Designing an enterprise machine learning platform that drives real decisions.

Diagnosed the real problem with enterprise supply chain platforms

Forecasting platforms have been around for decades, with established players like SAP, Oracle and o9 shaping the category. But the experience often lags: heavy UI, steep onboarding and slow workflows that lean on the customer success team to fill the gaps.

Stemly’s opportunity was to build a modern product: a clear, fast interface that enterprise teams could learn quickly, without giving up the accuracy and depth that would drive real savings.

Reduced risk with rapid prototyping

For complex products, the fastest way to learn is to prototype and test early. We built interactive prototypes across planning, forecasting and optimisation workflows to validate assumptions before engineering investment.

Prototypes aligned product, data science and commercial teams on what mattered. They also helped sales tell a clearer story and bring back feedback from real enterprise users.

This approach let us iterate quickly, keep scope realistic and design workflows that felt simple even when the underlying models were not.

Developed a platform structure and shared language

Early on, teams had different mental models of how the platform should work. Some were using the same words to mean different things. That’s a fast way to create confusing UI and inconsistent product decisions.

We introduced a term dictionary to define domain concepts and UI naming. For data-heavy products, clarity in language is part of the product experience.

We then shaped the platform structure by studying the SaaS patterns that worked and adapting them to Stemly’s needs. An organisation → workspace → module model gave us a scalable foundation.

Delivered a design system built for shipping

To scale quality and speed, we built a design system informed by Google's Material UI 2 and the Vuetify 2 UI framework, tuned for a data-dense enterprise product.

The system was built alongside the brand refresh. It went into the product first, then the marketing site and the interfaces around it.

We focused on reusable, build-ready components mapped to the front-end library, reducing one-off UI and helping engineering ship faster without quality drift.

Designed buildable products

In data-heavy products, precision is non-negotiable. Naming, behaviour, states and edge cases have to be explicit, or engineering and users will read the same screen differently.

Over time, prototypes shifted from ideal to shippable. To get there, the team kept learning across data science, forecasting and visualisation, so product choices stayed grounded in how the system actually worked.

Prioritised enterprise-ready capabilities

Stemly serves enterprise teams with large datasets and high upside from better forecasting accuracy. We prioritised a set of capabilities needed to operate confidently at scale.

Multi-tenancy was core: an organisation/workspace structure designed for data isolation, security and scalable administration.

We then took on identity and access management: users, groups, roles and data permissions. It was a complex area and we simplified it without losing control or auditability.

Finally, we designed connectors for ERP and data integrations via APIs, supporting large volumes and reliable ingestion for real-world enterprise environments.

Designed search that makes data usable

In data-first products, search is a workflow of its own. Users need to find the right time series, hierarchy or range quickly, then turn it into charts and tables without friction.

We designed a next-gen search experience that blended speed with control: type-to-search with guided properties across time series, frequency, date ranges and hierarchy, so users could refine without getting lost.

Created a bold brand built for trust

As the product matured, Stemly needed a brand that matched the value it delivered. The brand must be credible for enterprise, distinctive in a crowded category, and consistent across product and go-to-market.

We refined the story and visual identity to signal precision and momentum. The calming blue and energising lime palette supported a clear, modern feel, backed by a brand book and asset library for consistent use.

Valuable takeaways

Designing for technical experts needs radical clarity. Terminology, behaviour and edge cases are part of the UX, and you cannot design any of it without learning how the system works.

Feature work needs ruthless focus. Aligning on trade-offs early prevents an endless backlog and keeps delivery tied to outcomes, not opinions.

Early-stage teams move faster with constraints. Use proven building blocks for non-core features and invest energy into real differentiation.

Data visualisation looks simple but rarely is. Good charts and tables need clear models, consistent logic and careful interaction design.

Clickable prototypes can close the gap in enterprise sales, especially when procurement cycles are long and stakeholders need to feel confident early.