Build an AI CRM data hygiene and update workflow

Stale opportunity fields, incomplete notes and inconsistent CRM updates make pipeline reviews unreliable.

I will prototype a focused AI CRM hygiene workflow that converts unstructured sales updates into structured, reviewable CRM recommendations.

The prototype can accept one agreed input type, such as:

- Sales call notes
- Meeting summaries
- Account-manager updates
- Email text
- Slack or Teams updates supplied as text
- Existing CRM record exports

The workflow will:

- Extract relevant CRM fields
- Validate required information
- Compare proposed values with existing values
- Flag missing or conflicting information
- Generate evidence-backed update recommendations
- Present changes for human approval
- Produce CRM-ready structured output

The base Offer includes:

- One input format
- Up to five CRM fields
- One structured output format
- Up to 20 test records
- Human approval step
- Basic confidence or evidence field
- Test report
- Architecture and handover documentation

My design principle is simple: AI recommends, humans approve and important updates retain their supporting evidence.

Buyer requirements

- CRM platform
- Export of existing fields or sample records
- Required fields
- Field definitions
- Example notes or updates
- Rules for updating each field
- 10-20 representative samples
- Expected output format
- Access to a sandbox, test CRM or spreadsheet

Deliverables

- Working prototype
- Field extraction logic
- Validation rules
- Before-and-after comparison
- Approval-ready output
- Test report
- Workflow diagram
- Handover documentation

Not included

- Live production write-back
- Full Salesforce or HubSpot implementation
- More than five CRM fields
- Multiple communication channels
- Enterprise permissions
- Continuous monitoring
- Historical database cleanup
- Paid platform licences

## Portfolio proof

Primary:

- CRM Hygiene Agent case study

Use the positioning:
No more forecast calls based on stale CRM fields.