Be the Brand AI Recommends in Your Category

The shortlist now forms inside the prompt

A growing share of buying journeys begins with a question typed into an assistant rather than a search box, and the assistant answers with a shortlist of named vendors before the buyer ever reaches a results page. That shortlist is the new top of the funnel, and it is invisible to every tool built for classic search. AI search visibility is the discipline of understanding which prompts your buyers use, measuring who gets named in the answers, and running the program that makes it you. It matters because absence from the shortlist prompt is the most expensive kind of invisibility there is: the buyer never learns you exist at the exact moment they are forming their consideration set. The encouraging part is that the prompts are knowable. Five interviews with recent customers surface the literal questions they asked, and running those questions across the platforms on a schedule turns a fuzzy new channel into a scoreboard you can manage.

Comparison content is the retrieval bait models crave

The way buyers prompt maps almost perfectly onto a specific content type: alternative-to and X versus Y questions. Assistants answer those prompts by retrieving comparison content, and that content exists whether you participate or not, because review sites, affiliates, and competitors are already supplying the retrieval pool with their framing of your product. Honest comparison pages with real specifics, actual pricing, feature tables with versions and limits, and a clear verdict per use case get retrieved constantly, because they answer the prompt as asked. When you write them, you set the frame for every answer about your category; when you leave them to others, someone else sets it. This is one of the highest-leverage content investments in the entire discipline, and most brands never make it because it feels uncomfortable to name competitors directly, which is precisely why the brands that do own the comparison layer.

Open the door to AI crawlers, then prove they came

None of the visibility work matters if the assistants cannot read your pages. GPTBot, ClaudeBot, PerplexityBot, and Google-Extended cannot cite what they cannot fetch, and blocks hide in CDN and firewall rules as often as in robots.txt. For a commercial brand the trade is straightforward: visibility inside AI answers is worth more than the theoretical benefit of blocking retrieval on public marketing pages, so we open the retrieval agents on public content, deploy a standards-based llms.txt guide at the root, and then confirm in server logs that the agents actually crawl rather than assuming they do. Verifying crawl in the logs is the step almost everyone skips, and it is the one that catches the silent block that would otherwise cap the entire program at zero. Access is not glamorous, but it is the precondition for everything else, and it is the first thing we check.

Correcting what the assistants say about you

Brands often discover that an assistant is confidently wrong about them: an outdated price, a discontinued product, a competitor's feature attributed to you. You cannot edit the model, but you can change what it retrieves. We trace the retrieval source feeding the error, correct or displace it, and re-test, and corrections usually propagate within one or two refresh cycles. This is the honest version of reputation work in the AI era: there is no backdoor and no way to simply tell the model what to say, and anyone claiming otherwise is selling a fiction. What you can do is make the accurate, well-structured, well-sourced version of the truth the easiest thing for the model to find, which over a cycle or two crowds out the stale or hostile source that was feeding the error. Sentiment of your mentions matters too, because being recommended beats being merely listed, which beats being cautioned against.

Six board-ready KPIs, billed by the hour

AI visibility earned a bad reputation from vendors who sold activity dressed up as progress. We report six numbers a CFO can audit: citation count on a fixed prompt set, share against named competitors, query coverage, mention sentiment, source-domain wins, and an on-page readiness score, with the raw answers logged as evidence for every figure. You can reproduce any of them yourself, which is the entire point and the standard you should demand from any vendor, including us. Because the work is concentrated on a defined prompt set, a specific comparison-content build, and a list of retrieved domains, we bill hourly against a written estimate rather than as a retainer. Most clients start with prompt-journey mapping and the first comparison-content sprint, then scale into source-pool work only where the scoreboard moves. You approve the hours, you see them logged, and you own every account and asset we create along the way.