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Data Validation

Common patterns for validating data quality and filtering based on business rules.

Validate Company Against ICP

Use Case: Check if a company matches your Ideal Customer Profile using AI.

Validate Job Title

Use Case: Check if a person’s job title matches your target persona.

Validate Email Quality

Use Case: Check if an email address is valid and not a generic/role-based email.

Validate Company Size

Use Case: Filter companies by employee count range.

Validate Website Quality

Use Case: Check if a website is valid and not a social media profile.

Validate Location

Use Case: Filter companies by geographic location.

Validate Data Completeness

Use Case: Ensure a lead has all required fields before proceeding.

Score and Filter Leads

Use Case: Score leads based on multiple criteria and filter low scores.

Validate Against Exclusion List

Use Case: Check if a company or person is on an exclusion list.

Validate Hiring Signals

Use Case: Check if a company has relevant hiring signals.

Validate Technology Fit

Use Case: Check if a company uses compatible or competing technology.

Deduplicate Records

Use Case: Check if a record already exists in your sheet.

Validate Contact Info Quality

Use Case: Ensure contact information meets quality standards.

Multi-Criteria Validation

Use Case: Validate against multiple criteria before proceeding.

Validate Company Growth Signals

Use Case: Check for signals that indicate a company is growing.

Validate Industry Match

Use Case: Check if a company is in your target industries.

Validate with AI Classification

Use Case: Use AI to classify and validate complex criteria.

Best Practices

When validation fails, use ctx.halt() to stop the workflow and provide a clear reason. This saves credits on downstream operations.
Run validation checks as early as possible in your workflow to avoid wasting resources on bad data.
Always include a descriptive message in ctx.halt() so you understand why records were filtered out.
For nuanced criteria, use AI classification rather than rigid rules. It handles edge cases better.
Store validation results and scores so you can analyze your filtering effectiveness.
Use multiple validation criteria together for more accurate filtering.
Check for null/undefined values before validating to avoid errors.