Every signal each layer of the framework measures, from AI Readability through Recommendation Intelligence to Revenue. Choose a layer on the left to read it.
Can AI read your business?
AI systems cannot understand what they cannot read. This layer measures how accessible and machine-readable your business is across five signals: Structured Data, Product Data, Content Structure, Crawlability, and Accessibility.
Readability is the foundation layer of the AI Commerce Intelligence Framework™. If AI cannot read this, nothing above it can follow, whether that is understanding, trust, or recommendation.
Can AI understand your structured information?
Structured Data measures how effectively products, pages, reviews, organizations, and business entities are described using machine-readable markup. AI systems rely on structured signals to identify entities, attributes, relationships, and business context.
Why it matters
Without structured data, AI has to infer facts from layout and prose. With it, the same facts are delivered as explicit, machine-readable statements.
What it measures
Schema Markup, Product Schema, Organization Schema, Review Schema, and Entity Relationships.
Can AI extract product information accurately?
Product Data measures the completeness and quality of product information available to AI systems. Missing attributes, weak descriptions, and incomplete specifications reduce machine understanding.
Why it matters
If core product attributes are missing or buried, AI cannot represent your product accurately, and an incomplete product is hard to match to a buyer's request.
What it measures
Product Attributes, Product Specifications, Product Descriptions, Pricing Information, and Availability Signals.
Can AI parse your content efficiently?
Content Structure measures how clearly information is organized within the HTML document. Well structured content improves extraction, chunking, and retrieval.
Why it matters
Clean structure lets AI chunk and retrieve the right passage. A flat or chaotic document forces guesswork and weakens extraction.
What it measures
Heading Hierarchy, Content Segmentation, Semantic HTML, Information Density, and Content Organization.
Can AI agents access your content?
Crawlability measures whether AI crawlers can discover, access, and retrieve content without restrictions.
Why it matters
If AI crawlers are blocked or cannot reach a page, nothing on it counts. Access is the precondition for everything else.
What it measures
Robots Accessibility, Crawl Depth, Internal Linking, URL Structure, and Indexability.
Can AI and humans access the same information?
Accessibility measures whether content is available regardless of device, browser, or assistive technology. Accessible websites are often easier for machines to process.
Why it matters
Content that depends on rendering or interaction to appear is often invisible to machines. Accessible content tends to be machine-readable content.
What it measures
Alt Text, Accessibility Standards, Navigation Structure, Readability, and Content Availability.
Can AI understand what you sell?
Being readable does not mean being understood. A system can see your product page and still misread what you sell, who it is for, or when it should be recommended. This layer measures whether AI correctly interprets your business across five signals: Product Clarity, Category Clarity, Entity Recognition, Semantic Consistency, and Intent Alignment.
AI Understanding sits between readability and trust. AI cannot trust or recommend a business it does not correctly interpret.
Does AI understand what you sell?
Product Clarity measures how clearly products are described, categorized, and differentiated, so AI can tell exactly what you sell and who it is for.
Why it matters
If AI cannot tell exactly what a product is and who it is for, it cannot match it to the right buyer questions, no matter how readable the page is.
What it measures
Product Definition, Use Case Clarity, Differentiation, Feature Description, and Target Customer.
Does AI understand your category?
Category Clarity measures how effectively products and services are mapped into recognized categories that AI already understands.
Why it matters
Being mapped to the wrong category, or no category, means AI never considers you for the questions that belong to your category.
What it measures
Category Mapping, Taxonomy Alignment, Subcategory Coverage, Catalog Structure, and Category Signals.
Does AI recognize your business as an entity?
Entity Recognition measures whether AI systems consistently identify and connect your brand, products, founders, and business as one entity.
Why it matters
If AI does not recognize your brand as a stable entity, it cannot connect your products, reputation, and mentions into one understanding.
What it measures
Brand Entity, Product Entities, People Entities, Entity Linking, and Knowledge Graph Presence.
Does your information remain consistent across sources?
Semantic Consistency measures whether descriptions, categories, messaging, and attributes remain aligned across your pages and the wider web.
Why it matters
When your descriptions and claims contradict each other across pages and sources, AI builds a blurry, low confidence picture of your business.
What it measures
Description Consistency, Messaging Alignment, Attribute Consistency, Cross-Page Consistency, and Cross-Source Consistency.
Does AI connect you to the right customer intent?
Intent Alignment measures how effectively your business appears for the relevant questions, needs, and buying situations of your category.
Why it matters
Matching keywords is not the same as matching intent. Alignment decides whether you appear for the situations that actually lead to a purchase.
What it measures
Query Coverage, Use-Case Coverage, Need Matching, Buying-Situation Fit, and Intent Breadth.
Can AI trust your business?
Trust has always mattered. The difference now is that machines evaluate it alongside humans. This layer measures whether a business is trustworthy across five signals: Reviews, Brand Mentions, Authority Signals, Reputation, and Consistency.
Trust is the gate before recommendation. Weak signals here pull a business out of the running even when everything below is strong.
Do customer reviews strengthen AI trust?
Reviews measures the quality, quantity, recency, and consistency of customer reviews available to AI in a machine-readable form.
Why it matters
Machine-readable reviews give AI verifiable evidence of satisfaction. Star images a human sees do not count unless AI can parse them.
What it measures
Review Volume, Review Quality, Review Recency, Rating Consistency, and Review Schema.
Is your business discussed across the web?
Brand Mentions measures third party references, citations, discussions, and mentions of your business across the web.
Why it matters
AI learns from the whole web. A brand discussed across credible sources reads as real and established, while one with no footprint reads as unverified.
What it measures
Third-Party References, Media Citations, Community Discussions, Mention Volume, and Mention Quality.
Does AI view your business as authoritative?
Authority Signals measures expertise, recognition, backlinks, citations, and industry presence that mark your business as credible.
Why it matters
Authority tells AI you are a legitimate, recognized option in your category, which raises the baseline trust before any recommendation.
What it measures
Expertise Signals, Industry Recognition, Backlink Authority, Citations, and Domain Authority.
Does AI perceive your brand positively?
Reputation measures sentiment, customer satisfaction, complaints, and public perception as AI can read them.
Why it matters
Sentiment and unresolved complaints shape how safe AI considers it to recommend you to a buyer.
What it measures
Sentiment, Customer Satisfaction, Complaint Signals, Public Perception, and Reputation Stability.
Are your trust signals consistent everywhere?
Consistency measures whether business information, claims, branding, and customer experience stay aligned everywhere AI encounters you.
Why it matters
Inconsistent identity, claims, or contact details look like risk to a machine and undermine trust even when each individual signal is fine.
What it measures
Business Information, Claim Consistency, Branding Consistency, Contact Consistency, and Experience Consistency.
Will AI recommend you?
This is where commerce changes. Recommendation is the moment a system decides one option is better than another for a specific intent. A business recommended consistently gains a powerful advantage, not because it gets more traffic, but because it becomes the answer. This layer measures that advantage across five signals: Recommendation Frequency, Recommendation Position, Recommendation Share, Competitor Comparison, and Intent Match.
Recommendation Intelligence is the core of the framework. This is where readability, understanding, and trust turn into a real commercial outcome.
How often is your business recommended?
Recommendation Frequency measures how frequently AI systems suggest your brand when answering relevant buyer questions.
Why it matters
Frequency is how often you are in the room when buyers ask. Low frequency means most of the demand in your category is decided without you.
What it measures
Appearance Rate, Prompt Coverage, Recommendation Volume, Repeat Recommendation, and Category Frequency.
Where do you appear in recommendations?
Recommendation Position measures your recommendation ranking and prominence within an AI answer.
Why it matters
Being named first and confidently is worth far more than a weak afterthought. Position changes how much an appearance is actually worth.
What it measures
Rank Position, First-Pick Rate, Prominence, Endorsement Strength, and Placement Context.
How much recommendation market share do you own?
Recommendation Share measures the percentage of recommendations captured relative to competitors. It is the AI-era equivalent of market share.
Why it matters
Recommendation Share turns visibility into a single comparable number against competitors, the AI-era equivalent of market share.
What it measures
Share of Voice, Competitive Share, Category Share, Intent Share, and Share Trend.
How do competitors outperform you?
Competitor Comparison measures the recommendation gaps between your business and the competitors AI surfaces alongside or instead of you.
Why it matters
Knowing where competitors are recommended instead of you shows exactly where the recommendation gap, and the revenue opportunity, sits.
What it measures
Recommendation Gap, Head-to-Head Rate, Competitor Set, Win Rate, and Gap Drivers.
For which intents are you recommended?
Intent Match measures your recommendation performance across the different buying intents in your category.
Why it matters
Appearing for the right buying intents matters more than raw volume. Recommendations on high-intent questions are the ones that convert.
What it measures
Intent Coverage, High-Intent Match, Intent Relevance, Buying-Stage Fit, and Intent Gaps.
Will customers feel confident enough to buy?
Recommendation alone is not enough. The customer still has to decide. When confidence is low, hesitation appears, and hesitation kills conversions. Most conversion problems are really confidence problems: people are not saying no, they just are not sure enough to say yes. This layer measures that confidence across five signals: Trust Signals, Clarity, Proof, Risk Reduction, and Decision Friction.
Decision Confidence is the last layer before purchase. A recommended business can still lose the sale here.
Do customers feel safe choosing you?
Trust Signals measures the visible trust indicators that reduce uncertainty at the moment of decision.
Why it matters
Visible proof of legitimacy at the moment of decision removes the doubt that quietly kills conversions.
What it measures
Trust Badges, Policy Visibility, Guarantees, Security Signals, and Social Proof.
Can customers understand your value instantly?
Clarity measures communication clarity and how instantly a customer understands your offer and value.
Why it matters
If a buyer has to work to understand the offer or price, hesitation creeps in. Clarity removes the friction of figuring you out.
What it measures
Value Proposition, Offer Clarity, Pricing Clarity, Messaging Clarity, and Information Findability.
Can customers verify your claims?
Proof measures the evidence, testimonials, case studies, and validation a customer can use to verify your claims.
Why it matters
Claims without evidence ask buyers to take a risk on faith. Proof lets them verify before committing.
What it measures
Testimonials, Case Studies, Evidence and Results, Third-Party Validation, and Demonstrations.
How much purchase risk do you remove?
Risk Reduction measures the guarantees, policies, transparency, and confidence boosters that lower perceived purchase risk.
Why it matters
Returns, guarantees, and support lower the perceived cost of being wrong, which makes saying yes easier.
What it measures
Return Policy, Guarantees, Transparency, Support Availability, and Confidence Boosters.
What prevents customers from saying yes?
Decision Friction measures the obstacles, confusion, hesitation, and purchase barriers that prevent customers from saying yes.
Why it matters
Every unnecessary step or unanswered question is a chance to abandon. Reducing friction protects the sale you were recommended into.
What it measures
Checkout Friction, Information Gaps, Confusion Points, Hesitation Triggers, and Purchase Barriers.
The transaction. The point where readability, understanding, trust, recommendation, and confidence turn into a real buying action.
Purchase does not carry its own set of signals to optimize. It is the outcome of every layer above it working together, the moment all six layers of the framework either close the sale or let it slip.
The outcome. Sustainable revenue is the result of being chosen, again and again, by both humans and the AI systems guiding them.
Revenue is not a separate skill to master. It is the compounding effect of being readable, understood, trusted, recommended, and chosen with confidence, repeated across every AI system and every buyer question in your category.
Find out exactly what AI agents see when they evaluate your store against all seven layers of the framework.
Illustrative example · single-site signal for atomfoundry.dev.
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