Sartorial
Sartori helps online retailers prepare their product catalogs for AI shopping agents by enriching product data with the reasoning signals agents need to make recommendations. The company's audits show most Shopify stores fail to expose social proof, fit details, and substitution logic through the Universal Commerce Protocol, and Sartori provides the tools and content strategy to close that gap.
- Retail Technology
- Commerce & Shopping
- Artificial Intelligence
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
AI shopping agents now query product catalogs through standardized protocols like the Universal Commerce Protocol, but most retailers' product feeds were built for search engines and human shoppers, not for AI reasoning. These feeds lack the social proof, fit details, substitution logic, and policy information that agents need to make informed recommendations, causing AI-referred shoppers to bypass stores that are technically reachable but practically invisible to agentic commerce.
Solution
Sartori provides AI-driven shopper referral optimization for online retailers by transforming product catalogs into reasoning surfaces that AI agents can evaluate effectively. The company audits merchant endpoints to identify gaps in description depth, tag taxonomy, review exposure, and structured variant data, then helps retailers author product copy and metadata that supports agent decision-making. Sartori's approach is grounded in field data showing that AI-referred shoppers are 2.75× more likely to land directly on product pages and generate up to 14% higher average order value, while descriptions with substantive fit, fabric, and occasion details give agents the substrate needed to match products against shopper prompts.
Target Audience
Primary customers are Shopify-hosted e-commerce merchants and online retailers who want to capture AI-referred traffic and improve conversion from agentic shopping sessions.
Features
- Catalog audits across the Universal Commerce Protocol surface, measuring description length, tag density, compare-at pricing, alt text, and review/rating exposure
- Brand truth framework covering five dimensions agents need but feeds lack: fit, substitution, policy, routing, and voice
- Product copy optimization designed for AI reasoning rather than search ranking, avoiding persuasion density that capable models penalize
- Structured tag taxonomy development, including machine-readable Key_Value and colon-namespaced conventions
- Social proof integration to expose star ratings and review data at the agent boundary, the single most influential signal in agent selection per HBR research
- Worked examples and agent-ready product page templates based on real merchant audits