Work

Product Design

NameStudio Brainstorming

A domain name search tool powered by machine learning that helps people discover available .com names—even when they think all the good ones are gone.

COMPANY

Year

2018

Skills

UX Design

Product Design

Tools

“There Are No Good .coms Left”

That's what people believe when they search for domain names and hit a wall of "Not Available" messages. But great names still exist—if you have the right tools and a little creativity. NameStudio was designed to prove that.

The Challenge

Finding an available, memorable .com domain name feels daunting. Users assume all the good names are taken after seeing endless "Not Available" results. The challenge was to build a tool that could help people discover great domain names by expanding their search in smart, creative ways—without overwhelming them with complexity.

Goals

Help users discover available .com domain names through intelligent keyword expansion

Balance machine learning capability with simple, intuitive design

Create a scalable system that could power both standalone tools and partner integrations

Validate design decisions through real user testing and iteration

The Approach

From Keywords to Smarter Suggestions

Working with engineers, product managers and executive leadership, I helped shape an early machine learning prototype. It broke queries into keywords, generated synonyms, prefixes, and suffixes, and then checked each variation against live domain availability. The raw system proved the concept but needed refinement to become usable.

Behind the scenes, NameStudio expanded queries and checked each variation live.

What Real Users Taught Us

To understand how people would interact with the prototype, I led in-person usability sessions. Most participants navigated it easily and responded positively—but testing surfaced one critical design question:

Should we only show available domains, or also display unavailable results?

Users told us unavailable names were actually valuable. They provided context, revealed trends, and sparked new directions for brainstorming. This feedback became a cornerstone of our UX direction.

Should we only show available domains, or also display unavailable results?

Users told us unavailable names were actually valuable. They provided context, revealed trends, and sparked new directions for brainstorming. This feedback became a cornerstone of our UX direction.

Should we only show available domains, or also display unavailable results?

"Would unavailable results still aid discovery?"

Color-coded results helped users distinguish availability while learning from unavailable names.

Key Contributions

User Research & Testing

I led moderated in-person usability sessions and ran multiple unmoderated studies via UserTesting.com to validate design choices. Testing revealed that mixing available and unavailable results actually improved the experience—users had no trouble interpreting color coding, and the mix inspired more creative thinking.

Armed with insights from testing, I partnered with engineering, product leads, and executives to evolve the prototype into a scalable experience. Our priorities were:

Clarity: Simplify the interface so the complexity stayed behind the scenes.
Scalability: Build modular patterns that could support API integrations and campaigns.
Flexibility: Allow the tool to expand across refinements like synonyms, prefixes, suffixes, and TLDs.

The result was a beta release that paired machine learning capabilities with a clear, modular interface — establishing the foundation for the platform that followed.

UI Design & Information Architecture

I redesigned the interface to clearly distinguish between available and unavailable domains while keeping the complex machine learning invisible to users. The design prioritized three things:

Clarity: Simplifying the UI so machine learning stayed behind the scenes
Scalability: Building modular patterns that could support API integrations and marketing campaigns
Flexibility: Allowing the tool to expand across refinements like synonyms, prefixes, suffixes, and TLDs

Armed with insights from testing, I partnered with engineering, product leads, and executives to evolve the prototype into a scalable experience. Our priorities were:

Clarity: Simplify the interface so the complexity stayed behind the scenes.
Scalability: Build modular patterns that could support API integrations and campaigns.
Flexibility: Allow the tool to expand across refinements like synonyms, prefixes, suffixes, and TLDs.

The result was a beta release that paired machine learning capabilities with a clear, modular interface — establishing the foundation for the platform that followed.

Screenshot of the phase 1 NameStudio interface

Phase 1 NameStudio Design

Design Decisions Based on Data

We ran multiple studies comparing different approaches:

Displaying only available results
Displaying mixed results with color cues
Testing alternative visual treatments

The outcome was clear: showing only available results sometimes improved conversion potential, but those results were often less relevant or confusing. Unavailable keywords gave users valuable context and helped them understand how the tool worked. More importantly, they inspired new ideas.

We ran multiple studies via UserTesting.com to validate design choices:

Displaying only available results
Displaying mixed results with color cues
Testing alternative visual treatments

The outcome was clear: showing only available keywords sometimes improved conversion potential, but those results were often less relevant and even confusing. By contrast, unavailable keywords didn’t provide a direct solution, but they gave users valuable context — helping them understand how the tool worked and inspiring new ideas.

Users had no trouble interpreting the color coding, and many said the mix of available and unavailable results actually made the experience more useful. We decided to keep them in.

This decision also solved another challenge.

We ran multiple studies via UserTesting.com to validate design choices:

Displaying only available results
Displaying mixed results with color cues
Testing alternative visual treatments

Users had no trouble interpreting the color coding, and many said the mix of available and unavailable results actually made the experience more useful. We decided to keep them in.

This decision also solved another challenge.

Having a mix of available and unavailable results proved to help engagement and creativity.

Solving the "Empty Results" Problem

Short or generic searches often returned few relevant results when limited to only available options. By including unavailable keywords as context, the tool maintained momentum in the brainstorming process—giving users inspiration even when the "perfect" domain wasn't immediately available.

"Sometimes the best UX decision isn't about what's available—it's about what's understandable."

“Sometimes the best UX decision isn’t about what’s available— it’s about what’s understandable.”

When the tool only suggested available domain names, it could sometimes offer few options.

The Results

In 2019, the project evolved from prototype to platform. NameStudio launched as a branded API powering domain search for major platforms—and it's still actively maintained and updated today.

Impact:

U.S. Design Patent awarded (named contributor)
Adopted by major partners including Shopify, Wix, and more
Still in active use across multiple platforms

In 2019, the project evolved from prototype to platform. The branded NameStudio search API launched as a core product, powering domain search for major platforms and continuing to evolve.

Impact Highlights:

U.S. Design Patent awarded (named contributor)
Adopted by partners including Shopify, Wix, and more.
Actively maintained and updated

In 2019, the project evolved from prototype to platform. The branded NameStudio search API launched as a core product, powering domain search for major platforms and continuing to evolve.

Impact Highlights: