Devo app UI refactor
My Role
I was involved not only in project planning and prototyping all application screens, but also in implementing the required changes directly in code. This has been a huge project, so many times we needed to do the changes from the design department.
My responsibilities also included defining the new design system architecture, and ensuring visual and functional consistency across a vast library of data visualization components.
In addition,I collaborated closely with data engineers, product managers, and front-end developers, providing ongoing guidance and support throughout the process.
The Challenge
The original platform had evolved through years of incremental development, resulting in a fragmented experience. Data tables, dashboards, and charts were built inconsistently by different teams, using separate codebases and styling approaches. This led to visual clutter, reduced usability, and high maintenance costs.
The main challenge was to unify this ecosystem under a single scalable design system that would preserve analytical power while improving clarity and performance.
The redesign also needed to support new use cases, such as more customizable dashboards, new data tables based in components library, new navigation architecture and main menu, advanced data filtering... etc. without adding cognitive overload.
Research
We started with a full product audit, mapping all pages, widgets, and interactive elements used across different modules of the platform. We ran interviews with data analysts, business users, and internal developers to identify pain points in the workflow. The biggest issues were related to discoverability (hidden features), visual overload, and lack of standardization in visualizations.
In parallel, we benchmarked leading analytics tools to understand best practices in handling dense data interfaces. The key insight was that consistency and clarity were far more valuable than visual novelty. These findings guided our decision to build a flexible system around composable, data-first components.
In addition, we used a previous comprehensive research from my own design department, about pain points, inconsistencies, navigation problems, and unclear user flows or actions.
Design System
The new design system, internally named "Genesys", was conceived as a modular ecosystem covering UI components, data visualizations, and interaction patterns. We defined design tokens for color, typography, spacing, and data states, ensuring visual harmony across tables, charts, and dashboards.
To ensure adoption, we created Figma libraries linked to a Storybook instance used by developers. Each component was documented with accessibility notes, responsive behavior rules, and usage examples. This dramatically reduced design-developer handoff time and standardized visual consistency across teams.
Visual & Interaction Redesign
We simplified the interface by introducing a clean, data-first visual language that prioritized readability and focus. The new color palette was built around neutral backgrounds and high-contrast accent colors for data visualization, improving interpretability for complex processes and interfaces.
Interaction patterns were redesigned to make complex analytical actions (filtering, grouping, and exporting) feel effortless. Persistent filters, contextual tooltips, and keyboard shortcuts were added to streamline repetitive workflows. The overall goal was to make powerful analytics feel approachable without sacrificing depth.
Key UI improvements include:
- More purposeful use of brand colors.
- Rationalized color usage by context.
- Improved color hierarchy.
- Reduced icon overuse.
- Structured use of elevation and depth.
- Creation of a dedicated Table component.
- Refined secondary navigation.
- Simplified main menu.
- Standardized messaging system.
- Expanded contextual assistance.
Implementation & Collaboration
Close collaboration between design and development was crucial. We established a shared naming convention and design token pipeline that connected Figma styles to code variables. Weekly cross-team sessions ensured every component was validated against technical feasibility and real user needs.
Through the new Storybook-based workflow, designers could preview coded components live, accelerating iteration cycles and reducing ambiguity. This continuous collaboration also helped maintain a single source of truth between the design library and the production UI.
One of the most important tasks I had to accomplish during the refactor was supporting the development teams, as they needed to migrate numerous screens (each originally built with different technologies and languages) to React, while adopting the new libraries for components, design tokens, and icons, among others.
Anyway, in many cases, the design team (including myself) also had to take part in the code refactor, migrating legacy screens to React and implementing the new design system libraries. Once this process was completed, the developers integrated the frontend with the backend.
Process Snapshot
Audit & Discovery
Understanding the legacy system and identifying pain points. Full interface inventory, usability insights, and feature map.
Research & Benchmarking
Analyzing user workflows and comparing with market standards. Benchmark grid and summarized design principles.
Visual Redesign & Prototyping
Creating new interface visuals and testing interactive flows. High-fidelity prototypes validated through usability tests.
Implementation & Collaboration
Syncing with development via shared libraries and design tokens. Figma-Storybook pipeline and dev alignment.
Validation & Rollout
Gradual deployment and measurement of impact. A/B test results, NPS increase, and component reuse metrics.
Impact & Outcomes
Clarity and consistency
The redesigned interface brought a substantial improvement in clarity and efficiency across all user workflows. By rethinking visual hierarchy and interaction patterns, users could now interpret and act on complex datasets with significantly less cognitive effort, reducing unnecessary friction and helping analysts focus on insights.Analytical context
Beyond usability, the visual refresh strengthened the product's sense of purpose and trustworthiness. The refined color system, typography, and spacing created a calmer, more analytical atmosphere, aligning the interface with the mental model of professional data users.Reliability
Consistent motion cues and microinteractions improved perceived responsiveness, making the platform feel more polished and reliable. In the same way, the improvements for feedback messages and contextual help, have improved the user experience.Learnings and Next Steps
One major learning was the importance of designing for flexibility within constraints. In data-heavy environments, small inconsistencies can lead to confusion or misinterpretation, so maintaining strict system governance became essential. Another takeaway was that early developer involvement accelerates adoption and ensures scalability.
Moving forward, we plan to extend Genesys to support dark mode, and responsive layouts for secondary devices and scpecific sections which are able to manage in a mobile device. The system will also evolve into a shared design infrastructure for all internal analytics tools, unifying visual language company-wide.