Red Velvet AI helps admissions teams process transcripts and documents, rank candidates, and evaluate supporting evidence faster, while keeping decision-makers in control.

We support a wide range of formats including:

• PDFs for academic transcripts

• Plain text and structured syllabi

• Resumes, portfolios, and written statements

• Audio and video files as evidence of practical or informal learning

Our goal is to accommodate the diverse ways learning happens, whether it is through formal education, vocational training, or real-world experience. We are constantly expanding our input capabilities to support more evidence types over time.

Recognition of Prior Learning (RPL), as known as Credit for Prior Learning (CPL), is a formal assessment process that evaluates a student’s prior study, work experience, or informal learning to determine eligibility for academic credit. ​ In Australia, it must align with the Australian Qualifications Framework (AQF) and meet compliance standards set by TEQSA or ASQA. For institutions, RPL involves interpreting complex rules, verifying evidence, and ensuring fair, defensible credit decisions across diverse cohorts.

Yes! Institutions can set their own curriculum structures, equivalency rules, and approval workflows. We support full override and review before any output is final.

Absolutely! Institutions can configure logic, approve or reject matches, and provide human commentary in the process. The feedback will be used for future evaluations.

No! Our platform suggests matches, but academic staff always make the final call. Think of us as a co-pilot, not an autopilot.

We offer REST APIs to integrate with student information systems (SIS), CRMs, and learning platforms, as well as CSV export. Our tools can work in the background or appear as part of your current workflow.