Catalog raises $3M to build the data infrastructure for AI commerce.
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2 MIN READ
Catalog, the data infrastructure layer for agentic commerce, today announced a $3 million USD ($4.2M AUD) pre-seed round led by Acrew Capital, with participation from WndrCo and Hustle Fund. The company is launching its platform to help merchants structure and distribute product data so they can be understood, ranked, and recommended by AI systems.
The raise comes as a fundamental shift takes hold in how people shop online. Consumers are moving from browsing websites to asking platforms like ChatGPT what to buy. Instead of navigating pages and blogs, they describe intent in plain language and receive personalized recommendations. In this new model, AI becomes the storefront - filtering, comparing, and increasingly completing transactions on behalf of users.
But the underlying infrastructure has not caught up. Product data today is built for human browsing, not machine reasoning. It lives in inconsistent formats, buried in copy, and lacks the structure required for AI systems to interpret it reliably. As a result, even strong products are often excluded from AI-driven discovery.
Founded in San Francisco in 2025 by Hamish Gunasekara and Dylan Farrell, Catalog addresses this gap by transforming merchant catalogs into structured, machine-readable product data designed for AI systems.
Catalog’s platform is built around four core components:
Ingestion without integration: Catalog extracts product data directly from merchant websites and commerce platforms, requiring no schema redesign or engineering lift.
Normalization and enrichment: The system structures variants, resolves inconsistencies, and extracts key attributes such as material, dimensions, and use cases, producing deterministic product objects optimized for machine reasoning.
Distribution to AI ecosystems: Catalog delivers product data to AI platforms through protocols such as ACP and UCP, as well as agent-driven storefronts and APIs, ensuring products can be discovered and recommended across AI interfaces.
Continuous synchronization: Pricing, availability, and catalog changes are monitored and updated in real time so AI systems operate on accurate, up-to-date data.
With this approach, Catalog enables merchants to participate in AI-driven commerce without rebuilding their existing infrastructure. Setup takes hours, not months.
“Catalog is building critical infrastructure for how commerce will work in an AI-first world,” said Lauren Kolodny, General Partner at Acrew Capital. “As AI agents take on a larger role in discovery and purchasing, structured and reliable product data becomes essential. Catalog ensures merchants can show up and compete in that environment.”
Catalog plans to use the funding to expand its engineering team, deepen integrations with major AI platforms, and continue building toward a broader vision of agentic commerce infrastructure.
As AI becomes the interface for how people search, compare, and buy, visibility will depend on whether products can be understood by the systems making those decisions. Catalog is building the layer that makes that possible.
Catalog, the data infrastructure layer for agentic commerce, today announced a $3 million USD ($4.2M AUD) pre-seed round led by Acrew Capital, with participation from WndrCo and Hustle Fund. The company is launching its platform to help merchants structure and distribute product data so they can be understood, ranked, and recommended by AI systems.
The raise comes as a fundamental shift takes hold in how people shop online. Consumers are moving from browsing websites to asking platforms like ChatGPT what to buy. Instead of navigating pages and blogs, they describe intent in plain language and receive personalized recommendations. In this new model, AI becomes the storefront - filtering, comparing, and increasingly completing transactions on behalf of users.
But the underlying infrastructure has not caught up. Product data today is built for human browsing, not machine reasoning. It lives in inconsistent formats, buried in copy, and lacks the structure required for AI systems to interpret it reliably. As a result, even strong products are often excluded from AI-driven discovery.
Founded in San Francisco in 2025 by Hamish Gunasekara and Dylan Farrell, Catalog addresses this gap by transforming merchant catalogs into structured, machine-readable product data designed for AI systems.
Catalog’s platform is built around four core components:
Ingestion without integration: Catalog extracts product data directly from merchant websites and commerce platforms, requiring no schema redesign or engineering lift.
Normalization and enrichment: The system structures variants, resolves inconsistencies, and extracts key attributes such as material, dimensions, and use cases, producing deterministic product objects optimized for machine reasoning.
Distribution to AI ecosystems: Catalog delivers product data to AI platforms through protocols such as ACP and UCP, as well as agent-driven storefronts and APIs, ensuring products can be discovered and recommended across AI interfaces.
Continuous synchronization: Pricing, availability, and catalog changes are monitored and updated in real time so AI systems operate on accurate, up-to-date data.
With this approach, Catalog enables merchants to participate in AI-driven commerce without rebuilding their existing infrastructure. Setup takes hours, not months.
“Catalog is building critical infrastructure for how commerce will work in an AI-first world,” said Lauren Kolodny, General Partner at Acrew Capital. “As AI agents take on a larger role in discovery and purchasing, structured and reliable product data becomes essential. Catalog ensures merchants can show up and compete in that environment.”
Catalog plans to use the funding to expand its engineering team, deepen integrations with major AI platforms, and continue building toward a broader vision of agentic commerce infrastructure.
As AI becomes the interface for how people search, compare, and buy, visibility will depend on whether products can be understood by the systems making those decisions. Catalog is building the layer that makes that possible.