AI enrichment has made one thing cheap: generating suggestions. Run a single pass over a large catalog and you can produce tens of thousands of proposed changes, from new titles and filled-in attributes to rewritten descriptions and generated Q&A. Producing them was never the hard part. Deciding which ones to accept is.
For a catalog manager, that decision doesn't scale by hand. Forty thousand suggestions stops being a queue you work through and becomes a backlog you abandon. Teams usually cope in one of two ways, and both make the catalog worse. Either the queue goes unreviewed and the enrichment never reaches the storefront, or someone bulk-accepts everything and low-confidence guesses ship next to the good data. Neither is a policy. Both are a shrug.
Talk to anyone who reviews enrichment suggestions and a pattern shows up fast. High-confidence attribute fills get accepted almost every time. Low-confidence Q&A gets rejected almost every time. The real work, the part that genuinely needs a person, sits in the band in the middle, where confidence is ambiguous and the call depends on context.
So most of the queue is a judgment the team already makes the same way, over and over. That is exactly the kind of decision worth encoding once instead of repeating forty thousand times.
The obvious way to automate this is a single confidence cutoff per field: accept above the line, reject below it. We built that first, and it was wrong. A single line forces every suggestion into one of two buckets, including the ones sitting right at the cutoff where the model is genuinely unsure. Those are the suggestions a person should see, and a hard line buries them.
So each field gets two thresholds instead of one: an auto-reject floor, an auto-accept ceiling, and a Manual Review band in between that the system deliberately leaves alone. That middle band is the most important part of the design. It is where the product says, plainly, that a person should make this call.
Each field (Title, Description, Attributes, and Q&A) gets a single confidence slider with three zones. Below the low threshold, suggestions auto-reject. Above the high threshold, they auto-accept. In between, they wait for Manual Review.
The defaults carry judgment too. Titles auto-accept only at very high confidence (95% and above); attributes accept at a lower bar (80%). Higher stakes, higher bar. Any field can also be switched off, so you review all of its suggestions by hand.
Once saved, the thresholds apply to new suggestions automatically. The confident ones resolve themselves on every future enrichment run, so the queue stops refilling faster than the team can keep up. For the suggestions already waiting, Apply to Existing Enrichments runs the current settings against the queue and shows exactly how many will be affected before you commit. Everything processes in the background, so a run touching tens of thousands of suggestions never locks up the queue.
This is the part worth being precise about: automation here isn't about removing the human. It's about putting the confidence score to work. The suggestions a reviewer would approve every time and the ones they'd reject every time stop consuming attention, and what's left is the Manual Review band — where a person's judgment actually changes the outcome. The human stays in the loop exactly where the loop is worth their time.
A few honest boundaries. Enrichment Queue Automation applies the accept and reject policy you set to suggestions you already control. It does not generate product data, and a person sets every threshold. It acts only on AI suggestions in the Enrichment Queue, so it never touches your published product data directly. It applies your thresholds deterministically and does not learn or adjust them on its own. And every field keeps a Manual Review band, so the suggestions in the middle are never auto-decided.
Enrichment Queue Automation lives in the Enrichment Queue, behind the Automation button. Each field has its own slider and On/Off toggle, and Apply to Existing Enrichments runs your current settings against the queue whenever you want. Watch the full demo here.
Agentic Commerce Optimization depends on product data staying current across a catalog that is continuously re-enriched. At enterprise scale, the constraint has moved from generating better suggestions to reviewing them fast enough that the good ones reach shopping surfaces while they still matter. Automation rules turn the enrichment queue into a standing policy: the team's judgment, encoded as confidence thresholds and applied consistently and auditably across the whole catalog. That is what keeps high-volume enrichment sustainable instead of letting it become a backlog that quietly goes stale.
Enrichment Queue Automation is live now. Open the Enrichment Queue, toggle on a single field, nudge the auto-accept mark, and Apply to Existing will show how much of your current queue it clears. Or book a demo to see it against your own catalog.