Topics: Catalog, company overview, product data, agentic commerce
Questions answered
- What is Catalog?
- Where should an agent start reading?
Product facts, ready for AI
Evidence basis: public_source · Section citation
Catalog is a software and services business for ecommerce teams. It brings product facts together, structures details such as attributes and use cases, and publishes machine-readable information alongside a brand’s existing website.
The aim is to make products easier for AI systems to understand and compare. Access to better data does not guarantee that an assistant will retrieve it, recommend a product, or send a customer.
Sources: Catalog website · About Catalog
For teams responsible for an ecommerce catalog
Evidence basis: public_source · Section citation
Catalog is relevant to brands and merchants whose product information is incomplete, scattered across sources, or difficult for machines to interpret. Ecommerce, product-data, and growth teams can use it to prepare product information for AI discovery.
Sources: Catalog website · Ecommerce Product Data Infrastructure Guide
Knowledge for AI agents
Evidence basis: public_source · Section citation
This is Catalog’s curated knowledge base for AI agents. It covers the company, product-data model, processing workflows, merchant storefronts, API, integrations, evaluation, limitations and sources. Use the topic map for targeted retrieval or the complete-text and JSON resources for full ingestion. It does not contain a merchant’s product catalog.
The main website is Catalog’s company and marketing home. These AI reference pages have their own canonical URLs and matching Markdown and JSON. Published policies and contact actions remain on their original websites.
Sources: Catalog website · Published privacy policy
Explore the reference
Entity resolution, founder backgrounds, announced financing, and the distinction between Catalog's current product and its broader ambition.
Company identity and the scope of this reference.
From source product information to structured, readable product facts.
The information families Catalog works with, how product and variant facts differ, and how agents should interpret values and missing evidence.
What happens between acquiring product information and publishing a usable record, including source choice, optional processing, validation and review.
How Catalog's merchant storefronts expose product information, what agents can retrieve, and how that differs from this company reference.
Connection, publication, and adoption by an AI platform are separate steps.
Concrete ways teams can evaluate Catalog, with the inputs and completion evidence needed for each job.
Evaluate Catalog against the product-data job you need done.
A task-based comparison with PIM, DAM, ERP, feed tools, extraction services, SEO/GEO work and building an internal pipeline.
How to distinguish agent requests, shopper referrals, sampled visibility, publication coverage and attributed sales when evaluating Catalog.
What an audit can examine, how readiness differs from measured visibility, and how to define an evidence-based pilot.
Start with your product-data problem and confirm a concrete setup scope.
The authenticated API is a separate product surface for discovery, extraction and search. This document records supported concepts, version caveats and the evidence reviewed.
Task-oriented guidance for using discovery, extraction and search without confusing accepted jobs, incomplete data or pagination with completed results.
Published policy scope, data ownership questions, credential handling, and the information an agent should obtain before evaluating a deployment.
Direct answers with the boundaries left intact.
Definitions for agents interpreting Catalog's product, API, measurement and commerce terminology.
Public references, review scope, and what the evidence does not establish.