We focus on setting clear user expectations. The UI must provide tools for users to guide the AI, refine outputs, and easily undo or discard results. We design feedback loops so users feel in control, even when the output is variable.

It's about making the AI's reasoning understandable to the user. This can be through visualizing feature importance, showing source data, or providing simple natural language explanations for a recommendation or decision.

While we may not know exactly how a model works, we can design the 'wrapper' around it to build trust. This includes showing the data it was trained on, its confidence score, and allowing users to give feedback on its output.

We start with a 'human-first' approach. What problem is the user trying to solve? We then prototype ways the AI can assist, testing different levels of automation and user control to find the optimal balance.

While our core team consists of designers and front-end engineers, we partner closely with our clients' data science and ML teams. We speak their language and are adept at translating model capabilities into user-centric features.

Our team is passionate about AI and dedicates time to continuous learning. We experiment with new models, APIs, and design patterns, bringing the latest best practices to our client projects.