AI in the loop. Validation before the production system.
Generating product data with AI is easy. Trusting it is hard. Claro scores, sources and routes every AI-generated change before it writes back — so a value that looks right but is not never reaches your ERP.
A confident wrong value is more expensive than a missing one
The moment AI enrichment reaches production scale, the real question stops being "can it generate an attribute" and becomes "can we prove it is correct." A value that looks plausible but is wrong is often more expensive than a value that is simply missing, because nothing flags it before it spreads downstream. Claro treats every AI-generated or AI-assisted change as a claim to be checked: a calibrated confidence score, a link back to the source document, and a rule check — with your rules, that confidence score and the impact of the change together deciding whether it auto-applies, waits for review, or gets blocked.
What "AI in production" breaks without validation
AI can fill 90,000 attributes with no visibility into which ones are actually right.
There is no way to see the source or evidence behind a generated value.
No audit trail means no way to explain what happened when something breaks downstream.
How AI output validation works with Claro
A calibrated confidence score on every match, enrichment and classification.
Every value linked to its source document, field and version.
Required fields, allowed values and business rules enforced.
Auto-approve, send to review, or block — by your rules, confidence and impact.
Who needs validation in front of the ERP
What goes in, what comes back
Claro writes back through files and APIs rather than certified connectors, so this list is a guide, not a limit.
AI output validation: common questions
Does this replace our review team?
No, it makes their time count. Changes are grouped by fix-pattern, so a reviewer approves 312 similar changes as one decision instead of clicking through 312 rows.
What gets blocked rather than reviewed?
Anything whose blast radius is large regardless of score — merges, un-merges and reclassification. Confidence never buys those lanes; a named reviewer signs them off.
Do you train on our data?
No. Customer data is never used to train shared or third-party models.
What is in the audit trail?
What was proposed, what published, what was held, which check held it, which rule and impact tier applied and who approved it — enough to explain a downstream error months later.
Can it validate output from our own models, not just Claro's?
Yes. Validation is a separate layer from generation, so it can sit in front of your existing pipeline or a third-party enrichment vendor.
Related work
See it work on your own catalog.
Bring one supplier file and we'll run ai output validation on your real data — matched, classified and reviewable.