Buying path audit
When AI agents find your shop but can't complete the purchase.
Output: drop-off list, risk map, evidence gaps, and the first fixes for the path that matters most.
Use cases
Agents stop when product facts, rules, comparisons, or checkout steps are not clear enough to trust.
When AI agents find your shop but can't complete the purchase.
Output: drop-off list, risk map, evidence gaps, and the first fixes for the path that matters most.
When your product pages convince humans but leave agents without enough facts to cite you.
Output: product evidence spec, comparison model, trust requirements, schema backlog, and concrete copy examples for key product pages.
When your checkout rules only humans understand and agents leave before reaching them.
Output: checkout risk report for shipping, returns, accounts, payment, bundles, and support, with impact per fix.
When ChatGPT, Perplexity, and shopping agents can't confidently cite or recommend you.
Output: answer-surface checklist, crawlable proof inventory, and evaluation prompts for ChatGPT, Perplexity, and shopping agents.
I separate agent ambiguity from the UX work that actually matters.
Every fix gets an owner, cadence, and success measure.
Your team inherits standards instead of reading recommendations.