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UX/UI Design
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AI workflow
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Prototyping
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Demo Experiences
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Design Systems
✎
Accessibility
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Interaction Design
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Figma Specialist
✎
Wireframing
↟↟↟ UX/UI Design ⁕ AI workflow ✎ Prototyping ↟↟↟ Demo Experiences ⁕ Design Systems ✎ Accessibility ↟↟↟ Interaction Design ⁕ Figma Specialist ✎ Wireframing
A design studio by Marcelo Ambiel.
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Project Highlight 1
Overview
r.Potential is an AI decision-intelligence platform for CEOs, designed to turn live market and industry information into clearer workforce decisions.
Scope of Work
Product exploration · Information architecture · User flows · UX/UI · Onboarding · Design-system foundations · Interactive prototyping
Project Highlight 2
Overview
I designed a responsive workspace that brings forecasts, expense data, reporting, and AI-assisted analysis into one connected product experience. The goal was not to create another information-heavy dashboard—it was to make the path from “something changed” to “here is what to do next” feel obvious.
Deliverables
Desktop dashboard · Forecast analysis flow · AI assistant experience · Mobile views · Clickable Figma prototype
Project Highlight 3
Overview
Salesforce needed a high-impact demo experience to showcase Agentforce capabilities during Dreamforce 2025. The goal was to turn a complex enterprise AI workflow into a clear, believable, and presentation-ready prototype for a large non-technical audience.
The experience simulated how an enterprise user could create a custom AI agent inside Agentforce, guided by an in-product AI assistant. The final prototype was delivered as a clickable Figma experience and used as the foundation for recorded demo material presented during the event.
Role & Contribution
Translating product requirements into a clear prototype flow
Defining the demo narrative and required screen sequence
Structuring Figma files for team collaboration and client review
Leading async designer handoffs through Loom walkthroughs and daily reports
Designing the AI assistant conversation
Validating prototype coverage against the PRD
Supporting fast iteration under late-stage stakeholder changes

