AI Operation
AI in the loop. Validation before the production system
AI validation is the discipline of running every AI-generated or AI-assisted catalog change through confidence scoring, rule-based checks, provenance verification, and human review thresholds before it writes into production systems. Claro is built validation-first: every match, enrichment, classification, and update carries a confidence score, a provenance link, and a path through either auto-approval or human review depending on impact. This is what makes AI catalog automation safe for ERPs, PIMs, search, procurement, and pricing systems.
The problem
Generating product data is easy. Trusting it is hard.
AI fills 90,000 attributes. Your team has no clear way to tell which 10,000 are wrong, where they came from, or which ones can safely write back into production. After two visible errors, the project is shelved.
AI values that "look right" but are wrong
No control over which updates write back
No audit trail when something breaks
How it works
From AI candidate to production-grade update.
Every change runs through confidence, provenance, validation, and routing before it touches a system of record.
Step 1
Score every change
Every update — match, enrichment, classification — produces a confidence score calibrated to your data and your review history.
Step 2
Verify provenance
Every value links back to its source: the document, the field, the location, the version of the input. Audit-ready.
Step 3
Validate against rules
Required fields, allowed values, type checks, taxonomy constraints, business rules. All enforced before write-back.
Step 4
Route the decision
Auto-approve, route to review, or block — based on configured thresholds and impact rules. Reviewed decisions feed back into the system.
What you get
AI in production. Without the AI risk.
Production catalog systems aren't a sandbox. A wrong unit on a part, a wrong category on a regulated product, a wrong spec on a B2B SKU — these have operational, commercial, and sometimes regulatory cost. Claro is built around the question: "Is this update trusted enough to change the system of record?" Every change carries confidence, provenance, validation, and a routing decision. AI helps generate candidates. The loop decides what's true enough to trust.
Who is it for
Built for teams deploying AI on production catalogs.
Data, AI, engineering, risk, and compliance leadership at companies where catalog data feeds operational systems and the cost of AI errors is non-zero.
Currently piloting or deploying AI on catalog data
Burned by a failed AI rollout in the past
Need provenance and audit for compliance review
Want to deploy AI on production data, not just demos
Validation is the contract. AI is one of the engines.
Claro uses AI inside the loop — but the product is the loop, not the agent. The loop is built around confidence, provenance, validation, and write-back. AI generates candidates. The loop decides what's true enough to trust.
Problems solved:
LLM-generated catalog updates with no provenance
no control over auto-write vs. review thresholds
no audit trail when something breaks
AI projects shelved after two visible errors
Hours of Work. Done in Minutes.
Production-grade AI catalog automation with confidence, provenance, validation, and audit built in.
Book a demo
Provenance Trails
Every value links to its source: document, field, location, version. Click through, verify.
Routing Decisions
Auto-approve above threshold, route to review below, or block when configured. Per-workflow, per-attribute.
Confidence Scoring
Every change carries a calibrated confidence score. Configure thresholds per category, attribute, or workflow.
Validation Rules
Required fields, allowed values, type checks, business rules, taxonomy constraints — enforced before write-back.
External AI Integration
Plug your existing AI tools into Claro's validation, scoring, and write-back layer. The loop catches what models miss.
FAQ
Frequently asked questions
Is Claro an AI agent?
Can we plug external AI tools into Claro's validation?
How are confidence scores calibrated?
What does the audit trail include?
Is this compatible with our compliance requirements?
