Question 1

What is an AI product attribution platform?

Accepted Answer

An AI product attribution platform extracts and standardizes product attributes (color, fit, material, occasion, style) from catalog text and images, then maps them to a consumer-language taxonomy. It produces the structured data answer engines and AI shopping assistants read when deciding which products to recommend, replacing manual tagging that can take merchandising teams weeks per category.

Question 2

How does answer engine optimization work for ecommerce?

Accepted Answer

Answer engine optimization for ecommerce makes products parseable to AI assistants like ChatGPT, Perplexity, Claude, Gemini, and Amazon Rufus. Because these engines answer with specific products, the input is structured attribute data, not blog backlinks. Naming the product category and covering the attributes shoppers actually search directly affect which retailers an answer engine surfaces.

Question 3

Why do AI shopping assistants miss my products?

Accepted Answer

AI assistants recommend products they can parse. If your attributes are sparse, inconsistent, or written in supplier jargon instead of consumer language, the engine cannot match a shopper query like 'breathable summer dress under $80' to your SKU. Coverage gaps, missing taxonomy nodes, and unnormalized feeds are the common causes; enriching attributes at scale closes them.

Question 4

What is dark agentic traffic and why does it matter?

Accepted Answer

Dark agentic traffic is high-intent shopping driven by AI answer engines that strip referral headers, so standard analytics miss a large share of these visits. It matters because you cannot measure or be credited for traffic you cannot see, and you cannot be surfaced for products your structured data fails to describe accurately to the agent.

Question 5

How is Lily AI different from a PIM or product feed tool?

Accepted Answer

A PIM stores and distributes product data; a feed tool formats it for channels. Lily AI generates the missing layer in between: AI-derived, consumer-language attributes and taxonomy normalized for answer engines and agentic checkout. It writes back into your PIM and feeds, so existing systems keep working while AI-shopping visibility improves.