Alexis is a qualitative interview agent designed to study emotional patterns in everyday digital behavior. It's a conversational system that asks context-aware questions, follows up naturally, and captures rich qualitative data without overwhelming the user.
Traditional qualitative interviews are slow and expensive. I wanted to explore if an AI agent could collect rich emotional data while keeping users comfortable, leveraging the "low friction" nature of automated interactions to help people open up.
Mapped intricate conversation flows for emotional, behavioral, and reflective questioning paths.
Designed the language style and pacing to feel natural, refining prompts to avoid "scripted" robotic responses.
Built a backend that stores conversational context, allowing the model to guide the interview intelligently.
Identified patterns in how users share vulnerable information with AI, testing where interactions felt natural vs. mechanical.
This project sits at the intersection of UX research, behavioral science, and AI. Alexis isn't just a bot; it's a validated research tool. It demonstrates my ability to balance trust, emotional safety, and research validity while building functional technical systems.