Designing the platform when the product is a research tool

Context

Exabyte, later Mat3ra, is a material-science platform for simulating synthetic materials through atom-by-atom crystal lattice models and computational workflows. The product was scientifically deep: users were scientists in universities and corporate R&D labs, and a poor interaction model could mean wasted setup time before expensive computation.

The company had a bigger ambition than a single expert tool. It needed to become a platform that organisations could onboard into, search across, compare materials within, and use as part of a repeatable research workflow. The IA, navigation, and visual language had not caught up to that shift.

Role & team

What I led

I led three repositionings:

• •Discovery and reframing: I moved the mental model from "a tool you log into" to "a platform that onboards an organisation." That changed the default actions, first-run experience, account model, and IA root.
• •IA, search, and navigation: I rebuilt the navigation around scientists' actual workflows: finding materials, comparing candidates, configuring simulations, reviewing results, and returning to previous research states.
• •Move to Material: I adopted Google's Material foundation rather than continue maintaining a bespoke language at startup scale. That freed design time for the product-specific complexity: computation setup, simulation results, organisation administration, and scientific search.

Process — three acts

Act I — Discovery

I started with field-study work: identifying the principal users, interviewing scientists, mapping their needs into flows, and comparing the actual research workflow with the product's assumed workflow. The output included personas, an information-architecture schema, and principal user flows for the founders and engineering team.

The key move was to treat onboarding as part of scientific work, not as a generic account setup problem. A university lab, a corporate R&D group, and an individual scientist needed different defaults, permissions, and paths into the same computational system.

Act II — IA + system

Search became especially important. I worked through standard search and a smarter scoring model that could surface better materials from Exabyte's own algorithmic understanding, so the product could help scientists narrow the field before committing to deeper analysis.

Act III — Scale and handoff

The handoff focused on patterns the small engineering team could extend without me in the room. I documented the new IA, left design files and prototypes, and designed core surfaces around reusable Material patterns so the team could keep shipping without maintaining a fragile bespoke UI system.

One important surface was a single-page Material Editor: a workspace with live results and guided pre-rendering cues, designed to help users understand whether a material setup was worth running before spending compute on a full analysis.

Outcome

• •Platform reframe: repositioned the product narrative from tool to platform for organisations, universities, and research teams.
• •IA and navigation: shipped a new information architecture, search model, and navigation structure around scientific workflows.
• •Product surface: produced 200+ mobile and desktop wireframes across onboarding, dashboards, material search, comparison, compute, and help flows.
• •System focus: moved the UI onto Material so the startup could spend more attention on domain-specific product problems.
• •Future-facing work: explored AI and scientific-computing UI ideas early, including guided search and natural-language style interaction concepts.

What I'd do differently

I would move faster toward Material adoption. We stayed in wireframing too long before applying the brand skin — and in hindsight we should have applied it immediately, even imperfectly. The cost of fidelity debt compounded: decisions made in greyscale had to be revisited once the visual layer arrived. The right move was to get the branded surface in front of people early and iterate from there, reserving the careful design energy for the parts only Exabyte had: scientific search, simulation setup, comparison, and compute-aware decision support.