Check your Machine-Readiness Score

By Industry

The store whose products the AI shopping answer actually names

For online store owners and ecommerce operators on Shopify, WooCommerce, BigCommerce or a custom stack who are watching shoppers ask an assistant for a product instead of browsing to their site, and who suspect their catalog is missing from the answer.

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Every engagement is directed by a technical specialist and reviewed before delivery.

What this is

The Ecommerce Visibility System is our done-for-you program for online stores that now get shortlisted, or skipped, inside an AI shopping answer before a buyer ever loads a product page. We engineer the thing those answers are actually built from: clean, complete, corroborated product data. We reconcile your Google Merchant Center feed with the Product, Offer and Review schema on your store's pages so they tell one identical story, close GTIN and attribute gaps, make your catalog eligible across the agentic surfaces that now recommend products, and stand up compliant, real-customer reviews that feed the product card. It is one coordinated build run against your Machine-Readiness Score. The result is a catalog that reads as one trustworthy source, shows up cleanly where product decisions now happen, and converts the shoppers who still click. Reviews come from real customers only, under FTC rules. We measure and move your share of the answer. We never promise a ranking or a sales figure.

The problem

Why this matters now

A shopper used to start at Google, click to a store, and browse the catalog it designed. Increasingly they start by asking ChatGPT, Perplexity, Gemini or Google's AI Mode for the best product under a budget, with the constraints they care about, and get back a short list of specific items with prices and reasons. When none of your SKUs are on that list, the cause is rarely being out-priced or out-reviewed. You were never in the consideration set, and nothing in your analytics will show that it happened.

For a good-looking store, the reality is that AI shopping does not browse a site the way a human does. It does not read lifestyle photography or brand story. It reads structured product data: the feed, the Product and Offer schema, titles, GTINs, availability, price and review fields. A store with beautiful pages and a thin, stale feed loses to a plainer competitor whose product data is complete and accurate. Feed quality has quietly become infrastructure, and it is the least glamorous, least maintained part of most online stores.

Then there is the split-surface problem. The same product can live on your Shopify store, in Google Shopping, and maybe on Amazon or a marketplace, reading three different ways with three different titles and sometimes three different prices. When your feed and your on-page schema disagree on price, availability or rating, engines do not pick a winner. They distrust both and quietly deprioritize the SKU, so an unnoticed conflict costs you across every surface at once.

None of this is a traffic problem or an ad-budget problem. It is a data-integrity and structure problem. The exact signals that decide whether products are recommended, a clean feed, matching schema, complete attributes, real reviews and a fast product page, are the ones an online store is least likely to have anyone actively maintaining.

How it works

The mechanism, made checkable

  1. 01

    We read your commerce surface first

    We run your Machine-Readiness Score with weight on the pillars that decide a product decision, AI Answers and Share-of-Answer and the Technical Foundation, then audit your Google Merchant Center feed health, the on-page Product, Offer and Review schema, your GTIN and attribute coverage, and where your catalog does or does not appear across a frozen panel of the real product questions your buyers ask. We set scope in writing from this reading before anything is touched.

  2. 02

    We make your catalog one source of truth

    The single most damaging thing this work turns up is disagreement: the feed says one price, the page schema says another, the marketplace listing says a third. We reconcile your Merchant Center feed and your on-page Product and Offer schema so they pull from the same source of truth on price, availability and rating. When the feed and the page agree, engines trust the SKU. When they conflict, engines deprioritize both, which is the quiet leak we close first.

  3. 03

    We close the product-data gaps AI shopping reads

    AI shopping does not reward creative pages, it rewards complete, structured data. We fill the fields engines weight: precise titles, GTINs where a known brand requires them, brand, availability, accurate pricing, high-quality image references, and the recommended attributes most feeds leave blank, including review fields and a buyer question-and-answer block. Google requires a GTIN for products from known brands, and per Google Merchant Center guidance reported through 2026, valid GTINs are associated with materially higher impressions and clicks. We treat that as a documented lever, not a promise.

  4. 04

    We make your catalog eligible across the agentic surfaces

    Each AI shopping surface ingests product data its own way. We make your catalog eligible where it belongs: through Google's product graph that now powers AI Mode and agentic flows, through OpenAI's Agentic Commerce Protocol or Shopify's agentic storefront syndication for ChatGPT Shopping, and through the merchant programs that Perplexity and others run. We map which surfaces your buyers actually use, and prioritize eligibility there, rather than chasing every protocol at once.

  5. 05

    We turn real reviews into a product-card signal

    On an AI product card, ratings and review text are a ranking and trust input, not decoration. We stand up a compliant system that requests reviews from real customers at the right moment, captures them into the review markup and feed fields, and answers them in your voice. We earn reviews from genuine customers only, never fabricate them, incentivize for positivity, or gate them to hide criticism, in line with FTC rules. Steady, legitimate review velocity is what compounds on the card; manufactured reviews are a liability we will not create.

  6. 06

    We measure share-of-answer, hold and compound

    We re-read your Machine-Readiness Score on an agreed cadence, tracking how often your products appear across the frozen question panel, per engine, with the engine, locale and date stamped on every reading. Because answer engines are not deterministic, we report appearance as a rate with a confidence band, not a single number. Catalogs drift, prices move and surfaces change monthly, so a visibility win is a position to hold, not a build to shelve.

What is included

What is delivered

  • Commerce Machine-Readiness Score read across the pillars that decide a product decision, with named competitors scored on the same buyer-question panel.
  • Google Merchant Center feed audit and remediation: titles, GTINs, brand, availability, pricing accuracy, image references and the attributes most feeds leave incomplete.
  • Product, Offer and Review schema engineering on your product pages, reconciled with the feed so both pull from one source of truth on price, availability and rating.
  • GTIN and attribute coverage build across your catalog, prioritized on your top revenue-driving SKUs first.
  • Eligibility setup across the agentic surfaces that fit your store: Google's product graph and AI Mode, ChatGPT Shopping via the Agentic Commerce Protocol or Shopify agentic storefronts, and the relevant merchant programs.
  • A buyer question-and-answer block and review fields structured into your product data so the fields AI product cards weight are actually present.
  • Compliant, real-customer review acquisition, capture into the review markup, and response in your voice, kept inside FTC and platform rules.
  • Share-of-answer tracking across a frozen panel of your business's real product questions, sampled per engine and dated, with variance shown.
  • A ranked catalog fix list you keep, and, on the retainer, a reviewed report on the agreed cadence as SKUs, prices and stock change.

The outcome

What it moves

  • A product feed and on-page schema that agree with each other on price, availability and rating, so engines trust your SKUs instead of quietly deprioritizing them for conflicting data.
  • Complete, structured product data across your catalog, with GTIN and attribute gaps closed and the recommended fields most feeds leave blank actually filled, engineered to how AI shopping reads a catalog rather than how a human reads a page.
  • Catalog eligibility across the agentic surfaces your buyers use, from Google's product graph and AI Mode to ChatGPT Shopping and merchant programs, prioritized where it matters rather than chased everywhere at once.
  • A measured read of how often your products appear in AI shopping answers, reported per engine as a rate with a confidence band and stamped with engine, locale and date, so you finally know whether your catalog is in the consideration set.
  • A steady flow of reviews from real customers, captured into your product markup and answered in your voice, so the rating on your product card reflects genuine sentiment under FTC rules.
  • A commerce surface that stays current on cadence, because we maintain your feed, your schema and your reviews as new SKUs, prices and stock changes flow through, not abandon them after launch.

What you get

What you get, and how it is priced

The Ecommerce Visibility System runs at two levels: a one-time Catalog Foundation build that fixes and engineers your product data, feed and eligibility across the surfaces that matter, and an ongoing Catalog Care retainer that holds and compounds it, because prices change, stock moves, SKUs get added, and the agentic surfaces keep changing what they read. We scope both against your Machine-Readiness Score before any work is committed. Below is what each level covers, how we produce the outcome, and the deliverables inside it.

Catalog Foundation (one-time build). The whole product-data surface, fixed and engineered once. Commerce Machine-Readiness Score read, Merchant Center feed remediation, Product and Offer and Review schema reconciled with the feed, GTIN and attribute coverage across the catalog, eligibility setup on the agentic surfaces that fit your store, and the stand-up of compliant real-customer review capture. You finish with a catalog that reads as one trustworthy source and a ranked fix list you keep. Best when the feed and schema have drifted apart and need to be put right before anything is maintained. Scoped in writing against your Machine-Readiness Score.Quoted
Catalog Care (ongoing retainer). The standing retainer that holds and compounds visibility after the build. Continuous feed and schema hygiene as SKUs, prices and stock change, ongoing review acquisition and response, eligibility monitoring as the agentic surfaces change what they read, and a Machine-Readiness Score re-read with share-of-answer tracking on an agreed cadence. Month to month, no lock-in, cancellable in the same number of steps it took to start. Best for active catalogs where the product data changes constantly and needs someone maintaining it. Scoped in writing.Quoted

You see the full deliverables and cadence first, then a price built for your business, confirmed in writing.

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Straight answers

Questions about Ecommerce Visibility System

AI shopping pulls from a feed, so why not just export a feed and be done?

Because an exported feed is where the problem starts, not where it ends. Most feeds we read in this work have missing GTINs, thin or duplicated titles, blank recommended attributes, and prices or availability that disagree with the very schema on your store's own product pages. When the feed and the page contradict each other, engines distrust both and quietly deprioritize the SKU. Our work is reconciling the two into one source of truth, filling the fields AI product cards actually weight, and keeping them accurate as your catalog changes. A raw export does none of that.

Is any of this synthetic feed spam or churned-out listings?

No. The work is expert-led and human-reviewed. A technical specialist directs and reviews the work before delivery. We use proprietary technology to read your catalog and feed faster and more precisely, but a specialist directs the feed decisions, the schema reconciliation, the attribute work and every review response, and checks the delivery before it ships. Sloppy, conflicting product data is exactly what gets a SKU distrusted by engines, which is the opposite of what this work exists to fix.

We sell on a marketplace and on our own store. Does this help both?

It helps the surfaces you own and the data engines read about you everywhere. An Amazon or marketplace algorithm is outside anyone's control, but we can make your owned catalog, your Merchant Center feed and your product-page schema tell one consistent story, which is what the AI shopping surfaces and Google's product graph read. When your store is the clean, corroborated source of truth on a product, it holds a stronger position across every surface that references it. We scope which surfaces your buyers actually use, and set priority there.

Why is this scoped instead of a fixed price?

Because a catalog is not standard. One store has 40 clean SKUs and needs a tune, another has twelve thousand with missing GTINs, conflicting titles and a feed that has never been reconciled with its own pages. Publishing a single number for both would be a fiction, and quoting by how big a catalog looks would be dishonest. We publish the full deliverables and cadence here, read your commerce surface, then confirm the exact figure in writing.

How do you measure whether my products show up in AI answers?

We fix a panel of the real product questions your buyers ask, phrased the way they phrase them, and run it across the engines those buyers use, many times, because a single check is worthless when the engines are not deterministic. We report how often your products appear as a rate with a confidence band, stamped with the engine, locale and date. Engines also differ sharply in how they surface and cite products, which is exactly why we sample per engine rather than checking once. We report movement plainly, including where it is flat.

Can you guarantee I get recommended by ChatGPT or ranked in Google Shopping?

No system can promise that. AI product selection is undocumented and volatile, engine behavior changes, and eligibility rules shift. We commit to engineering every signal that can be legitimately moved, clean feed, reconciled schema, complete attributes, real reviews and a fast product page, and to measuring the result with variance. We never promise a recommendation, rank, or sales figure, because those depend on engines and buyers no outside firm controls.

My product pages look great already. Is that not enough for AI shopping?

Beautiful pages and readable product data are different things, and AI shopping reads the second. The surfaces that now recommend products lean on structured data, the feed and the Product, Offer and Review schema, far more than on visual design or lifestyle copy. Per analyses reported through 2026, plainer stores with complete, accurate product data routinely appear in AI recommendations ahead of larger sites with creative but poorly structured content. We leave your design as it is; we engineer the data underneath it, so both the engine and the human who clicks are served.

Do reviews really affect whether my products get recommended?

On an AI product card, ratings and review text are a trust and ranking input, not decoration, and the recommended product-data fields include review fields for exactly that reason. We stand up a system that earns reviews from real customers at the right moment, captures them into the review markup, and answers them in your voice. They must be genuine: never bought, incentivized for positivity, or gated to suppress criticism, per FTC rules. Legitimate review velocity is what compounds on the card, and manufactured reviews are a liability we will not create.

Related

Where this connects

AI Answer & GEO System

When the priority is being named and recommended inside generative answers, the program that engineers the entity, content and structured-data signals AI engines draw on, sampled and measured as share of answer.

Explore

Reputation Engine

Because ratings and review text are a ranking input on the AI product card, the compliant real-customer review acquisition and response system, kept inside FTC and platform rules.

Explore

Surface Intelligence Audit

Most catalogs start here: a scored read of exactly where your products stand across all four pillars, returned as a ranked fix list that tells you whether your feed and data are the gap.

Explore

Provenance

Sources

  • OpenAI Developers, Agentic Commerce, product feed specification (required attributes item_id, title, price, availability, brand, image_url; recommended gtin, review fields and a question-and-answer block), developers.openai.com, accessed July 2026.
  • Google Merchant Center Help and guidance on product data and GTIN requirements for products from known brands, with reported impression and click uplift for valid GTINs, as summarised by industry guides in 2026; treated as directional, not a guarantee.
  • Google Search Central and Google Merchant Center, product feed as the interface to Shopping, Performance Max, AI Mode and agentic (Universal Commerce Protocol) eligibility; feed and on-page schema must agree on price, availability and rating, 2026.
  • Aggarwal and colleagues, GEO: Generative Engine Optimization, KDD 2024, arXiv:2311.09735 (peer-reviewed): structured content and entity signals as levers for inclusion in generated answers, applied here as direction, not guarantee.
  • US Federal Trade Commission, Rule on the Use of Consumer Reviews and Testimonials, 16 CFR Part 465, effective 2024: prohibits fake, incentivized-for-positivity and suppressed reviews.

Begin with where the business stands.

Submit a query See the method

No obligation. The deliverable is a measured starting position and the corrections that move it most.