An Excel Sheet With a Billion-Dollar Problem

When I joined Interos through Neudesic in early 2021, the company was moving fast — adding headcount, closing enterprise deals, and racing toward a valuation that would eventually cross $1 billion. The product, however, was a different story.

The legacy platform presented supply chain risk data exactly like an Excel spreadsheet: rows and rows of data points with no narrative, no hierarchy, and no path to action. Enterprise analysts managing thousands of suppliers across global tiers were barraged with information and left to interpret it entirely on their own. For a platform selling clarity around supply chain risk, it was doing the opposite of what it promised.

The new platform needed to do the inverse: condense complex datasets into meaningful aggregates, provide continuous monitoring through events and risk score changes, and give users a clear path from high-level overview down to the details that actually matter.

I was embedded in Interos's product team as a UX Feature Lead — sitting inside agile sprints alongside PMs, engineers, and their internal design team. My scope grew over time as trust built. By the end of the engagement I was leading design across five parallel workstreams, mentoring a junior designer, and acting as a bridge between design and an engineering team that had historically worked in isolation from UX.

The homescreen is the most valuable real estate in any platform — it sets the mental model for everything else. The legacy homescreen was an overwhelming data dump. I started the redesign with one question: what does a user need to know within 30 seconds of logging in?

The answer shaped everything. Supply chain analysts needed a fast read on their overall risk posture — not a list of raw scores. Executives needed a birds-eye snapshot. Managers needed to know what had changed since yesterday. The homescreen had to serve all three without fragmenting into three different products.

I started on paper. Filling a notebook with layout concepts before opening Figma — working out information hierarchy, card groupings, and drill-down patterns in rough sketches first. That process surfaced a key structural decision early: lead with risk posture at the aggregate level, then surface the most urgent changes, then allow drill-down. Simple hierarchy, but it required fighting the impulse to show everything at once.

Interos added over 200 employees in two years. The company's valuation crossed $1 billion during the engagement. That kind of growth is exhilarating — and it creates real friction. Processes that work at 50 people break at 250. Design decisions that should have taken a day sometimes took a week to get in front of the right people. Keeping five to ten product pods aligned without a formal design process meant enormous coordination overhead.

Engineering was its own challenge. When I joined, engineers were not used to having their work reviewed by UX — and in some cases, not used to having their work stopped. The existing culture was build-first, design-after. That gap between design intent and what shipped was significant, and it showed in the product.

I worked with the team to propose a formal UX Review process: a critical quality checkpoint where the UX team would review in-progress engineering work before it went to QA. Some old-school engineers pushed back hard — they perceived it as a loss of control, a slowdown, a new layer of oversight they hadn't signed up for. What it actually was: a quality check that caught issues early rather than after they were already built.

Getting this off the ground required going above the immediate team. We brought the proposal to the VP of Product at Interos, who understood the cost of shipping broken design experiences to enterprise clients. With their backing, the UX Review process was implemented and became a standard part of the development cycle — not without friction, but successfully.

I joined the company profile midway through development — which meant inheriting someone else's half-finished design. The existing work had real problems: poor contrast, inconsistent hierarchy, and dark UI patterns that weren't following established best practices. Text was hard to read. Interactive elements weren't distinguishable. Risk score data didn't breathe.

I was also inheriting a collaboration arrangement. A junior designer on the east coast was already on the project. We worked in a tag-team setup across time zones — when he logged off, I took over. It wasn't just a design challenge; it was a mentorship dynamic that I hadn't expected but came to value. Getting him to a point where he could own decisions independently was as much a part of my contribution as the pixels I shipped.

The redesign established a proper dark UI system: layered backgrounds, consistent elevation, and a typographic hierarchy that made risk scores legible and actionable. The core principle was progressive disclosure — surface the aggregate first, gate the detail behind intentional interaction.

Supply chain risk doesn't follow business hours. A Tier 2 supplier hit by ransomware. A country placed under new sanctions overnight. A critical component manufacturer flagged for labor violations. Users needed to know about these events whether they were in the platform or not.

The alerts system was designed around three surfaces: in-app alerts for active users, an events feed for investigation, and a branded email digest for awareness outside the platform. The hardest design problem wasn't making the alerts work — it was managing volume. For large supply chains, the number of events could reach into the hundreds per day. Without ruthless prioritization, an alert system becomes noise.

The solution was configurable risk thresholds tied to categories and supply chain tiers — giving analysts control over what triggered an alert, at what severity, and for which suppliers. The email digest followed the same logic, surfacing only the highest-priority changes rather than blasting everything.

A third-party company had produced designs for the events timeline — a chronological view of supply chain events across the entire ecosystem. The brief was clear: the designs were visually complex, hard to parse, and needed a significant cleanup before they could be built.

My task was to simplify elements, fix the visual hierarchy so filters were prominent, and restructure the layout to surface the most important information first. The before/after speaks for itself — the original design buried the most critical information under layers of visual noise. The redesign pulled the filters to the top, cleaned the timeline rows, and established a consistent scanning pattern from left to right.

Alongside the feature work, I led a one-month research engagement that wasn't about the product at all — it was about Interos itself. The company had grown fast enough that nobody had a clear picture of their own data acquisition process. Which data sources had been purchased? Which were active? Who owned what? The answer depended entirely on who you asked.

I interviewed every department head across the data acquisition lifecycle — mapping each team's role, identifying where handoffs broke down, and tracing the process across every Jira board it touched. The output was an end-to-end journey map of the data lifecycle, with pain points annotated and recommendations structured as good / better / best options so stakeholders at different levels of investment could act on it.

Platform V2 launched in July 2021 — the first major redesign of the Interos product experience. Five workstreams delivered. Three flagship features shipped. A junior designer mentored through the engagement. And a data lifecycle map that gave the company a single source of truth it didn't have before.

The platform they shipped looked nothing like the Excel spreadsheet they started with. It had hierarchy, narrative, and a clear action path from aggregate to detail. That's the work.

Interos taught me what it looks like to do UX inside a company that's outpacing its own process. The product was ambitious, the team was smart, and the design challenges were genuinely hard — but the infrastructure to support good design was still being built in real time while I was trying to use it.

The most valuable skill I developed here was designing for data density — finding the right level of abstraction for complex enterprise data so it becomes legible without losing fidelity. Supply chain risk is inherently complex. The job isn't to simplify it into something false. The job is to give it just enough structure that users can act on it.

If I were starting over, I'd push harder earlier to establish a shared design system. Across five workstreams and multiple collaborators, the lack of a unified component library created inconsistency that we spent time reconciling rather than eliminating. That's a lesson I've carried into every embedded engagement since.