RPL Robot

COMING SOON

AI-Assisted. Assessor-Led. Better RPL Outcomes.

RPL Robot is an AI-Assisted platform that helps RTOs streamline the Recognition of Prior Learning Process. It guides candidates to submit the right evidence and gives assessors the tools to make consistent, well-informed decisions.

guided
Guided Evidence Collection
assessor control
Assessor Control
structured workflow
Structured Workflows
clear reporting
Clear Outcome & Reporting
RPL Robot
AI Assisted RPL Robot
AI Assisted

1. AI-Assisted Evidence Collection

Help candidates submit better evidence the first time

RPL Robot guides candidates through a structured evidence collection process using AI-powered prompts. The system helps them explain their experience, identify suitable evidence and understand how it relates to unit requirements — ensuring submissions are relevant, complete and well structured.

  • ✔ Smart prompts & guidance
  • ✔ Explain experience & context
  • ✔ Upload relevant evidence
Structured Evidence Mapping

2. Structured Evidence Mapping

Evidence organised against competency requirements

Submitted evidence will be easily organised and mapped against unit requirements, giving assessors a clear overview and reducing manual administration. The platform encourages complete, well-structured submissions, making the assessment process easier to manage and more consistent across candidates.

Images shown are from a product currently under development and may not represent the final product.
Structured Evidence Mapping
Structured Evidence Mapping
Assessor Review & Feedback

3. Assessor Review & Feedback

Keeps assessors in control

AI supports the process — it never replaces the assessor judgement. Assessors review submitted evidence within a structured interface where they can mark evidence as suitable, partially suitable or not suitable, select feedback reasons, provide comments and request additional evidence where required. Every decision is transparent, consistent and fully auditable.

Reporting & Outcomes

4. Reporting & Outcomes

Produce clear, structured RPL reports

At the completion of the assessment, RPL Robot generates comprehensive reports including assessment outcomes & assessor feedback, outstanding competency gaps and audit history — providing a complete record of the RPL process and supporting organisational quality assurance.

Images shown are from a product currently under development and may not represent the final product.
Reporting & Outcomes

Why RPL Robot?

ai-assisted evidence guidance
AI-assisted evidence guidance
structured evidence mapping
Structured evidence mapping
consistent assessor workflows
Consistent assessor workflows
clear audit trails
Clear audit trails
improved reporting
Improved reporting
secure australian cloud platform
Secure Australian cloud platform

Expression of Interest

Be the first to hear about RPL Robot!

Register your interest below to receive updates, product annoucements and early access opportunities.


Read More

Competency Isn’t Enough: Rethinking Digital Skills in 2026

Competency-based training still matters, but on its own, it’s not enough for digital skills. It does a great job of proving someone can perform a task under known conditions. The problem is that digital environments don’t stay consistent. Tools change, workflows shift, and AI introduces new layers of complexity. Learners can be “competent” in training and still struggle when those conditions change.

The gap isn’t in effort or ability; it’s in underlying understanding. That’s where capability comes in. Capability is what allows someone to adapt, question outputs, and transfer their skills into new or unfamiliar systems. It’s what keeps performance intact when the environment evolves. The takeaway is simple:

Keep competency as the outcome & start building capability as the method.

That shift is what turns short-term success into long-term effectiveness.

There’s nothing inherently wrong with competency-based training. In fact, it’s one of the strongest features of the Australian VET system. It gives us clarity. It defines expectations. It creates a shared understanding of what “good” looks like in the workplace.

But when it comes to digital skills, something isn’t quite lining up anymore.

Not in a dramatic, system-breaking way. More in the quiet, familiar sense that learners can complete the training, tick the boxes, and still feel uncertain when they hit the workplace. Or worse—they feel confident right up until the moment something changes.

And in digital environments, something always changes.