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Test the idea before you build it.

We turn concepts and designs into realistic experiences so your team can test assumptions, communicate ideas, and learn before development.

Design Feedback Comes Too Late

When users or stakeholders see a product only after it's built, changing the experience can take more time, effort, and budget.

Make Changes While They're Easier

We bring your product to life as a prototype, creating an early way to review, test, and improve the experience before development locks decisions in.

Let check what we do

Prototype
Make ideas tangible.

Test
Observe real interactions.

Learn
Find friction and uncertainty.

Refine

Improve what matters.

Test scenarios

And result

Interactive prototypes

Usability findings

Design recommendations

Refined experience

Works we worked

AI Customer Support Assistant
Problem
A company wanted to introduce an AI assistant into its customer-support workflow but was unsure how much control users would want over AI-generated actions. Building the full experience would require significant engineering effort.
Context
- New technology with uncertain user expectations.
- Several possible interaction models.
- High cost of changing the product after development.
Process
- Created multiple interaction concepts.
- Built realistic prototypes for key scenarios.
- Tested them with support agents.
- Iterated based on observed behavior and feedback.
RESULT
Testing showed that users preferred AI suggestions with clear control rather than fully automated actions. The prototype helped the team validate the interaction model before committing to development.
LIVE PREVIW
Banking App Redesign
Problem
A bank was considering a major redesign of its mobile app but had several competing concepts for navigation and account management. The team needed evidence before choosing a direction.
Context
- Three competing navigation concepts.
- Large existing customer base.
- High risk associated with changing core workflows.
Process
- Created realistic prototypes for each concept.
- Tested key journeys with existing customers.
- Compared comprehension, task completion, and preference.
- Refined the strongest direction based on findings.
RESULT
The team was able to eliminate two concepts before development and move forward with greater confidence. The prototype also helped stakeholders align around the final direction much earlier.
LIVE PREVIW

AI Customer Support Assistant
Problem
A company wanted to introduce an AI assistant into its customer-support workflow but was unsure how much control users would want over AI-generated actions. Building the full experience would require significant engineering effort.
Context
- New technology with uncertain user expectations.
- Several possible interaction models.
- High cost of changing the product after development.
Process
- Created multiple interaction concepts.
- Built realistic prototypes for key scenarios.
- Tested them with support agents.
- Iterated based on observed behavior and feedback.
RESULT
Testing showed that users preferred AI suggestions with clear control rather than fully automated actions. The prototype helped the team validate the interaction model before committing to development.
LIVE PREVIW
Banking App Redesign
Problem
A bank was considering a major redesign of its mobile app but had several competing concepts for navigation and account management. The team needed evidence before choosing a direction.
Context
- Three competing navigation concepts.
- Large existing customer base.
- High risk associated with changing core workflows.
Process
- Created realistic prototypes for each concept.
- Tested key journeys with existing customers.
- Compared comprehension, task completion, and preference.
- Refined the strongest direction based on findings.
RESULT
The team was able to eliminate two concepts before development and move forward with greater confidence. The prototype also helped stakeholders align around the final direction much earlier.
LIVE PREVIW

AI Customer Support Assistant
Problem
A company wanted to introduce an AI assistant into its customer-support workflow but was unsure how much control users would want over AI-generated actions. Building the full experience would require significant engineering effort.
Context
- New technology with uncertain user expectations.
- Several possible interaction models.
- High cost of changing the product after development.
Process
- Created multiple interaction concepts.
- Built realistic prototypes for key scenarios.
- Tested them with support agents.
- Iterated based on observed behavior and feedback.
RESULT
Testing showed that users preferred AI suggestions with clear control rather than fully automated actions. The prototype helped the team validate the interaction model before committing to development.
LIVE PREVIW
AI Customer Support Assistant
Problem
A company wanted to introduce an AI assistant into its customer-support workflow but was unsure how much control users would want over AI-generated actions. Building the full experience would require significant engineering effort.
Context
- New technology with uncertain user expectations.
- Several possible interaction models.
- High cost of changing the product after development.
Process
- Created multiple interaction concepts.
- Built realistic prototypes for key scenarios.
- Tested them with support agents.
- Iterated based on observed behavior and feedback.
RESULT
Testing showed that users preferred AI suggestions with clear control rather than fully automated actions. The prototype helped the team validate the interaction model before committing to development.
LIVE PREVIW