# Choose which product-record correction comes first > Review corrections across three products, understand each destination’s priorities and apply only source-backed fields to the record you can copy. - Author: Ratik - Published: 2026-09-10 - Category: Concept Walkthroughs - Canonical: https://www.arclift.ai/blog/product-catalogue-concept Daily Essentials is a bottle of 60 capsules. Its current catalog record says 30 and leaves other facts incomplete. Plain Blend and Mineral Drops have their own record problems. The first useful question is which correction to make next, and what evidence supports it. The AI-Ready Product Catalog turns that question into a working review. Choose a destination, inspect a prioritized worklist across the three products and apply the fields you select. The JSON then reflects those applied changes. A missing fact does not become true because the interface offers a confident replacement. This is a fictional design walkthrough using PLAIN / LIVING products and AI-generated imagery. Source declarations and destination contracts are authored examples. Changes stay in the page; the concept does not enrich a live catalog, submit a feed or demonstrate improved discovery. Explore the Concept: [AI-Ready Product Catalog](/concepts/product-catalogue#compare) ## Start with something you can inspect The source chapter identifies Daily Essentials as Capsules in a 60-capsule bottle, Plain Blend as Powder in a 300 g canister and Mineral Drops as Liquid in a 30 ml bottle. These are the same product facts used in the related PLAIN / LIVING shopping concepts. Sharing the catalog keeps one experience from silently describing a different product. Each correction needs a source suited to the field. The product description comes from the declared catalog entry. Format and pack size can be checked against the pictured label and matching source facts. A SKU comes from the declared record source; the label photograph is not evidence for every internal code. Identifiers and reviews need their own evidence. Missing identifier information is not proof that none was assigned. A verified declaration that an identifier is unassigned is a different state, even though both leave its value empty. A review value also needs a source for that product. The workspace keeps those distinctions visible instead of filling the gaps with invented data. Plain Blend and Mineral Drops use assignment and page-rating records already authored for Agent Readiness. Both source records use the example rating of 4.6 from 214 reviews. These are declared fixtures, not supplier verification or verified customer reviews. Plain Blend’s working rating starts at 4.3 from 198 reviews so you can inspect and apply the alignment; Mineral Drops already matches its source. Every GTIN stays null. Plain Blend starts with an authored verified-unassigned status. Mineral Drops starts as not checked and records verified unassigned only after you apply its assignment correction. Daily Essentials has neither assignment nor rating evidence in this workspace. Its unknowns remain unresolved even after every available correction is applied. ## Give priority a destination The concept has two destination contracts: Storefront product record and SKU-based partner export. Both are local examples with declared field requirements. They are not representations of Google, an AI model or a named commerce platform’s current rules. Choose a destination and the worklist explains which fields need attention for that use. It spans products so the next useful correction is visible even when another item is selected. The reason belongs beside the priority: a required field with an available source presents a different task from information whose owner still needs to supply evidence. Plain Blend’s SKU is a concrete example. Its working record carries C-02, while its authored catalog source supplies P-01. Correcting that field is Recommended for the storefront but Required for the partner export, which identifies products by SKU. A missing description moves in the other direction: Required for the storefront, Recommended for the partner. Format and pack size are Required in both contracts. The storefront recommends rating alignment and marks identifier assignment for Review. The partner contract does not consume ratings or GTIN information. An excluded field does not become verified or correct; it remains outside that destination’s worklist and output. Changing destinations changes the review context. It does not change how many capsules are in the bottle, invent an identifier or undo a correction already applied. In a real implementation, the team would agree each destination’s contract before relying on that priority order. ## Show the correction, not a score Select a product to inspect its fields. Current value, proposed value and source appear together with the reason for the correction. Description, format, pack size, SKU, identifier provenance and review-source alignment can each be reviewed on their own terms. There is no aggregate readiness score or certification. Only a source-backed correction can be selected. An unresolved item remains a visible task, not a selectable guess. Matching fields do not need to be rewritten to make the worklist look more substantial. A review that leaves a fact unresolved can be the correct result when the source does not support a change. The workspace uses a document layout for this operational task. Product context stays alongside the working area on desktop. On a narrow screen, each value retains its field label and the source remains available without relying on a distant column heading. ## Apply a decision, then inspect its effect Selection is a review step. It does not alter the record. Apply the selected corrections to change the working values, leaving every unselected field as it was. This makes a partial correction inspectable: the output should show what you approved, not every proposal the interface could make. Applied values stay with their product while you switch products, chapters or destinations. Pending selections clear on each of those changes. That distinction prevents a choice made for one review context from becoming an unnoticed selection in another, while preserving the work already applied. Start Again restores the original records, the first product, the initial destination and the source chapter. Reloading the page also restores the baseline. There is no account or background catalog update behind the interaction. ## Read the record you actually changed The final chapter exposes the selected destination’s working fields as JSON. Before applying a correction, its original value remains in the output. Apply one field and only that field changes. Merely comparing the values does not replace the record with an idealized version. The storefront output includes identifier and rating values with their evidence status, including unresolved entries. The partner output omits GTIN, identifier status and ratings because that contract does not consume them. Those working facts remain in local state when you switch destinations. A narrower export does not erase an unresolved task or undo an applied correction. The copy action copies that same visible text. If the browser cannot use the clipboard, the record stays selectable for manual copying. Feedback clears when the context or working values change so a successful copy message does not describe a different record. This output is plain data for inspection. It is not a complete merchant feed or Product schema implementation, and copying it submits nothing to a platform. The useful proof is the relationship between source, selected correction, applied value and exported text. ## What production would need A real project would begin with authoritative product sources, named owners and the destinations that consume the record. Those destinations might include a storefront, app, partner export or shopping guide. Their requirements would need to be checked individually, with a clear distinction between required fields, useful additions and information they do not consume. The approval process matters as much as the interface. Who can accept a correction? What happens when a supplier file conflicts with the current label? Who confirms whether an identifier is assigned? Where does a review figure come from, and when was it last checked? The concept makes those questions visible without pretending to answer them for a real business. A bounded first release could cover a representative product group and the fields that already have reliable sources. Its acceptance would check selected and untouched values, the correct product, destination requirements and the output actually published. An unresolved-evidence queue should remain part of that process rather than disappearing after the easy corrections are made. ## What this demonstrates The concept demonstrates a reasoned correction order, field-level review and a working record that follows your applied decisions. It does not establish fewer support requests, more sales or better AI placement. After a real release, correction accuracy, unresolved conflicts, approval time and mismatches reaching published destinations could help evaluate the process, with definitions and a baseline agreed in advance. Explore a destination, review a product and apply a subset of its corrections. Read the output, change context and return to the same product. Then bring us one record that disagrees with your source material. We can use it to discuss a focused prototype and the decisions needed to keep your product information consistent. ## Your brand’s next step [Product Data & AI Discovery](/services#product-data) [Start a Conversation](/contact?service=product-data&example=product-catalogue) ## Related reading [Product records for AI discovery: what to fix, what not to promise](/blog/product-record-readiness)