TL;DR
FACT: generative engine optimization is the practice of making your brand selectable, citable and accurately described inside AI-generated answers.
INFERENCE: the order matters more than the effort. Identity before amplification: a model that cannot tell which business you are will not credit you for anything you publish.
Step one: lock the entity
Decide on one canonical name, one description, one founding date, one set of contact details — and make every page, profile and listing agree. Where variants exist (a shortened name, a former name), declare them explicitly as variants of the same entity rather than letting machines guess.
FACT: this is the step we formalized as the Entity Lock Protocol, and it is the step most GEO programs skip. Our own founder page declares every name variant explicitly so machines read one person, not four.
Step two: publish canonical definitions
For every term you want to be cited on, publish one page that defines it in the first sentence, in quotable words, and mark it as the canonical version. One page per term cluster — duplicates split the signal.
FACT: when we found our site defining the same terms on multiple pages, we merged them into single canonical pages and redirected the rest. One term, one address.
Step three: earn citations where models already look
FACT: in our 4 October pilot, the most-cited sources for how-to questions were primary documentation and government guidance — not marketing blogs. Models with web search reach for sources they already trust.
INFERENCE: the practical consequence is that off-site mentions — directories, press, podcast show notes, industry associations — are GEO work, not just PR. A citation on a trusted third-party site teaches the model who you are in a voice it did not hear from you.
Step four: measure on a schedule
Fix a set of buyer questions, run them against the same models on a schedule, save every answer word for word, and score selection, citation, position and entity accuracy. Change one thing at a time, then re-run.
FACT: we run this loop on ourselves and publish the results, including the zeroes. Our 9 October stand-in run: 12 answers, 35 cited links, none to backtier.com — including for "bounded AI stack," a term we coined. That result is why we built a canonical definition page for the term.
What to expect
INFERENCE: expect weeks between a change and a measurable shift, because search engines and AI tools revisit pages on their own schedule. Expect single runs to be noisy. The trend across scheduled runs is the signal, and the entity work compounds: every consistent mention makes the next one easier for a machine to count.
Canonical definitions used in this post
GEO and its neighbors: [GEO, AEO, AIO, HEO and SEO, defined together](/answers/geo-aeo-aio-heo-seo). Bounded AI stack: [the canonical definition](/bounded-ai-stack). Other terms: [the glossary](/glossary).