AI search optimization / answer engines / getting cited, not just ranked
People ask ChatGPT, Gemini, Claude, and Perplexity for recommendations before they ever open a search results page. Those systems pull from pages, entities, structured data, and third-party sources far more than they pull from a keyword list. I have worked in organic search since 2013, and I have spent the last several years watching how these systems choose the sources they name. This page covers what I actually change and how I measure it.
01 / How AI systems pick what to cite
I think of an answer engine as a reader with a retrieval system behind it. These models work from patterns and from retrieval, then assemble an answer from passages and entities. An AI answer engine reads the useful passage and the entity around it instead of treating a whole page as one undifferentiated ranking signal. When I put a clear, direct answer near the top of a page, it is easier for a system to pick up and quote.
Consistent entity signals across the site and the open web help a brand become recognizable. Structured data and FAQ content give a page more context and make useful lines easier to quote. The exact recipe behind commercial systems stays private, so I focus on signals that show up repeatedly across engines and can be checked over time. I connect this work to Local AEO, ranking tracking, and the questions I explore in my AEO and GEO article.
Choose your next step
02 / What I work on
03 / What you get
04 / How it works
I keep the sequence close to the questions your buyers ask and the sources answer engines can read.
I review where you appear across answer engines and where competitors get named.
I test the actual questions your buyers ask.
I fix the page-level signals and structure.
I track citations and crawler activity over time.
I report in plain language and choose the next move.
05 / Who this is for
I work with operators in categories where advertising is restricted, businesses whose buyers research heavily before purchase, brands that already rank well and want the next layer of visibility, and teams that need reporting they can actually read.
Some engagements happen through Evergreen Digital, with founder Brady Madden setting strategic direction on most accounts. I build strategy myself on some engagements, with the scope clear from the start.
Guaranteed placements in AI answers and fixed timelines sit outside the offer because no one controls those systems.
FAQ
AI search optimization is the work I do to make a brand's pages and public signals easier for answer engines to interpret, quote, and connect to a real entity. I work on direct answers, structure, schema, profiles, and the paths crawlers use.
Traditional SEO helps pages earn visibility in search results through relevance, technical access, and authority. AI search optimization adds answer-ready passages, entity clarity, citation tracking, and question-level testing so I can see where a system names a source.
I test ChatGPT, Gemini, Claude, and Perplexity, then track the engines that matter to the audience and category. I also review the search and profile sources those systems use when they assemble an answer.
Citations depend on recrawl timing, source changes, question phrasing, and the engine. I establish a baseline, make the changes, and review citation movement over time. Guaranteed placements and fixed timelines sit outside the offer.
I build a question set, record the cited sources, compare competitor mentions, and connect the findings to crawl activity, rankings, organic traffic, and revenue. The report shows the question, engine, source, and next action.
Start with the questions buyers ask, answer them directly near the top of useful pages, keep business details consistent, add appropriate schema, and make key pages accessible to crawlers. I can audit the starting point and show the next set of fixes when the team wants help.
A useful next step
