Our process begins with a "Model & User" discovery. We collaborate with your data scientists to understand the model's inputs, outputs, and limitations. Simultaneously, we interview the end-users to understand the real-world business decisions they need to make with it

Our process focuses on "Explainable AI" (XAI). We design interfaces that clearly communicate the confidence of a prediction and surface the key factors that influenced the model's output. This transparency is the foundation of user trust.

We design for probability. Our process avoids binary "yes/no" interfaces. Instead, we use visualizations to communicate confidence scores and ranges, and we design workflows that guide users on how to act based on different levels of certainty.

We act as embedded partners. Our designers join your workflow, viewing early model outputs and providing feedback on what's usable. This collaborative process ensures the final data product is both technically sound and human-centered.

It's a two-part process. First, we test the usability: can users navigate the interface? Second, we test for comprehension and trust: do users understand what the model is telling them, and do they trust it enough to make a decision?

A generalist designer will be stopped in their tracks by the complexity. When you hire a data science design expert, you get a specialist whose process is already built to handle the unique challenges of machine learning, from "human-in-the-loop" workflows to designing for probabilistic outcomes.