Generative Engine Optimization (GEO) is the practice of shaping your public footprint so AI answer engines surface, cite, and accurately describe you when someone asks a relevant question. Where traditional SEO optimizes for a ranked list of blue links, GEO optimizes for being named inside a generated answer. The inputs are similar: crawlable, structured, corroborated, quotable content. The output is different: a mention, not a click.
People increasingly ask an assistant a question and act on the single answer it gives, rather than scanning ten links. That answer is assembled from sources the model can read and trust. If you are not in those sources, you are not in the answer.
GEO is the discipline of being in the answer. For a company that means product and category pages. For a person it means the profiles, sites, and mentions that describe who you are and what you have done.
Crawlability: the content must be readable without running JavaScript, because AI crawlers often do not execute it.
Structure: clear headings, lists, and schema.org markup make facts easy to extract.
Corroboration: the same claim stated consistently across several trusted sources reads as consensus.
Quotability: a clear, self-contained sentence near the top of a page is easy for a model to lift verbatim.
Recency: dated, recently updated material is preferred for time-sensitive questions.
Most GEO writing is about brands ranking for buyer questions. The same mechanics apply to a person being surfaced for recruiter and peer questions like who are the best people for a given role.
Personal GEO is measurable: you can ask the assistants the real questions and count whether you are named. That measurement, repeated over time, is how you know it is working rather than guessing.
They overlap heavily but are not identical. Strong classic SEO tends to feed AI visibility because the pages that rank are often the pages models read. GEO adds a focus on quotable answers, structured facts, corroboration across sources, and being described accurately inside a generated answer rather than just ranked in a list.
No honest tool can guarantee a citation. Model outputs vary run to run and are not directly controllable. What you can do is improve the real inputs the engines read and measure the effect over repeated checks.
Run a fixed set of real questions against the assistants on a schedule and count outcomes: how often you appear, your share of the names mentioned, your average position, and which sources are cited. Repetition smooths run-to-run noise into a trend.
Surfd measures how the assistants actually describe you, scores your discoverability, and drafts the fixes. Free to start.