Your AI companion for Customer Fields.

Field definitions deserve a review before they become a data problem.

A field can look harmless until different people interpret it differently.

Strawberry can inspect the visible schema, linked documentation, and sample records to surface ambiguous labels, overlapping concepts, and missing rules without silently rewriting the structure.

Incomplete records can be sorted by what is actually knowable.

Blank data is not always bad data: some values are unavailable, some live elsewhere, and some need a decision. Strawberry can assemble a correction queue that identifies evidence-backed updates separately from records where the source does not justify a guess.

Data-quality findings are easier to resolve when the evidence travels with them.

A useful audit links each proposed correction to the field rule or source page that supports it. Strawberry can prepare that evidence packet so the person approving a change does not have to reconstruct the case from scratch.

A regular hygiene pass keeps field drift visible.

Each Monday, have Customer Fields compare newly incomplete records, rule violations, and conflicting values against the field definitions. An unchecked schema or bulk-record edit can break downstream reports and overwrite the values teams rely on, so the named data owner reviews every proposed change.

Frequently asked questions