Designing Bullground's AI Wealth Management Platform

Bullground set out to democratize wealth management for individual investors across Latin America. Before WiMA existed, getting professional investment advice meant either hiring an expensive private advisor or navigating complex platforms alone.

As the only UX Designer of the team, my role was to build the entire product from scratch. Working directly with the CTO and two developers over 8 months, I had full creative ownership — from research and information architecture to the final interface and design system.

This was a complete 0-to-1 build—no existing product, no users, no design patterns to reference. I had to define everything: information architecture, interaction patterns, visual language, and the entire design system.

The result: a platform where individual investors can manage their entire portfolio from a single screen, with an AI assistant that doesn't just answer questions — it executes tasks and provides personalized recommendations.

Individual investors want to grow their wealth, but most fintech platforms assume you're either a complete beginner or a day trader. There's little middle ground for people who want professional guidance without surrendering complete control.

The core design challenge was fourfold:

Information overload. Wealth management involves dozens of metrics — ROI, alpha, net worth evolution, portfolio distribution, realized gains, individual stock performance, crypto positions. Users needed access to this data, but showing everything at once would overwhelm them.

Complex workflows in a simple interface. Adding investments and recording transactions involved relatively few steps (around 5), but each step required multiple financial inputs. The challenge wasn't reducing steps — it was deciding how to break up dense input forms so they felt manageable for non-expert users.

Making AI feel central, not bolted on. The long-term vision was to evolve WiMA into a generative AI wealth manager. The AI couldn't live in a sidebar or feel like an afterthought — it needed to be the primary interface.

Building trust in AI. For users to trust AI recommendations with their personal wealth, we needed radical transparency. Users had to understand not just what the AI recommended, but why — without drowning them in technical explanations or financial jargon.

Most fintech dashboards follow a predictable pattern: navigation on the left, content in the center, maybe a sidebar for details. I proposed something different.

I designed the platform around a three-column layout: a collapsible navigation on the left, WiMA (the AI conversational agent) in the center, and a live dashboard with real-time portfolio data on the right.

The AI chat sits front and center because we wanted users to feel they could ask WiMA anything about their money. The dashboard updates in real time as context for the conversation — users can see their portfolio performance while discussing investment strategies with the AI.

This layout was designed to scale: as users grow more sophisticated, they can access deeper analytics. As the AI evolves, it can surface more complex insights. The foundation supports both the beginner investor asking "What should I do with $1,000?" and the experienced investor analyzing sector performance.

After I left, the team evolved the layout based on real-world usage data. The dashboard moved to the center as the primary view, and WiMA shifted to a contextual panel on the right. The navigation structure, data hierarchy, and design system I built all remained intact — the foundation was designed with flexibility in mind.

Research happened through continuous conversations with Bullground's existing clients and target users throughout the build process. I conducted user interviews across different experience levels — from first-time investors to people who'd been managing portfolios for years.

The key insight: trust was the primary concern. Users wanted AI recommendations, but they needed to understand the reasoning. This shaped every subsequent design decision.

I validated design decisions through rapid prototyping and usability testing at multiple fidelity levels — from paper sketches to functional Figma prototypes. This iterative approach allowed us to refine the information hierarchy and input flows before development.

The most iterative part was the information architecture. The solution was progressive disclosure across multiple levels: a summary view for the 5-second pulse check, tab-based deep dives for specific asset classes, and stock-level detail that exists but never competes with the overview.

The input-heavy flows went through multiple rounds of restructuring based on user testing — not to reduce fields, but to find the right way to group related inputs so each step felt focused rather than overwhelming, even for users with limited financial literacy.

The platform successfully launched with 1,000 initial clients across Latin America. Within the first phase post-launch, we grew to 1,500 users—a 50% increase driven primarily by word-of-mouth and user satisfaction.

We achieved a 14% churn rate, significantly outperforming the 20-25% industry average for fintech wealth management platforms. This retention success validated our core design principles: users stayed because the platform was intuitive, the AI felt trustworthy, and the progressive data hierarchy made complex financial information accessible.

Post-launch feedback from users highlighted: - The three-column layout that kept AI guidance and portfolio data visible simultaneously - Progressive disclosure that surfaced the right information at the right time without overwhelming beginners - The ability to manage their entire investment portfolio from a single screen - AI recommendations that explained the "why" behind every suggestion, building trust over time

The design system and information architecture I established continued to scale as the product evolved, supporting new features and increased user load without requiring foundational redesigns.

WiMA taught me that in data-heavy products, the designer's most important job isn't making things look good — it's making decisions about what not to show. Every metric that earned a spot on the summary dashboard had to justify its presence to users who might be seeing their investment portfolio for the first time.

The three-column layout was a risk — it's unconventional and demanded more from the development team. But it established a product architecture that could grow with the AI's capabilities and the users' financial literacy. It sent a clear message: the AI isn't a feature, it's the experience.

What I'd do differently: Looking back, I would have involved engineering earlier in the design system creation. We had some iteration on component complexity that could have been avoided with earlier technical validation. I'd also have pushed for more structured post-launch user interviews to quantify which specific features drove our strong retention numbers — understanding the "why" behind our 14% churn rate would have made the success even more actionable.