Calibrated confidence
Surfaces signal model confidence and uncertainty visibly to the user.
Responsive AI framework
A design system for AI surfaces that earn trust.
Patterns for AI UX that scale with user confidence - from suggestion to draft to autonomous action - with calibrated transparency, consent, and reversibility at every step.
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Pillars
Five principles of Responsive AI
Grounded answers
Every claim ties to a citation, a source, or a tool result the user can inspect.
Consent and reversibility
Autonomous actions ask permission and are always reversible by design.
Human-in-the-loop scaling
Surfaces shift between suggestion → draft → action based on user trust over time.
Continuous evaluation
User feedback flows back into evals - every interaction calibrates the next.
FAQs
Frequently asked questions
It includes UI patterns - but they're starting points. The framework is mostly about the logic of trust calibration, which you can apply to any UI system.
Yes - every embedded copilot we ship embodies Responsive AI principles by default. You get calibrated confidence, citations, consent flows, and feedback loops out of the box.
Yes - the design tokens, component patterns, and usage guidelines are available as a standalone framework license. Reach out via the contact form.
Build AI surfaces users actually trust.
We'll share the full framework and offer a working session with our design and engineering leads.