I didn't start with AI.
I started with a problem.
I'm Amelia Ph. Nguyen — an early-career operator with experience in creator content, commerce and data. I build practical tools to make decisions clearer and workflows more reliable.
From real work to better systems.
I’ve worked across product launches, international creator campaigns and aftersales analytics. Across those roles, I kept noticing the same friction: fragmented information, repetitive steps and unclear review criteria. That curiosity led me to workflow automation — and eventually to SIGNAL.
Follow the journeyFrom operations to
AI evaluation.
Four chapters, one evolving curiosity. Select a chapter to see what I worked on and what it taught me.
Execution taught me to think commercially.
My first chapter brought me into digital commerce, creator partnerships and product launches. Co-leading TikTok Shop livestreams taught me to connect shopper insights to concrete decisions and measurable results.
Measured experience.
Real operational impact.
Results from my roles at INNO, 4AM and Mercedes-Benz Vietnam.
These outcomes come from professional experience, not SIGNAL.
Manual screening and reporting showed me how much work follows repeatable patterns. I started automating those steps — then asked a harder question: how can we recognize an incorrect automated decision?
See what that question became ↗Meet SIGNAL.
A creator evaluation experiment that became a study of criteria, missing evidence and human judgment.
SIGNAL.
A working prototype for traceable rule-based decisions, human review and rubric retesting.
Interactive rubric demo with synthetic data; not production model benchmarking.
Explore the interactive SIGNAL demo ↗
ACTUAL PROTOTYPE SCREENSHOTI like people who ask better questions.
I'm interested in thoughtful technology, operations, data quality and the human decisions behind reliable systems. Happy to connect or exchange ideas.