Room Area Viewer Editor

PROJECT AT A GLANCE

Led end-to-end UX/UI design and collaborated with a team of five to deliver a Revit data analysis plugin that streamlined spatial classification, centralized data management, and improved workflow efficiency, adopted by 400+ users across six studios.

Role:
Sole Product Designer
Figma, Miro, & XD

Timeline:
20+ Weeks – 2023 Beta Testing, 2024 MVP Launch

Client:
SmithGroup, A&E Firm

Product Solution

1.0 Final Prototype (Figma)

What is R.A.V.E?

R.A.V.E. is a data analysis plugin for Autodesk Revit designed to simplify spacial classification process & centralize data management.

What does that mean for users?

After – Streamlined Process With R.A.V.E.

Before – Complex Process

1.0 Streamlined Workflow Implementing RAVE

Jump To Designs

2. Project Roadmap

Problem

Architects struggle to classify spatial usage datasets efficiently, leading to inconsistent data practices, slower project analysis, and limited insights across studios.

Goal

Optimize how architects classify and access spatial data to reduce manual effort, increase efficiency, and support data-driven design decisions.

Success (KPI)

– Reduced manual spatial classification and data retrieval time by at least 25%.

– Achieved adoption of R.A.V.E. in at least 3 in-house studios.

– Engaged 100+ active users firmwide.

Digging Deeper

Understanding the Why Starts with Listening

Employee surveys surface the real-world roadblocks users face.

Insight-Driven Opportunities

Key themes revealed through survey feedback, highlighting where to focus UX improvements.

Need for Centralized Workflow, Demand for Enhanced Visualization, & Strong Desire for Efficiency
Jump To Designs

Uncovering workflow friction and hidden drop-offs through personas and user journeys. (Figure 3.0)

Bringing Insights to Life

3.0 Personas & User Journeys

Mapping New Worflows

Information Architecture

3.1 IA Structure Mapping Key User Workflows

Technical
Constraints

Developers explored the framework early, establishing technical constraints that guided my design exploration.

50+
Wireframes

Rapid ideation focused on technical feasibility, streamlined data input, clear categorization, low cognitive load, and intuitive visuals.

Initial
Solution

I structured the interface around user actions, prioritizing intuitive data entry and aligning with the three insights driving design: centralized workflow, enhanced visualization, and efficiency.

User Testing

Collaborated on eight moderated usability tests using a Figma prototype I created to validate design decisions before development.

01 Design Update

Before –
Unclear Flow, Mixed Purpose

Disorganized panel left users uncertain about steps and mixed input with output.

After –
Guided Steps, Cleaner Panel

Structured data entry flow with staged disclosure and clear grouping for better task clarity.

01 Design Update

03 Design Update

Before –
One View,
Low Clarity

Stacked bar with crowded labels, limited readability, no flexibility.

After –
Flexible Views, Finer Control

Toggle between a circle graph for building totals and bars for easy space-type comparisons.

03 Design Update

Evaluating Impact

423 projects classified totaling 40,000,000+ sqft classified to date
by over 400+ users
by six in-house studios.

Lessons Learned

Validate
Assumptions With Testing

Testing prototypes in users’ actual workflow uncovers true priorities and guides iterative refinement.

Design for Scalability

Building modular systems that apply to the full interface and extend across other platforms like Power BI dashboards.

Integrate Early with Technical Teams

Embedding design in engineering discussions early prevents misalignment and reveals opportunities for automation.

Next Steps

  1. Enhance Onboarding Experience

    • Design a smoother entry point for new users and those returning after a break.

    • Consider interactive tutorials, contextual tips, or guided workflows to help users get oriented quickly.

  2. History & Revision Tracking

    • Introduce a history area where users can view changes they’ve made.

    • Enable easy rollback or edits to correct mistakes and maintain confidence in data integrity.

  3. Leverage AI for Data Entry Automation

    • Explore opportunities to automate repetitive data entry tasks.

    • Use AI suggestions or predictive inputs to speed workflows while keeping user control.

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