For E-commerce
The store whose products the AI shopping answer actually names
For Shopify, WooCommerce, BigCommerce and custom-stack store owners who suspect their catalog is missing from ChatGPT Shopping, Google's AI Mode, Perplexity, and Google's Shopping results, and cannot see it in their normal analytics.
Every engagement is directed by a technical specialist and reviewed before delivery.
What this is
When a shopper asks an AI assistant for the best product under a budget, the engine builds its answer from structured product data, your Merchant Center feed, your on-page Product and Offer schema, your GTIN and review fields, not from your homepage. The Ecommerce Visibility System reconciles that data into one consistent, complete story: it closes GTIN and attribute gaps that trigger Google's "limited performance" suppression, makes your feed and your page schema agree with each other everywhere they appear, and stands up a compliant, real-customer review engine that feeds the product card AI surfaces read. It runs as one coordinated build against your Machine-Readiness Score, not a scatter of disconnected feed tweaks. We measure your share of the answer, dated and sampled. We never promise a ranking, a citation, or a sales figure.
The problem
Why ecommerce stores lose here
A shopper used to click to your store and browse the catalog you designed. Increasingly they ask ChatGPT, Perplexity, Gemini or Google's AI Mode for the best product under a budget and get back a short list of specific SKUs with prices and reasons attached. OpenAI's own developer communications, when it relaunched shopping inside ChatGPT in February 2026, named inaccurate product data and merchant onboarding difficulty, not weak demand, as the reason it pulled back from letting shoppers buy inside the chat. That is direct, first-party evidence that clean structured data, not a redesign, is what actually decides whether you are on the list.
The same product frequently lives in three places at once, your Merchant Center feed, your store's own Product schema, and maybe a marketplace listing, each with its own price, availability and title. When those three disagree, engines do not pick a winner. They deprioritize the SKU. Google's own Merchant Center documentation states plainly that a missing GTIN triggers demotion to "limited performance," a direct, non-quantified but unambiguous statement that incomplete feed data suppresses visibility.
Meanwhile the majority of US product search now starts on Amazon rather than Google, per independent survey estimates ranging from roughly 55 to 74 percent, and AI-referred traffic to US retail sites grew 393 percent year over year in Q1 2026, converting 42 percent better than average traffic that March. A store not structured to be legible across these surfaces at once is competing for attention on a shrinking share of where its buyers actually start looking.
The evidence
What the numbers show
OpenAI cited inaccurate product data and merchant onboarding difficulty, not weak demand, as the reason it pulled back from in-chat purchase completion in its 2026 ChatGPT shopping rollout.
established OpenAI developer announcements, 2026-02 and 2026-03; CNBC, 2026-03-24.
AI-referred traffic to US retail sites grew 393% year over year in Q1 2026, and converted 42% better than non-AI traffic in March 2026.
established Adobe Digital Insights, Quarterly AI Traffic Report, 2026.
Products missing a required GTIN are demoted to "limited performance" in Google Merchant Center.
established Google Merchant Center Help, support.google.com/merchants.
Estimates of US shoppers starting product search on Amazon range from roughly 55% to 74% across independent surveys.
emerging eMarketer; Jungle Scout, Amazon Advertising Report, 2026.
Agentic commerce could represent $190 to $385 billion, or 10 to 20%, of US e-commerce spend by 2030.
established Morgan Stanley, Agentic Commerce Market Impact Outlook, 2026.
How it works
The work, made checkable
- 01
Read your ecommerce Machine-Readiness Score first
We audit your Merchant Center feed against Google's specification, your on-page Product and Offer schema, your GTIN and attribute completeness, and your presence in AI answers for a frozen panel of real buyer questions in your category. We set scope in writing from this reading before anything is touched.
- 02
Reconcile your feed and your on-page schema into one story
We rebuild titles to lead with what a shopper actually searches, fill the attributes Google reads to match and rank products, close GTIN and identifier gaps, and make your feed price and availability match your page schema exactly, so no engine has a reason to distrust either one.
- 03
Clear disapprovals and structural blockers at the root
A blank GTIN field, a price mismatch, a stale availability status, a policy-triggering word in a title, each suppresses a product or puts your whole account at risk. We find and fix the cause, not just the symptom Google's dashboard flags.
- 04
Stand up compliant, real-customer reviews
Review fields are structured data AI shopping surfaces read directly. We request reviews from your real customers at the right moment, monitor what arrives, and never buy, incentivize, or gate them, in line with the FTC's 2024 rule against fake and suppressed reviews (16 CFR Part 465).
- 05
Measure your share of the answer, with the method disclosed
We track your presence across ChatGPT, Perplexity, Gemini, Copilot and Google AI Mode for a frozen panel of real category questions, and report it as a share-of-answer rate with a confidence band, dated and sampled, never as a promised citation.
- 06
Hold and compound the position
Feeds drift, reviews decay in freshness, and AI engines change how they summarize products month to month. On the Catalog Care retainer, we re-read your Machine-Readiness Score on an agreed cadence and keep the reconciliation current as your catalog changes.
Included
What is delivered
- Ecommerce Machine-Readiness Score read across feed, schema, reviews and technical foundation, with your closest competitors scored alongside on the same category-question panel.
- Google Merchant Center feed audit and remediation: titles, attributes, GTIN and identifiers, price and availability sync.
- Product and Offer schema engineering on your store's pages, reconciled to match your feed exactly.
- Disapproval root-cause resolution across your catalog, not a symptom-only fix.
- Compliant real-customer review acquisition and monitoring, built to FTC 16 CFR Part 465.
- Share-of-answer tracking across ChatGPT, Perplexity, Gemini, Copilot and Google AI Mode for a frozen panel of real buyer questions, sampled and dated.
- A ranked fix list you keep, and, on the Catalog Care retainer, a specialist-reviewed report on the agreed cadence.
The outcome
What it moves
- A Merchant Center feed and on-page Product/Offer schema that agree with each other everywhere they appear, so no engine has a reason to distrust either one.
- GTIN and attribute gaps closed across your listed products, clearing the specific cause of Google's "limited performance" suppression.
- A compliant, real-customer review engine feeding the product-card data AI shopping surfaces read directly, never bought or incentivized.
- A measured, dated read of your share of the answer across major AI shopping surfaces, benchmarked against your closest competitors.
- A catalog structured to be legible across your own site, Google's product graph, and marketplace-adjacent surfaces at once, not optimized for one channel at the expense of the others.
- A ranked fix list you keep, and, on the retainer, ongoing maintenance as your catalog, pricing and reviews evolve.
Straight answers
Questions
You are based overseas. How do you engineer feed and schema work for a US ecommerce store?
Raveneye Global is operated by RavenGroup Global Tech Private Limited, and billing runs in USD. The work is engineering your Merchant Center feed, your on-page schema, and your review system, all done on your own store and listings, to Google's and the FTC's published standards. A technical specialist directs and reviews every project before delivery.
Is any of the product data work synthetic or automated without review?
No. The work is expert-led and specialist-reviewed. We use proprietary technology to audit your feed and schema faster and more precisely, but a specialist directs every fix, every schema change, and every review-flow decision, and checks the delivery before it ships. Feed and schema work is exactly where an unreviewed automated pass can create disapprovals rather than fix them.
Can you guarantee my products will appear in Google Shopping or an AI shopping answer?
No. AI-answer selection is undocumented and changes constantly, and Shopping eligibility depends on factors including your own pricing and inventory that are outside any agency's control. We engineer every signal that can legitimately be moved, then measure the result as a share-of-answer rate with a confidence band. We never promise a ranking, a citation, or a sales number.
Why is this scoped instead of a fixed price?
Because no two catalogs arrive in the same state. A 40-product feed with clean data is a different job from a 5,000-SKU catalog with hundreds of disapprovals and three conflicting price sources. We publish the full deliverables here, read your actual feed and schema, then agree the exact figure in writing before anything starts.
Do you also handle Amazon or marketplace listing optimization?
This system engineers your own site's data, Google's product graph eligibility, and AI shopping surface visibility, which share a common data foundation. Dedicated Amazon marketplace ranking, which weighs different signals like sales velocity and on-listing conversion, is a related but distinct discipline; we scope and discuss it if it is relevant to your business rather than treating the two as identical work.
Provenance
Sources
- OpenAI developer announcements, 2026-02 and 2026-03; CNBC, "OpenAI revamps shopping experience in ChatGPT after struggling with Instant Checkout," 2026-03-24 (established)
- Adobe Digital Insights, Quarterly AI Traffic Report, 2026 (established)
- Google Merchant Center Help, GTIN [gtin] and Limited performance due to missing value: GTIN, support.google.com/merchants (established)
- Federal Trade Commission, Rule on the Use of Consumer Reviews and Testimonials, 16 CFR Part 465, effective 2024 (established)
- eMarketer; Jungle Scout, Amazon Advertising Report, 2026 (emerging)
- Morgan Stanley, Agentic Commerce Market Impact Outlook, 2026 (established)