Aurum is not a black box. A deterministic engine proposes matches and records, but a data steward reviews, corrects and approves everything before it becomes part of the master.
Automated data cleaning that you can’t inspect just moves the trust problem. Aurum keeps a human in control: the engine and, on demand, an LLM propose clean records with rationale, but a data steward reviews, corrects and approves each one — so the master reflects decisions a person is willing to stand behind.
A deterministic engine proposes; a human steward stays in control.
Stewards review, correct and approve every record.
For a bad grouping, an LLM proposes a clean record with rationale and citations — a human approves.
No AI proposal is published without human approval.
A quality dashboard tracks match rate, unmatched spend and more for sign-off.
No. The engine proposes, but a human steward reviews, corrects and approves every record before it is published.
For a bad grouping it proposes a clean record with a rationale and citations; a human still approves before anything is published.
A quality dashboard tracks match rate, unmatched spend, missing attributes and reclassification rate for sign-off.
Turns scattered records into one trusted golden master, with humans in control.
A deterministic engine clusters lines describing the same item and collapses duplicates.
Places every record in the UNSPSC taxonomy so spend rolls up by standard category.
Full audit trail and source lineage per record, with reversible edits.
See the complete product: problem, features, how it works and deployment.
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