The Aisle scanner · public beta
Aisle runs 27 checks across discovery, readability, transactability, posture, trust, and agent-native content. You get a letter grade an agent can trust, in under a minute.
The thesis
The web is being rebuilt for agents. When a user asks ChatGPT, Perplexity, or Claude to buy a pair of shoes, a model reads structured data, parses policies, and decides where to send the purchase. Sites that make that easy get the sale. Sites that don't, don't.
Aisle measures readiness in 27 concrete checks. Can an agent find your products? Can it read the price without running JavaScript? Can it complete checkout without creating an account? Can it tell a legitimate sale from a fake countdown timer? The scanner runs against any public URL and returns a letter grade with evidence.
The rubric is agent-native on purpose. It overlaps with SEO in places, because agents read some of the same signals search engines do, but the dimensions that matter most are the ones that only matter when the buyer is a model rather than a person.
How it works
No SDK, no integration. A URL is all Aisle needs to return a full report and a prioritized list of fixes.
Step 01
Aisle visits your storefront the way an AI shopping agent would. Identified, polite, indistinguishable from real agent traffic.
Step 02
Clerk, Aisle's AI assistant, runs 27 checks across 6 dimensions of agent readiness. Every signal an agent weighs before recommending a purchase, scored.
Step 03
One letter grade, a dimension breakdown, and evidence for every finding. Each report has a permanent URL you can share.
Waitlist
Aisle is invite-only. Drop your email to join the waitlist. We will also send occasional dispatches on agent-readiness patterns across categories.