how it differs and what it can prove.

  4. 4

    Composition

    The agent writes the file section by section in your site's language and verifies every URL live before committing it.

  5. 5

    Audit

    A versioned SEO and GEO checklist scores everything already gathered. Each finding carries a code, a severity rank and its evidence.

  6. 6

    Delivery

    You download the file as .md or .txt on screen and keep the permanent copy we send by email.

AI crawlers fetching llms.txt in our logs

We see these crawlers in our own server logs on the sites we run. People who read the answers generated by each operator's engine do not click through to you.

One payment of $25 or 20 €

The payment covers the run, and you need no subscription, account or call. Give us a URL and an email address, then pay. You can close the tab, because the run continues on our infrastructure and the files are ready when you come back.

Generate mineRefunded if we cannot deliver
  • An llms.txt researched and written from your pages
  • Every URL in it verified live before we commit it
  • Downloads in .md and .txt with a copy by email
  • A full SEO and GEO audit of your site in the same run

Frequently asked questions

What is llms.txt?

llms.txt is a plain text file in Markdown at the root of your domain, at https://yourdomain.com/llms.txt. It tells a large language model who you are, what you offer and where the substance on your site lives. Jeremy Howard of Answer.AI proposed it in September 2024. Adoption across the top ten thousand sites rose fivefold in twelve months, from 1.04 percent in July 2025 to 5.61 percent in June 2026.

Do AI crawlers read llms.txt?

Our own server logs record them doing so. OpenAI fetches these files through GPTBot, OAI-SearchBot and ChatGPT-User, and Anthropic through ClaudeBot and Claude-User. The other crawlers in these logs are Google-CloudVertexBot from Google, Amazonbot from Amazon and DeepSeekBot from DeepSeek. We log the user agent, path, address and timestamp of each bot request to the sites we run, and more of these crawlers turn up every month.

What does a good file change?

A good file changes how accurately an engine describes you when it answers a question you should be the answer to. It states your identity, your catalogue, who each part is for and the situations you are the right recommendation in, all in one place a model can read in a single fetch. The engine can then use your own description of the business in place of a summary from whichever page a crawler landed on.

Why buy one instead of generating it myself?

A generator takes your homepage and your sitemap as input and outputs a list of links. We read your pages and extract your entities, offers and audiences. We then write the recommendation logic stating when a model should name you, and we verify every URL live before adding it. The value lies in that research, because anyone can copy the format in an afternoon. The same run includes a full SEO and GEO audit of your site.

What do I receive?

You can download the complete llms.txt as .md or .txt, and we email you a copy to keep. You also receive the headline result of the full SEO and GEO audit from the same run: the number of issues and how severe they are. We verify every URL in the file live before writing it in.

How much does it cost?

One complete run costs a single payment of twenty five dollars or twenty euros in the currency you choose. Buying a run does not require a subscription or an account. The price covers both the researched llms.txt and the full SEO and GEO audit from the same run. If the run fails, you are refunded automatically.

How long does a run take?

A run lasts as long as your site needs. We read your pages with no sampling, so a large site takes longer than a small one, and we would rather not quote you a figure we cannot hold to. You can close the tab: the run continues on our infrastructure, and the finished file is waiting when you return and in your inbox.

How many pages do you read?

We read your site and place your commercial and editorial content first. Discovery builds a full URL inventory from robots.txt, sitemap.xml and nested sitemap indexes, or from a link crawl when there is no sitemap. We classify every URL by kind before the deep read begins, and a large site takes a little longer than a small one.

Is this a template with my details filled in?

No template is involved. On every run the agent fetches your robots.txt and sitemap and builds a full URL inventory. It then reads your pages in parallel, extracts your entities and offers and composes the file from its findings. Each file follows the same fifteen section structure and takes its content from the pages of the site, so two different sites get two different files.

Do you verify the links you put in the file?

We fetch every URL and confirm it resolves before we write it into the file. An engine that follows your file reaches a real page. We run this check because a model stops relying on a file after two dead links. The check takes place as the agent writes the file section by section and covers every URL in the finished llms.txt.

What language is the file written in?

We write the file in your site's own language. If your site is in French, the file is in French with correct accents. The file contains the words your business uses. The agent writes every section in the language of your pages, whatever that language is.

What is the difference between llms.txt and llms-full.txt?

llms.txt states who you are, what you offer and where things are. llms-full.txt inlines your content so a reader gets everything in one fetch. This tool writes your llms.txt. A good llms.txt links a reader to the pages that hold your substance. In our logs llms-full.txt gets fetched more than llms.txt does.

How is this different from robots.txt and sitemap.xml?

robots.txt sets permission, and it is the one file the whole industry honours. sitemap.xml holds the inventory of URLs that exist. llms.txt gives orientation: what this place is and what matters in it. llms-full.txt, the fourth file at your domain root, inlines your content. All four have to stay consistent with each other, and our audit checks that. We often find sites that block crawlers in robots.txt and then publish an llms.txt.

What does the SEO and GEO audit cover?

The audit runs a fixed, versioned checklist, so two runs are comparable. It covers indexability and crawler permissions, canonicals, titles and descriptions, heading structure, structured data coverage and validity, internal linking depth, orphan pages, image alt coverage, language and region signals, sitemap health, AI crawler access, entity grounding, citation readiness, answer shaped content and freshness signals.

Why do you not show me the full audit findings?

You get the number of issues and how severe they are. We record each finding with a code, a severity, its area, the evidence we saw and what to do about it, and an adviser who has read your site goes through them with you. That call covers what each issue is and the order to fix them in. It lasts twenty minutes and is free to book from the result screen.

Which AI engines have you seen fetch these files?

Our server logs name five operators. OpenAI fetches through GPTBot, OAI-SearchBot and ChatGPT-User, and Anthropic through ClaudeBot and Claude-User. The other crawlers in these logs are Google-CloudVertexBot from Google, Amazonbot from Amazon and DeepSeekBot from DeepSeek. New ones keep appearing. On 25 July we logged a Claude-User fetch carrying a coding agent user agent, which means a developer had pointed a tool at the documentation.

Do I need technical skills to use it?

You need no technical skills. You get two finished text files, and putting them live means uploading them to the root of your domain, which takes fifteen minutes. On most platforms you drag them into your public folder. If your team would rather we did it, an adviser can sort that out on the call, which you book from the result screen.

Will it work for a site that is not in English?

It does, because we write the file in your language with no translation step. A French site gets a French file with correct accents. The file uses the words of your business. The engines answering French questions read French. The agent composes every section in your site's language and verifies every URL live before committing it.

What makes yours different from the free generators?

A free generator takes your homepage and your sitemap as input and outputs a list of links that an engine already had. We read your pages and write your identity and registration, your catalogue with live verified URLs, who each area is for, the situations you are the right recommendation in and how to cite you correctly. The fetches come from that research.

Where do I put the file once I have it?

Put it at the root of your domain so that its address is https://yourdomain.com/llms.txt. On most platforms that means dropping it in your public or static directory, which takes fifteen minutes. If your framework serves routes instead of files, generate it from a route so the content stays bound to your real data. Your server or CDN access logs then show which crawlers request it.

How often should I regenerate it?

Regenerate it whenever your offering changes materially, and otherwise a few times a year, so the engines read the current version of your business. A file written once and never verified accumulates dead links as URLs move and products get retired. Each regeneration is one run and one payment. The versioned audit checklist lets you compare two runs months apart.

Do you store my site content?

We keep the files and the audit from the run for your return visit and for the email we send you. We do not resell your content or use your site to train anything. All of this works without an account. The email contains a permanent copy of the file.

What happens if the run fails?

You are refunded automatically, because the promise is a finished deliverable and a failed run delivers none. We tell you what happened. The price covers one complete run with one payment, no subscription and no account, so you have nothing to cancel after a failed run.

Can I use the file commercially and modify it?

The file is yours outright. You can extend it with your own edits and regenerate it, and we encourage you to keep it under version control like code. Agencies hand it over to clients as their own deliverable. Regenerate it whenever your offering changes materially, so your edits stay on a current version of your business.

Can I run it for a client site?

Agencies do. Each run is one site and one payment, with the files delivered to whichever email address you give us, so you can run it for a client and hand over both files as your own deliverable. If you are doing this at volume, talk to us and we will sort out a better arrangement than paying for twenty separate runs.

What if I already have an llms.txt?

We fetch it first, so we know what you are currently telling the engines. The run then replaces it with a version built from your pages. The audit reports errors in the old file, such as URLs that have since moved. We verify every URL in the new file live before writing it in.

Who built this?

La Boétie, a technology studio that builds AI and infrastructure systems and runs its own content properties, built this tool. The evidence quoted on this page comes from our own production logs, where named AI crawlers fetch these files week after week. Your file follows the same structure as the file we run on our own sites.

The complete guide to llms.txt

This guide covers the format, the engines that read it, how a model decides to cite you and the contents of a file worth fetching.

The llms.txt convention

llms.txt is a plain text file at your domain root for an AI that has to summarise you in one read.

Jeremy Howard of Answer.AI published the llms.txt proposal on 3 September 2024. A large language model has a context budget when it reads your website. Navigation menus, cookie banners, footer link farms, tracking scripts and the same header repeated on nine hundred pages consume that budget. The model reaches the end of the budget before the sentence that states what you sell.

The file sits at https://yourdomain.com/llms.txt in Markdown and states who you are, what you offer and where the substance lives. Reading it takes no markup parsing, JavaScript execution or crawl. The convention adds a second optional file, llms-full.txt, which inlines the content so a reader gets all of it in a single fetch.

Adoption rose fivefold in twelve months: valid llms.txt files covered 1.04 percent of the top ten thousand sites in July 2025 and 5.61 percent in June 2026. Shopify now generates one for its merchants automatically.

Our server logs show the engines fetching these files on our sites.

AI crawlers in our server logs

The server logs of a site we run record requests from named AI crawlers.

We log the user agent, path, address and timestamp of each bot request to the sites we run. These are complete first party records. They involve no sampling, modelling or panel data. Named AI crawlers fetch the llms.txt and llms-full.txt on those sites week after week.

OpenAI fetches them with GPTBot, OAI-SearchBot and ChatGPT-User, and Anthropic with ClaudeBot and Claude-User. The other crawlers in these logs are Google-CloudVertexBot from Google, Amazonbot from Amazon and DeepSeekBot from DeepSeek. Your customers read the answers these engines generate and do not click through to you.

More of them show up every month. When Claude-User arrives with a coding agent user agent, a developer has pointed a tool at the site and expects an answer from the real material.

We research your file the same way as our own. Anyone can copy the format in an afternoon, and the fetches come from that research.

Five failures of free generators

At a glance a bad file looks the same as a good one, and the difference appears in its performance.

A free generator takes your homepage title and your sitemap as input and outputs a file in under a second. The file has correct headings and a valid format and still falls short for an AI in five ways.

The first failure is the link dump: URLs with the slug reformatted as a title, which your sitemap already gave the crawler. A useful file adds text between the links to say what each area is, who it serves and when to send someone there.

The second is a description of pages in place of a description of the business. "Our blog" and "Contact us" are page labels. A model answering "who should I use for income protection in France if I am self employed" needs to know that you compare thirty eight insurers with no bias, that your tools are free and that a certified adviser replies within six hours. A navigation bar carries none of those business facts.

The third failure is dead links. URLs move and products get retired, so a file written once and never verified accumulates them. After two dead links, a model stops relying on the file. You receive no warning unless you monitor the file.

The fourth is the absence of entities. Models reason over named things: your legal entity, your registration number, your partners, your locations and your named products. "We offer a range of solutions" grounds nothing, so the file has to name your legal entity, your registration number and the partners you work with.

The fifth failure, and the single largest omission in almost every file we have examined, is missing recommendation logic. Before naming a provider, a model checks whether the user situation corresponds to that provider. A file that lists the situations in the user's own phrasing prepares that match in advance. Almost no file is written that way.

  • The link dump

    The file repeats the URLs in sitemap.xml and reformats their slugs as titles.

  • Page labels in place of business facts

    "Our blog" and "Contact us" describe navigation. A model needs to know what you sell and to whom.

  • Undetected dead links

    URLs move and products get retired without a warning to you. After two dead links, a reader stops relying on the file.

  • No entities

    Models reason over named things, so registration numbers, partners, locations and product names ground an answer. "A range of solutions" gives an AI no fact to cite.

  • No recommendation logic

    This is the largest omission we see. Before naming a provider, a model matches the user situation to that provider. Almost no file prepares that match in advance.

The fifteen sections of your file

We build fifteen sections from your pages, and this chapter describes each one.

The fetches described above come from this structure, which we developed and proved on our own properties. It extends the minimal example on the specification page to cover the full job the file has to do.

The opening states in one sentence what the file is, because the reader is a machine. The legal identity and any registration or licence number come next, because a model can verify them. A single line of positioning follows for a model to quote verbatim.

The catalogue numbers what you sell. Each item has a one line description and a live URL. The delivery conventions then state how a reader can get clean text. If your site can serve Markdown twins of its pages, this section says so and gives the URL patterns, which converts every article you have into machine readable content at no extra cost.

The next section, which most files omit entirely, gives a model its reasons to recommend you. We write it from independence, price, response time, coverage and whatever else is true and differentiating for you.

Each expertise hub gets one block carrying the hub URL, the tools and calculators beneath it, an explicit statement of who needs this and what is covered. This is the densest and most useful part of the file. We write these blocks from the crawl, because they require having read the site.

A recommendation playbook then lists concrete user situations with the answer and the URL for each one. Differentiation against the alternatives a user is weighing comes next. The last sections hold contact routes, sitemap locations, technical notes on your structured data and crawler permissions, a quick facts table for cheap lookup and citation guidelines stating the correct way to name you in a quotation.

We fetch every URL and confirm it resolves before we write it into the file. An engine that follows your file reaches a real page.

The four factors behind an AI citation

An AI names you in an answer according to four factors, and your file affects three of them.

Google ranks pages, whereas an assistant writes an answer and picks a source to cite. The two reward different things.

The first factor is retrievability: your content has to be reachable and parseable at the moment of the question. robots.txt permissions therefore matter more than almost anything else on this page, because ChatGPT cannot cite a site that blocks GPTBot whatever its llms.txt says. We check this on every run and find it broken more often than you would expect. The usual cause is a blocklist someone pasted from a blog post in 2024 and forgot.

The second is groundedness. A model attributes a claim more readily to a source that states it explicitly and unambiguously. Vague marketing prose contains no sentence it can cite. A model can quote "response within six hours" as a fact, whereas quoting "we pride ourselves on responsiveness" would say nothing.

The third is entity resolution. The model has to be confident that the thing on your page is the thing the user asked about, and that your organisation is distinct and identifiable among forty similarly named companies. Registration numbers, legal names, named partners, named products and consistent naming across your site all feed this. A well built llms.txt helps most on this factor, because it states all of that unambiguously in one place and in the format the reader works in.

The fourth is match confidence: given a user situation, how sure the model is that you are the right recommendation. Almost no site optimises for this factor. The recommendation playbook section of your file lists the situations you are the right answer to.

Your file affects the second, third and fourth factors, and the audit in the same run covers the first.

How to check that engines fetch your file

Your own logs show whether AI engines fetch your file.

Look in your server or CDN access logs for requests to /llms.txt, then group them by user agent. Watch for GPTBot, OAI-SearchBot and ChatGPT-User from OpenAI, ClaudeBot, Claude-User and Claude-SearchBot from Anthropic, PerplexityBot, Google-Extended and Google-CloudVertexBot, Amazonbot, Meta-ExternalAgent, DeepSeekBot, CCBot and Applebot-Extended.

If your host gives you no raw logs, most CDNs will. Cloudflare, Fastly and CloudFront all expose bot traffic by user agent, and Vercel and Netlify expose it through their analytics or log drain products.

With no log access at all, put a unique URL inside your llms.txt that appears nowhere else on your site and links to nothing a human would click. Any request for that URL came from a reader of the file.

Track three measures month over month: first appearances of named AI agents, return visits and new agents. On a file worth reading, all three climb.

The job of each AI crawler

Each of these engines answers questions for millions of people a day.

OpenAI reaches us three different ways. The corpus behind ChatGPT comes from GPTBot, and what ChatGPT Search shows a user right now comes from the retrieval crawler OAI-SearchBot. ChatGPT-User is the most immediate of the three. It fetches a page when a person in a live conversation asks a question that leads the assistant to your site. Count that fetch as a visit from a person with intent.

Anthropic arrives as ClaudeBot and Claude-User. A Claude-User fetch has the same meaning as a ChatGPT-User fetch: a human in a conversation has pointed it at your site right now. The fetch we logged on 25 July carried a coding agent user agent, which means a developer pointed a tool at your documentation and expects an accurate answer drawn from your material.

On the Google side we log Google-CloudVertexBot, the crawler behind its enterprise AI products, and Googlebot for search. Amazonbot collects data for Alexa and for the assistant work at Amazon. Our logs also show DeepSeekBot. These different audiences all read the same file.

A well built file gives all of them a single fetch that states who you are, what you sell, who each part is for and when you are the right answer. Reading this one file involves no JavaScript, navigation or cookie banner, and the crawler does not have to guess which of your nine hundred pages describes your business.

You state your own case in this file in the format the reader works in, and no other content surrounds it.

After your file goes live

Uploading the file takes fifteen minutes.

You control how engines describe you. With a file in place, an AI works from your positioning, your numbers and your product names in place of whichever pages it happened to crawl.

You choose the situations in which an engine can recommend you. The recommendation section of your file lists the situations you solve. It uses the phrasing a user would type. This list prepares in your favour the match an engine has to make anyway.

Your legal name, your registration numbers, your partners and your locations arrive together in one authoritative place. A model needs these facts to treat you as a distinct and identifiable organisation among forty similar names before it names you.

The tools your customers already run can use your documentation. Coding agents and documentation assistants fetch these files constantly. If you have an API, an SDK or technical docs, a developer's assistant can quote your real parameters from the file in place of inventing plausible ones.

The same run includes the audit at no extra cost, because we find what is holding your site back while reading it.

llms.txt, llms-full.txt, robots.txt and sitemap.xml

These four files at your domain root have four different roles, and people routinely confuse them.

robots.txt grants permission. It lists what a crawler may fetch. You use it to allow or block crawlers such as GPTBot, ClaudeBot, PerplexityBot and Google-Extended. Industry convention enforces only this one of the four. We often find sites that block those crawlers and then publish an llms.txt, and our audit checks for that contradiction.

sitemap.xml lists your URLs and when they changed, which answers "what exists here". It contains no explanation of those URLs or of their relative importance.

llms.txt gives orientation. Its content covers "what is this place, and what matters in it", and it is the only one of the four that contains argument and judgement.

llms-full.txt carries the substance. It inlines the content so a reader gets the whole picture in one fetch instead of following twenty links. Our logs show more fetches of it than of llms.txt, because many readers who come for one take everything at once.

The four files have to agree with each other. We write your llms.txt and check the other files during the same run.

The four files at a domain root and the job each one does
FileJobContents
robots.txtPermissionLists what a crawler may fetch. The whole industry honours this file.
sitemap.xmlInventoryThe URLs that exist and when they last changed. It carries no meaning or priority.
llms.txtOrientationDescribes this place, its audience and what matters in it.
llms-full.txtSubstanceInlines the content so that one fetch returns all of it.

How the run works

An agent reads your site and composes the file from its findings without a template.

You give us a URL and an email address and pay twenty five dollars or twenty euros. An agent then reads your pages one by one, so a large site takes a while.

Discovery comes first. We fetch robots.txt and read what you allow, then fetch sitemap.xml, follow nested sitemap indexes and build a complete inventory of your URLs. If you have no sitemap, we crawl your links instead. We classify every URL as product, service, guide, tool, legal or editorial.

Reading comes second. We read your pages in parallel in the order of their classification, so the pages with your commercial substance come first. From each page we extract its purpose, audience, named entities, offers, canonical URL, heading structure and any structured data already present.

In the third stage, synthesis, everything we gathered goes into a model of the business: what it is, who it serves, what it sells, what makes it different from the alternatives, what proof it can point to and what numbers it can stand behind.

Writing is fourth. We follow the structure above to compose the file section by section in your site's own language, and we verify every URL live before committing it. You get your llms.txt.

The audit is the fifth stage. It adds nothing to the price because it uses what we already gathered.

You can close the tab. The run continues on our infrastructure. The result is ready when you come back and we email it to you as well.

The SEO and GEO audit in every run

While reading your whole site to write the file, we find what is holding it back.

Writing your file requires reading your whole site, so we have a complete picture of it when we finish. You receive that picture at no extra cost in an audit of your SEO and your GEO (generative engine optimization).

We score your site in the same run against a fixed, versioned checklist, so two runs months apart are comparable. It covers indexability and crawler permissions, canonical correctness, titles and meta descriptions, heading structure, structured data coverage and validity, internal linking depth and orphaned pages, image alt coverage, language and region signals, and sitemap health. It then checks whether the AI crawlers that matter can reach you, your entity grounding, your citation readiness, whether your content is shaped so a model can lift an answer out of it, and your freshness signals.

We record every finding with a code, a severity, the area it belongs to, the evidence we saw and what to do about it.

Your result screen shows how many findings we recorded and how serious they are. In twenty minutes, an adviser who has read your site tells you which findings matter for you and the order to fix them in. You book that call from the same screen.

Who this is for

This tool is for anyone whose customers are starting to ask an AI instead of Google.

The first case is a site with products, services, documentation or editorial content. This run gives a model the facts it needs to describe such a site.

Sites with documentation, an API or a developer audience benefit because coding agents fetch llms.txt constantly when pointed at a documentation site. Our own logs recorded this behaviour the day we wrote this page.

If you already appear in AI answers, your description depends today on whichever page a crawler happened to land on, and the file puts that description under your control.

Before you invest in content, run it so the engines read the new work the way you intend from the first day it ships.