Obility Editorial · · 4 min read
Buyers ask in the words they already have
Many young companies describe themselves with a term they made up: a revenue orchestration platform, a customer intelligence layer, a spend intelligence suite. On a homepage that can work, because the visitor has already arrived and can read on. In an AI answer it works badly. A buyer who has never heard of you does not type your term. They ask for the best tool to do a job they can already name, such as chasing overdue invoices, forecasting staffing or keeping track of software spend.
An answer engine builds its shortlist from that wording. It searches for pages that match the question, reads what comes back and combines it with what the model learned in training about which companies do that kind of job. A page that never uses the buyer’s words is a weak match for that search. A company that other sources never describe in those words is unlikely to be part of the model’s sense of who does the job.
The engine files you under the label other people use
Even when an assistant knows your company, it still has to decide what you are. It takes that from the most consistent description across the pages it can see: directory listings, review profiles, press coverage, partner pages and your own site. If your homepage says one thing, your oldest directory profile says another and a journalist picked a third, the answer settles on one of them, and it may not be the one you would choose. A company described as a reporting tool is unlikely to be named when the question asks for planning software, however well it plans.
This produces a pattern that puzzles a lot of teams. Ask an assistant what your company is and the answer is accurate. Ask it for the best tools for the job your product does and you are missing. The engine knows who you are. It has not connected you to the question.
Three versions of the question locate the problem
For each buying question you care about, run three versions: one that uses your own category term, one in the plainest words a buyer would use for the job, and one that simply asks what your company is. Take an illustrative company that calls itself a spend intelligence platform. It appears in answers to a question about the best spend intelligence platforms, because it wrote most of the pages that use the phrase. It is absent from a question about the best software for controlling SaaS spending at a mid-size company, which is how its buyers actually ask. Asked what the company is, the assistant calls it an expense reporting tool, a label lifted from a directory listing written three years earlier.
Each result points somewhere different. Presence on your own term proves very little, since the only competition for that phrase is you; count it as a vanity check, not visibility. Absence on the plain question alongside an accurate description means the label is the problem. A wrong description means the pages about you disagree with each other, and the sources the engine cites for that answer tell you which ones to fix first.
Put the familiar label beside your own
None of this means abandoning your positioning. It means putting the buyer’s words next to it. Your homepage title and opening lines, your product pages and your About page can name the category buyers recognise and the jobs they ask about, in the same sentence as your own term: a spend intelligence platform, software that finds and controls SaaS spending. Retrieval works on text, so that page now matches both questions.
Then bring the outside descriptions into line. Directory and review sites usually ask you to choose a category for your profile; choose the one buyers search for, and use the same one-line description on every profile you control. Where a page the engines cite files you under the wrong label, ask its owner to update it.
Obility is built around this kind of consistency. Context Manager records your approved product names, descriptions, intended audiences and claims, so every brief and page starts from the same description rather than whichever version a writer found first. Answer Engine Insights groups tracked prompts by audience, buying stage and market and keeps the full answer with each observation, so a reviewer can see which label an engine attached to you and which competitors it recommended instead.
A new category appears in answers when other people use it
Be realistic about the coined term itself. An engine starts to treat it as a category once independent sources describe the market that way: analysts, reviewers, buyers writing in forums, competitors adopting the phrase. Repeating it on your own pages cannot do that for you. Until it happens, track the coined-term question as a narrow signal and judge progress on the plain-language questions your buyers really ask.
Avoid the opposite mistake as well. Stuffing every adjacent label into your pages to catch more questions blurs the description engines rely on, and it can get you filed under a category where you lose the comparison. Pick the one or two familiar labels that fit what you sell and where you would want to be compared, and leave the rest alone.