N^o 1Context & Scope

The Challenge

Voltus operates a complex energy platform used by internal teams to monitor load, manage hardware, and report environmental impact — all under time-sensitive, revenue-critical conditions.

As the product scaled, UX challenges emerged across:

• Data-heavy reporting
• Operational hardware workflows
• Inconsistent design foundations

My Role

Senior Product Designer

Led research, interaction design, and system improvements across multiple high-impact initiatives.

N^o 2Design Philosophy

Expert Tools for High-Stakes Environments

Designing for Voltus meant operating in a space where "UX debt" had real-world consequences. These were not consumer flows; they were mission-critical tools where:

• Accuracy: Incorrect data directly translates to lost revenue for enterprise partners.
• Speed: Slow workflows in hardware management lead to missed dispatch events.
• Scalability: Inconsistent design systems increase engineering friction as the product grows.

My focus was to centralize fragmented workflows and create a shared language between design, data, and engineering.

N^o 3Deep Dive #1: Carbon Impact Reporting

Problem

Customers and internal teams lacked a clear, trustworthy way to understand the carbon impact of demand response events — despite having the data.

• Was hard to interpret
• Required domain knowledge
• Didn’t clearly communicate value

Approach

• Partnered with stakeholders to clarify business and user goals
• Designed flows that translated complex energy data into understandable insights
• Balanced precision with readability

This feature has since been publicly released and referenced by Voltus.
View public product overview →

N^o 4Carbon Reporting — Outcomes & Tradeoffs

Outcomes

• Introduced a premium reporting experience aligned with customer expectations
• Improved clarity around environmental impact
• Strengthened trust in Voltus’ data and reporting capabilities

Key Tradeoffs

• Prioritized interpretability over raw data density
• Deferred edge cases to avoid overwhelming first-time users

N^o 5Deep Dive #2: Hardware (Voltlets) Management

The Problem: Operational Fragmentation

Troubleshooting "Voltlets" (physical monitoring devices) was a manual nightmare. Field engineers had to jump between Slack, internal database lookups, and external monitoring systems just to perform a simple reset.

The Solution: A Unified Control Plane

• Consolidation: I designed an in-app hardware management experience that pulled real-time device health into a single view.
• Self-Service: Enabled remote resets and diagnostic triggers, removing the "Engineering bottleneck" for routine maintenance.
• Role-Specific Clarity: Created tailored views for Field Engineers (diagnostic-heavy) vs. Dispatch Ops (status-heavy).

N^o 7Deep Dive #3: Design System & Color Tokens

From 100+ Colors to a Scalable Token System

As the platform scaled, the UI suffered from "Color Drift." We had over 100 hard-coded hex values, making global changes risky and confusing engineers.

The Initiative: Semantic Systematization

• The Audit: I led a cross-functional audit to reduce the color library by ~40%.
• Semantic Tokens: I introduced a token-based system (Surface, Component, State) that moved the conversation from "What color is this?" to "What is this color’s purpose?"
• Engineering Alignment: Synced Figma styles with Storybook and code, reducing UI-related PR cycles by 15%.

N^o 9Design Leadership & Collaboration

How I Worked

• Partnered closely with Product and Engineering from discovery through delivery
• Balanced near-term delivery with long-term system health
• Advocated for UX in operational, data-heavy, revenue-critical contexts

What I Learned

• Small system decisions compound quickly
• Internal tools deserve the same UX rigor as customer-facing products
• Clarity is a growth lever, not a nice-to-have

N^o 10Closing

Why This Matters

At Voltus, I wasn’t just shipping features — I was helping the platform mature.

This experience shaped how I think about:

• Designing under operational and technical constraints
• Scaling systems alongside fast-growing products
• Creating clarity in complex, data-heavy domains