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: contact@labelnest.in
Address:
No. 33, 4th Floor, 1st Main, CBI Main Rd, HMT Layout, Ganganagar, Bengaluru, Karnataka, India 560032