AI-Enhanced Recognition of Prior Learning (RPL) for RTOs and Universities

Red Velvet AI provides an AI-powered platform that supports Recognition of Prior Learning and skills assessment for RTOs, universities, and admissions teams. Our technology analyses resumes, videos, third-party evidence, work samples, and course documentation to assist assessors in mapping evidence against competency frameworks and assessment criteria.

Deakin Applied AI Initiative
HolonIQ 2025 Australia & New Zealand Edtech 50

Skills Recognition

VelvetPath – Streamlines Recognition of Prior Learning (RPL) & Skills Pre-Assessment.
Maps evidence to Units of Competency, identify gaps, keep workflows structured, transparent, and easy to follow.

VelvetPath Platform Image
VelvetPath RPL platform, human-in-the-loop design
VelvetAssess Assessment Validation Platform
VelvetAssess Assessment Validation Tool

Assessment Validation Tool

VelvetAssess – Intelligently Detects Patterns in Learner Responses to Improve Assessment Quality and Reduce Audit Risk.
Analyses multiple learner responses in minutes, revealing patterns, anomalies, and inconsistencies that normally take hours to find.

Transcript Intelligence & Course Mapping

Intelligent GPA Conversion, Ranking & Credit Mapping.
Automatically extracts student, course, and grade information from scanned or digital academic transcripts – no more manual data entry or copy-paste errors!

Transcript Intelligence & Course Mapping
Transcript Intelligence & Course Mapping Platform

What did our clients think?

Highly recommend!

As a trainer in Early Childhood Education and Care, I’m genuinely impressed by how easy it is to use—for both trainers and students. The feedback is accurate, relevant, and flexible enough to meet curriculum requirements for RPL.

My students found the platform very easy to navigate, and I appreciated the ability to upload additional evidence, such as recorded Zoom sessions, as well as add follow-up questions where clarification or extra evidence was required.

It has saved me a significant amount of time and has quickly become part of my daily routine. I highly recommend it to anyone considering purchasing this AI App for their organisation.

Liz Powell

Senior Trainer/ Assessor, The Management Edge (TME)

Love it!

VelvetPath has cut my RPL pre-assessment time by more than half. The platform is intuitive, I can upload evidence, see the mapping instantly, and focus on the judgement calls that really matter. It removes the repetitive admin, but still keeps me fully in control of the assessment process. I wouldn’t go back to doing it manually.

Grace T.

Trainer & Assessor in Diploma of Leadership and Management

Having questions?

What is Recognition of Prior Learning (RPL)?

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.

How does VelvetPath support skills assessment?

VelvetPath does the grunt work in the assessment process.. It analyses applicant evidence (such as qualifications, work references, and portfolios) and automatically maps them against the relevant OSCA and ANZSCO occupation, competency framework, or assessment criteria.

Assessors can then review, adjust, and finalise AI-generated recommendations, maintaining control while benefiting from automation and data consistency.

What kinds of documents can your platform handle?

We support a wide range of formats including:

  • PDFs (Resumes, work samples, portfolios, and written statements)
  • Plain text and structured syllabi
  • Photos evidence
  • 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.

Can I customise the matching rules or override AI suggestions?

Absolutely! You can configure the logic, approve or reject matches, and add your own comments. Your feedback will be used to improve future evaluations.

The AI does not make decisions automatically. It only suggests matches. You always have the final say and make the judgement. Think of it as a co-pilot, not an autopilot.

How does Red Velvet AI integrate with my existing systems?

Red Velvet AI offers REST APIs to integrate with student information systems (SIS), CRMs, and learning platforms, as well as CSV & PDF exports. Our tools can work in the background or be part of your current workflow.

How can I get started?

You can book a demo to enquire further, or contact us at partners@theredvelvet.ai.

Red Velvet AI scales with your organisation

We usually start with a free trial or pilot program!

Registered Training Organisations (RTOs)

We know every RTO operates differently, so our pricing is based on:

  • Volume of applications processed
  • Number of qualifications supported

For RTOs, plans typically range from $200 to $10,000 (excl. GST) per month, depending on your scale and needs.
Start a free trial to explore the platform!

Larger Organisations (TAFEs, Universities, Skills Assessment Bodies)

For larger institutions, pricing is tailored to reflect the complexity and scale of assessments.
Factors include:​

  • Number of frameworks or qualifications
  • Application volume
  • Custom integrations and reporting needs

Book a demo with us to get a quote tailored to your needs!

© 2026 by Red Velvet AI. All rights reserved.
Email: partners@theredvelvet.ai

Red Velvet AI acknowledges the Traditional Custodians of Country throughout Australia and their continuing connection to land, waters, and community. We pay our respects to Elders past and present.