Loading project case study
All work

Personal Projects / 2025

AI Persona Generator

Helped product teams move from raw interview transcripts to reviewable personas and opportunity maps in minutes, leaving more time to interpret evidence and explore solutions.

Visit live app Read the case study
AI Persona Generator

Value and impact

Transcript to first synthesis
Weeks → Minutes
Evidence to opportunity map
Hours → Minutes
Source material reviewed
Higher Coverage

01

Overview

AI Persona Generator is a tool I designed and built with Cursor and Google Gemini to bridge the gap between raw research insights, opportunity mapping, and design exploration.

It generates detailed personas and opportunity solution trees from research transcripts and insights. I took it end to end, from concept and interaction design to implementation and deployment, using AI to amplify my existing design and coding workflow.

  • Agentic Build
  • AI/ML
  • UX Research
  • Cursor

Challenge

Product teams faced significant challenges in manually synthesizing research findings into actionable personas and opportunity solution trees.

The process typically took weeks of manual effort, often resulting in inconsistent outputs and missed insights. This bottleneck delayed design exploration and strategic decision-making.

Approach

I designed and implemented the application with Cursor, moving from concept to a deployed product in days rather than weeks.

I used Google Gemini to analyze research transcripts and extract patterns, user needs, pain points, and opportunities, then designed an interface for uploading source data and refining the generated outputs.

04

Solution

Created a web application that accepts research transcripts and insights as input, processes them through Gemini AI, and generates detailed personas covering demographics, goals, pain points, and behavioral patterns.

The system also automatically constructs opportunity solution trees, mapping user needs to potential solutions and design opportunities.

05

Impact

Researchers and designers can move from raw transcripts to a structured first synthesis in minutes rather than weeks, and create an opportunity map in minutes rather than hours.

Teams gain broader, more systematic coverage of the source material while retaining human review for interpretation and judgment. The faster starting point gives product organizations more time to compare opportunities and test directions instead of spending the discovery cycle formatting artifacts.

06

Key learnings

Building with Cursor showed how AI can amplify a designer's ability to implement and ship a complete product quickly.

AI can synthesize research systematically when it is properly guided, but human review remains essential. Structured outputs such as personas and opportunity trees helped turn the synthesis into action.

PreviousLena's Kitchen View all projects