How can I simulate an AI shopping mission against my Shopify catalog?
You can simulate an AI shopping mission by feeding real buyer natural-language prompts (such as 'waterproof trail shoes size 10 under $130') into headless frontier LLMs equipped with search tools. Relayeo automates this process at scale: it prompts ChatGPT, Gemini, and Perplexity with high-intent shopping queries, inspects which products survive candidate filtering, and diagnoses the exact schema or variant break when products are dropped.
Simulating shopping missions before seasonal promotions allows merchants to recover an average of 18–26% in lost conversational search sales caused by silent variant dropouts.
Manual querying in ChatGPT is slow, subject to user personalization biases, and lacks underlying telemetry. Automated mission simulation isolates the deterministic catalog parsing layer.
# Multi-model simulation runner via Relayeo CLI
npx relayeo-check yourstore.com --jsonStep-by-Step Calibration Sequence:
- 1Identify your category's top high-intent buyer prompts.
- 2Run the prompts through Relayeo's Shopping Mission Library.
- 3Inspect the dropped products matrix to find variant or pricing drift.
- 4Calibrate catalog feeds and retest.
Relayeo Shopping Mission Engine
Explore pre-computed synthetic category missions at relayeo.com/missions. Run automated custom shopping missions against your own catalog at app.relayeo.com.
npx relayeo-check yourstore.comThe 1,284-Store AI Shopping Benchmark
Read the empirical telemetry report detailing why 68.2% of multi-variant Shopify catalogs dropped out of ChatGPT Search.
Simulated Buyer Mission Teardown
Inspect the step-by-step synthetic query evaluation and dropped candidate products for this shopping vertical.