Product-data quality checker: who it is for
The checker looks for mechanical problems in a catalogue: missing attributes, inconsistent units and sizing, duplicated or near-identical descriptions, unassigned categories, and fields that contradict each other between the title, the summary and the detailed specification.
( The Detail )
What this tool tells you
Findings tell you where the catalogue will behave badly in search, filtering and feeds. They do not tell you whether the copy is persuasive, whether the range is right, or which products deserve commercial attention. It also cannot verify that a stated specification is true, only that it is present.
Using it well
This is aimed at owners and operators who already suspect there is a better way to run a recurring part of the business, but do not yet have language for what is actually wrong. It assumes no technical background and no existing AI work.
It is less useful if you have already scoped a specific build and know the data sources, the rules and the owner. At that point the constraint is delivery rather than diagnosis, and a direct conversation will get you further than a structured questionnaire.
How to approach it
Begin with the decision rather than the tool. Name the recurring judgement this affects, the information it depends on, and the person accountable for acting on the result. That framing keeps the first build small enough to inspect and useful enough to matter.
Keep a human review point in the loop until the quality and the failure modes are understood. A system that shows its working - what it drew on, where it is uncertain, and what it deliberately left alone - is one a business can keep running after the initial build.
( Next Step )
Start small enough to review, but on a workflow important enough to show whether a better system is worth building.