AI shopping agents evaluate every store across 8 scored factors before making a recommendation. Understanding each factor and what to fix first is the technical foundation of AI Commerce Intelligence.
The AI Commerce Score is a composite 0 to 100 measurement across 8 factors. Each measures a specific dimension of how well AI shopping agents can understand, trust, and recommend a store. Version 3.0 added Semantic Visuals and Image Clarity as a new factor reflecting how AI vision systems now evaluate product images directly.
AI vision systems now read product images, not just text. GPT-4o Vision and Apple Intelligence compare what is actually in a photo against surrounding text and alt attributes. A generic alt tag like "image_322.jpg" tells AI nothing. "Black leather Chelsea boot, size 8" is rich semantic signal. This factor is new in v3.0 and reflects how rapidly AI vision is becoming part of recommendation evaluation.
Structured data is the primary language AI agents use to understand what a store sells, at what price, and how trustworthy it is. Covers JSON-LD Product, Offer, and AggregateRating schema, Organization schema for merchant identity, and llms.txt quality. 72% of stores are missing critical schema markup.
Can AI crawlers actually access and parse the store? Covers robots.txt configuration (are GPTBot and ClaudeBot blocked?), Core Web Vitals via PageSpeed API (LCP under 2.5s, CLS under 0.1), and DOM structure quality including heading hierarchy. A technically broken store is an invisible store.
Machine-readable trust signals AI checks before routing a buyer to a store. Return policy in crawlable HTML (not a JS modal), checkout accessibility for autonomous agents, and legal identity transparency in footer or contact pages. 64% of stores fail this check.
Price visible in HTML without JavaScript, schema price and priceCurrency present, inventory availability consistent across feed and live page. When an AI agent tells a buyer a price and the checkout shows a different one, that agent has failed the buyer. 58% of stores fail commerce accuracy checks.
AI matches stores to buyer intent semantically, not by keywords. Specific positioning wins. "Organic collagen supplements for joint recovery in endurance athletes" matches very differently from "wellness products." Measured via LLM semantic embedding comparison against actual buyer prompt language in that niche.
The social proof signals AI can actually parse. AggregateRating schema with a real ratingValue and reviewCount from a verified review platform. Sentiment analysis on accessible review content. AI systems use this to predict post-purchase satisfaction and adjust how confidently they recommend a store.
Brand mentions in external LLM indexes, Reddit discussions, Perplexity citations, and AI-readable databases. Small weight now, expected to grow significantly by 2027 as LLM training data expands. A store with zero external presence is treated as unverified regardless of on-site quality.
Apply what you just learned. Get your real AI Commerce Score across all 8 v3.0 factors in 30 seconds.