CRM data cleanup
Every CRM accumulates noise: duplicate contacts, stale company records, leads stuck in 'New' for months, sales-stage values that don't reflect reality. The cost is real — sales reps waste time on dead leads, marketing emails the same person three times, executives base forecasts on phantom pipeline. Most teams attempt a quarterly cleanup, then drift back to chaos within weeks.
The manual reality
Cleanup is not a one-time project. Bad data accumulates faster than humans can fix it: forms create duplicate contacts, sales reps create new companies instead of finding existing ones, integrations push inconsistent values. Manual cleanup batches help briefly, then the same problems return. The structural fix is continuous — catch the dirt as it enters, not after it accumulates.
The WorkAist approach
The WorkAist CRM hygiene agent runs daily across your HubSpot, Pipedrive, or Salesforce. It detects duplicate contacts (fuzzy match on email, phone, name + company), proposes merges, identifies stale leads that haven't progressed in N days and proposes a status update or archive, flags companies with conflicting fields (different industry tags from different reps), and validates email addresses against the supplied SMTP signal. Every action goes through a review queue — nothing destroys data without sign-off.
Implementation in 5 steps
- 1Connect your CRM (HubSpot, Pipedrive, Salesforce, Close, Attio).
- 2Define what 'duplicate' means for your business: email match alone, or email + company, or fuzzy name-and-domain.
- 3Define stale-lead thresholds (e.g. 'lead with no activity in 60 days → propose archive').
- 4Approve the initial cleanup batch (typically 200-2,000 actions on first run for a year-old CRM).
- 5The agent runs daily after that, with a weekly digest email summarising actions taken and exceptions surfaced.
Connectors & agents involved
FAQ
Won't the agent destroy data?▼
Every action has a review queue and an audit log. The first 30 days run in 'propose only' mode — actions are listed, not executed. Once you trust the matches, you graduate to 'execute' mode with confidence thresholds: high-confidence matches auto-execute, low-confidence wait for human approval.
What about validation against external sources (LinkedIn, Clearbit)?▼
Yes — the agent can be paired with a Clearbit, Apollo, or LinkedIn connector to validate company-size and industry fields against an external source. Mismatches surface as exceptions, not silent overwrites.
Can it also enrich missing fields?▼
Yes. Once duplicate detection is stable, the agent can enrich missing company-size, industry, and HQ-country fields from a paired data source. This is configured as a separate stage so you can adopt cleanup-only first and enrichment later.
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Open-source, self-hosted, AGPL-3.0. Your data stays in your infrastructure.
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