Google Shopping
Free listings and Shopping ads are matched from your feed against your landing page. A price or availability that doesn’t match is one of the most common reasons an item is disapproved.
Catalogue truth for ecommerce
Your product data disagrees with itself.
A price that differs between the store record and the product feed. A product with no identifier. Stock that contradicts what the page says. Every system reading that catalogue — Google Shopping, marketplace feeds, ads, and now AI assistants — inherits the same contradiction. KoreLens reads every source, shows each value with the date it was read, and never guesses which one is right.
Free · No sign-up · Nothing stored unless you ask · What happens to my data? →
Free · No sign-up · Real answers, word for word — nothing simulated · Every number is measured — or it isn’t shown.
I run a store
Connect Shopify or WooCommerce and KoreLens reads your real product records — then ranks what to fix first, each finding showing the exact values and dates that triggered it.
See what to fix first →
I run an agency
Audit a prospect before the pitch, check a whole client list in one table, and send a branded before/after report that shows exactly what you fixed and what moved since.
See the agency workflow →
We measured it: 2% of verified stores (5 of 247) expose machine-readable product data on the homepage — 249 verified UK Shopify and WooCommerce storefronts, 16 July 2026. The UK Catalogue Truth Index →
What’s actually broken
This was broken long before AI arrived.
Prices that differ between the store and the feed. Products with no identifier. Stock that contradicts what the page says. For years it quietly cost Shopping impressions and Merchant Center approvals, and nothing in the stack was watching. AI didn’t create the problem — it made it impossible to ignore. An assistant answering a buyer’s question needs facts it can verify. If yours aren’t readable, it recommends a store whose facts are.
A disapproved item earns zero Shopping impressions — you keep paying for traffic that can’t see the product.
Merchant Center
Missing GTINs, brand, or condition hold items in review. The account-level warnings that follow are usually a data problem, not a policy one.
Marketplace feeds
Marketplaces and comparison engines read the same identifier fields. One wrong or absent identifier breaks every listing built on top of it.
AI assistants
An assistant answering a direct product question either has facts it can verify or recommends a store whose facts it can. Contradictions read as unverifiable.
One fix clears all four, because all four are reading the same fields. What KoreLens proves is that the sources agree afterwards — never that a particular listing, impression, or recommendation follows.
The UK Catalogue Truth Index · 2026 edition — sector medians and the open dataset →
The gap diagnosis
The customer you lost never told you.
When a store’s own systems disagree about a price, a stock level, or a returns policy, nothing in the stack says so. The feed states one thing, the product page states another, and every system reading them picks one and describes the product wrongly. No bounce report, no missed call. The customer simply never appears.
When the answer names a competitor, see what their cited page has.
Most AI-visibility tools stop at “you weren’t mentioned.” KoreLens shows who was cited instead — and the exact product facts the cited page exposes that yours doesn’t. Then it follows the fix all the way: the correction, an independent re-read confirming it landed, and the same buyer question asked again.
Everything else measures one half. Tools that watch AI answers never see the catalogue underneath; tools that check product data never re-ask the question. KoreLens runs a closed loop — we don’t just find the problem and call it done, we re-ask the question afterwards and report the verdict, even when it’s inconclusive.
Illustration with fictional stores — not a live model answer.
For agencies · Prospecting
Turn a prospect list into pitch material.
Paste up to 25 prospect stores in your dashboard and get every real readiness score in one table — each store’s top blockers named, the lot downloadable as CSV for your pitch deck. A site we can’t reach says so; it’s never given a made-up number.
Between pitches, the embed widget prospects for you: drop the audit on your agency site with one script tag, visitors run their real check, and the ones who want their full fix list leave their email — consent recorded — as leads in your dashboard. How the widget works →
For agencies · The client deliverable
The monthly answer to “what am I paying you for?”
Generated from the client’s record and led by the change record: the tracked question, the answer before with its date, the answer after with its date, the fix logged in between — word for word, with your agency’s name and logo on top. The readiness score rides along as the supporting number. Nothing to edit before you send it. One link, or the PDF.
A small “checked with KoreLens” credit line stays on reports by default — your clients should always know what ran the checks. Enterprise agencies can remove it for fully white-labelled deliverables.
Sample report with fictional businesses — not a live model answer.
Find out where your sources disagree.
What this is — and isn’t
What we won’t tell your clients: that we can guarantee AI citations, rankings, or sales. No honest tool can — the platforms decide. What we prove is the part you control: whether AI systems can read, understand, and verify your client’s data — and exactly what changed after you fixed it.
Readiness, not placement. Listing on any AI shopping surface is approval-dependent and decided by the platform — we check whether product data is ready, and we never say a store is live or approved when it is not.
Examples marked as illustrations use fictional stores.