Ai Search
Answer Engine Optimization, Done at the Passage Level
Answer engine optimization is the work of making your website the source an answer engine quotes when someone asks a question. It targets the systems that return a written answer with citations, ChatGPT, Perplexity, Google AI Overviews, and Gemini, rather than the ranked list of blue links beneath them.
That is a different target than classic rankings. An answer engine does not scroll a results page. It retrieves a handful of passages it judges relevant and trustworthy, writes an answer grounded in them, and names some of those passages as its sources. Answer engine optimization is the practice of becoming one of the passages it retrieves and one of the sources it names.
What answer engine optimization actually optimizes
Most of the value sits in two mechanics that classic SEO never had to care about: retrieval and grounding. Retrieval is the step where the engine pulls candidate text to read before it writes anything. Grounding is the step where it ties each claim in its answer back to specific text it retrieved. If your page is never retrieved, nothing else you do matters. If it is retrieved but your claims are vague, the engine grounds the answer on a competitor who stated the same fact plainly.
So answer engine optimization works at the passage level, not the page level. The real question for any page is whether a single paragraph can be lifted out, read in isolation, and still answer the prompt completely. That one constraint changes how pages get written, ordered, and structured.
How an answer engine decides what to cite
Retrieval and generation are two separate steps
The model you chat with does not read the whole web in real time. A retrieval layer selects a small set of sources first, then the language model writes prose grounded in them. This pattern is called retrieval augmented generation, and it means you are optimizing the retrieval step, not the model's personality. That is good news. Retrieval responds to the same things crawlers and search have always respected: pages that load and are reachable, clear topical relevance to the prompt, and corroboration from other credible sources that name the same facts.
A citation is not the same as a mention
A mention is when an engine names your business in an answer without linking to you. A citation is when it attaches your page as the source behind a claim, usually as a footnote or a link the reader can click. Both have value, but they come from different places. Mentions tend to grow out of your reputation across the wider web, the forums, directories, and articles that already talk about you. Citations tend to come from a single page that answered the question better than anything else the engine retrieved. Answer engine optimization pushes on both, but the page you own is where you have the most direct control, so that is where the method starts.
What we do
We treat answer engine optimization as build work, not a slide deck. The deliverables are concrete changes to the site itself and to the signals around it, carried out in a deliberate order rather than all at once.
On the page
Around the page
An llms.txt file is plain text that lives at the root of your domain. Each line points to a page and describes what that page answers, which in effect hands an AI crawler a table of contents instead of making it guess. It does not force an engine to cite you. It raises the odds that a crawler reaches your strongest answer rather than a thin page, and that the engine reads that answer in the right context.
The monitoring step matters because answer engines change their answers as their sources change. A single audit is a snapshot that goes stale. The work is a loop: structure the content, measure what the engines actually retrieve and cite, find where a competitor got grounded instead of you, correct the page, and check again. Treating it as a one-time project is the most common way the effort quietly stops working.
Why we only do this on sites we build
Answer engine optimization depends on controlling the HTML, the content structure, the schema, and the crawler rules. On a site we did not build, we can recommend those changes, but we cannot promise they ship correctly or stay correct over time. So we scope this as part of a program that includes a custom website, on a published monthly retainer with a 90 day initial term. We also take only one SEO client per industry per city, because answer engines usually name a short list, and we will not work both sides of the same local race.
The full commercial model and the deliverables live on the SEO service page at /services/seo/, and the published rates are at /pricing/seo/. This page is about the method, not the invoice. If you want to see exactly what an engagement includes and how it is scoped, start on those two pages and then come back to the specifics here.
Where to go next
If you are still mapping the vocabulary, the guide at /guides/ai-search/what-is-answer-engine-optimization/ defines the term plainly without the sales gloss. Generative engine optimization at /services/ai-search-optimization/generative-engine-optimization/ covers the broader label some vendors use for the same goal. To understand how a source actually gets named inside an answer, the AI citations service at /services/ai-search-optimization/ai-citations/ walks through the citation mechanism end to end.
We do SEO on websites we build. AI search optimization is part of that same engagement.
Questions
Is answer engine optimization different from SEO?
Yes, though they overlap. Classic SEO aims to rank a page inside a list of links. Answer engine optimization aims to get a specific passage retrieved and cited inside a written AI answer. The technical foundation is shared, but the content work targets self-contained answers a machine can lift out and attribute.
Can you guarantee my business gets cited by ChatGPT?
No, and you should distrust anyone who does. No vendor controls what an engine returns, and the engines change their answers as the web changes. What we control is the method: structuring content, fixing entity and schema signals, keeping crawlers unblocked, and monitoring what the engines say so we can correct course.
How is this different from generative engine optimization?
They describe nearly the same work under different labels. Answer engine optimization leans toward engines that return a direct answer with citations. Generative engine optimization is the broader umbrella term. In practice the deliverables are the same, and the page at /services/ai-search-optimization/generative-engine-optimization/ covers the overlap.
Do I need an llms.txt file for this to work?
It helps, but it is not the whole job. An llms.txt file points AI crawlers at the pages that carry your best answers, which improves the odds the right passage gets read. It does nothing on its own if those pages are thin or the content is not structured as clear, self-contained answers.
Why do you only offer this on websites you build?
Because the work lives in the HTML, the content structure, the schema, and the crawler rules, and we cannot stand behind changes we do not control. Bundling a custom site means the structure is right from the first line instead of retrofitted later. The full model and rates are on the SEO service and pricing pages.
