AI can turn a messy CRM into a reviewable cleanup queue.

Start by deciding what clean means.

A CRM is not messy because one field is blank.

It is messy when records no longer support the work people expect from them. A sales team may care about duplicate accounts and missing owners; customer success may care about renewal dates, open tickets, and contact roles.

Write down the fields that are required, the fields that are allowed to be unknown, and the rules for a duplicate. Then ask Strawberry to inspect the records in scope and separate clear defects from cases that need judgement.

Let evidence travel with every proposed fix.

A company name match alone is not enough to merge two accounts.

A good review queue shows the matching website domain, the overlapping contacts, the recent activity, and the field values that disagree.

Strawberry can work from the CRM in your signed-in browser and the tabs that explain the record. It can prepare a field-level recommendation instead of turning an uncertain match into an irreversible decision.

Fix the records that are blocking work first.

A ten-year-old lead with no owner may be less urgent than an active opportunity with no decision-maker, source, or next step. Sort the queue by the work it affects rather than trying to normalize every historical record in one pass.

That turns a cleanup project into a useful operating rhythm: resolve active duplicates, repair the fields used in current reporting, then work through older records in deliberate batches.

Keep the cleanup from becoming next quarter’s problem.

Data hygiene lasts when each new record has an owner and a minimum standard.

Save the audit rules as a skill, then run a routine that reports newly incomplete or conflicting records before they spread into forecasting and outreach.

The routine can collect exceptions and prepare a review list. Changes to customer records should remain subject to the approval policy your team chooses.

Frequently asked questions