The six types of buyer questions AI answers about your category
B2B buyers ask AI assistants six types of questions on the way to a purchase: problem discovery, category discovery, comparison, high purchase intent, brand validation and location-specific discovery. Each stage has its own question shape, its own answer format - and its own way of leaving you out. Mapping your AI visibility across all six is how you find where revenue actually leaks; it is also exactly how a structured audit builds its question set.
Stages 1-2: problem and category discovery
Problem discovery sounds like "how do we reduce picking errors in our warehouse" - no category named, no vendors expected, but assistants often end these answers by naming approaches and example providers. Category discovery is "best warehouse management software" - the classic shortlist question where two to five names carry the answer. Losing stage 2 is the loudest failure; losing stage 1 is the quietest, because you never learn the buyer existed. What wins here: classifiable category language and educational content that connects the problem to your category.
Stages 3-4: comparison and purchase intent
Comparison is "X vs Y for a company like ours" - assistants build feature-by-feature verdicts from whatever comparable evidence exists, and vendors without published, structured facts get represented by third-party guesses. High purchase intent is "X pricing", "how to migrate to X", "X implementation time" - questions from buyers close to a decision. If your own site does not answer them in machine-readable form, the assistant answers from someone else's version of your facts. What wins here: comparison pages, transparent pricing signals and concrete implementation detail.
EntityRise measures all six stages for your real questions - then fixes the stages that leak →
Stages 5-6: validation and location
Brand validation is the last-mile check: "is X legitimate", "X reviews", "who is behind X". Assistants synthesise reviews, press, founder information and complaint patterns - and thin or inconsistent public records read as risk. Location-specific discovery ("X providers in Poland", "near us") filters the category map by geography, where directory data and local citations decide presence. What wins here: consistent entity facts, named people, verifiable customers and accurate local listings.
Measuring all six question stages - not just your favourite
Most companies check stage 2 once, see a result they like or hate, and stop. But the stages fail independently: strong shortlist presence with weak validation answers still kills deals late. A meaningful measurement runs real questions from every stage, repeatedly, across ChatGPT, Claude, Gemini, Grok and Perplexity, and reports each stage separately - the same structure EntityRise's methodology uses, with clients reviewing and editing the questions before the audit runs.
Frequently asked questions
Which stage should I fix first?
The one your measurement shows is leaking most against your deal flow. As a pattern, category discovery and validation move revenue fastest - but patterns are not your data.
Do buyers really ask assistants all six?
Not every buyer asks every stage, but across a buying committee the journey usually touches most of them - and each stage reached is a moment you are either present or absent.
Can I supply my own questions for an audit?
Yes - generated questions are reviewed and edited by the client before an EntityRise audit runs, and you can add the ones that matter most to your business.
Continue reading
Six stages, six ways to be absent.
EntityRise measures where you leak, implements the fixes and remeasures the same questions.