Approve AI-generated product data before it goes live

AI can fill thousands of attributes in minutes, but nothing should reach your website without a human decision. SKULaunch keeps AI-generated and AI-extracted values in review until someone approves them, so you get the speed of automation with control over what is published.

The problem

Teams worry about two failure modes. Publish AI output unchecked and one wrong rating on a safety-critical product causes real harm. Check every value by hand and automation saves nothing. Most tools offer neither a sensible middle path nor a clear record of what was checked.

Before and after

An illustrative example from an enrichment run.

Before: the dilemma

8,000 values filled overnight by an AI tool and pushed straight to the website. Two weeks later a customer finds a wrong voltage on a charger.

After: in SKULaunch

• Enriched values held for review, not published
• Reviewer filters to safety-related attributes first and checks them fully
• Remaining values spot-checked by category, then approved in bulk
• Every approval recorded against the product

How it works in SKULaunch

1. Enrich into review. AI Tools write values to products awaiting approval.
2. Review in the grid. Choose columns, filter by category or attribute, and compare with sources.
3. Approve or correct. Approve in bulk, edit values that need it, or reject.
4. Publish approved data only. Channels receive approved records.

A practical review standard

Review by risk, not volume. Check every value on attributes with safety, legal or compatibility consequences. Spot-check a fixed number of products per category for everything else, and widen the check if errors appear. Record the standard so every reviewer works the same way. That keeps accuracy high without turning review into the new bottleneck.

What changes

Automation does the volume, people make the decisions, and nothing unchecked goes live.

Common questions

Do we have to review every value?

No. Review by risk and sample the rest.

Can approval be in bulk?

Yes, after review of the filtered set.

Will confidence scores be shown?

Confidence scoring is on the roadmap. Today, review is by filter, source and sample.

See the approval workflow, filling missing attributes, and product data enrichment.