01
Create a scenario
Describe the buyer, product need, constraints, competitors, and what a successful purchase would require.
Agent-commerce QA
Will AI shoppers choose your store?
ShopperProof runs realistic AI shopping scenarios, finds the evidence gaps that make agents choose competitors, and proves whether fixes improve the next run.
Scenario report
Sensitive-skin pet shampoo
Scenario success
72
AI shopper completed the buying task with enough evidence.
Evidence gaps
9
Missing or unclear facts found across product, policy, and schema.
Competitor wins
3
Prompts where the agent found clearer proof elsewhere.
Rerun lift
+18
Measured after applying fixes to product and policy evidence.
Why the agent chose a competitor
The winning competitor stated fragrance-free, puppy-safe, and two-day shipping in citeable language. Your product page implied gentle care but did not prove those buyer constraints.
Missing claimPolicy unclearCompetitor cited
Proof layer
Protocols tell agents what is possible. ShopperProof tests what actually happens.
Every report connects a buyer task to transcript evidence, missing facts, competitor proof, and a rerun plan.
Workflow
Test, fix, and retest the whole AI shopping path.
The product loop is built around outcomes, not dashboards full of disconnected signals.
02
Run AI shoppers
Run repeated tests across supported models and capture transcripts, citations, recommendations, and handoff signals.
03
Review evidence gaps
See which facts were found, missing, contradicted, or easier for competitors to prove.
04
Fix and rerun
Apply merchant-controlled fixes, rerun the same scenario, and compare before/after outcomes.
Differentiation
Visibility tools improve inputs. ShopperProof tests the buyer outcome.
The point is not only whether a store is machine-readable. The point is whether an AI shopper can confidently recommend it.
| Current tool | What it misses | ShopperProof layer |
|---|---|---|
| AI visibility tracker | Shows whether a brand was mentioned. | Shows whether the buying scenario succeeded and why. |
| UCP or MCP checker | Validates a protocol surface. | Tests whether the protocol, page, policy, and product facts produce a better shopper outcome. |
| SEO or schema audit | Improves machine-readable inputs. | Measures whether agents cite the right evidence and choose the right product. |
Free tools
Start with the surfaces AI shoppers already inspect.
Each tool is designed to lead into a real scenario run, not stop at a disconnected score.
AI Shopper Readiness Scan
Check whether a storefront gives AI shoppers enough product, policy, schema, and protocol evidence to recommend it.
Open toolUCP and MCP Checker
Review agent-facing discovery surfaces and compare them with the storefront evidence AI shoppers use.
Open toolProduct Evidence Gap Checker
Find product facts that are missing, vague, or easier for competitors to prove.
Open toolPolicy Answerability Checker
Test whether AI shoppers can answer shipping, returns, warranty, subscription, and compatibility questions.
Open toolResources
Build the category around proof, not hype.
The first content cluster teaches merchants how to evaluate AI shopper outcomes and protocols.
Start with category creation, then support it with protocol and Shopify-specific guides.
AI shopper outcomes
What Is Agent-Commerce QA?
Agent-commerce QA is the process of testing whether AI shoppers can complete real buying scenarios with accurate evidence.
Shopify AI readiness
Shopify AI Readiness Checklist For AI Shopping Agents
A practical checklist for Shopify merchants preparing product, policy, schema, and protocol surfaces for AI shoppers.
Agent commerce protocols
UCP vs ACP vs AP2 vs MCP
A merchant-readable comparison of the agent-commerce protocols shaping AI shopping.
Ready to test the loop