ProLearn — Learning Platform
From internal training tool to white label product: designing a learning platform built to scale across organisations
The Problem
Professional development inside most organisations is a mess of disconnected systems. Mandatory compliance modules in one place, optional courses somewhere else, manager-led programmes nowhere near either. Employees don't know what's available, can't track their own progress, and have little incentive to engage beyond the minimum required.
ProLearn started as a brief to fix this for a single company. It became something more ambitious. A white label learning platform deployable across multiple clients and industries, each with their own content, branding, and workforce needs, without the product fragmenting into something different for every customer.
My Role
I came onto this project as Product Design Director, with overall responsibility for design direction, quality of output, and strategic alignment. Rather than being hands-on in day-to-day production, my role was to set the vision, hold the standard, and make sure the team, three experience designers, a product manager, and two developers, were producing work that met both the brief and the bar this level of engagement requires.
In practice this meant owning the visual design direction from the outset, running stakeholder sessions to align on goals and priorities, and making the calls that kept the project on course when the direction shifted from a single internal deployment to a white label platform. That pivot didn't just change the technical requirements; it changed what success looked like entirely. Steering the team through that transition without losing momentum or quality was as much a leadership challenge as a design one.
The Core Challenge
The hardest design problem on this project had nothing to do with features. It was about learning behaviour itself.
The research I directed consistently surfaced two distinct types of learner. People who wanted quick, digestible content they could fit into ten spare minutes, and people pursuing structured, in-depth programmes over weeks or months. These aren't just different preferences. They require fundamentally different information architectures, pacing models, and motivational mechanics.
Getting both groups into one product without creating a bloated or confusing experience for either was the central tension I had to resolve. The instinct in situations like this is to build more. More modes, more settings, more options. Part of my job was holding the team back from that instinct, because it usually makes things worse. The decision I kept coming back to was about subtraction rather than addition. Finding the underlying structure that made both learning styles feel at home in the same product, which required a lot of throwing things away.
The Decisions That Mattered
The first structural decision I made was to reorganise around learning intent rather than content type. Early concepts the team had developed organised the platform by format. Courses here, microlearning there. User testing showed quickly that this created friction, because people didn't think in terms of format. They thought in terms of what they were trying to achieve. I directed the team to restructure the entire architecture around goals and career context, letting the platform surface the right format for the right moment rather than asking users to make that decision upfront.
When the product direction shifted to white label, I made the call to rebuild from the inside out rather than retrofit customisation onto what already existed. I established multi-tenancy as a first principle in the design system. Visual tokens, content slots, and configurable components that could carry any client's brand without requiring bespoke design work each time. It cost time in the short term and saved considerably more in every deployment that followed.
The third decision was about when to introduce AI personalisation, and my answer was: not yet. I held the team back from leading with AI-driven recommendations until we had enough user behaviour data to make them meaningful. My approach was to use a rules-based system initially, with AI personalisation layering in as users built a history on the platform. That decision avoided the hollow "personalised for you" experience that plagues so many learning tools at launch, where the algorithm has nothing to go on and the recommendations are essentially guesswork.
Outcome
Tested with 25 users, the redesigned platform delivered a 20% improvement in task completion speed, with users describing the experience as noticeably smoother and more intuitive than previous tools they had used. Engagement and course completion rates improved, and the white label architecture successfully enabled deployment beyond the original client, which was ultimately the whole point of the pivot.
The more interesting result, for me, was what the research phase taught us about the gap between what organisations think their employees need from a learning platform and what those employees actually need. That gap is almost always larger than anyone expects.