Watch how AI agents
choose what to recommend
Biasque runs a real AI shopping agent across Shopify's global catalog and records exactly which products it picks, which it skips, and why. An open, neutral view of how AI ranks the things people buy.
Results reflect Biasque's own AI agent and may differ from ChatGPT, Perplexity, or other services.
Try a search to see which products the AI picks — and why it skips others.
Top picks by category
Products ranked by how often Biasque's AI selected them for real user searches. The fraction next to each price (e.g. 2/2) shows how many times it was chosen out of total appearances in search results.
Electronics & Tech
8 selected
Home & Living
8 selected
Sports & Outdoors
7 selected
Fashion & Apparel
6 selected
Health & Wellness
6 selected
Jewelry & Accessories
6 selected
Other
6 selected
Toys & Kids
5 selected
Pets
4 selected
Books & Art & Stationery
2 selected
Food & Drink
1 selected
Selected & not selected — with reasons
Live results from Biasque's AI shopping agent — showing which products it picked (or skipped) for real user queries, along with its reasoning.
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By Category
FAQ
Quick answers about how Biasque works, what the metrics mean, and how to use the platform effectively.
+What is Biasque?
Biasque is an independent observatory for AI shopping behavior. It records what an AI agent searches, which products it considers, and what it finally recommends across stores — an open, neutral view of how AI agents read intent and decide which products win. Anyone — shoppers, researchers, or merchants — can see not just what was picked, but the agent's reasoning for each choice.
Pronounced by-ask. The name combines “bias” and the suffix “-que” — evoking pick, with the French-inflected feel of words like boutique or unique.
+How does Biasque collect data?
Every time a user runs a search on Biasque, the AI agent queries Shopify's public product catalog and logs which products appeared, which were selected, and the agent's reasoning for each decision.
Currently, all searches are processed by a single AI model. The data reflects how that one model evaluates products — not an aggregate across multiple AI systems. Selection rates should be read as signals from one AI perspective.
+Does Biasque show how ChatGPT or other AI services rank my products?
No. Biasque uses its own AI agent implementation and does not reproduce or simulate the behavior of ChatGPT, Google Gemini, or any other third-party AI service. Results reflect how Biasque's AI agent evaluates products via Shopify's catalog data, and may differ from what those services would return. Use Biasque as a directional signal for AI discoverability — not as a prediction of any specific platform's output.
+What does selected vs not selected mean?
For a given search query, Selected means the product was included in the AI agent's final recommendation. Not selected means the product was evaluated as a candidate for that same query, but was not chosen in the final recommendation.
+How is selection rate calculated?
Selection rate is selections divided by appearances over a time window — how often the AI agent picked a product out of the times it appeared as a candidate for a search.
+How fresh are price and stock signals?
Product data is based on recent catalog snapshots. Prices and stock availability may differ slightly from the store's current listing — always check the product page before purchasing.
+How is account and usage data handled?
Authentication and usage data are used to operate the service, secure accounts, and deliver analytics. See Privacy Policy for details.
+What is UCP (Universal Commerce Protocol)?
UCP is an open standard that lets AI agents communicate with e-commerce stores in a uniform way — without custom integrations per store. It covers capabilities like search, cart, checkout, and discounts. Shopify leads the spec, and Biasque uses it to run AI shopping queries across thousands of stores. See ucp.dev for the full specification.
+What is ACP (Agentic Commerce Protocol)?
ACP is OpenAI's protocol for enabling AI agents to act on a buyer's behalf during a shopping flow, using delegation tokens with defined spending limits and expiry. Where UCP focuses on capability negotiation between agents and stores (discovery, search, cart, checkout), ACP focuses on the authorization layer. The two protocols are complementary: UCP handles the commerce infrastructure side, ACP handles the trust and authorization side. Note: OpenAI initially explored completing purchases entirely within the chat interface, but has since shifted to a model where buyers are redirected to the store's checkout — giving merchants more control over the payment flow and post-purchase experience. Biasque currently uses UCP for product discovery and reasoning data.