News
White Paper
Why Enterprise AI Fails at Scale
Key Takeaways
- Catalog raised $3M pre-seed led by Acrew Capital to structure merchant product data for AI shopping agents.
- OpenAI and Google are building competing protocols that both require brands to restructure product data for agentic indexing.
- Catalog's platform ingests, normalizes and distributes product data without requiring merchants to redesign their existing infrastructure.
Catalog, a San Francisco-based startup building data infrastructure for agentic commerce, announced a $3 million pre-seed round led by Acrew Capital, with participation from WndrCo and Hustle Fund.
The company simultaneously launched a platform aiming to help merchants structure and distribute product data so that AI systems can understand, rank and recommend their products.
Catalog plans to use the funding to expand its engineering team and deepen integrations with major AI platforms.
Table of Contents
- How Catalog's Platform Works
- Two Protocol Standards Emerge
- AI Search Goes Beyond Text — And Product Data Must Keep Up
- AI Agents Move From Recommending Products to Buying Them
- Bad Data Is the Bottleneck AI Commerce Can't Engineer Around
- Catalog Background
How Catalog's Platform Works
Catalog's new platform is built around four components designed to prepare merchant data for AI systems.
| Feature | How It Works |
|---|---|
| Ingestion without integration | Extracts product data from websites and platforms; no schema redesign needed |
| Normalization & enrichment | Structures variants, resolves inconsistencies, extracts key product attributes |
| AI ecosystem distribution | Delivers data via ACP, UCP protocols and agent-driven storefronts |
| Continuous synchronization | Real-time updates for pricing, availability and catalog changes |
Two Protocol Standards Emerge
OpenAI's Agentic Commerce Protocol (ACP) and Google's Universal Commerce Protocol (UPC) represent competing visions for agentic commerce infrastructure.
- ACP focuses on conversation-to-buy experiences where agents reason through product selection via dialogue.
- UCP extends search into agentic discovery across Google's ecosystem, prioritizing browse-to-buy patterns driven by indexed brand content and inventory signals.
Both protocols require brands to structure product data for agentic indexing — enabling AI systems to surface, evaluate and recommend products based on intent rather than keywords.
AI Search Goes Beyond Text — And Product Data Must Keep Up
AI-powered search platforms now process text, images and video simultaneously, auto-generating keywords and attributes for product catalogs at scale. These multimodal generative AI systems upend product discovery by allowing customers to upload photos alongside written queries.
Conversational AI platforms deliver optimal results when they access product data, inventory and order status in real time — guiding discovery, explaining options and completing transactions through natural dialogue.
AI Agents Move From Recommending Products to Buying Them
Agentic AI platforms observe customer journeys in real time, adapting strategies dynamically. These systems enable:
- Agent-led product discovery that surfaces products based on shopper intent
- Autonomous shopping assistants that automate product selection, purchase and returns with minimal user input
- Tokenized AI payments enabling secure transactions without exposing financial data
- Real-time trust fabric validating agent identity and explaining autonomous decisions
Organizations implementing autonomous AI systems reported a 28% improvement in issue resolution time and 19% increase in first-contact resolution rates.
Bad Data Is the Bottleneck AI Commerce Can't Engineer Around
"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."
- Lauren Kolodny
General Partner, Acrew Capital
Industry experts emphasize that AI innovation depends on data quality.
Even advanced AI systems struggle when content bases are outdated, inconsistent or incomplete. AI remains only as effective as the information it consumes — making clean, structured and trustworthy data foundational to any agentic commerce strategy.
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Catalog Background
Founded in 2025 by Hamish Gunasekara and Dylan Farrell, Catalog targets a gap in online commerce infrastructure. As consumers shift from browsing websites to asking AI tools like ChatGPT what to buy, product data — often built for human browsing — lacks the structure AI systems need to interpret it reliably.
Catalog offers API-driven solutions that structure retailer product pages into normalized product objects for AI shopping agents.