Netizen Labs

Bridging the UX research gap
with AI integration

Summary
With more teams being asked to “do UX research” without any background, Netizen Labs aimed to bridge that knowledge gap with helpful structure, automation, and AI all in a single platform.
I was the only designer full fledge in Labs before a product team was up and running. That meant catching my future PO PMs for requirements, organizing the chaos and giving shape to ideas.
Role
Lead UX Designer
Responsibility
End-to-End UX & UI Design Process
Collaborator
Product manager, product execs, developers, QA
Period
2023 - present
Goal of building Netizen Labs

Workflow

Streamline UX research into a single platform

Efficiency

Increase and scale UX research projects efficiently

Beginner friendly

Empower anyone to conduct UX research

Isolated AI features

AI was only added to read recordings of testing sessions and provide analysis, but the tedious and tricky task for creating discussion guide was still a pain point for many users.

Insights

❌ I still have to ChatGPT my way into creating Discussion Guide. The front part of preparation is still as time consuming to me

Relook into full integration

We reimagined a more seamless integration to fully play the role of a research assistant:

Create a project with a single prompt

Removes manual project creation and prompt AI to create project and discussion guide in minutes

Auto generate a discussion guide

Quickly focus on important part of UX research: Editing discussion guides rather than creating project from scratch and further prompt AI to refine the guide, view results and how AI writes it.

AI follow up questions

Moderated testing is challenging for beginners. They tend to ask close ended or leading questions. AI can listen and prompt follow up question to probe deeper insights.
One prompt home page

The results

Saved 2 to 3 man-days per research cycle

Reduced post-interview analysis time by more than 50%

Improved scheduling and session access clarity

Before

3 - 5 man-days needed to complete a discussion guide from scratch
Teams spent 1–2 hours per interview compiling notes and quotes for analysis
Manually schedule respondents and send multiple session links observers and interpreters

After

Complete post analysis in 1 - 2 mandays with AI assistance
Analysis are automatically compiled and ready in under 1 hour
Bulk role assigning and link sharing led as a scalable feature to support larger projects

Saved 4–5 man-days per research cycle

Before

2 people needed 3 - 5 man-days to complete a discussion guide from scratch

After

1 person can complete it in 1 - 2 mandays with AI assistance

Reduced post-interview analysis time by 70–80%

Before

Teams spent 1–2 hours per interview compiling notes and quotes for analysis

After

Analysis are automatically compiled and ready in under 1 hour

Improved scheduling and session access clarity

Before

Teams spent 1–2 hours per interview compiling notes and quotes for analysis

After

Analysis are automatically compiled and ready in under 1 hour
Discussion Guide with AI assistance
AI Analysis

What changed

I stepped into a product manager role, organizing meetings, gathering requirements, and helping define product direction before a full product team was finally assembled a year later.
As the product matured, so did our processes. We managed to shift our focus on improving both the user experience and the team workflow behind it.

Vibe coding

Introduced usability tests with vibe coded prototypes to quickly test concepts

Prioritization

Prioritized features that saved users time and clarified roles

Communication

Helped the team speak a shared language between product, tech, and design

What I learnt

Product-building isn’t linear, it’s alive

We shifted, redefined, scrapped, and reinvented features after every milestone’s testing and feedback.

Design is a shared process

Never design blindly; rather,  it takes effective communication from each team  pull in context from each team until the problem makes sense.

UX isn’t just outputs, it’s translation

I learned to read the room, spot knowledge gaps, and ask questions that aligned teams.