BITNOVA
2025
Bitnova is an AI-powered Web3 investment platform designed to help users navigate volatile digital asset markets with clarity. Through intelligent market analysis and adaptive portfolio insights, Bitnova surfaces meaningful opportunities and guides users toward more informed, value-focused decisions.
FOCUS
UI/UX
TIMELINE
2 Weeks
TOOL
Figma
*Group project with Sirui Liu, Georgia Lyu
CONTEXT
CHALLENGE
This project focused on designing how an existing help crypto investors better understand market movements and evaluate decisions, rather than simply react to price changes?
Translating High-Frequency Market Data into Legible, Actionable Insight
Making AI Insights Understandable Without Replacing User Judgment
Enabling Confident Decisions Through Comparison and Risk-Free Exploration
DESIGN OUTCOME
An AI-powered investment experience that is Contextual, Transparent, Exploratory.
RESEARCH
This research explored how investors interpret complexity in live portfolio data and how AI insights are woven into their decision-making. The emphasis was on uncovering mental models, hesitation points, and sources of confidence as users navigate potential actions, with particular attention to how comparison and simulation shape users’ sense of control in uncertain, high-stakes environments.
This behavior mapping illustrates how users’ thinking becomes increasingly self-focused and risk-oriented, while emotional valence shifts from neutral to negative. The pattern highlights a lack of interpretive support prior to decision-making.
INSIGHTS
EXPLORING NEW FEATURES
Based on customer needs, we brainstormed and generated ideas to solve their problems, and out of all the ideas generated, we picked 6 ideas to finalize.
DESIGN
With the user flow in place, I developed low-fidelity wireframes to map out key interactions, then refined them into high-fidelity wireframes to define the visual style and interface details.
REFLECTION
Working on Bitnova deepened my understanding of how AI can function as an AI Copilot within user experience, rather than as a background optimization system. Designing an AI copilot required me to think beyond output accuracy and consider how AI communicates intent, uncertainty, and relevance through interaction.
This project helped me clarify the distinction between AI as an agent and AI as an interface element. Instead of treating the AI as a decision-maker, I explored how it could act as a conversational and contextual layer, responding to user intent, surfacing explanations, and supporting reasoning without asserting authority. This shifted my approach to designing AI systems from “what can the model do” to “what role should the model play in the user’s cognitive process.”
I also developed a stronger understanding of progressive disclosure in AI-driven interfaces. Through chart annotations, portfolio comparisons, and simulations, AI insights were revealed in response to user actions rather than presented upfront. This reinforced the idea that effective AI UX is not about maximizing intelligence, but about pacing, framing, and timing.
Overall, Bitnova became a framework for thinking about AI copilots as collaborative tools, a systems that extend human judgment through dialogue, context, and exploration, rather than replacing it. This perspective now informs how I approach AI-assisted design across both product and speculative contexts.