The language of
The AI Commerce Graph™

The AI Commerce Score is the composite 0 to 100 measurement of how well AI shopping agents can understand, trust, and recommend a store across 8 scored factors. Every store Atom Foundry scans receives one.

Recommendation Share is the percentage of high-intent buying prompts relevant to a brand's niche in which that brand appears in AI recommendations. It is the AI-era equivalent of search market share.

AI Visibility Risk is the score zone for stores with an AI Commerce Score below 50. Stores in this zone are not ranked lower. They are actively excluded from AI recommendation flows entirely.

The AI Commerce Graph is the complete network of relationships that AI systems build between brands, products, trust signals, citations, buyer intents, and semantic relevance. It is the emergent structure across ChatGPT, Perplexity, Google AI Mode, and Alexa for Shopping that determines which brands get recommended, in which contexts, to which buyers.

Recommendation Routing describes the dynamic process by which AI systems direct specific buyer queries to specific brands. Two brands can both be present in the AI Commerce Graph but receive completely different query traffic based on their positioning signals, trust scores, and semantic alignment.

Persistent Merchant Identity measures how consistently and accurately AI systems understand and represent a brand across ChatGPT, Perplexity, Google AI Mode, Alexa for Shopping, and Apple Intelligence. A brand with low PMI gets understood differently by different systems, resulting in inconsistent recommendation outcomes.

The AI Trust Layer is the pre-recommendation verification step AI systems run before evaluating a brand. It confirms a brand is real, operational, and safe to recommend. A brand that fails the AI Trust Layer is excluded from recommendations entirely, regardless of how well it scores on other factors.

The Semantic Commerce Layer is the structured layer of product copy, FAQ content, schema descriptions, and llms.txt positioning that translates a store's products into the semantic signals AI uses to match brands to buyer queries. Without it, a brand with perfect technical infrastructure still fails to appear in the queries that drive revenue.

Machine-Readable Commerce is the complete architectural approach to making a store legible to AI agents. It goes beyond schema markup. It covers every layer where AI needs to extract, verify, or act on your data.

Recommendation Confidence measures how strongly AI endorses a store when it appears. A weak mention and a first confident recommendation are very different commercial outcomes. Confidence is built through verified reviews, clear policies, and accurate commerce data.

Semantic Clarity measures whether AI can clearly understand a store's product category, target customer, unique positioning, and use case. Generic copy scores zero. Specific positioning scores high.

AI Extractability measures how easily AI can chunk, parse, and extract answers from a store's content. High extractability means AI can pull answers effortlessly. Low extractability means AI skips the store even if the content is good.

Prompt Visibility is whether a brand appears when buyers submit purchase-intent prompts to AI systems. A brand can have a high AI Commerce Score but zero Prompt Visibility if its positioning does not match buyer query language.

Recommendation Velocity measures how quickly a brand's AI Commerce Score and Recommendation Share are changing over time. Positive velocity means the brand is becoming more visible. Negative velocity means competitors are gaining ground.

Recommendation Density measures how broadly a brand appears across adjacent buyer intents and related query clusters. A brand with high Recommendation Density is more resilient to changes in how buyers phrase queries and more deeply embedded in AI recommendation flows.

Hidden Authority describes a store that has real authority and trust signals but scores poorly on AI Extractability. AI may recognize the brand but cannot extract structured answers. The result: AI knows about the brand but cannot confidently recommend it.

Intent Misalignment describes a store that is credible and technically solid, but whose positioning, copy, and content are misaligned with how buyers actually ask AI for recommendations in that category. The fix is content strategy, not technical optimization.

The AI Trust Graph is the network of external signals AI uses to validate a brand's trustworthiness. Reddit mentions, press coverage, review platform presence, citations in articles. AI does not just trust what a brand says about itself.

Recommendation Position tracks where in an AI response a brand appears and with what level of endorsement. The four positions are first recommendation, supporting mention, comparison mention, and weak citation. Position determines conversion quality, not just visibility.

The Recommendation Graph is the network of relationships AI systems build between brands, products, trust signals, citations, buyer intent, and semantic relevance. It is the underlying structure AI uses to decide which brands belong together and which brand best answers a given buyer query.

AI Visibility is the foundational measure of whether an ecommerce store exists in the eyes of AI shopping systems. A store with high AI Visibility is discoverable, interpretable, and surfaceable by AI agents when relevant buyer queries are submitted. AI Visibility is necessary but not sufficient for recommendation. A store can be visible to AI and still not get recommended.

Agent-Readable Commerce is the next layer beyond Machine-Readable Commerce. Where Machine-Readable Commerce ensures AI can parse and understand a store, Agent-Readable Commerce ensures autonomous AI agents can navigate the entire commerce flow end-to-end: from product discovery through price verification through checkout completion. It is the infrastructure requirement of the agentic commerce era.

AI Commerce Infrastructure is the umbrella layer describing the complete technical and semantic stack that enables AI systems to discover, evaluate, trust, and transact with ecommerce stores. It encompasses structured data, trust signals, semantic commerce layers, agent-readable flows, and persistent merchant identity. Atom Foundry maps AI Commerce Infrastructure across the ecommerce internet as the foundation of the AI Commerce Graph.

Agentic Commerce is the emerging model of ecommerce in which AI agents act as autonomous buyers. A human delegates a purchase intent to an AI agent, and the agent independently discovers products, compares stores, evaluates trust and pricing, and completes the transaction. Shopify launched its Agentic Dashboard in May 2026, generating llms.txt, llms-full.txt, and agents.md for every store to enable this model.

AI Discoverability measures the ease with which AI systems can find, parse, and surface a store in response to buyer queries. It is broader than Prompt Visibility, which measures appearance in specific prompts, and broader than AI Visibility, which measures whether AI can read the store. AI Discoverability encompasses the full path from AI crawling to query matching to surfacing in responses.

Merchant Interpretability is the degree to which AI systems can build an accurate, complete understanding of a merchant entity from all available signals. It combines Semantic Clarity, Persistent Merchant Identity, structured data completeness, and external signal consistency. High Merchant Interpretability means every AI system that encounters a brand builds the same accurate understanding of what it sells, to whom, and why.

AI Recommendation Eligibility is the binary state of whether a store has met the minimum requirements to enter AI recommendation flows. It is not a score or a ranking. It is a gate. A store that fails the AI Trust Layer, has no structured data, or is blocked to AI crawlers is ineligible for recommendation regardless of product quality, brand recognition, or marketing spend. Eligibility must be established before any optimization is meaningful.

The Commerce Knowledge Graph is the semantic knowledge structure that AI systems construct to understand the ecommerce internet. It connects merchant entities to product categories, use cases, buyer intents, price points, trust signals, and competitive relationships. Unlike the AI Commerce Graph which maps recommendation flows, the Commerce Knowledge Graph maps semantic understanding. Atom Foundry measures each merchant's position and strength within this graph as part of the AI Commerce Score.

The AI Visibility Layer is the complete stack of technical, semantic, and trust signals that determine how visible and recommendable a merchant is within AI commerce ecosystems. It is the new optimization layer in ecommerce, sitting above SEO and paid acquisition. The AI Visibility Layer includes structured data, crawl accessibility, semantic commerce signals, trust verification, external citation networks, and agent-readable commerce flows.

The Commerce Protocol Layer is the standardized stack of machine-readable files, APIs, and structured formats that allow AI agents to interact with ecommerce stores at the protocol level rather than the HTML level. It includes llms.txt, llms-full.txt, agents.md, Schema.org markup, product feed APIs, and checkout agent APIs. The Commerce Protocol Layer is to AI commerce what HTTP was to the web. It defines the rules by which AI agents and merchants communicate.

AI Readability measures how easily AI crawlers can parse, chunk, and extract meaningful content from a store. It covers heading hierarchy, server-rendered text, FAQ schema, semantic HTML structure, and the absence of JavaScript-only content blocks. High AI Readability means AI agents can extract the store's full signal set on a single crawl pass.

Recommendation Visibility is the binary and graded measure of whether a brand appears when AI systems process a relevant buyer query. Unlike Prompt Visibility which measures appearance rate, Recommendation Visibility focuses on the specific query-response moment. A brand with high Recommendation Visibility is consistently surfaced in the queries that matter most to its niche.

Trusted But Invisible describes a store that has built real-world trust, authority, and brand recognition but is systematically excluded from AI recommendations because its content cannot be extracted by AI crawlers. The problem is not trust. The problem is technical accessibility. It is the most frustrating AI visibility failure because the brand has done the hard work but cannot benefit from it.

Machine Trust Signals are the structured, crawlable signals that AI systems verify before including a store in recommendation flows. They are distinct from visual trust cues that humans see. Machine Trust Signals include AggregateRating schema, return policy text in HTML, SSL certificate presence, legal identity in footer, shipping information in crawlable text, and contact details. They must be machine-readable to count.

The Recommendation Economy is the structural shift from a search-and-click model of commerce to an AI-mediated model where intelligent systems recommend, compare, and transact on behalf of buyers. In the Recommendation Economy, the entity that controls recommendation determines who gets the sale. Google controlled the Link Economy. AI agents control the Recommendation Economy.

The Recommendation Gap is the quantified difference between a brand's current AI Recommendation Share and the share held by the top competitor in its category. It represents the revenue opportunity available through AI Commerce optimization. A large Recommendation Gap means significant revenue is flowing to competitors through AI recommendation channels.

AI Recommendation Rate is the percentage of tested purchase-intent prompts in which a merchant appears as a recommendation, in any position and at any confidence level. It is the broadest measure of AI visibility. A merchant with a high AI Recommendation Rate appears across many queries. A merchant with a low rate is systematically excluded from most recommendation flows in its category.

Recommendation Authority is the aggregate strength of a merchant's AI-verifiable signals across trust, semantic clarity, external validation, and commerce accuracy. A merchant with high Recommendation Authority is not just visible to AI but deeply credible to it. AI systems recommend high-authority merchants first, most often, and with the highest confidence. Recommendation Authority compounds over time as external signals accumulate.

The Recommendation Eligibility Score quantifies how close a store is to the minimum threshold required to enter AI recommendation flows. Unlike the AI Commerce Score which measures overall recommendation quality, the Recommendation Eligibility Score focuses specifically on the gate-level requirements. A store below eligibility threshold gets zero recommendations regardless of its score on other dimensions. The Recommendation Eligibility Score shows exactly how far away eligibility is and what is blocking it.