An AI SEO agency for brands that need to be the answer, not just a result
What an AI SEO agency actually does
An AI SEO agency works on a surface that did not exist five years ago: the answer a language model gives when someone asks it to recommend a company like yours. ChatGPT, Google’s AI Overviews, Perplexity, Copilot and Gemini do not return ten blue links. They return a short answer, naming a handful of brands, drawn from sources the model judged credible.
That work is usually called AI search optimization, and it splits cleanly into two halves. The first is on your own site: whether your pages state things plainly enough to be quoted, whether your entity is unambiguous, whether your structure survives being parsed rather than read. The second is off it: whether the sources these models actually cite — trade press, review sites, forums, industry roundups — mention you at all.
Most agencies selling AI search optimization services only do the first half, because the first half is a checklist. The second half is earned media, and it is slower. Strategic Pete has run earned-media programmes for a decade; that is the part of this we were already doing before it had a new name.
Where brands lose AI visibility, in our experience
The page never answers the question directly
Models extract answers. A page that circles a question for four paragraphs before arriving at it gives them nothing clean to lift. The fix is usually structural, not a rewrite.
Nothing outside your own domain mentions you
Language models weight third-party sources heavily, because a brand describing itself is weak evidence. If no trade publication, roundup or review site names you, there is very little for a model to draw on.
Your entity is ambiguous
Two companies with similar names, an inconsistent description across profiles, a founder whose bio says something different on every site — ambiguity gets resolved by dropping you rather than guessing.
The structure is invisible to a parser
Headings that are styling rather than hierarchy, key facts locked inside images, schema that does not match what is on the page. A person reads past all of it; a parser does not.
You are measuring rankings and calling it visibility
Position tracking tells you nothing about whether a model named you. These are different surfaces with different winners, and a stable ranking alongside falling traffic is the signature of the gap between them.
How the engagement runs
1. Establish what is true. We run a fixed set of buying-intent prompts across the models that matter to your category, and record what comes back: whether you are named, who is named instead, and which sources each answer cites. That is the baseline, and it is measured rather than assumed.
2. Fix what is fixable on the site. Entity consistency, answer-shaped content, structure and schema that match what is visible on the page. This is the fast half and it is usually a matter of weeks.
3. Earn the citations. We work the sources the models were actually citing in step one — not a generic list of publications, the specific ones your category’s answers draw from. This is the slow half, and it is the half that moves the result.
4. Report from the system of record. Prompt-level tracking of where you appear, alongside Search Console for the classic surface. No blended vanity figure that hides which half is working.
Who this suits
Companies in considered-purchase categories, where a buyer asks an assistant for a shortlist before they ask a colleague. B2B services, software, healthcare and financial services all behave this way now. It suits you least if your category is impulse retail or if you already dominate a small, well-defined market by name.
It also suits companies that already invest in earned media, because the two programmes share a spine. If you have never been covered anywhere, expect the citation half to take longer.
Two parts of this are deep enough to have their own pages. If the priority is being named inside composed answers, start with generative engine optimization. If it is being the source a direct answer is built from, start with answer engine optimization services.
Where to start: GEO, AEO, or the whole surface
Most engagements begin with one of two narrower pieces of work, and which one depends on where your buyers actually ask their questions.
Generative engine optimization is the right entry point when the concern is composed answers — a buyer asking ChatGPT or Perplexity to recommend a company like yours, and getting three names that are not you. It is the more competitive surface and the one moving fastest.
Answer engine optimization services are the right entry point when the concern is direct answers of every kind, including featured snippets and People Also Ask. It is broader, slower-moving, and usually the better first step for a company with deep expertise that has never been written down plainly.
The full engagement runs both, plus the measurement that ties them together. If what you need first is simply a reading of your AI brand visibility — how often you are named, against whom, and from which sources — that is a short piece of work and a sensible place to start.
Frequently asked questions
AI search optimization is the practice of making a brand appear, accurately, in the answers that language models give — ChatGPT, AI Overviews, Perplexity, Copilot and Gemini. It combines on-site work (clear answers, consistent entity data, parsable structure) with off-site work (being cited by the sources those models draw on). It overlaps with SEO but is not the same discipline, because the ranked list and the generated answer have different winners.
They share foundations — a crawlable, well-structured, credible site helps both. They diverge on what counts as success. SEO optimises for a position in a list of links. AI search optimization optimises for being named inside a single generated answer, where there is no second page and often only three or four brands mentioned. The work that moves the second is weighted much more towards third-party citation than towards on-page tuning.
AI visibility is how often, and how accurately, a model names your brand when asked a question a buyer would actually ask. We measure it by running a fixed prompt set on a schedule and recording three things: whether you were named, which competitors were named instead, and which sources the answer cited. Because model outputs vary between runs, a single check is anecdote — the measurement is the trend across a repeated prompt set.
No, and you should treat any agency that guarantees it with suspicion. Model outputs are non-deterministic and the providers change their retrieval and ranking behaviour without notice. What can be committed to is the work: a measured baseline, the on-site fixes, a citation programme aimed at the sources your category’s answers actually draw from, and honest reporting of what moved.
The on-site half shows up quickly — often within weeks, because it depends on your own pages being re-crawled. The citation half runs on the timeline of earned media, which is months rather than weeks. Expect the first measurable shift in prompt coverage in one quarter and a meaningful one in two, assuming the citation work is actually being done rather than just the checklist.
Want to know what AI says about your brand today?
A short call and a baseline prompt run are enough to tell whether the gap is on your site or off it. That distinction decides everything that follows.