What We’re Seeing
AI features can work technically and still fail with users.
Copilots get stuck in loops. Agents act without enough context. Recommendations appear at the wrong moment. Generated content is difficult to verify or edit. The capability may be strong, but the experience leaves users unsure about what the AI is doing, whether they can trust it, or what they should do next.
It’s not just a usability issue. Trust, control, recovery, and feedback need to be designed into every AI interaction. AI feature experience design turns complex AI behaviour into clear flows users can understand, test, and adopt.
Portfolio: Vocable · Simpla.ai · Moment.ai · Insphere.ai
The AI interaction patterns your product needs.
Conversational AI
A focused workspace for asking questions, creating content, completing tasks, and working naturally with AI.
Prompts that gather missing context and smoothly connect users with human support when needed.
Clear updates that show what the agent is doing and when user approval is required.
Simple controls for reviewing AI actions, approving changes, and receiving useful progress updates.
Clear signals that show how certain the AI is and why an output was generated.
An editable space for refining AI outputs, correcting mistakes, and sharing feedback without breaking the flow.
How we work
A practical path from AI feature to clickable prototype.
We conduct a deep dive into the prioritized feature, user goals, product requirements, technical constraints, and the outcomes the AI experience needs to support.
Deliverables:
We map the complete interaction, including primary user journeys, alternative paths, edge cases, escalation points, and potential failure modes.
Deliverables:
We design the interface, AI interactions, conversation scripts, trust signals, approval controls, and feedback patterns required for the feature.
Deliverables:
We build a high-fidelity clickable prototype with simulated AI responses, support user testing, and refine the experience based on feedback.
Deliverables:
We finalize the experience and document how each interaction, state, response, and failure path should work during development.
Deliverables:
What You Get
What you’ll get in your design discovery.
Prototypes and interaction flows
High-fidelity clickable experiences with realistic AI responses and complete user journeys
Conversation scripts
AI dialogue, clarification prompts, recovery paths, and escalation flows structured for implementation
Trust patterns
Confidence indicators, explanations, approval controls, and feedback loops built into the experience
AI state design
Loading, processing, uncertainty, success, failure, and recovery states designed as one system
Implementation notes
Behaviour specifications and prompt guidance your engineering team can work from
Test scenarios
Edge cases and failure conditions documented before the feature moves into development
Two kinds of teams come to us.
You know what your AI product should do, but the experience still exists as a collection of ideas, prompts, and technical capabilities. You need clear interaction flows, trust patterns, and a testable prototype that turns the product vision into something users can understand, control, and adopt.
You’re adding copilots, agents, recommendations, or generative features to an existing product. The AI capability is defined, but the experience needs to fit naturally into current workflows, match the rest of the product, and give users enough clarity and control to use it confidently.
The questions every team has.
Start with AI Opportunity Mapping first. This engagement is built for teams who already know what to build.
Generative AI UX design involves shaping prompts, defining output boundaries, presenting drafts as editable starting points, showing confidence indicators, and allowing users to review, approve, or override AI-generated content before action.
This engagement focuses on the product experience. We design the flows, interfaces, states, and behaviour specifications your AI and engineering teams need for implementation.
Agentic UX defines how users interact with AI systems that complete tasks independently. It covers permissions, progress, approvals, explanations, intervention, and recovery.
We use realistic simulated responses and predefined scenarios to show how the feature should behave across successful, uncertain, and failed outcomes.
Yes. We design around the capabilities, limitations, response patterns, and latency of the model or AI system your product uses.
We can focus on a specific feature, conversation flow, trust pattern, agent experience, or prototype rather than redesigning the full AI experience.
Take the deliverables to your engineering team and build independently — or extend into team extension if you want design embedded through the build phase.
Connect with us
Let’s talk about your product.
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