CV Import turns faculty CVs into structured data in minutes. Paste CV content into Scholarly’s flexible intake form and AI will map entries to your institution’s CV fields quickly and accurately—reducing manual entry, saving time, and improving data quality with human oversight at every step. Trust is foundational: access is permission-aware and auditable, data is encrypted, Scholarly is SOC 2 Type II–compliant, and faculty data is never used to train AI models. (source, source, source)

• What is CV Import? * An AI-assisted intake that converts pasted CV text into structured records mapped to your organization’s configured CV fields—always with human validation before saving.
• How does it work? * 1) Copy any text from a faculty CV and paste it into the Scholarly UI.
  * 2) Scholarly’s AI identifies categories and fields.
  * 3) Data is mapped into your desired schema.
  * 4) Review, confirm, and finalize your entries.
• Which CV sections are supported? * Academic Training, Appointments, Research, and Lectures—mapped to your institution’s schema for consistency across records. (source)
• Why does this matter? * It reduces data-entry time and the risk of manual errors, while improving consistency and quality across faculty profiles—freeing faculty and staff to focus on higher-impact work and enabling more accurate insights across your institution.
• Is it secure and under my control? * Yes. Each suggested mapping shows its source context so you can validate, edit, and approve. Data access is role-based and auditable; data is encrypted in transit and at rest; Scholarly is SOC 2 Type II–compliant; and faculty data is not used to train AI models or retained by AI vendors. (source)