UX/UI design powered by AI for modern digital products.
Research, interface design, prototypes and design systems work together to create clearer and more usable digital experiences.
UX design starts before the interface.
Before designing screens, a project needs to connect user needs, business goals and real constraints. UX design turns that context into decisions that guide structure, flows, content and interface design.
AI strengthens this phase because it helps collect information, compare patterns, explore scenarios and organize research faster.
Experience still matters: the value comes from asking the right questions, reading the context correctly and turning raw information into useful design decisions.
- Understand who will use the digital project and in which context.
- Identify pain points, goals and moments of friction.
- Test ideas before development absorbs time and budget.
- Plan releases around real priorities, not internal assumptions.
- Design interfaces that support clearer and more useful interactions.
UX as a continuous method.
The UX process continues over time through qualitative and quantitative research, user feedback, testing and iteration, with the purpose of improving the digital product as real needs evolve.
This way of working shaped my mindset early on, during my experience at Capgemini Ernst & Young, where I learned the value of structured thinking, user-centered logic and multidisciplinary collaboration.
The role was different from what I do today, but that approach still matters: good digital projects need method, context and the ability to connect different perspectives.
AI now extends this mindset. It helps organize research, compare scenarios and reduce complexity, while design experience keeps the process selective, realistic and useful.
UX gives the project a reasoned structure before the interface takes shape. UI translates that structure into hierarchy, interaction, rhythm and visual clarity, so the experience feels coherent instead of merely polished.
Adaptive UX for real projects.
A complete UX process works best when teams can collaborate from the early stages of a project. Workshops, research, user flows, prototypes and testing help reduce risk and create a stronger foundation before design and development move too far.
Real projects rarely offer perfect conditions. Ecommerce platforms, WordPress sites, existing frameworks and pre-built systems often come with limits, fixed structures and technical constraints. A good UX/UI approach still adds value in these contexts.
The work shifts from “design everything from scratch” to making better decisions inside real boundaries: clearer flows, better content hierarchy, more useful interactions, stronger visual consistency and fewer obstacles between people and action.
AI inside UX/UI work.
I use AI across the entire UX/UI process: research synthesis, competitor analysis, user flows, information architecture, UX writing, interface checks, naming, content structure and design system logic.
AI helps explore alternatives faster, compare different solutions and identify weak points in flows, messages and layouts.
AI also helps clarify technical aspects when UX decisions need to consider frontend logic, platform limits, integrations or implementation complexity.
AI does not replace design judgment. It increases the amount of exploration that can happen before a decision, then experience defines what deserves to move forward.
From UX knowledge to UI execution.
The final interface should translate research, structure and priorities into something clear, balanced and usable. A good UI does more than look polished: it helps people understand where they are, what they can do and what should happen next.
This includes layout, hierarchy, typography, spacing, interaction states, accessibility, visual rhythm, responsive behaviour and brand consistency.
Figma and FigJam are central tools in this process because they allow fast exploration, structured prototypes, design system work and clearer collaboration.
I also pay strong attention to UX writing, because buttons, labels, forms, empty states, notifications and microcopy often decide whether an experience feels clear or confusing.
Visual details, icons and small motion elements can add clarity and character when they support the experience instead of decorating it.
UX/UI design process and activities.
Research, discovery and analysis.
- Market research and competitive analysis.
- Quantitative data analysis, including KPIs, analytics, heatmaps and surveys.
- Qualitative analysis through user interviews and feedback.
- Heuristic evaluation and UX review.
- AI-supported synthesis of research, patterns and documentation.
Context and problem definition.
- Problem validation.
- User goals and pain points.
- User stories and How Might We statements.
- User personas and audience profiles.
- Affinity maps and research clustering.
- AI-supported organization of insights and scenarios.
Structure and experience design.
- Brainstorming and ideation.
- User flows and user journeys.
- Prioritization.
- Information architecture.
- Low-fi and mid-fi prototypes.
- Wireframes and early validation.
User validation.
- Prototype testing.
- Feedback collection and analysis.
- UX copy review.
- Flow refinement.
- AI-supported comparison of alternatives and weak points.
UI and detailed design.
- User interface design.
- High-fi prototypes and mockups.
- Visual hierarchy and interaction states.
- Responsive layout priorities.
- Accessibility checks, including contrast, readability and interface states.
- Design system structure, reusable components and interface consistency rules.
- Icon sets, visual assets, micro-interactions and motion design when relevant to UX.
Development support and implementation.
- Collaboration with developers.
- Interface handoff and design specifications.
- Frontend logic review.
- Technical feasibility checks.
- Implementation support to preserve UX, UI and design system consistency.
Testing, launch and optimization.
- Functional and usability testing.
- Performance and behaviour review.
- Launch support.
- Post-launch metric monitoring.
- Continuous feedback collection.
- Iteration based on real data, user behaviour and business priorities.
