Multiverse places apprentices inside employers, funded through the UK apprenticeship levy. In 2024 that business ran on manual handoffs. Applying took an hour or more, eligibility and motivation were checked by people, and only a quarter of customers ever sent their staff a skills survey. Separately, the company was testing a software product in the US that diagnosed a team's skills gaps and prescribed learning.

Working with the VP Product and Product Director, I co-created the vision to bring the two together: Funded Learning as a Service, running the levy business on the same product across three moments: diagnose, prescribe and treat. In August 2024 it was aligned with the exec team, and it became the foundation for a new product group.

I was design owner for the first levy version, then for the learner skills scan through every change of ownership, and customer experience lead until September 2026. For long stretches I was the only designer across the top and middle of the funnel.

The vision: levy today against the target experience

The vision replaced the hour-long application with one skills scan of about 15 minutes, covering eligibility, role fit, prior learning and motivation, with programme recommendations at the end. The first levy version proved the idea in a quarter: 71% of prospects started the scan and 91% finished it.

Then we hit the catch. A shorter application meant less commitment. Learners took the scan as cold prospects, often because their employer asked them to, and it was their leader who chose the programme, not them.

So the design problem became: how do you keep the speed of a short scan while making sure the people who finish it actually want, and are suited to, the programme they're placed on?

Ask for commitment plainly

I designed a commitment moment at the end of the scan. The first version asked learners to rate their interest on a scale across a range of programmes. Internal teams couldn't interpret the answers, and the feedback exposed a deeper flaw: we were asking people to commit to programmes they knew nothing about.

I used that feedback to get the sales team behind a clearer design. Learners rank their recommended programmes, then answer one direct question: will they enrol in their first choice? Yes, Maybe or No, with a reason if it isn't Yes. When learners told their managers the ranking wasn't working, I replaced it with a familiar drag-and-drop pattern, and later split ranking and commitment across two pages to make each decision lighter.

Commitment moment, first version and final

Fix the funnel one question at a time

The scan was fast but it wasn't fair. In the pilot, 69% of applicants were auto-rejected, against 4% in the old process. With our learning and workforce partners, I worked through the scan question by question.

• A rule rejecting anyone working fewer than five hours on relevant tasks caused most of the rejections. We split it into smaller bands and reworded it to look forward.
• We audited every role-fit question and answer, and wrote rules that all new questions now follow.
• Learners see their recommendations straight after the scan, rather than waiting for sign-off.
• Branching skips follow-up questions when a learner has no knowledge of a skill.
• Operational questions moved into the scan, so staff stopped chasing them by hand.

The hours question, before and after

Make the recommendation explain itself

The last step was trust. Customers pushed back when a recommendation felt like a black box. One wanted their staff on a programme the scan didn't recommend, and nothing on screen explained why.

The skills scan became Programme Matcher. Instead of three steps and a yes or no eligibility result, learners see ranked matches, a best match and how their role aligns with each programme. For customers, a taxonomy linking business pains to skills gaps to programmes put the reasoning on screen.

After we added a step where learners map their day-to-day work, confirmation of their duties rose from 23% to 61% in two weeks.

Programme Matcher, before and after

The 2025 pilots missed most of their targets. Learners rated the new scan 2.4 out of 5, against 4.1 for the application it replaced, and only 35% were approved against a target of 75%. The product was faster, but it wasn't yet fairer.

In October 2025 we stopped adding new deals for a quarter. Earlier that year I had written up the three possible ways to run the product, and that framing became the basis for the reset: one path, three fixes. Then at our largest launch, in February 2026, fewer than 200 people using the scan at once caused it to time out.

Both setbacks came from scaling ahead of the platform. They're also why the later numbers mean something: the relaunch was built on fixing what the pilots exposed.

By June 2026 every long-form programme ran through the product. In the first full quarter:

The relaunch quarter beat its targets: 31 deals against 25, and 1,619 learners committed against 1,000. At our largest launch, 508 learners enrolled against a target of 231.

One gap remains: customer satisfaction is 67%, below the 76% baseline.

Since August 2026 I've been design lead on the next version, rebuilding the prescribe step as an AI-driven, conversational experience. My early prototype drove two decisions at Tech Leadership: sensitive personal data is captured by a structured form rather than passed through the AI agent, and design effort goes first into explaining the recommendation.

What I'd do differently

• Design for the managers sooner. Line-manager approval came late, after learners had already committed.
• Treat the emails as the product. Many complaints came from messages sent at the wrong moment, not from the screens.
• Push harder on the platform before scaling, rather than finding its limits at our largest launch.