About Me
I make complicated products easier to act on.
The products have changed. The work has not: understand the real problem behind the interface, make the important parts easier to see, and help people move forward with confidence.
Sometimes that means simplifying a dense workflow for an expert. Sometimes it means bringing order to a new product direction before a team spends months building the wrong thing. Sometimes it means designing an AI interaction with enough context and control that people know what they are looking at—and what to do next.
I enjoy the moment when a messy problem starts to reveal its shape. That is where I bring curiosity, visual thinking, and a practical bias toward making something useful.
The moment that
shaped my work
In 2017, I was working on an AI-assisted auditing platform for Brazil's Federal Court of Accounts. In a usability session, a senior director known for being rigorous and skeptical read a draft of a quasi-legal document generated by the system.
He read it once. Then again.
Then he said it could turn work that normally took months into hours.
What stayed with me was not that the system had produced text. It was the change in the room: a careful expert had found enough context, control, and value to take the output seriously.
The platform later reported a 63% reduction in audit-instruction writing time (Shipped; pilot-context measurement). For me, the larger lesson was simpler: a product does not earn confidence through polish or persuasion. It earns it when people can understand what is happening, ask the right questions, and recover when something is wrong.
That lesson has travelled with me—from public-sector workflows to fintech and data products, from component libraries to prototypes for new interaction models.
How I work
Translate complexity into direction
Dense workflows, expert users, data-heavy screens: I start with the system, not the screen. Flows, diagrams, and information architecture that give teams a shared picture — then an interface that makes the complexity feel obvious.
Design the failure states, not just the happy path
Any system fails sometimes; non-deterministic ones guarantee it. The craft lives in what happens then: confidence cues, partial results, override paths, and audit trails — so people can rely on the output without trusting it blindly.
Stack & Tools
I use the right level of fidelity for the question in front of us.
Figma
Cursor
Claude
Codex
Gemini
Perplexity
Antigravity
Photoshop
Illustrator
Google Analytics
Heap
Storybook
Sass
Github
FAQ's
Daniel Canabrava Torres
dnltrs.com
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