AI-Powered Trading. Human-Centered Intelligence.
Empowering investors with autonomous AI trading bots, real-time market analysis, and personalized investment strategies to automate trades, identify opportunities, manage risk, and make faster, data-driven decisions through conversational AI.
- My role
- Principal Product Designer
I led end-to-end product design for an AI-assisted trading experience, connecting account setup, automation, and portfolio monitoring. I focused on making bot controls easier to understand and performance easier to inspect.
- Client
- NeuroTrade
- Timeline
- 2 weeks
- Tools
- Figma
- FigJam
- Framer
01 / DESIGN DECISIONS
Complex automation. Clearer decisions.
I focused the design on clarity before automation and oversight after activation.
- Problem
- Trading platforms can overwhelm people with dense information, unfamiliar controls, and uncertainty about AI-driven decisions.
- My decision
- I organized the experience around distinct tasks: understand the portfolio, choose a bot, configure its settings, then monitor its activity.
02 / AI & AGENT WORKFLOWS
From AI capability to a workflow people can follow.
I designed a four-step automation journey with clear setup controls and ongoing oversight.
01Connect
Choose an exchange account and establish the connection before configuring a bot.
02Configure
Select the bot approach, risk setting, investment amount, and relevant trading parameters.
03Activate
Move through the setup and activation states so the transition into automation is visible.
04Monitor
Return to active-bot cards and portfolio views to inspect activity, investment, runtime, and performance.
03 / PRODUCT SCREENS
Explore the trading workspace.
Move from portfolio context to action: review holdings and market signals, configure AI agents, and ask the NeuroTrade Copilot about your account.
04 / RESEARCH & NEXT STEPS
Research & supporting flows
I reviewed requirements and competitor patterns, then used interviews, surveys, and personas to frame three concerns: interface complexity, trust in automated decisions, and the learning curve for beginners.
Account access & recovery
Original FigJam flow showing PIN and password sign-in paths, notification checks, and recovery decisions.
Sign-up & verification
Original FigJam flow showing registration, account review, verification, and the route into login.
Data model
A map of NeuroTrade API entities, account relationships, and the operations that connect user, wallet, asset, and trading data.
What I would validate next.
I would test whether people can understand bot setup, monitor performance, and recover from connection issues. These are next-step priorities, not measured project results.