Skip to main content
WISEPIM AI Quality Review scoring the catalog across data-quality dimensions with one-click fixes WISEPIM AI Quality Review scoring the catalog across data-quality dimensions with one-click fixes A rules-based score tells you a field is empty. The AI Quality Review tells you whether the content actually does its job: does the description read well, do the images match the attributes, is the price sane for the family, will the page convert? It reads each product across 12 dimensions, scores it 1 to 5, and turns the findings into a ranked list of fixes you can apply in one click. What acting on it enables: ship cleaner listings faster, lift conversion on the products that need it most, cut returns caused by misleading content, and lock in the gains with guard rules so quality does not slip back.
A review runs on demand and analyzes the products you choose. Vision dimensions (checking images against your data) use AI credits, so the cost scales with catalog size and the dimensions you pick. Results are queued and typically ready in a few minutes; they never expire, so you can revisit any past run.

​
Run a review

1

Open AI Quality Review

Find it in the Analytics sidebar under the quality section, or trigger it straight from a product selection in the Products table.
2

Choose what to review

Review your current selection or everything matching your active filters. Start small (one family or category) if you just want a read on a specific area.
3

Pick an intent preset

Pre-launch sweep (every dimension, including AI vision), SEO health, Image audit (the vision checks), or Conversion focus. Each preset selects the dimensions that matter for that goal. Custom lets you choose dimensions yourself.
4

Add optional context

Industry vertical defaults to your project’s primary industry. Open the picker to choose another industry, or a narrower segment or assortment, for more specific expectations. You can also add up to three buyer personas to simulate how different shoppers react, or a freeform instruction for anything specific you care about.
5

Run and review

The modal shows an estimate of the credits and minutes before you click Start Review. The review processes in the background. When it finishes, the dashboard fills with your score, the dimension breakdown, and a prioritized list of issues to act on.

​
Find matches or check one product

The review modal has three tabs. Full review is the run described above. The other two are quick previews that answer one question. They never change your catalog.
Select between 1 and 20 products first. Under Describe the products to match, write a question of up to 500 characters, for example “Products suitable for outdoor use”. Choose the Fields to inspect (name, description, short description, brand, supplier, categories or attributes), then click Preview matches.The result splits the products into Matches, Does not match and Unknown. Unknown means the fields you picked did not support a decision, so those products are kept apart from the non-matches. Click Select N matches to narrow your current product selection to the matches.
Select exactly one product. Pick a check and click Run semantic preview:Most checks end in Supported, Needs review or Not enough evidence. The result shows a model probability. “Model probability is not calibrated accuracy. Review the evidence before acting.”

​
The 12 dimensions

Each product is scored on these dimensions. The two image dimensions require vision and use additional AI credits.

​
What the scores mean

Each product also gets a priority (Critical, High, Medium, Low) from its overall score, so you always know where to start. The dashboard compares your overall score against an industry benchmark (Industry avg) and shows an Open suggestions counter with the number of open items rated N critical. Those are the suggestions from this review you have not applied, snoozed or dismissed yet. WISEPIM does not predict the score you would reach by applying them: apply the fixes, then re-run the review to see what changed.
The benchmark follows your industry as precisely as the data allows. A segment or assortment gets its own benchmark once at least 200 products from at least 3 projects sit in it. A top-level industry needs 50. Until then, the nearest broader industry is used, and the result says which one. Your project’s own grade thresholds still win over the benchmark.
Findings, observations, tips and terminology notes are written in the project owner’s interface language, independent of the catalog language or the locale under review. Quoted evidence from your products stays verbatim.

​
Reading the results

  • The dimension radar shows your shape at a glance. One spoke pulled toward the center is the dimension to fix first.
  • The Issues to fix list is the heart of the page: every issue is ranked by how many products it touches and how severe it is, with a plain-language title and the affected count.
  • Confidence and consensus chips tell you how sure the AI is. A suggestion flagged for review (consensus dissent) shipped anyway but deserves a human glance before you apply it in bulk.
  • The Insights tabs add depth: priority breakdown, score trends across past runs, a conversion-readiness checklist, buyer-persona findings, and open-ended observations the AI surfaced outside the fixed dimensions.
  • The per-product findings table lets you drill into any single product to see its per-dimension scores and the exact findings behind them.

​
Category FAQ suggestions

The review can also suggest buyer questions for a category, shown as “N questions for” followed by the category name. Click Preview questions to read them, then click Add to plus the category name (for example Add to Hiking boots) to add them to the category’s FAQs. Every product directly in that category shows them. Subcategories don’t get them. See Product FAQs.

​
Fix it, or guard against it

WISEPIM Quality Guard rules list with severity and scope WISEPIM Quality Guard rules list with severity and scope Every issue offers two actions, and the difference matters:
  • Fix applies the suggestion now. Depending on the issue that might create a Quality Guard rule, adjust a scoring setting, add a missing attribute to a family, or fill a value the AI derived from an image.
  • Add guard rule creates a Quality Guard rule (left inactive for you to review and enable) so the same issue is caught automatically on every future import and edit.
Use Fix to clean up what is wrong today. Use Add guard rule to make sure it stays fixed. The strongest workflow is to do both: fix the current products, then guard the rule so the problem never silently returns.

​
Act on what you find

Open the dimension radar and find the lowest spoke, then filter the Issues list to that dimension and work top-down by affected count. Apply the highest-impact fixes first, then re-run the review and compare the new score against this run. Outcome: the fastest possible lift, because you are fixing the issues that touch the most products.
A single weak dimension (often SEO Compliance or Attribute Coverage) is usually one systemic gap, not a thousand unique problems. Apply the matching suggestion (for example, raising the minimum description length or adding a missing attribute to a family) to fix it across the catalog at once. Outcome: a broad quality jump from a single action.
Recurring issues are a data-discipline problem, not a one-off. Use Add guard rule to create a Quality Guard rule that blocks it at import and edit time, then enable it. Outcome: the issue stops reappearing, so each future review starts from a cleaner baseline.
When the consensus pass disagrees with itself, the suggestion still ships but is flagged. Open the affected products and confirm before applying in bulk. These are the cases where AI is least certain and a human eye pays off most. Outcome: you keep the speed of automation without applying a wrong fix at scale.
Image-vs-data findings (a red product photographed in blue, three items shown for a single-unit listing) are common after supplier imports. Fix the attribute or the image, or fill the AI-derived value where it is confident. Outcome: images and data finally agree, which reduces returns and channel rejections.
The Conversion Readiness tab lists the signals buyers expect for your vertical (size charts, what’s-in-box, certifications) and how many products are missing each. Add the missing attributes, starting with the signals marked critical. Outcome: listings carry the information that turns a browse into a purchase.

​
How it relates to the other quality tools

  • Data Quality is the always-on, rule-based health monitor: it tells you what is missing. AI Quality Review is the improvement engine on top: it tells you what is wrong or weak and how to fix it.
  • Quality Guard is the enforcement layer. AI Quality Review discovers the rules worth having; Quality Guard keeps them enforced.
  • Linguistic Review is the language-quality counterpart, focused on translation quality rather than product data.

Data Quality Analytics

The rule-based completeness score that runs continuously across your catalog.

Quality Guard Overview

Turn review findings into rules that block bad data before it lands.

Linguistic Review

The same idea applied to translation quality across your locales.

Enriching Products with AI

Fix the content the review flags with targeted AI enrichment.

Product FAQs

Write and manage the buyer questions the review suggests per category.