AI User Research Interviews. Qualitative insights beyond just a survey.
Reach is an AI-powered platform for scalable qualitative research. It replaces slow, manual interviews with real-time, adaptive voice conversations - conducted, transcribed, and analyzed entirely by AI.
The system lets researchers upload questions, run interviews at scale, and extract structured insights from an interactive dashboard.
The project was driven by a belief that AI can democratize deep user understanding. Designing and building Reach taught me how to ship fast, validate assumptions, and push the boundaries of what language models can do in real-world contexts.
Backed by the Wharton MBA Venture Fund, Winner of Penn's Venture Labs Implementation Award
Team: Matthew Porteous, Max de Castro
Reach - Demo Page
User research and Reach's vision for insights into markets.
User research today is slow, expensive, and hard to scale. Interviews require recruiting, scheduling, transcription, and manual analysis, often taking weeks and costing $20–$50 per participant. Surveys are faster but shallow, failing to capture the nuance of real conversations.
Imagine a Starbucks executive wants to understand how their University City store is performing beyond the numbers. A customer walks in, gets a prompt on the app, and speaks to an AI interviewer for a free coffee. Minutes later, the executive sees rich, structured feedback, no scheduling, no delays.
Creating interviews - Building step by step
AI conversations that feel natural, enabling valuable user insights.
Most teams rely on static surveys, scripted chatbots, or costly agencies. We found that competitors charge upwards of $20,000 and still require users to find their own participants. Others, lack real-time conversational capabilities, making insights rigid and less actionable.
Reach Demo
Building a product end to end. Try Reach here.
Building Reach taught me the importance of rapid iteration: shipping early, testing often, and learning directly from real users. Each development cycle revealed new edge cases, UX challenges, and technical constraints that helped refine the product.
It reinforced that great products aren’t built all at once, they’re shaped through continuous feedback and adaptation.
Designing with AI means thinking beyond static interfaces, it’s about creating systems that adapt, listen, and respond in real time. This project taught me to consider the entire user journey, from recruitment to incentive, and to reduce friction at every step.
I also learned how to design for both web and mobile, ensuring a seamless experience no matter where users engage.