Find out which product data is wrong — before your customers do.
Validation re-checks the data you already have against fresh sources. Every attribute comes back with a confidence score, the reasoning behind it, and the page it was read from. Every product comes back with an overall quality score.
Validation VLD-2291
Cordless impact driver, 18V brushless
Overall quality 71 · 1 attribute contradicted
Enrichment has a finish line. Accuracy does not.
Filling a catalog is a project. It gets a budget, a deadline, and someone who owns it. Keeping it true is nobody’s project.
So specifications drift. A manufacturer revises a model and keeps the part number. A supplier updates a datasheet nobody reads. Someone pastes the wrong dimensions into row 4,812 and the mistake propagates into a feed, a marketplace listing, and a customer’s expectations.
From the outside the catalog looks finished. It is complete and quietly wrong, and the first person to notice is usually the one filing the return.
What 0 to 100 means
The number is only useful if you know what to do with it. Three bands, three responses.
- 85 – 100
- Multiple current sources agree with the value on record. Safe to publish without review.
- 65 – 84
- Broadly supported, but with a caveat — one source disagrees, the wording is ambiguous, or the figure depends on a measurement condition. Worth a glance.
- 0 – 64
- The value on record is contradicted, unsupported, or impossible to establish. These are the rows to work through.
Attribute scores roll up into an overall quality score for the product, which is what makes a catalog-wide reading possible — one number for a range, a brand, or a supplier, tracked across runs.
A score with its reasoning attached
A bare number invites an argument. A number with the contradicting source attached ends one.
The comparison
What you have on record, next to what current sources say. Stated plainly, in the same row.
The reasoning
Written out: how many sources agreed, what they disagreed about, and why the score landed where it did.
The citation
The page it read. Open it and settle the question yourself in ten seconds.
Four moments that justify a sweep
After a supplier feed lands
Before a marketplace submission
After a migration or acquisition
On a cadence for the top of the range
How a validation job runs
- STEP 1
Point it at existing data
Select products you already have. Validation needs no new input — the data on record is the thing under test.
- STEP 2
It re-researches from scratch
Each attribute is looked up again from current sources, independently of whatever is stored.
- STEP 3
Compare, score, explain
Stored value against found value, scored 0–100, with the reasoning written out and the source linked.
- STEP 4
Work the exceptions
Filter by score, re-enrich the failures, or export the exception list for whoever owns that part of the catalog.
Built to run against everything.
Validation is only useful if it is cheap enough to run on the whole catalog and repeat it next quarter.
- Bulk selection
- Select from the products table, or target a whole catalog or category in one job.
- Parallel processing
- Products are checked concurrently with live progress. No per-job SKU cap.
- Webhooks
- A completion event fires when the job finishes, so a scheduled sweep can drive your own workflow.
- Job history
- Every run is stored with its inputs, model tier, cost, and results.
- API
- Create and read validation jobs programmatically, the same as any other agent.
What people ask once they see the scores
How is validation different from enrichment?
What does the score actually measure?
Does it change our data?
Can we validate only some attributes?
What does it cost to validate a whole catalog?
Can we track quality over time?
Six agents, one catalog
Product Enrichment
Market Analysis
AI Import
Brand Enrichment
Category Enrichment
Point it at a hundred products you believe are fine.
This is the demo that tends to change the conversation. Pick a range you are confident in and see what the scores say.
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