Frequently Asked Question

Data Quality Assurance & Credit Reinstatement Policy

Last Updated 3 months ago

Maintaining accurate, reliable, and up-to-date data is a core priority. The platform continuously improves its data coverage and validation processes to ensure high-quality results across both Keyword Scan and Tech Lookup.

Data Accuracy & Updates

The database is regularly updated to:

Include newly emerging technologies and tools
Improve detection accuracy across websites
Validate and refine existing datasets

This ongoing process ensures that results remain relevant, comprehensive, and aligned with real-world technology usage.

Credit Reinstatement Policy

Credits are reinstated under the following conditions:

Inaccurate Data: If the downloaded dataset contains clear inaccuracies or incorrect information
System Issues: In cases of errors, bugs, or server-related problems that affect report generation or data delivery

To request credit reinstatement, users must raise a support ticket under “Account & Billing.” Each request is reviewed before credits are restored. Once verified, the corresponding credit will be restored to your account.

User Responsibility

Credits are not reinstated in the following case:

If an incorrect dataset is downloaded due to user error (e.g., selecting the wrong keyword, filters, or report)

For Keyword Scan, users can review sample results before downloading, helping ensure the dataset matches their intent. As a result, credits are generally not reinstated for these cases.

Reporting Issues

Users are encouraged to report any inconsistencies or suspected inaccuracies. This helps improve overall data quality and ensures faster resolution.

Key Takeaways

Data is continuously updated and validated for accuracy
Credits are reinstated for verified inaccuracies or system-related issues
Credits are not reinstated for incorrect downloads due to user selection
Keyword Scan provides preview data to help avoid mistakes
Users can report issues to improve data reliability across the platform

This approach ensures fairness, transparency, and continuous improvement in data quality.