Raspberry Voice Assistant AI
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Raspberry Voice Assistant AI
Hi, Mario! Here I am
What's your approach to designing AI-driven interfaces for enterprise platforms? Specifically around component architecture and scalability.
Great question. When designing AI interfaces for enterprise, I focus on three core pillars.
01 · Transparency — users must always understand what the AI is doing, its confidence level, and when it's uncertain. This means explicit state indicators, not just loading spinners.
02 · Progressive disclosure — start with the essential output. Let users drill into reasoning, sources, and alternatives on demand. Cognitive load management is critical in enterprise contexts.
03 · Error resilience — AI fails. Design for graceful degradation. Every AI action should have a clear fallback, an undo path, and a way to override.
01 · Transparency — users must always understand what the AI is doing, its confidence level, and when it's uncertain. This means explicit state indicators, not just loading spinners.
02 · Progressive disclosure — start with the essential output. Let users drill into reasoning, sources, and alternatives on demand. Cognitive load management is critical in enterprise contexts.
03 · Error resilience — AI fails. Design for graceful degradation. Every AI action should have a clear fallback, an undo path, and a way to override.
How does that translate to component architecture in a design system like Figma with MCP integration?
With MCP + Figma, you close the gap between design intent and implementation. The key is treating design tokens as a single source of truth — colors, spacing, typography all flow from Figma variables directly into code.
In practice this means your component library in Figma maps 1:1 with your production components. When a designer adjusts a token in Figma, the change propagates through MCP into the codebase automatically.
The architecture looks like: Figma variables → MCP bridge → design tokens JSON → component props → rendered UI. No manual handoff, no translation layer where intent gets lost.
In practice this means your component library in Figma maps 1:1 with your production components. When a designer adjusts a token in Figma, the change propagates through MCP into the codebase automatically.
The architecture looks like: Figma variables → MCP bridge → design tokens JSON → component props → rendered UI. No manual handoff, no translation layer where intent gets lost.
That's a solid pipeline. What about multi-role enterprise platforms where different user personas need different levels of AI autonomy?
This is where parameterization at the role level becomes essential. Each persona — say an operator vs. an analyst vs. an admin — gets a different AI autonomy profile.
Operators might see AI suggestions but always approve actions. Analysts get full AI output with citations. Admins can configure autonomy per workflow. The underlying AI is the same; what changes is the trust surface exposed to each role.
Design-wise, this means your component states need to encode permission level. A button that triggers an AI action looks and behaves differently depending on whether the current user can act on it autonomously or needs to confirm. Same component, different state expression — this is where a well-structured design system pays off significantly.
Operators might see AI suggestions but always approve actions. Analysts get full AI output with citations. Admins can configure autonomy per workflow. The underlying AI is the same; what changes is the trust surface exposed to each role.
Design-wise, this means your component states need to encode permission level. A button that triggers an AI action looks and behaves differently depending on whether the current user can act on it autonomously or needs to confirm. Same component, different state expression — this is where a well-structured design system pays off significantly.
Demo only — no live model connected.
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