OPAL Senegal case study.
UX for crisis response.
OPAL Senegal case study.
UX for crisis response.
OPAL Senegal case study.
UX for crisis response.
OPAL Senegal case study.
UX for crisis response.
OPAL Senegal case study.
UX for crisis response.
OPAL Senegal case study.
UX for crisis response.
OPAL Senegal case study.
UX for crisis response.
OPAL Senegal case study.
UX for crisis response.
TL;DR / Quick Snapshot
OPAL is a decision-support platform designed for governments and analysts to coordinate responses to floods in Senegal. As the solo UX/UI designer, I built the product from scratch—designing interfaces, shaping flows, and growing a UX culture along the way.
Impact: From research to rollout, I helped shape a tool now used to inform high-stakes decisions in flood-prone areas.
TL;DR / Quick Snapshot
OPAL is a decision-support platform designed for governments and analysts to coordinate responses to floods in Senegal. As the solo UX/UI designer, I built the product from scratch—designing interfaces, shaping flows, and growing a UX culture along the way.
Impact: From research to rollout, I helped shape a tool now used to inform high-stakes decisions in flood-prone areas.
TL;DR / Quick Snapshot
OPAL is a decision-support platform designed for governments and analysts to coordinate responses to floods in Senegal. As the solo UX/UI designer, I built the product from scratch—designing interfaces, shaping flows, and growing a UX culture along the way.
Impact: From research to rollout, I helped shape a tool now used to inform high-stakes decisions in flood-prone areas.
TL;DR / Quick Snapshot
OPAL is a decision-support platform designed for governments and analysts to coordinate responses to floods in Senegal. As the solo UX/UI designer, I built the product from scratch—designing interfaces, shaping flows, and growing a UX culture along the way.
Impact: From research to rollout, I helped shape a tool now used to inform high-stakes decisions in flood-prone areas.
TL;DR / Quick Snapshot
OPAL is a decision-support platform designed for governments and analysts to coordinate responses to floods in Senegal. As the solo UX/UI designer, I built the product from scratch—designing interfaces, shaping flows, and growing a UX culture along the way.
Impact: From research to rollout, I helped shape a tool now used to inform high-stakes decisions in flood-prone areas.
TL;DR / Quick Snapshot
OPAL is a decision-support platform designed for governments and analysts to coordinate responses to floods in Senegal. As the solo UX/UI designer, I built the product from scratch—designing interfaces, shaping flows, and growing a UX culture along the way.
Impact: From research to rollout, I helped shape a tool now used to inform high-stakes decisions in flood-prone areas.
TL;DR / Quick Snapshot
OPAL is a decision-support platform designed for governments and analysts to coordinate responses to floods in Senegal. As the solo UX/UI designer, I built the product from scratch—designing interfaces, shaping flows, and growing a UX culture along the way.
Impact: From research to rollout, I helped shape a tool now used to inform high-stakes decisions in flood-prone areas.
TL;DR / Quick Snapshot
OPAL is a decision-support platform designed for governments and analysts to coordinate responses to floods in Senegal. As the solo UX/UI designer, I built the product from scratch—designing interfaces, shaping flows, and growing a UX culture along the way.
Impact: From research to rollout, I helped shape a tool now used to inform high-stakes decisions in flood-prone areas.
The Chaos
Flood disasters are chaotic,
Multiple data sources,
Data can be dispersed,
Decision-making under stress is hard,
Machine learning is complex,
Different thinking styles,
Different needs,
And so on…
The Chaos
Flood disasters are chaotic,
Multiple data sources,
Data can be dispersed,
Decision-making under stress is hard,
Machine learning is complex,
Different thinking styles,
Different needs,
And so on…
The Chaos
Flood disasters are chaotic,
Multiple data sources,
Data can be dispersed,
Decision-making under stress is hard,
Machine learning is complex,
Different thinking styles,
Different needs,
And so on…
The Chaos
Flood disasters are chaotic,
Multiple data sources,
Data can be dispersed,
Decision-making under stress is hard,
Machine learning is complex,
Different thinking styles,
Different needs,
And so on…
The Chaos
Flood disasters are chaotic,
Multiple data sources,
Data can be dispersed,
Decision-making under stress is hard,
Machine learning is complex,
Different thinking styles,
Different needs,
And so on…
The Chaos
Flood disasters are chaotic,
Multiple data sources,
Data can be dispersed,
Decision-making under stress is hard,
Machine learning is complex,
Different thinking styles,
Different needs,
And so on…
The Chaos
Flood disasters are chaotic,
Multiple data sources,
Data can be dispersed,
Decision-making under stress is hard,
Machine learning is complex,
Different thinking styles,
Different needs,
And so on…
The Chaos
Flood disasters are chaotic,
Multiple data sources,
Data can be dispersed,
Decision-making under stress is hard,
Machine learning is complex,
Different thinking styles,
Different needs,
And so on…
Project Challenges
The project began with no UX foundation and tight deadlines. User roles were undefined. There were many features, but it was unclear who would use them. The high-stakes domain covered life, infrastructure, and complex data like satellite and flood data.
Stakeholders ranged from analysts to responders—roles were ambiguous. OPAL’s MVP-first mission: connect everything, keep it simple, powerful, and user-driven.
Project Challenges
The project began with no UX foundation and tight deadlines. User roles were undefined. There were many features, but it was unclear who would use them. The high-stakes domain covered life, infrastructure, and complex data like satellite and flood data.
Stakeholders ranged from analysts to responders—roles were ambiguous. OPAL’s MVP-first mission: connect everything, keep it simple, powerful, and user-driven.
Project Challenges
The project began with no UX foundation and tight deadlines. User roles were undefined. There were many features, but it was unclear who would use them. The high-stakes domain covered life, infrastructure, and complex data like satellite and flood data.
Stakeholders ranged from analysts to responders—roles were ambiguous. OPAL’s MVP-first mission: connect everything, keep it simple, powerful, and user-driven.
Project Challenges
The project began with no UX foundation and tight deadlines. User roles were undefined. There were many features, but it was unclear who would use them. The high-stakes domain covered life, infrastructure, and complex data like satellite and flood data.
Stakeholders ranged from analysts to responders—roles were ambiguous. OPAL’s MVP-first mission: connect everything, keep it simple, powerful, and user-driven.
Project Challenges
The project began with no UX foundation and tight deadlines. User roles were undefined. There were many features, but it was unclear who would use them. The high-stakes domain covered life, infrastructure, and complex data like satellite and flood data.
Stakeholders ranged from analysts to responders—roles were ambiguous. OPAL’s MVP-first mission: connect everything, keep it simple, powerful, and user-driven.
Project Challenges
The project began with no UX foundation and tight deadlines. User roles were undefined. There were many features, but it was unclear who would use them. The high-stakes domain covered life, infrastructure, and complex data like satellite and flood data.
Stakeholders ranged from analysts to responders—roles were ambiguous. OPAL’s MVP-first mission: connect everything, keep it simple, powerful, and user-driven.
Project Challenges
The project began with no UX foundation and tight deadlines. User roles were undefined. There were many features, but it was unclear who would use them. The high-stakes domain covered life, infrastructure, and complex data like satellite and flood data.
Stakeholders ranged from analysts to responders—roles were ambiguous. OPAL’s MVP-first mission: connect everything, keep it simple, powerful, and user-driven.
Project Challenges
The project began with no UX foundation and tight deadlines. User roles were undefined. There were many features, but it was unclear who would use them. The high-stakes domain covered life, infrastructure, and complex data like satellite and flood data.
Stakeholders ranged from analysts to responders—roles were ambiguous. OPAL’s MVP-first mission: connect everything, keep it simple, powerful, and user-driven.
Designing for Modularity
To make OPAL future-proof, we designed the platform with scalability at its core. Anticipating new features, such as poverty mapping, meant building a system that could grow and adapt without compromising usability. Every interface needed to remain flexible, ensuring the product could evolve alongside the organization’s needs.
From the start, modularity guided the design process. The back end was structured to seamlessly combine in-platform functionalities with external services through API integrations. The main challenge was creating a unified experience—bringing together diverse tools and services in a way that felt cohesive and intuitive.
To achieve this, we focused heavily on mapping user flows. By understanding the journeys of analysts, response advisors, and response managers, we ensured the platform was routed in a way that matched real-world workflows. This alignment between system architecture and user needs allowed us to design a platform that is both modular and user-centered.
Designing for Modularity
To make OPAL future-proof, we designed the platform with scalability at its core. Anticipating new features, such as poverty mapping, meant building a system that could grow and adapt without compromising usability. Every interface needed to remain flexible, ensuring the product could evolve alongside the organization’s needs.
From the start, modularity guided the design process. The back end was structured to seamlessly combine in-platform functionalities with external services through API integrations. The main challenge was creating a unified experience—bringing together diverse tools and services in a way that felt cohesive and intuitive.
To achieve this, we focused heavily on mapping user flows. By understanding the journeys of analysts, response advisors, and response managers, we ensured the platform was routed in a way that matched real-world workflows. This alignment between system architecture and user needs allowed us to design a platform that is both modular and user-centered.
Designing for Modularity
To make OPAL future-proof, we designed the platform with scalability at its core. Anticipating new features, such as poverty mapping, meant building a system that could grow and adapt without compromising usability. Every interface needed to remain flexible, ensuring the product could evolve alongside the organization’s needs.
From the start, modularity guided the design process. The back end was structured to seamlessly combine in-platform functionalities with external services through API integrations. The main challenge was creating a unified experience—bringing together diverse tools and services in a way that felt cohesive and intuitive.
To achieve this, we focused heavily on mapping user flows. By understanding the journeys of analysts, response advisors, and response managers, we ensured the platform was routed in a way that matched real-world workflows. This alignment between system architecture and user needs allowed us to design a platform that is both modular and user-centered.
Designing for Modularity
To make OPAL future-proof, we designed the platform with scalability at its core. Anticipating new features, such as poverty mapping, meant building a system that could grow and adapt without compromising usability. Every interface needed to remain flexible, ensuring the product could evolve alongside the organization’s needs.
From the start, modularity guided the design process. The back end was structured to seamlessly combine in-platform functionalities with external services through API integrations. The main challenge was creating a unified experience—bringing together diverse tools and services in a way that felt cohesive and intuitive.
To achieve this, we focused heavily on mapping user flows. By understanding the journeys of analysts, response advisors, and response managers, we ensured the platform was routed in a way that matched real-world workflows. This alignment between system architecture and user needs allowed us to design a platform that is both modular and user-centered.
Designing for Modularity
To make OPAL future-proof, we designed the platform with scalability at its core. Anticipating new features, such as poverty mapping, meant building a system that could grow and adapt without compromising usability. Every interface needed to remain flexible, ensuring the product could evolve alongside the organization’s needs.
From the start, modularity guided the design process. The back end was structured to seamlessly combine in-platform functionalities with external services through API integrations. The main challenge was creating a unified experience—bringing together diverse tools and services in a way that felt cohesive and intuitive.
To achieve this, we focused heavily on mapping user flows. By understanding the journeys of analysts, response advisors, and response managers, we ensured the platform was routed in a way that matched real-world workflows. This alignment between system architecture and user needs allowed us to design a platform that is both modular and user-centered.
Designing for Modularity
To make OPAL future-proof, we designed the platform with scalability at its core. Anticipating new features, such as poverty mapping, meant building a system that could grow and adapt without compromising usability. Every interface needed to remain flexible, ensuring the product could evolve alongside the organization’s needs.
From the start, modularity guided the design process. The back end was structured to seamlessly combine in-platform functionalities with external services through API integrations. The main challenge was creating a unified experience—bringing together diverse tools and services in a way that felt cohesive and intuitive.
To achieve this, we focused heavily on mapping user flows. By understanding the journeys of analysts, response advisors, and response managers, we ensured the platform was routed in a way that matched real-world workflows. This alignment between system architecture and user needs allowed us to design a platform that is both modular and user-centered.
Designing for Modularity
To make OPAL future-proof, we designed the platform with scalability at its core. Anticipating new features, such as poverty mapping, meant building a system that could grow and adapt without compromising usability. Every interface needed to remain flexible, ensuring the product could evolve alongside the organization’s needs.
From the start, modularity guided the design process. The back end was structured to seamlessly combine in-platform functionalities with external services through API integrations. The main challenge was creating a unified experience—bringing together diverse tools and services in a way that felt cohesive and intuitive.
To achieve this, we focused heavily on mapping user flows. By understanding the journeys of analysts, response advisors, and response managers, we ensured the platform was routed in a way that matched real-world workflows. This alignment between system architecture and user needs allowed us to design a platform that is both modular and user-centered.
Designing for Modularity
To make OPAL future-proof, we designed the platform with scalability at its core. Anticipating new features, such as poverty mapping, meant building a system that could grow and adapt without compromising usability. Every interface needed to remain flexible, ensuring the product could evolve alongside the organization’s needs.
From the start, modularity guided the design process. The back end was structured to seamlessly combine in-platform functionalities with external services through API integrations. The main challenge was creating a unified experience—bringing together diverse tools and services in a way that felt cohesive and intuitive.
To achieve this, we focused heavily on mapping user flows. By understanding the journeys of analysts, response advisors, and response managers, we ensured the platform was routed in a way that matched real-world workflows. This alignment between system architecture and user needs allowed us to design a platform that is both modular and user-centered.
Understanding the Users
No UX culture, no direct user access—creativity required. I mapped assumptions into proto-personas covering four key groups: Government Decision-Makers, Field Responders, Analysts, and Admins.
I dove into stakeholder research across ministries and organizations, and ran internal testing before external validation to define the core user experience.
Understanding the Users
No UX culture, no direct user access—creativity required. I mapped assumptions into proto-personas covering four key groups: Government Decision-Makers, Field Responders, Analysts, and Admins.
I dove into stakeholder research across ministries and organizations, and ran internal testing before external validation to define the core user experience.
Understanding the Users
No UX culture, no direct user access—creativity required. I mapped assumptions into proto-personas covering four key groups: Government Decision-Makers, Field Responders, Analysts, and Admins.
I dove into stakeholder research across ministries and organizations, and ran internal testing before external validation to define the core user experience.
Understanding the Users
No UX culture, no direct user access—creativity required. I mapped assumptions into proto-personas covering four key groups: Government Decision-Makers, Field Responders, Analysts, and Admins.
I dove into stakeholder research across ministries and organizations, and ran internal testing before external validation to define the core user experience.
Understanding the Users
No UX culture, no direct user access—creativity required. I mapped assumptions into proto-personas covering four key groups: Government Decision-Makers, Field Responders, Analysts, and Admins.
I dove into stakeholder research across ministries and organizations, and ran internal testing before external validation to define the core user experience.
Understanding the Users
No UX culture, no direct user access—creativity required. I mapped assumptions into proto-personas covering four key groups: Government Decision-Makers, Field Responders, Analysts, and Admins.
I dove into stakeholder research across ministries and organizations, and ran internal testing before external validation to define the core user experience.
Understanding the Users
No UX culture, no direct user access—creativity required. I mapped assumptions into proto-personas covering four key groups: Government Decision-Makers, Field Responders, Analysts, and Admins.
I dove into stakeholder research across ministries and organizations, and ran internal testing before external validation to define the core user experience.
Understanding the Users
No UX culture, no direct user access—creativity required. I mapped assumptions into proto-personas covering four key groups: Government Decision-Makers, Field Responders, Analysts, and Admins.
I dove into stakeholder research across ministries and organizations, and ran internal testing before external validation to define the core user experience.
Understanding the Context
Research involved mapping Senegal’s flood landscape, uncovering pain points and stakeholders, and reviewing competitors. Every insight helped shape key requirements for the platform.
Understanding the Context
Research involved mapping Senegal’s flood landscape, uncovering pain points and stakeholders, and reviewing competitors. Every insight helped shape key requirements for the platform.
Understanding the Context
Research involved mapping Senegal’s flood landscape, uncovering pain points and stakeholders, and reviewing competitors. Every insight helped shape key requirements for the platform.
Understanding the Context
Research involved mapping Senegal’s flood landscape, uncovering pain points and stakeholders, and reviewing competitors. Every insight helped shape key requirements for the platform.
Understanding the Context
Research involved mapping Senegal’s flood landscape, uncovering pain points and stakeholders, and reviewing competitors. Every insight helped shape key requirements for the platform.
Understanding the Context
Research involved mapping Senegal’s flood landscape, uncovering pain points and stakeholders, and reviewing competitors. Every insight helped shape key requirements for the platform.
Understanding the Context
Research involved mapping Senegal’s flood landscape, uncovering pain points and stakeholders, and reviewing competitors. Every insight helped shape key requirements for the platform.
Understanding the Context
Research involved mapping Senegal’s flood landscape, uncovering pain points and stakeholders, and reviewing competitors. Every insight helped shape key requirements for the platform.
Framing the Problem
How could OPAL connect workflows and stakeholders to make sense of crisis moments? Problem framing was crucial to guide every decision.
Framing the Problem
How could OPAL connect workflows and stakeholders to make sense of crisis moments? Problem framing was crucial to guide every decision.
Framing the Problem
How could OPAL connect workflows and stakeholders to make sense of crisis moments? Problem framing was crucial to guide every decision.
Framing the Problem
How could OPAL connect workflows and stakeholders to make sense of crisis moments? Problem framing was crucial to guide every decision.
Framing the Problem
How could OPAL connect workflows and stakeholders to make sense of crisis moments? Problem framing was crucial to guide every decision.
Framing the Problem
How could OPAL connect workflows and stakeholders to make sense of crisis moments? Problem framing was crucial to guide every decision.
Framing the Problem
How could OPAL connect workflows and stakeholders to make sense of crisis moments? Problem framing was crucial to guide every decision.
Framing the Problem
How could OPAL connect workflows and stakeholders to make sense of crisis moments? Problem framing was crucial to guide every decision.
Making Complexity Feel Simple
My approach: turn complexity into clarity—always prioritizing simplicity. User flows were rooted in real crisis needs, not just business priority.
I used rapid mock-ups, collaborative iteration with developers, and constant heuristics to keep designs both powerful and approachable.
Making Complexity Feel Simple
My approach: turn complexity into clarity—always prioritizing simplicity. User flows were rooted in real crisis needs, not just business priority.
I used rapid mock-ups, collaborative iteration with developers, and constant heuristics to keep designs both powerful and approachable.
Making Complexity Feel Simple
My approach: turn complexity into clarity—always prioritizing simplicity. User flows were rooted in real crisis needs, not just business priority.
I used rapid mock-ups, collaborative iteration with developers, and constant heuristics to keep designs both powerful and approachable.
Making Complexity Feel Simple
My approach: turn complexity into clarity—always prioritizing simplicity. User flows were rooted in real crisis needs, not just business priority.
I used rapid mock-ups, collaborative iteration with developers, and constant heuristics to keep designs both powerful and approachable.
Making Complexity Feel Simple
My approach: turn complexity into clarity—always prioritizing simplicity. User flows were rooted in real crisis needs, not just business priority.
I used rapid mock-ups, collaborative iteration with developers, and constant heuristics to keep designs both powerful and approachable.
Making Complexity Feel Simple
My approach: turn complexity into clarity—always prioritizing simplicity. User flows were rooted in real crisis needs, not just business priority.
I used rapid mock-ups, collaborative iteration with developers, and constant heuristics to keep designs both powerful and approachable.
Making Complexity Feel Simple
My approach: turn complexity into clarity—always prioritizing simplicity. User flows were rooted in real crisis needs, not just business priority.
I used rapid mock-ups, collaborative iteration with developers, and constant heuristics to keep designs both powerful and approachable.
Making Complexity Feel Simple
My approach: turn complexity into clarity—always prioritizing simplicity. User flows were rooted in real crisis needs, not just business priority.
I used rapid mock-ups, collaborative iteration with developers, and constant heuristics to keep designs both powerful and approachable.
Wireframing
Extensive wireframes and mock-ups brought ideas to life, visualizing functionality and testing possibilities fast.
I usually do different exercises to brainstorm examples by myself, sketching ideas to come up with a few unique designs. Additionally, I did some Crazy 8s by putting a marker to paper and sketching 8 ideas in 8 minutes to rapidly explore different directions.
I like to use AI to assist and upscale some of my work, so I uploaded pictures of my top 3 sketches into Figma Make in order to get different ideas than the ones I sketched. To do that I like to provide context about the problem statement, user needs and findings of the research, so the model has enough information to reach the expected outcomes. Once I got some interesting visual wireframes, I cherry pick the best ideas and try to get the best snippets of each design, and I bring it back to Figma so I can rework the wireframes (my ideas + the AI-generated ideas).
Wireframing
Extensive wireframes and mock-ups brought ideas to life, visualizing functionality and testing possibilities fast.
I usually do different exercises to brainstorm examples by myself, sketching ideas to come up with a few unique designs. Additionally, I did some Crazy 8s by putting a marker to paper and sketching 8 ideas in 8 minutes to rapidly explore different directions.
I like to use AI to assist and upscale some of my work, so I uploaded pictures of my top 3 sketches into Figma Make in order to get different ideas than the ones I sketched. To do that I like to provide context about the problem statement, user needs and findings of the research, so the model has enough information to reach the expected outcomes. Once I got some interesting visual wireframes, I cherry pick the best ideas and try to get the best snippets of each design, and I bring it back to Figma so I can rework the wireframes (my ideas + the AI-generated ideas).
Wireframing
Extensive wireframes and mock-ups brought ideas to life, visualizing functionality and testing possibilities fast.
I usually do different exercises to brainstorm examples by myself, sketching ideas to come up with a few unique designs. Additionally, I did some Crazy 8s by putting a marker to paper and sketching 8 ideas in 8 minutes to rapidly explore different directions.
I like to use AI to assist and upscale some of my work, so I uploaded pictures of my top 3 sketches into Figma Make in order to get different ideas than the ones I sketched. To do that I like to provide context about the problem statement, user needs and findings of the research, so the model has enough information to reach the expected outcomes. Once I got some interesting visual wireframes, I cherry pick the best ideas and try to get the best snippets of each design, and I bring it back to Figma so I can rework the wireframes (my ideas + the AI-generated ideas).
Wireframing
Extensive wireframes and mock-ups brought ideas to life, visualizing functionality and testing possibilities fast.
I usually do different exercises to brainstorm examples by myself, sketching ideas to come up with a few unique designs. Additionally, I did some Crazy 8s by putting a marker to paper and sketching 8 ideas in 8 minutes to rapidly explore different directions.
I like to use AI to assist and upscale some of my work, so I uploaded pictures of my top 3 sketches into Figma Make in order to get different ideas than the ones I sketched. To do that I like to provide context about the problem statement, user needs and findings of the research, so the model has enough information to reach the expected outcomes. Once I got some interesting visual wireframes, I cherry pick the best ideas and try to get the best snippets of each design, and I bring it back to Figma so I can rework the wireframes (my ideas + the AI-generated ideas).
Wireframing
Extensive wireframes and mock-ups brought ideas to life, visualizing functionality and testing possibilities fast.
I usually do different exercises to brainstorm examples by myself, sketching ideas to come up with a few unique designs. Additionally, I did some Crazy 8s by putting a marker to paper and sketching 8 ideas in 8 minutes to rapidly explore different directions.
I like to use AI to assist and upscale some of my work, so I uploaded pictures of my top 3 sketches into Figma Make in order to get different ideas than the ones I sketched. To do that I like to provide context about the problem statement, user needs and findings of the research, so the model has enough information to reach the expected outcomes. Once I got some interesting visual wireframes, I cherry pick the best ideas and try to get the best snippets of each design, and I bring it back to Figma so I can rework the wireframes (my ideas + the AI-generated ideas).
Wireframing
Extensive wireframes and mock-ups brought ideas to life, visualizing functionality and testing possibilities fast.
I usually do different exercises to brainstorm examples by myself, sketching ideas to come up with a few unique designs. Additionally, I did some Crazy 8s by putting a marker to paper and sketching 8 ideas in 8 minutes to rapidly explore different directions.
I like to use AI to assist and upscale some of my work, so I uploaded pictures of my top 3 sketches into Figma Make in order to get different ideas than the ones I sketched. To do that I like to provide context about the problem statement, user needs and findings of the research, so the model has enough information to reach the expected outcomes. Once I got some interesting visual wireframes, I cherry pick the best ideas and try to get the best snippets of each design, and I bring it back to Figma so I can rework the wireframes (my ideas + the AI-generated ideas).
Wireframing
Extensive wireframes and mock-ups brought ideas to life, visualizing functionality and testing possibilities fast.
I usually do different exercises to brainstorm examples by myself, sketching ideas to come up with a few unique designs. Additionally, I did some Crazy 8s by putting a marker to paper and sketching 8 ideas in 8 minutes to rapidly explore different directions.
I like to use AI to assist and upscale some of my work, so I uploaded pictures of my top 3 sketches into Figma Make in order to get different ideas than the ones I sketched. To do that I like to provide context about the problem statement, user needs and findings of the research, so the model has enough information to reach the expected outcomes. Once I got some interesting visual wireframes, I cherry pick the best ideas and try to get the best snippets of each design, and I bring it back to Figma so I can rework the wireframes (my ideas + the AI-generated ideas).
Wireframing
Extensive wireframes and mock-ups brought ideas to life, visualizing functionality and testing possibilities fast.
I usually do different exercises to brainstorm examples by myself, sketching ideas to come up with a few unique designs. Additionally, I did some Crazy 8s by putting a marker to paper and sketching 8 ideas in 8 minutes to rapidly explore different directions.
I like to use AI to assist and upscale some of my work, so I uploaded pictures of my top 3 sketches into Figma Make in order to get different ideas than the ones I sketched. To do that I like to provide context about the problem statement, user needs and findings of the research, so the model has enough information to reach the expected outcomes. Once I got some interesting visual wireframes, I cherry pick the best ideas and try to get the best snippets of each design, and I bring it back to Figma so I can rework the wireframes (my ideas + the AI-generated ideas).
Iteration
Not all ideas survived technical constraints. The best were rapidly iterated and turned into prototypes.
Iteration
Not all ideas survived technical constraints. The best were rapidly iterated and turned into prototypes.
Iteration
Not all ideas survived technical constraints. The best were rapidly iterated and turned into prototypes.
Iteration
Not all ideas survived technical constraints. The best were rapidly iterated and turned into prototypes.
Iteration
Not all ideas survived technical constraints. The best were rapidly iterated and turned into prototypes.
Iteration
Not all ideas survived technical constraints. The best were rapidly iterated and turned into prototypes.
Iteration
Not all ideas survived technical constraints. The best were rapidly iterated and turned into prototypes.
Iteration
Not all ideas survived technical constraints. The best were rapidly iterated and turned into prototypes.
Prototyping
Wireframes became high-fidelity, testable prototypes—now in active development.
Prototyping
Wireframes became high-fidelity, testable prototypes—now in active development.
Prototyping
Wireframes became high-fidelity, testable prototypes—now in active development.
Prototyping
Wireframes became high-fidelity, testable prototypes—now in active development.
Prototyping
Wireframes became high-fidelity, testable prototypes—now in active development.
Prototyping
Wireframes became high-fidelity, testable prototypes—now in active development.
Prototyping
Wireframes became high-fidelity, testable prototypes—now in active development.
Prototyping
Wireframes became high-fidelity, testable prototypes—now in active development.
Testing, Testing, Testing
Users included ministers, analysts, and decision-makers—each with unique needs. We relied on scenario-based tasks, proactive internal testing cycles, and planned drills to simulate floods and measure experience.
Feedback was captured in checklists, focusing on what worked and what was confusing—spanning usability, clarity, and accessibility across UX categories.
Testing, Testing, Testing
Users included ministers, analysts, and decision-makers—each with unique needs. We relied on scenario-based tasks, proactive internal testing cycles, and planned drills to simulate floods and measure experience.
Feedback was captured in checklists, focusing on what worked and what was confusing—spanning usability, clarity, and accessibility across UX categories.
Testing, Testing, Testing
Users included ministers, analysts, and decision-makers—each with unique needs. We relied on scenario-based tasks, proactive internal testing cycles, and planned drills to simulate floods and measure experience.
Feedback was captured in checklists, focusing on what worked and what was confusing—spanning usability, clarity, and accessibility across UX categories.
Testing, Testing, Testing
Users included ministers, analysts, and decision-makers—each with unique needs. We relied on scenario-based tasks, proactive internal testing cycles, and planned drills to simulate floods and measure experience.
Feedback was captured in checklists, focusing on what worked and what was confusing—spanning usability, clarity, and accessibility across UX categories.
Testing, Testing, Testing
Users included ministers, analysts, and decision-makers—each with unique needs. We relied on scenario-based tasks, proactive internal testing cycles, and planned drills to simulate floods and measure experience.
Feedback was captured in checklists, focusing on what worked and what was confusing—spanning usability, clarity, and accessibility across UX categories.
Testing, Testing, Testing
Users included ministers, analysts, and decision-makers—each with unique needs. We relied on scenario-based tasks, proactive internal testing cycles, and planned drills to simulate floods and measure experience.
Feedback was captured in checklists, focusing on what worked and what was confusing—spanning usability, clarity, and accessibility across UX categories.
Testing, Testing, Testing
Users included ministers, analysts, and decision-makers—each with unique needs. We relied on scenario-based tasks, proactive internal testing cycles, and planned drills to simulate floods and measure experience.
Feedback was captured in checklists, focusing on what worked and what was confusing—spanning usability, clarity, and accessibility across UX categories.
Testing, Testing, Testing
Users included ministers, analysts, and decision-makers—each with unique needs. We relied on scenario-based tasks, proactive internal testing cycles, and planned drills to simulate floods and measure experience.
Feedback was captured in checklists, focusing on what worked and what was confusing—spanning usability, clarity, and accessibility across UX categories.
Design System
I led implementation of a living Design System—an evolving artifact that consolidates everything we use to build and iterate quickly.
Design System
I led implementation of a living Design System—an evolving artifact that consolidates everything we use to build and iterate quickly.
Design System
I led implementation of a living Design System—an evolving artifact that consolidates everything we use to build and iterate quickly.
Design System
I led implementation of a living Design System—an evolving artifact that consolidates everything we use to build and iterate quickly.
Design System
I led implementation of a living Design System—an evolving artifact that consolidates everything we use to build and iterate quickly.
Design System
I led implementation of a living Design System—an evolving artifact that consolidates everything we use to build and iterate quickly.
Design System
I led implementation of a living Design System—an evolving artifact that consolidates everything we use to build and iterate quickly.
Design System
I led implementation of a living Design System—an evolving artifact that consolidates everything we use to build and iterate quickly.
Impact
We launched the live MVP in less than a year. I served as a bridge from business to engineering, raised UX maturity, and embedded user-centricity into OPAL’s strategy—culminating in a full Design System rollout.
Impact
We launched the live MVP in less than a year. I served as a bridge from business to engineering, raised UX maturity, and embedded user-centricity into OPAL’s strategy—culminating in a full Design System rollout.
Impact
We launched the live MVP in less than a year. I served as a bridge from business to engineering, raised UX maturity, and embedded user-centricity into OPAL’s strategy—culminating in a full Design System rollout.
Impact
We launched the live MVP in less than a year. I served as a bridge from business to engineering, raised UX maturity, and embedded user-centricity into OPAL’s strategy—culminating in a full Design System rollout.
Impact
We launched the live MVP in less than a year. I served as a bridge from business to engineering, raised UX maturity, and embedded user-centricity into OPAL’s strategy—culminating in a full Design System rollout.
Impact
We launched the live MVP in less than a year. I served as a bridge from business to engineering, raised UX maturity, and embedded user-centricity into OPAL’s strategy—culminating in a full Design System rollout.
Impact
We launched the live MVP in less than a year. I served as a bridge from business to engineering, raised UX maturity, and embedded user-centricity into OPAL’s strategy—culminating in a full Design System rollout.
Impact
We launched the live MVP in less than a year. I served as a bridge from business to engineering, raised UX maturity, and embedded user-centricity into OPAL’s strategy—culminating in a full Design System rollout.