Designing Bullground's AI Wealth Management Platform

Bullground set out to transform how independent financial advisors manage wealth. Before the platform existed, advisors ran their businesses on PDFs, spreadsheets, and WhatsApp threads — a process that worked for a handful of clients but collapsed at scale.

I was brought on as the sole designer to build the entire product experience 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.

The result: a platform that lets advisors manage their entire client portfolio from a single screen, with an AI assistant that doesn't just answer questions — it executes tasks.

Independent financial advisors are relationship people, not spreadsheet people. But their day-to-day reality was drowning in data management: manually compiling portfolio reports as PDFs, tracking client performance across multiple spreadsheets, and coordinating everything through scattered WhatsApp conversations.

The core design challenge was threefold:

Information overload. Wealth management involves dozens of metrics — ROI, alpha, net worth evolution, portfolio distribution, realized gains, individual stock performance, crypto positions. Advisors told us they needed more data, but showing everything at once would overwhelm anyone.

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

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.

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 data on the right.

For B2C users (individual investors), the AI chat sits front and center because we wanted them to feel that they could ask WiMA anything about their money. The dashboard updates in real time as context for the conversation.

For B2B users (advisors managing multiple clients), the same layout scales — they can ask WiMA to surface best-performing strategies, identify at-risk clients, or execute portfolio changes.

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 advisors throughout the build process. As we built, advisors told us what they needed — and crucially, what they needed more of.

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 — not to reduce fields, but to find the right way to group related inputs so each step felt focused rather than overwhelming.

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.

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 it sent a clear message to users: the AI isn't a feature, it's the experience.