AI search, in plain English

Improving accessible, answer-first content while measuring answer-surface outcomes separately.

Publishing verifiable public evidence for retrieval systems and measuring sampled generated answers.

Observed mentions, citations, or presence across a defined prompt and answer-surface sample.

A system that answers a question directly instead of returning ten blue links.

Google's AI-generated summaries that appear above traditional search results.

An optional emerging navigation file that some AI-oriented tools may choose to interpret.

AI that recommends specific products when shoppers ask what to buy.

Machine-readable markup such as JSON-LD that explicitly describes visible entities and facts.

Structured data describing a product's name, price, availability, and attributes.

Structured data that labels genuine questions and answers already visible on the same page.

Making public brand facts and relationships consistent enough to identify and verify the same entity.

How AI search breaks one question into many sub-queries to assemble an answer.

A pattern that combines generation with retrieved sources or data at answer time.

When an AI answer references or links your store as a source.

A 0–100 readiness score for observable crawlability, structured-data, content, and entity signals—not a citation probability.

Commerce where software agents can research, compare, prepare carts, and—in supported, authorized flows—help transact under shopper or merchant instructions.

Improving public content and evidence for retrieval systems without promising model selection.

Applying search and publishing disciplines to public LLM-retrieval readiness and sampled outcomes.

A robots meta or X-Robots-Tag directive telling compliant search engines not to index a fetched URL.

A search that gets answered directly on the results page or inside an AI answer, so the person never clicks through to a website at all.