When AI recommends
a product, make it yours.
Shoppers ask ChatGPT and Perplexity what to buy, and get back a short list of named brands. We do the technical work that puts your catalog on that list: product entity graphs, conversational query coverage, and crawler access built for a store.
Your customer asked AI
what to buy. It named
five brands, not yours.
Someone types "best waterproof hiking boots for wide feet" into ChatGPT and gets back a handful of specific products with reasons attached. That is a purchase decision made before anyone opened a search results page, and it happened without a single click on your site.
Being absent from that answer is rarely a content problem. It is usually that GPTBot was never allowed past robots.txt, or the Product schema validates but quotes a price from last spring, or the model crawled four thousand filter permutations and never reached the product itself. Sometimes the catalog is perfectly crawlable and every description came from the manufacturer, so there is simply nothing to quote that a hundred other retailers do not also have.
Traditional SEO got your products findable. This work makes them recommendable. They are not the same job.
The signals that get
your products named.
Catalog-scale work rather than content-marketing advice: entity graphs wired to live inventory, crawler control for stores that generate tens of thousands of URLs, and copy built to be quoted.
Product Entity Graph
Product, Offer, AggregateRating, and Brand schema connected as a graph and wired to live stock and pricing, so AI shopping engines can identify what you sell, compare it against alternatives, and quote a price that is still true. Half the catalogs we audit have schema that validates and is six months stale.
Conversational Query Coverage
Nobody asks an AI for "running shoes". They ask for the best running shoes for flat feet, an alternative to a brand they already know, or something compatible with a product they own. Those are attribute and use-case queries, and most catalog copy answers none of them. We restructure product and category content around how people actually ask.
Crawler Access for a Catalog
A store generates far more crawlable surface than a content site: variations, taxonomies, filter combinations, feeds, and REST endpoints, most of which is noise to a model. We publish a valid llms.txt, set explicit rules for GPTBot, PerplexityBot, ClaudeBot, and Google-Extended, and point AI crawlers at the products rather than at 40,000 filter URLs.
Citable Product and Category Copy
AI models quote passages, not pages. A spec table and a manufacturer description give them nothing to lift. We build the comparison, use-case, and FAQ patterns that models actually extract, with the sourcing and freshness markers that make a passage safe to cite.
Agentic Commerce Signals
ChatGPT now has a shopping mode and an agent that browses on a buyer's behalf. Emerging protocols (x402, ACP, MPP) let agents discover, evaluate, and eventually transact. We expose the signals that work today, monitor the standards as they land, and keep the store positioned rather than retrofitting later.
Citation Tracking in Your Category
We track the buying questions in your category, the ones that end in a product recommendation, and monitor whether you get named across ChatGPT, Perplexity, Claude, and AI Overviews. When a competitor starts appearing where you used to, you hear about it from us rather than from a quarter of soft revenue.
Five layers we audit on every store.
The technical surface AI engines and shopping agents check before they trust, cite, or transact with a store. We score each layer and fix what is missing.
Discoverability
robots.txt, product feeds, XML sitemaps, llms.txt, and response headers that tell AI crawlers what you sell and where to look.
Content Accessibility
Clean HTML, markdown content negotiation, and product copy structured into passages a model can quote without guessing.
Bot Access Control
Explicit rules per AI crawler, so the engines that send buyers get your catalog and the scrapers that do not, don't.
Protocol Discovery
Product and Offer schema, entity graphs, MCP server cards, and the API surfaces that expose what your store can do.
Commerce Readiness
Live pricing and availability in structured data, plus the agentic commerce signals shopping agents are starting to require.
Scan, optimize, track. Repeat.
Scan
A free AI visibility scan checks AI crawler rules, llms.txt, product schema validity and freshness, entity signals, and whether you currently get named on the buying questions in your category. You find out where you stand before spending anything.
Optimize
Product entity graph deployed and wired to live stock and pricing, crawler directives fixed so models reach products instead of filter permutations, category and product copy restructured into citable passages, and llms.txt published. Everything ships through staging, because this work touches the templates that also take orders.
Track
Ongoing monitoring of the buying questions in your category across the major engines. When a citation is lost we trace it to the underlying signal, whether that is stale schema, a crawler rule, or a competitor who simply answered the question better.
Common questions.
Find out whether AI
knows your catalog exists.
The free scan checks AI crawler access, llms.txt, product schema validity and freshness, and whether you get named on the buying questions in your category. Real gaps, not a generic score.
The scan is free. The follow-up is a real person, not a sales sequence.