ClearStep

The problem was not translation. It was decision effort.

People can receive a bank notice, form prompt, or unfamiliar instruction and understand the words while still being unable to answer three practical questions: Does this apply to me? Is action required? What should I do next?

Existing workarounds often added explanation without reducing the decision. ClearStep reframed the task around moving from uncertain information to a confident next action.

Research and prototype work showed that users valued a clear action more than additional explanation. That changed the product from a general interpretation tool into an action-first decision-support experience.

The experience begins with the task, not a blank AI prompt. The system uses provided information, asks for one missing detail only when necessary, and returns guidance in a stable structure.

Interviews, card sorting, information-architecture evaluation, Wizard of Oz validation, and prototype testing were used at different stages. The work was not a single visual pass: labels, privacy guidance, follow-up behavior, and the result hierarchy changed in response to evidence.

The most important result was behavioral: users could identify whether action was required and what to do next with more confidence.

I owned the research, problem framing, product strategy, information architecture, interaction design, AI behavior, prototyping, testing, iteration, and final communication.

The current outcome demonstrates how uncertainty can be handled responsibly through a predictable interaction. Production infrastructure, model engineering, and commercial deployment remain outside the completed work.