Data Sources & Collection Policy

1. Purpose

The purpose of this policy is to:

• Explain data origins
• Describe collection practices
• Explain enrichment workflows
• Define limitations
• Improve transparency
• Set customer expectations

2. Data Collection Philosophy

LabelNest aims to create structured, usable, and actionable information through combinations of:

• Public research
• Customer-provided information
• Automated workflows
• Human validation
• Enrichment systems
• Monitoring systems

Data collection approaches may evolve over time.

3. Types of Data Sources

3.1 Public Sources

Publicly accessible sources may include:

• Company websites
• Press releases
• News publications
• Corporate disclosures
• Regulatory filings
• Public reports
• Government databases
• Public profiles
• Event announcements
• Industry publications
• Open datasets

Public availability does not guarantee completeness or accuracy.

3.2 Customer-Provided Sources

Customers may provide:

• Internal records
• Employee information
• Company information
• Documents
• Contacts
• Research requests
• Uploaded files
• CRM exports
• HR records
• Operational data

Customers remain responsible for permissions and lawful sharing.

3.3 Third-Party Sources

Information may originate from:

• Data providers
• Infrastructure partners
• Integrations
• APIs
• Public repositories
• Commercial vendors

Third-party restrictions may apply.

3.4 User Activity Signals

Systems may collect:

• Search patterns
• Product interactions
• Monitoring preferences
• Usage activity
• Export behavior
• Saved workflows

These signals may support personalization and product improvements.

4. Data Collection Methods

Collection methods may include:

Manual Research

• Research workflows
• Analyst workflows
• Validation activities
• Quality reviews

Automated Collection

• APIs
• Monitoring workflows
• Change detection systems
• Parsing systems
• Scheduled workflows
• Data synchronization

Customer Imports

• CSV uploads
• APIs
• Integrations
• Manual uploads
• Data migration workflows

5. Data Processing Lifecycle

Data may move through stages including:

`
Collection
↓
Extraction
↓
Normalization
↓
Deduplication
↓
Validation
↓
Enrichment
↓
Storage
↓
Monitoring
↓
Distribution
`

Processing workflows vary by product.

6. Data Normalization

Collected information may be transformed through:

• Standardization
• Deduplication
• Formatting rules
• Taxonomy mapping
• Categorization
• Relationship mapping
• Entity resolution

Normalization does not guarantee accuracy.

7. Enrichment Practices

Enrichment workflows may include:

• AI-assisted enrichment
• Human review
• Relationship mapping
• Classification
• Categorization
• Confidence scoring
• Research augmentation

Enrichment may introduce limitations.

8. Quality Assurance

Quality activities may include:

• Sampling
• Validation checks
• Duplicate checks
• Completeness reviews
• Exception handling
• Manual reviews
• Workflow monitoring

Quality processes may vary by dataset.

9. Update Frequency

Information freshness may vary.

Possible update frequencies:

• Real time
• Daily
• Weekly
• Monthly
• Event-driven
• Customer-triggered

Not all datasets update uniformly.

10. NestLens Data Practices

NestLens workflows may process:

• Entity information
• Relationship data
• Contact intelligence
• Monitoring signals
• Company information
• Research outputs
• Event intelligence
• User-created intelligence

Outputs may include synthesized information.

11. NestHR Data Practices

NestHR workflows may process:

• Employee information
• Team structures
• Leave information
• Attendance records
• Workflow information
• Organizational structures

Organizations remain responsible for compliance obligations.

12. Limitations

Data may contain:

• Delays
• Missing information
• Inconsistencies
• Public-source inaccuracies
• Outdated information
• Coverage gaps

Users should validate important decisions independently.

13. Data Ownership and Rights

Ownership treatment may vary depending on source:

Customer-submitted information:

• Typically controlled by customers

Platform-generated intelligence:

• May be governed by product terms

Third-party information:

• May remain subject to source restrictions

14. Prohibited Uses

Users may not:

• Misrepresent information sources
• Circumvent restrictions
• Bulk replicate datasets
• Use data unlawfully
• Remove attribution requirements

15. Policy Changes

This policy may change periodically.

Updates may be communicated through:

• Website notices
• Product notices
• Email communications

16. Contact Information

NestLens Queries: nestlens@labelnest.in

Operations: ops@labelnest.in

Privacy: privacy@labelnest.in

General Support: support@labelnest.in

Address:

No. 33, 4th Floor, 1st Main, CBI Main Rd, HMT Layout, Ganganagar, Bengaluru 560032