AI SEO is the practice of optimizing your content and brand so that AI systems surface them when they answer people's questions. It is the broadest of the names for this work: where traditional SEO chased rankings in a list of links, AI SEO targets presence inside the generated answer itself.

AIO, short for AI Optimization, is the practice of shaping your content and online presence so AI systems favor them when generating responses. The name is deliberately broad, covering any optimization aimed at AI outputs rather than classic search rankings.

AEO, or Answer Engine Optimization, is the practice of optimizing so your content becomes the answer an answer engine gives. Answer engines — AI assistants, voice search, and featured answers — return a direct response rather than a page of links, so AEO focuses on being the quoted or cited source for a question.

GEO, or Generative Engine Optimization, is the practice of optimizing for generative engines — the LLM-powered search and chat tools such as ChatGPT, Gemini, Perplexity, and AI Overviews. The goal is for your brand, pages, and ideas to appear within the text these systems generate.

The Open Knowledge Format, or OKF, is a new open standard from Google Cloud for packaging the knowledge an AI system needs so that any model or agent can read it. It takes the informal habit of keeping an "AI wiki" next to your work and turns it into a portable, vendor-neutral format. The announcement and the full specification are linked at the end.

The problem it solves

Foundation models are only as good as the context they are given, and in most organisations that context is scattered. Table schemas, metric definitions, runbooks, join paths, and the reasoning behind past decisions live in catalogs, wikis, shared drives, code comments, and the heads of a few senior people. Each tool stores this knowledge in its own shape behind its own API, so it does not travel. Every team that builds an agent re-solves the same job of gathering context, and every catalog vendor reinvents the same data model.

OKF answers this with a format that anyone can produce without an SDK, anyone can consume without an integration, and that survives being moved between systems. It lives in version control beside the code it describes, and the same file is readable by a person and parseable by an agent.

How it works

An OKF bundle is simply a directory of markdown files. The rules are deliberately small enough to fit on a single page.

1. One concept per file. A concept is anything worth capturing: a table, a dataset, a metric, a playbook. The file's path is its identity, so tables/orders.md is the concept tables/orders.
2. Markdown with YAML frontmatter. Each file opens with a small block of structured fields. The only required field is type. Recommended fields are title, description, resource (a link to the underlying asset), tags, and timestamp. You may add any other fields you like.
3. Links make a graph. Concepts reference each other with ordinary markdown links, which turns the folder into a graph of relationships rather than a plain tree.
4. Two reserved files. An index.md gives a directory listing so an agent can see what is available before opening files, and a log.md records changes over time, newest first.

Because it is just markdown and files, a bundle renders on GitHub, opens in any editor, ships as a tarball, and is indexed by any search tool. If you can read a file with cat, you can read OKF.

Three principles

1. Minimally opinionated. Exactly one field is mandatory. The spec defines how systems interoperate, not what you must write.
2. Producer and consumer are independent. A human can author a bundle that an agent consumes, or a pipeline can generate one that a person browses. The format is the contract, and the tools at each end are swappable.
3. A format, not a platform. It is tied to no cloud, database, model, or agent framework, and never requires an account to read or write.

What comes with it

Google shipped working proofs alongside the spec: a reference agent that drafts a bundle from a BigQuery dataset and then enriches it by crawling authoritative documentation, a self-contained HTML visualiser that renders any bundle as an interactive graph, and three sample bundles built from public datasets.

Why it matters for AI visibility

This is the same idea that sits under AI visibility, seen from the supply side. If you want models to represent your work accurately, the knowledge they rely on has to be legible to them. OKF is a clean way to make that knowledge portable and machine-readable, so the systems that answer questions about your domain can ground themselves in what you actually said rather than guessing.

Read more

AI Visibility is the broad outcome that AI-centric SEO work aims to achieve. It means being seen, cited, and recommended by AI systems when they answer people's questions. Traditional SEO aimed at ranking in a list of links. AI Visibility aims at presence inside the generated answer itself: whether your brand, your pages, and your ideas show up when a language model responds, and how prominently they do.

It is a desired outcome rather than a single number. Underneath it sits a family of measurable signals that together tell you how visible you are across AI answers.

How it is measured

People track AI Visibility through a set of related metrics. These divide into two kinds of presence: mentions, where your brand is named in the answer, and citations, where your pages are linked or referenced as a source. Each kind is measured by share, by absolute count, by how often it happens, and by how prominently it appears.

1. Share of voice. Your overall slice of the AI answer space for a topic, relative to competitors.
2. Share of citations. The proportion of cited sources in answers that are yours.
3. Share of mentions. The proportion of brand mentions in answers that are yours.
4. Number of citations. The absolute count of times your pages are cited.
5. Number of mentions. The absolute count of times your brand is named.
6. Frequency. How often citations and mentions occur across a body of queries and over time.
7. Rank of citations and mentions. How prominently each appears, for example whether your source is cited first or your brand named first, rather than sitting lower in the answer.

Read together, these tell a full story. Share metrics show your standing against competitors, counts show absolute reach, frequency shows consistency, and rank shows prominence. Strong AI Visibility means appearing often, as a meaningful share of the answer, and near the top of what the model surfaces.

Also known as

AI Visibility is the outcome. These are common names for the practice of working toward it: