Free Amazon AI Readiness Score

On May 13, 2026, Amazon retired the Rufus brand and folded its capabilities into Alexa for Shopping — the AI assistant that more than 250 million shoppers now use to discover products, compare options, and get recommendations. The name changed, but the underlying shift did not: a growing share of Amazon discovery no longer happens through a keyword search box. It happens through a conversation, where a shopper describes a need in plain language and the assistant decides which products it can confidently recommend. We wrote up the full transition in Amazon retires Rufus and merges it into Alexa for Shopping, and it is worth reading alongside this score.

"AI readiness" is simply a measure of how well your listing is structured for that conversation. A ready listing gives Alexa for Shopping enough explicit, evidenced context to recommend the product with confidence when a shopper's described intent matches it. An unready listing — even one that ranks well in traditional search — leaves the assistant guessing, and an assistant that has to guess usually recommends something else. The score on this page estimates that readiness on a 0–100 scale by reading your public listing text the way Amazon's AI systems do.

This is a content and semantics score, not an account-health or advertising score. It does not look at your sales, your reviews, or your ad spend. It looks only at whether the words on your listing establish the relationships and answer the questions that Alexa for Shopping and COSMO rely on. That makes it something you can act on directly — every gap it surfaces is a sentence you can add or rewrite.

For years the goal of Amazon listing optimization was keyword coverage: stuff the title, bullets, and backend with every phrase a shopper might type, and let the legacy A9 algorithm match the query to the text. That logic still drives some traffic, but it is not how COSMO works. COSMO is Amazon's semantic knowledge graph — it maps a product to shopper intent through explicit relationships, not through keyword frequency. It wants to know who the product is for, what it is used with, what it replaces or improves on, and what problem it solves. A keyword can appear a dozen times and still establish none of those relationships.

This is why a listing can rank on page one for hundreds of search terms and still score poorly here. The keyword density that satisfies A9 says nothing about whether the listing has told COSMO that this spatula is "for left-handed bakers," that it is "used with cast-iron and nonstick cookware," or that it "solves the problem of melting when you flip on high heat." Without those evidenced relationships, the product is invisible to a meaningful set of conversational queries — the very queries that are growing fastest. A high keyword rank and a low readiness score are not a contradiction; they are two different systems measuring two different things, and only one of them is where Amazon is heading.

The practical takeaway is that keyword optimization and AI readiness are complementary, not interchangeable. You still want to rank. But ranking buys you a spot in a results page that fewer shoppers scroll through every quarter, while readiness buys you a place in the assistant's short list of confident recommendations. The listings that win the next few years will do both, and most listings today do only the first.

The overall readiness number is a weighted blend of three categories, each scored from 0 to 100. The first and heaviest is COSMO Semantic Mapping. This checks whether your listing establishes the seven semantic relationships COSMO uses to connect a product to shopper intent: who it is for (Target Audience), what it does (Function / Use), where and when it is used (Use Context / Occasion), how it relates to alternatives (Compared To), what it pairs with (Used With), its concrete makeup (Material / Attribute), and the pain it resolves (Problem Solved). Coverage and the strength of the evidence both count. In practice, Target Audience, Compared To, and Used With are the relationships most listings are missing entirely, so their absence shows up clearly.

The second category is Alexa for Shopping Q&A Coverage. Here the analysis first generates the realistic buyer questions a shopper would actually ask Alexa for Shopping about a product in your category — questions about material and composition, size and fit, care and maintenance, compatibility, what's in the box, and intended use — and then checks whether your listing answers each one. A clear answer earns full credit, a vague or implied answer earns partial credit, and a missing answer earns none. Every unanswered question is a moment where the assistant has to say "I'm not sure" about your product to a ready-to-buy shopper.

The third category is Relationship Depth. For the relationships that are present, this asks whether there is enough concrete evidence — specific claims, real use cases, named complements — to map the product confidently, or whether it is just a thin keyword mention. A listing that name-drops "for the whole family" technically touches Target Audience but maps nothing; depth separates the relationships that genuinely inform a recommendation from the ones that only look like they do. The three categories together produce a single honest picture of how ready your words are for an AI shopping conversation.

Because this tool reads only public listing data, it works on any ASIN — including the competitor sitting directly above you in the search results. That turns the score from a self-audit into competitive intelligence. Run the ASIN of the product you are losing to and you will see exactly which semantic relationships it has established that you have not, and which buyer questions it answers that your listing leaves open. The gap between your score and theirs is, quite literally, a list of the sentences standing between you and the recommendation.

The competitor angle also reveals where a whole category is weak. If you score the top three listings for your main keyword and all three are thin on Compared To and Used With, that is an opening: the first listing to establish those relationships convincingly becomes the assistant's default recommendation for a large set of conversational queries that nobody is currently answering well. AI readiness is still new enough that most categories are full of high-ranking, low-readiness listings — and that is exactly the kind of asymmetry worth acting on early.

A low score is not a verdict on your product — it is a map of unwritten context. Start with the handful of highest-impact opportunities the breakdown surfaces, because they are ordered by impact for a reason. More often than not the biggest wins come from the three relationships most listings skip: name the buyer explicitly instead of writing "for everyone," state what the product is used alongside instead of describing it in isolation, and draw an honest comparison to the alternative the shopper is weighing. Each of these is usually one or two added sentences, not a rebuild.

From there, work through the buyer questions your listing answers with "no" or "partial." Every one of those is a concrete sentence you can add — the material it is made from, whether it fits a specific model, how to care for it, what arrives in the box. You are not inventing claims; you are surfacing facts you already know about your own product that the listing simply never stated. The point throughout is to replace keyword mentions with evidenced context: not "durable, premium, versatile" but "the 304 stainless steel head withstands 425°F, so it won't warp the way nylon does when you sear." That is the difference between a word COSMO can index and a relationship it can recommend on.

If the rewrite feels like a lot of careful, evidence-driven copy, that is the work SellerForge's Listing Builder is built for. It rewrites your title, bullets, and description to establish every missing relationship and answer the questions Alexa for Shopping asks, then re-scores the result so you can see the before and after. But you do not need a tool to start — the breakdown on this page is a complete, specific to-do list, and the first three fixes alone usually move the number meaningfully.