A Shopify-first tool that audits and fixes product catalog gaps that make merchants invisible to AI shopping agents.
Added Jun 22, 2026
Merchants are discovering that AI shopping agents do not evaluate stores the same way humans or search engines do. Missing GTINs, weak variant data, incomplete schema, stale inventory, unclear shipping rules, and absent protocol support can cause products to be skipped or converted poorly inside ChatGPT, Gemini, Copilot, and other agentic shopping surfaces. Standard ecommerce analytics also do not show whether products are being cited, recommended, displaced by competitors, or passed into checkout by AI agents.
Build a Shopify app that scans product catalogs for agent-readiness issues and produces a prioritized fix queue for schema, variants, identifiers, inventory, pricing, shipping, discounts, and checkout rules. The first version can connect to Shopify Admin APIs?, read product and order data, inspect storefront schema, and generate product-level remediation tasks. Later versions can monitor AI referral traffic, agent query visibility, competitive displacement, and readiness for ACP/UCP-style commerce protocols.
Shopify, Google, OpenAI, Stripe, and other commerce platforms are moving agentic shopping from experimentation into checkout infrastructure. Merchants now have a new workflow: making their catalog understandable and transactible by AI agents before competitors occupy that shelf.
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This is exactly what our scan data shows. The three things you mentioned — llms.txt, variant selectors, JSON-LD — are the right levers. In order of impact: 1. JSON-LD is the most critical. Without it, agents can't machine-read your price or availability. One brand we scanned ($50-75M revenue) had zero structured data — scored 26/100. Adding a single JSON-LD block would more than double their score. 2. Variant selectors are where most brands break. Custom JavaScript size/color pickers look great for humans but give agents nothing to interact with. Across 21 brands, the Add-to-Cart flow failed \~85% of the time — almost always because of non-semantic variant selectors. 3. llms.txt is the easiest win. Only a third of brands we scanned had one. It takes 15 minutes to write and gives agents context they'd otherwise have to guess at. The uncomfortable part: agents fail silently. No abandoned cart metric, no error log, no customer complaint. They just leave and try the next store. **Most brands don't know they're losing this traffic because there's no signal that it was ever there.** [github.com/.../agent-a](github.com/.../agent-a)
I built CatalogReady after running into a fairly boring but important problem: a product page can look complete to a person while still leaving a shopping agent guessing about the product identity, offer, availability, and supporting evidence. I wanted to know whether this was an edge case, so I ran the same audit across 50 live product pages from 50 different domains in fashion, electronics, home, beauty, and grocery. The result: * 50 pages were reachable * 40 needed work * Scores ranged from 1 to 91 * CeraVe scored 16 while The Ordinary scored 91 That last comparison is not about which product is better. It measures how clearly the fetched static HTML exposes product identity, offers, and evidence to machines. The audit deliberately does not use an LLM. It makes one GET request and applies deterministic rules, so the same input produces the same findings. No API key is required, and it never writes to the store. You can run it with the following command: `uvx --from catalogready-ai catalogready` [`your-store.com/.../example%60](your-store.com/.../example) The code, scoring methodology, 50-page benchmark, and every result are here: [github.com/.../ai-shopping-audit](github.com/.../ai-shopping-audit) I’m the builder, and the project is Apache-2.0 licensed. The parts I would most value criticism on: 1. Is 80/100 a defensible readiness threshold? 2. Which checks feel too strict or too noisy? 3. Is static HTML the right baseline, or should the benchmark also capture browser-rendered state? Happy to answer questions about the scoring or rerun any page people think is a useful counterexample.
Been going through the Editions release, and most of it is incremental stuff: Faster POS, better analytics dashboards, Sidekick doing a bit more. Fine. But the Agentic Storefronts thing is genuinely different. Your products can now show up in a ChatGPT or Microsoft Copilot conversation, and the shopper can buy without ever visiting your store. Order lands in your admin like normal. The catch Shopify buried in the small print: Products run through their new Catalog format convert at 2x the rate of products AI just scrapes off your site. So catalog quality is now a conversion lever, not just an SEO nicety. There's also a tool showing you what people actually type into AI to find products like yours, and whether you show up. That's basically keyword research for AI search. Didn't expect that to exist yet. The POS rebuild is worth mentioning for anyone with a physical store; they claim to save over a minute per transaction. For a busy weekend that adds up fast. Curious if anyone's actually got Agentic Storefronts live yet and what the traffic looks like. Is the catalog cleanup as much work as it sounds, or does Sidekick handle most of it?
AI agents now touch 73% of buying journeys. Are you ready? That working paper post about the "agentic shelf" vs the chat surface got me thinking, bc I learned this exact thing the hard way on my own store. I spent all of last year obsessing over how my brand shows up in ChatGPT chat. Turns out that's only half the battle. The autonomous shopping agents pulling live product data are a totally separate surface, and mine was basically invisible there. The number that reframed it for me: 73% of consumers now use AI agents somewhere in their purchase journey. And it's not a someday thing. AI platforms are projected to drive $20.9 billion in retail spending in 2026, roughly quadrupling 2025. Some forecasts put agentic commerce at redirecting $3 to 5 trillion in global retail spend by 2030. (yeah, trillion. I had to reread that.) What actually worked: I stopped thinking "content" and started thinking "machine-readable product data." For an AI agent to pick my product over a competitor, the data has to be complete, accurate, structured, and consistent everywhere. Missing a GTIN, vague variants, no real-time inventory and agents just quietly skip you. Schema was the lever again. Products with full Product schema show up 3 to 5x more often in AI-generated shopping recommendations. That's not a rounding error, that's whether you exist on the shelf at all. The catch: there are two protocols now (ACP and UCP) and merchants running both capture 40% more agentic traffic than ones running only one. Google's UCP just pulled ahead with 20+ partners behind it including Amazon, Microsoft, Visa and Mastercard. If you're on Shopify, a lot of this is literally one admin toggle that most people haven't even flipped. What happened: after I cleaned up my product data and schema, my AI-referred orders started climbing. (Shopify reported AI-attributed orders growing 13x platform-wide, and AI-referred visitors convert at like 4 to 23x ...
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