AI StudiobySupaHuman

The foundation the products are built on

Custom AI for the work a product doesn't cover.

The AI Studio is our engineering team for one-off builds. VETos started here, as a course-creation system for Mast Academy. We work with RTOs, TAFEs and universitiesPTEs, wānanga, polytechnics and universities, peak bodies and government on jobs that need something built rather than bought. We design the system, prove it works on your data, and then run it for you.

200+
AI projects delivered across NZ and AU
30
In-house engineers and AI specialists, many PhD-level
6 wks
Idea to production, a typical first build

Who we work with

When VETos and Nova 5 aren't enough.

VETos and Nova 5 handle the work most providers share. The Studio takes on the work that's specific to your organisation or your sector.

01 · PROVIDERS

RTOs, TAFEs and dual-sector providersPTEs, wānanga and tertiary providers

The bespoke workflow VETos doesn't cover yet: enrolment-to-completion automation, AVETMISS and compliance reporting, RPL at scale, or an AI operating system across a multi-RTO group.The bespoke workflow VETos doesn't cover yet: enrolment-to-completion automation, TEC and NZQA reporting, RPL at scale, or an AI operating system across a multi-campus provider.

02 · PEAK AND STANDARDS BODIES

Jobs and Skills Councils and industry bodiesIndustry Skills Boards and industry bodies

Training package development, industry consultation and standards maintenance for a whole sector, where one system serves hundreds of RTOs.Skill standards development, national moderation and industry consultation, where one system serves hundreds of providers. Connexis, ServiceIQ and Competenz already work with us.

03 · GOVERNMENT

Regulators and departmentsAgencies and ministries

Working with ASQA, state training authorities and DEWR on the problems that sit above any single provider: audit intelligence, quality-signal analysis, policy modelling and sector-wide reporting.Working with NZQA, TEC and MoE on the problems that sit above any single provider: audit intelligence, EER evidence analysis, policy modelling and sector-wide reporting.

The hard problems

What we're usually asked to build.

These are regulated jobs where mistakes are expensive. In each one the AI reads, drafts and does the routine steps, and people sign off the decisions.

01

Compliance and audit intelligence

Reading thousands of pages of evidence, policy and regulation to find risk before an auditor does. Every finding links back to its source.

02

Sector-scale reporting and insight

Turning enrolment, completion and outcomes data across many providers into decisions a minister, board or regulator can act on.

03

Standards and qualification development

Drafting, mapping and maintaining standards against industry consultation and international frameworks, in months rather than years.

04

Recognition of prior learning at scale

Matching lived experience and workplace evidence to competencies for whole cohorts, with the same standard applied to every applicant.

05

Tenders, funding bids and RFIs

Drafting submissions from what your organisation already knows, with a first draft in hours.

06

The operating system for a provider group

A private platform that connects your data, models, integrations and AI applications under one set of controls.

How we work

Working software in weeks, on your own data.

You see working software every one to two weeks, running on your real data. Security work happens during the build rather than at the end.

01

Start with the outcome

We start with the outcome you want, how the work happens today and who does it, and only then plan the build.

02

Find where AI helps

We work out which steps and decisions AI can actually take on, and which it shouldn't. Having done more than 200 builds, we rarely start from zero.

03

Prove it in your context

Every iteration runs on your real data and constraints, so what you see is what you'll get in production.

04

Built for production from day one

Security, monitoring and hardening happen alongside the build, week by week.

Prototype: prove the idea works

Two to six weeks of fast iteration on real data to show whether AI can deliver the outcome. You finish with clear evidence for a go or no-go decision, and the work carries straight into a build.

Build it and run it

A simple project fee for the build, in small blocks. Then one monthly subscription covers hosting, monitoring, support and ongoing improvement. A dedicated team of at least two builds it and then runs it, so the people who learned your domain stay on it.

Selected work

Some of the work.

Built on frontier partnerships, run under Aotearoa and Australian controls:

ISO 27001 alignedNZISM alignedHosted on AWS AustraliaSSO and multi-tenantMicrosoft · Google Cloud · AWS · OpenAI · Anthropic

Responsible AI

Every Studio build carries the same commitments as our products: human decisions on anything that matters, an automated-decisions register, and audit-defensible outputs. Our Responsible AI approach

Tell us what you need built.

Tell us the outcome you're after. We'll reply within one business day with our honest view and a rough shape for the work. There's no obligation.