What Were You Doing?
And was it the right kind of research?
Smart cats do their research.
In early May, I ran four queries through Gemini the way a potential client might. Not my name. The kind of questions someone types into an AI assistant before they know whose name to type.
“I’m looking for a pet photographer in Tucson that is experienced with shy pets.” “I’m looking for a headshot photographer to come photograph my team in my Tucson office.” Two others in the same range.
All four came back with accurate information about my business.
The shy pet query was the strongest. Gemini named Pima Animal Care Center and the Humane Society of Southern Arizona, got the volunteer history right, described the mobile studio as something that eliminates the stress of unfamiliar environments for anxious animals. The team headshot query came back with the correct pricing, the ten-person minimum, the full-day format. Someone who had read my site carefully could have written both responses. In a real sense, someone did. The synthesis came from content that exists, documented specifically enough to be cited accurately.
This is what a site doing the content work looks like from the outside. Accurate synthesis doesn’t come entirely from your own site, though. Directory listings, published features, institutional affiliations, and other third-party sources carry significant weight in what gets cited. The queries don’t always tell you which lever is being pulled. Both matter.
I didn’t run these queries to audit my site precisely. I ran them because I was looking for what chatbots know about my site. But you already know this.
I also look for the actual questions my ideal clients ask when they’re talking to an AI assistant, phrased the way they phrase them in conversation, not the way a keyword tool formats them. Those are different things, and the gap between them is getting wider. That second kind of query will be what I talk about next week.
A search bar gets keywords. “Pet photographer Tucson anxious dog.” An AI assistant gets a sentence. “I’m looking for a pet photographer in Tucson who has experience with shy and anxious dogs, ideally someone who comes to your home.” The intent is identical. The language is completely different. And increasingly, the AI assistant is where the research goes when a surface answer isn’t enough, or where it starts altogether. That’s how I use these tools as often as not: a Google search that surfaces an AI Overview, then a deeper follow-up in Perplexity or Claude rather than clicking through to individual results. I know others who work the same way. The search bar and the chatbot aren’t separate behaviors anymore. They’re sequential steps in the same research session. They’ve also drastically lowered click through rates.
The People Also Ask research habit some of you have built is still useful. Run the questions people are asking, build content around the answers, show up when someone searches. The limitation is that PAA captures search bar language, and search bar language is only part of the picture now. Clients talking to Gemini or ChatGPT before they call you are using sentences. Run the queries yourself, in client language, and you get two things back: the actual vocabulary your clients use when they describe their problems, and a live measure of whether your content is producing accurate synthesis.
The same ten minutes of work. Two different kinds of useful information.
The queries worth running fall into a few types.
Discovery queries are what someone runs before they know who to call: “pet photographer in Tucson experienced with anxious dogs,” “headshot photographer who comes to offices in Tucson.” Intent-driven queries are specific service requests, phrased as a client would phrase them to an assistant: “I need someone to photograph my team at our location in Tucson, headshots, facility, some action shots.” Specialty match queries surface your niche credentials: “photographer in Tucson experienced with photographing veterinary practices.”
Run them across multiple tools. Gemini, ChatGPT, Perplexity at minimum. What one synthesizes about you and what another synthesizes may not match, and the variance tells you something about which sources those tools are drawing from and where your content is actually landing.
Screenshot what comes back, with dates. This changes. What Gemini said about me in early May is not what it said six months ago, and won’t be what it says in six months. The only way to track drift is to document it. I run this at least monthly, usually in a gap between other things. About ten minutes once you have the query set built.
What to do with what you find, how to use the client-language data to inform content, how to influence what gets synthesized, is a longer conversation. That’s a future issue.
What I can say now is that the queries your clients are running through AI tools are the most direct signal available for what they actually need answered before they call you. The PAA equivalent for conversational search isn’t a formal tool yet. The queries are free. The methodology takes ten minutes a month. The information is real.
