AX Framework
Designtap: Hong Kong's Premier UX & AX Design Agency
Our Methodology: The DesignTap AX Framework
Discovery → Mapping → Interaction Model → Visual System → Validation
The DesignTap AX Framework is our proven approach to building AI experiences that feel intuitive, human, and trustworthy. Instead of treating AI like a traditional feature, we design around how users understand, interact with, and trust intelligent agents. Our methodology blends UX strategy, behavioral design, and AI‑specific interaction patterns to ensure every AI product communicates clearly, behaves predictably, and delivers real value from day one.
AI products fail when users feel confused, overwhelmed, or unsure what the AI is doing. The DesignTap AX Framework eliminates these pain points by making AI:
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Understandable
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Predictable
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Humanized
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Trustworthy
Framework
Step 1:
Discovery & Understanding the Agent
Step 2:
Interaction Mapping & Behavior Modeling
Step 3:
AX Interaction Patterns
Step 4:
Visual System & Identity for the AI
Step 5:
Validation & Real‑World Testing
We begin by mapping out what your AI agent can do, should do, and must not do. This step defines the agent’s role, personality, boundaries, and value to the user.
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Clarify the agent’s purpose
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Identify user expectations and fears
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Define the agent’s capabilities and limitations
Next, we design how the AI behaves in every scenario — from answering questions to taking actions.
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Conversation flows and decision paths
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Feedback states that show what the AI is doing
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Error handling that feels human, not robotic
We apply proven AX patterns that help users understand and collaborate with the AI.
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Guided prompts and suggestions
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Transparent reasoning cues
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Human‑AI collaboration models
AI needs a visual identity just like a brand. We craft a visual system that communicates intelligence, reliability, and personality.
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Motion cues that show thinking, learning, or acting
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Visual hierarchy that clarifies AI vs. user actions
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A consistent multi‑platform design language
We test the AX system with real users to ensure the AI feels intuitive and helpful.
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Usability testing with real prompts
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Behavior tuning based on user reactions
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Iterative refinement for clarity and trust
