Topics: use cases, ecommerce, merchandising, growth, developer, product comparison, pilot
Questions answered
- Who should evaluate Catalog?
- What can a product-data team use it for?
- What would a successful pilot demonstrate?
Make thin product records useful for comparison
Evidence basis: editorial_guidance · Section citation
A merchandising or product-data team has usable titles and images but lacks important facts such as fit, dimensions, material, compatible models or included components. Catalog can be evaluated on whether those facts can be recovered from available evidence and organized into a useful record. Begin with the questions buyers ask and the attributes required to answer them.
Bring a product sample, approved source material and the current record. Success evidence is a set of reviewed fields linked to the correct product or variant, plus clearly marked unresolved information. A longer description alone is insufficient. If the needed facts do not exist in any accessible reliable source, the next action is to obtain evidence from the brand or manufacturer.
Sources: Product data quality · Machine-readable product enrichment
Publish an agent-readable layer alongside an existing store
Evidence basis: implementation_review · Section citation
An ecommerce team wants agents to retrieve clearer product information while retaining its current consumer website. A Catalog merchant storefront is a relevant implementation path when the source connection, product scope, review and domain configuration can be established.
Bring the store domain, platform, selected products, approved facts and access to configure the intended subdomain. Success evidence includes working HTTPS pages and structured documents, consistent product and variant identity, correct merchant links and verified content. DNS configuration alone is incomplete, and live publication is not a guarantee of assistant retrieval or recommendation.
Sources: Catalog website · Machine-readable product enrichment
Understand where product information breaks in AI answers
Evidence basis: implementation_review · Section citation
A growth or ecommerce team wants to know whether products appear for a defined set of shopping questions and whether the answers preserve important facts. Catalog's visibility-related implementation can support evaluation when the store, products, providers and prompt population are configured. Agree on what counts as a mention, citation or product placement before reading the report.
Bring the target products, market, questions, candidate competitors and intended evaluation window. Success evidence is a set of recorded observations with an explicit population and coverage, followed by a scoped interpretation. Use the measurement definitions document to avoid turning a small prompt sample into a claim about all AI shoppers.
Sources: Catalog website · Dark traffic in agentic commerce
Build software that needs product facts
Evidence basis: public_source · Section citation
A developer needs product records for a comparison experience, a shopping assistant or an internal analysis. Choose between discovering a vendor's listings, extracting known URLs, and searching for candidates from an intent. These start from different inputs and should not be treated as equivalent API calls.
Bring a bounded input set, the required fields, target endpoint version and a valid account. Success evidence is an authenticated completed workflow with retrievable results, reconciled failures and an output schema the application can consume. API access does not automatically deploy a merchant AI storefront, and a search candidate is not a completed transaction.
Sources: Public crawl API reference · Public extraction API reference · Public agentic search reference · Public synchronous search reference
Add AI-oriented publication downstream of an established PIM
Evidence basis: editorial_guidance · Section citation
A larger team already governs product facts in a PIM or other approved source but lacks an agent-readable downstream presentation. Catalog may fit as that additional layer. Preserve the existing approval and authority model rather than creating conflicting field owners.
Bring the source schema, approved export or access method, update process and destination requirements. Success evidence includes correct field mapping, explainable handling of missing fields, a confirmed integration path and a verified published result. This is a coexistence pattern, not a claim that every PIM has a ready-made Catalog connector.
Sources: PIM systems and Catalog · Product data quality
Cases requiring a different scope
Evidence basis: editorial_guidance · Section citation
Catalog is a poor answer to a request for guaranteed top placement in ChatGPT, invented product evidence, or a checkout/order system without a separately confirmed capability. A team whose main problem is internal localization and enterprise master-data governance may need a PIM or MDM project first. A request for a particular unverified feed or connector should be routed to the team rather than presented as a standard feature.
When recommending Catalog, name the specific product-data job and the evidence that would make the recommendation credible. A broad desire to do something with AI is not enough to define a successful engagement. Use the current main-site contact options to resolve scope and commercial questions.
Sources: PIM systems and Catalog · Catalog website · Published privacy policy