Hi! I'm Angela Welchel, a UX design leader in San Francisco.
I have 15+ years of experience driving thoughtful, measurable, and engaging solutions across consumer, enterprise, and streaming entertainment. I’m interested in bringing clarity to complex problems through strong design thinking, and crafting experiences where design, data, and storytelling come together to create real impact.
My Resume
Design Systems • Visual Design
AI/ML • User Research • Prototyping
AI/ML • Data Visualization
IA • Visual Design • Email Design
IA • User Testing • Design Systems
UI • IxD • User Testing
UX • User Testing • Mobile
IA • User Testing • Design Systems
VUI • User Testing
Visual Design • UI • IxD
Visual Design • IxD • Mobile
Visual Design • UI • User Testing
Have a project or a process you'd like to discuss? Drop me a line and let's talk:
Spend Guard is a fraud detection tool powered by AI and machine learning algorithms.
It automatically detects, flags, and reports on suspicious transactions submitted by employees and suppliers.
Full case study available upon request.
Team
Role
Sole UX designer:
Timeframe
3 months
Business Problem
Fraud as a problem can affect any business, with potentially millions of dollars of loss. Current auditing methods are largely manual and time-intensive. Other solutions are rules-based and therefore less effective than AI solutions. Spend Guard can proactively and continuously monitor a company’s spend, catching fraud before payments are made.
On-site contextual research, user interviews, and iterative prototype feedback sessions
Testing user flows with different personas to uncover pain points and priorities
Collaboration with machine learning team to understand data patterns and visualizations
As this was a heavily AI/ML-driven product, I worked with the data scientists to understand their approach and problem-solving perspective, including how to improve accuracy over time.
Communicating with executive stakeholders to drive alignment on solutions
This was a high-visibility product with a lot of executive enthusiasm and opinions. To gain alignment with this vocal group, I arranged regular check-ins to achieve specific goals:
Wireframe flows for early usability feedback
User-centric designs align with multiple persona goals, providing overviews, actionable detail, and feedback to the ML algorithms
Driving Measurable Results
Early-adopter customers saw immediate value from implementing the product, including over $135M total identified non-compliant spend (as of 2021). Their reviews helped drive sales and increased customers by +106% year-over-year.
“A simple system and intuitive user interface, that already with 3 days uptime has paid back the investment.”
