I’m a systems-level UX writer.

I’m a UX writer with experience building language that helps people complete real tasks across onboarding, product UI, and support surfaces.

I trained in UX Writing at Berghs School of Communication, then applied that foundation in product environments like Varsity Tutors and Rive, where clarity has to work for both beginners and power users.

My focus is systems-level UX writing: vocabulary, patterns, and in-context education that reduces confusion the moment it happens.

Project example: Help Tool

Problem

Rive’s feature set was expanding fast, and churn interviews (~20) revealed a consistent root cause: the learning curve was too steep, especially around State Machines and the newer Data Binding workflow.

Idea —> Solution

A Help Tool inside the Editor: hover UI + hold H to reveal a short definition and a link to the right documentation. Lightweight, contextual, and available exactly when a user asks, “Wait, what’s this?”

Process

  • Partnered with Rive’s product designer and met biweekly over six months.

  • Built and refined a spreadsheet inventory of Editor UI by panel, each with a working definition + final definition + doc link.

  • Prioritized which tooltips to ship first based on churn interview themes (State Machines + Data Binding surfaces).

  • Authored and edited ~150 definitions, using a consistent microcopy system (tool = verb-led, concept = “A..” definitions, 1-2 sentences, concrete terms).

Measurement + iteration

Instrumented usage in Amplitude at the individual-definition level to track which terms were being pulled up most often. This created a quantitative signal of where users were confused and where docs/education needed reinforcement.

Cross-functional leverage

This project became a feedback loop connecting:

  • Design: surfacing which UI needed clearer naming, hierarchy, or affordances

  • Growth: tying churn interview insights to product learning friction

  • Docs/Education strategy: using Amplitude “most-opened definitions” to prioritize tutorials, doc IA improvements, and future onboarding

  • Product + Engineering: shipping a lightweight, measurable learning layer inside the Editor that can expand as the product evolves

Previous
Previous

Product Launches / Messaging

Next
Next

Customer Stories / Case Studies