AI search, tuned to your industry.
A traveler asking where to stay and a shopper asking for the best prenatal brand are running two different machines. The questions differ and so do the sources each assistant trusts. Put a single question to two assistants and you often get two different winners. Our category playbooks share one method and adapt everything downstream of it.
What changes from one industry to the next?
Four things change. The first is the prompt set: the specific questions whose answers move revenue in that market. The second is the corpus, meaning which sources a given assistant leans on when it builds those answers. Health publishers carry one category. In another, the answer comes almost entirely from metasearch and review communities.
The third is entity shape. A hotel group resolves as individual properties. A consumer house has to resolve as a portfolio of brands sitting under a parent. Get that wrong and the model answers about the wrong thing. The fourth is trust. The clinical citation a nutrition brand needs looks nothing like the review volume a resort needs.
The method underneath does not change. Benchmark the prompts, fix the entity layer, publish the answer a model can lift, then earn the third-party signals it checks before recommending anyone. That is the work described on our AI Search service. Plain definitions sit under what GEO is and what AEO is.
Categories with a page of their own.
Hotels, serviced apartments, airlines, OTAs & attractions
Where a shortlist of three or four names decides the itinerary before a booking site loads. Includes a dated, reproducible test of what ChatGPT and Google AI Mode answer today.
explore_travel →Multi-brand consumer houses
Where dozens of "best [category]" recommendations run at once, each decided before a shopper reaches your product page. Includes the same live test across two assistants.
explore_fmcg →Which categories do you work in?
Rates, fees & eligibility prompts
Assistants answer these from regulator pages and comparison sites. They hedge hard on anything that reads as advice. Getting named means giving them a citable, compliant source of your own.
"Best X under Y" prompts
Marketplace listings and review roundups carry the answer. That puts your own product data in direct competition with your resellers' versions of it.
Symptom & treatment prompts
Models weight medical publishers and named clinicians above everything else here, which turns a wrong or missing citation into a compliance issue.
Neighborhood & developer prompts
Answers are assembled from listing portals. Individual projects need to resolve as their own entities before an assistant can name them at all.
Program, fee & admissions prompts
Rankings sites and student forums answer these today. Course facts published as structured data on your own domain are what change that.
"Alternatives to X" prompts
Review platforms and comparison posts decide the vendor list an assistant reads out. Your own documentation rarely makes it in unless it is written to be extracted.
Your category still fits, even if it is not listed here.
The method is the same everywhere. Benchmark your brand against the prompts that decide your market, see who the assistants name and what they cite, then engineer your way into the answer. Start with an audit.