LLM Seeding™: verifiable information AI can cite.
A method for publishing clear facts, with a source and a date, on a brand's own pages and on trustworthy external sources, so that AI answer systems can understand and cite them accurately. No promise of position, citation or timing.
Method authorship declared by the author. Last reviewed: 14 October 2026.
When the answer arrives ready-made, clicks shrink
The Pew Research Center found that in Google searches with an AI summary, 8% of visits included a click on a traditional result, against 15% without one (900 US adults, March 2025). Being understood by the machine starts to count alongside being clicked.
how information usually sits
factsDifferent on every profile and directory
numbersNo source, no date
brandDescribed in adjectives, with no clear entity
how the method organizes it
factsOne official fact sheet, same wording everywhere
numbersWith source, date and method
brandNamed in full, with author and proof
Six stages, each with a success test
From a map of questions to measurement, with a verifiable result at every step. Read the full method.
- QuestionsWhich questions and entities must be right.
- AnswerCanonical sentences of 40 to 80 words.
- ProofSource, date and authorship behind each claim.
- StructureHTML robots can read, schema that matches.
- DistributionThe same facts on legitimate external sources.
- MeasurementFixed questions, archived answers, proportionate conclusions.
Six formats for six kinds of question
Each format requires its own kind of proof. None guarantees citation. See the formats.
Comparisons
Open criteria, a table and sources.
FAQ with direct answers
Real questions, self-contained answers.
First-person analyses
A real test, with method and limits.
Opinion with a thesis
Thesis, evidence and counterpoint.
Glossaries and definitions
One entry per term, with a source.
Original data and studies
Methodology, sample, date and limits.
What the method does not do
Google says that optimizing for AI in Search is SEO and that no additional requirements apply to its AI features. LLM Seeding works with that, and goes further into external sources and other platforms.
It is not manipulation
No hidden text, no instructions to models, no fabricated mentions or reviews. See the manifesto (Portuguese).
It is not a promise
No page guarantees position, citation, traffic, revenue or timing. See what we know and do not know (Portuguese).
It is not a magic file
llms.txt has no proven effect on citations. See llms.txt funciona? (Portuguese).
It does not replace technical access
A blocked crawler reads nothing. See robots.txt para robôs de IA (Portuguese).
Camillo Dantas, creator of LLM Seeding™
Computer scientist and creator of the method. He positions himself as Brazil's leading specialist in SEO for AI (the author's own statement). 21 years of SEO at Globo, more than 3.000+ students and author of the book SEO para Inteligências Artificiais (in Portuguese).
LLM Seeding is a method with declared authorship. GEO, AEO, RAG and query fan-out are market or third-party concepts and are credited on each page. The author's theses are presented as opinion grounded in experience.
Does LLM Seeding guarantee my brand will be cited?
No. Nobody controls an AI assistant's answer, and answers vary between platforms and between days. The method organizes the work to reduce ambiguity, raise the chance that the correct information is used and measure what changed. Promising citation, position or a deadline goes against the method's principles.
Is LLM Seeding the same as SEO, GEO or AEO?
No. SEO is an established field. GEO and AEO are market labels, and Google treats both as SEO. LLM Seeding is a specific method by Camillo Dantas for publishing verifiable information on pages and external sources. The differences are covered in Portuguese at LLM Seeding, GEO e AEO.
Start with the checklist
Eighteen checks in six groups show what to fix first. It runs in your browser and sends nothing anywhere.