The AI search playbook

You cannot buy placement in an AI answer and you cannot submit a sitemap to ChatGPT. What you can do is measurable: become the source engines retrieve, extract from, and cite.

When a shopper asks an AI engine what to buy, the answer is assembled from retrieved sources — and presence in that answer is winnable with specific, unglamorous work. This playbook runs it in three stages: baseline where you stand per engine and prompt, make your pages extractable and corroborated, and re-measure the same prompt set so movement is attributable. It is the working method behind the Visibility agent, powered by GEOly AI measurement.

Stage 1: Baseline before touching anything

Build a prompt set from real buyer questions in your category — per market and language, because engines answer differently by locale. Run it across ChatGPT, Perplexity, Gemini, and Google AI Overviews on a schedule and record three things: whether you appear, how you are framed, and which sources are cited instead of you.

  • Prompts from actual buyer questions, not vanity keywords

  • Presence, framing, and displacement recorded per engine

  • The competitor and third-party sources winning your citations

  • Locale-split results, which usually surprise

Stage 2: Fix retrievability, then corroboration

Engines cite pages they can retrieve, parse, and trust. Retrievability work: key claims stated plainly near the top, structured data that validates, clean server-rendered content, crawlers not accidentally blocked. Corroboration work: the claims on your pages should be checkable — specific numbers, named facts, consistency with what independent sources say. Pages that answer the prompt-set questions directly — comparisons, buying guides, spec pages — earn citations that product-grid pages cannot.

Stage 3: Re-measure the same set, monthly

Re-run the identical prompt set and diff: new mentions, framing shifts, displaced competitors. Movement attributes to the work shipped between runs. Where a third-party source keeps winning a citation you want, that names your next task — either earn presence on that source or build the page that outcompetes it for retrieval.

How it works

01

Build and run the baseline

A buyer-question prompt set, run per engine and locale, with presence and displacement recorded.

02

Ship the retrievability work

Extractable claims, valid structured data, and pages that answer the actual questions.

03

Diff monthly and iterate

The same set, re-run — movement attributed, next targets named.

What you get

  • A tracked baseline of AI-engine presence per prompt and locale

  • Pages engines can retrieve, extract from, and corroborate

  • A named list of sources to displace or earn presence on

  • Attribution between shipped work and citation movement

Frequently asked questions

Is this just SEO renamed?

It overlaps — clean structure and credible content serve both — but the measurement is different (citations, not positions) and some tactics diverge: extraction-friendly writing and corroboration matter more; some classic ranking tactics matter less.

How fast do citations move?

Slower than paid, faster than domain authority. Retrievability fixes can show within weeks on some engines; displacement of an entrenched source takes longer. Monthly measurement is the honest cadence.

Should small brands even try?

Especially them: AI answers reward specific, well-structured, credible niche content over domain size more than classic rankings do. Being the citable answer for a narrow question is the most winnable visibility game available to a small store.

What does GEOly AI do in this playbook?

It is the measurement layer — running the prompt sets, recording presence and displacement, per engine and locale. The Visibility agent consumes that data and does the page work.

Put the team to work on your store

Connect your storefront, analytics, and email stack, set a goal, and let the agents run the work end to end. Start on the free plan with your own model key.