AI Experience Design
The hardest part of AI isn’t the model — it’s the interface. How do you show users what the AI did? How do you build trust when the output isn’t always right? How do you design for uncertainty? We specialise in designing AI-native experiences: prompt interfaces, confidence indicators, human-in-the-loop workflows, model output presentation, and graceful fallbacks. The result is AI features that people actually use.
What you get
Real examples
AI-assisted document review
Design UX for AI-powered review tools — confidence indicators, inline suggestions, and approval flows that help users trust and adopt AI features.
AI copilot interfaces
Design agent-facing interfaces where AI suggests actions. Source citations, confidence scores, and clear override paths build the trust needed for adoption.
Common questions
Why does AI need special UX design?
AI outputs are probabilistic, not deterministic. Users need to understand confidence levels, know when to trust the AI, and have clear paths to override it. Standard UX patterns don’t cover this.
Do you design conversational AI interfaces?
Yes. Chat interfaces, voice UX, prompt design, and multimodal interactions. We design for both end users and internal teams using AI tools.
How do you handle AI errors in the UX?
We design for failure from day one — graceful fallbacks, confidence thresholds, human escalation paths, and clear communication when the AI isn’t sure.
Can you design for existing AI products?
Absolutely. We regularly redesign AI features that were built engineer-first. Better UX typically doubles adoption and reduces support tickets.
What’s your design process?
User research → AI-specific personas → wireframes with AI interaction patterns → prototype → user testing with real AI outputs → iteration → design system handoff.
Ready to get started?
Tell us about your project and we'll tell you honestly how we can help.
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