Predix

Case Study

Predix

Crypto · AI-Assisted Trading App

Overview

An AI-assisted trading app that surfaces crypto market signals to support better decision-making. The UX focuses on simplifying complex trading insights into digestible signals.

Role

Product Designer

Timeline

5 months (2023)

Platform

Mobile & Web

Team

1 Designer, 3 Engineers, 1 AI Specialist

Problem

Crypto trading is overwhelming with countless indicators and data points. Novice traders struggle to interpret signals while experienced traders suffer from analysis paralysis. There was a need for AI to surface actionable insights without replacing human decision-making.

AI predictions must be presented with appropriate confidence levels

Cannot appear as financial advice due to regulations

Must work for both novice and experienced traders

Approach

Interviewed 40+ crypto traders about their decision-making process. Mapped out how traders currently analyze markets. Designed AI insights to augment rather than replace human judgment. Created clear visual signals that communicate confidence and risk.

01

Research

02

Design

03

Testing

04

Iterate

Key Decisions

Signal Confidence Levels

Showed AI confidence alongside predictions, helping users understand when to trust signals and when to do additional research.

Visual Market Sentiment

Used clear visual indicators (color, icons, charts) to communicate complex market conditions at a glance.

Educational Context

Provided brief explanations of why AI surfaced each signal, helping users learn rather than blindly following recommendations.

Solution

Solution 1
Solution 2
Solution 3
Solution 4

Outcome

75% of users reported more confident trading decisions

Average portfolio performance improved 18%

2,000+ active traders in first quarter

4.5/5 app store rating

Featured in Top Crypto Trading Tools 2023

Reflection

Designing for AI-assisted decision-making requires careful balance - the AI should empower users, not replace their judgment. The most important design decision was showing confidence levels, which built trust in the system.

Next Steps

01

Add customizable signal types and thresholds

02

Implement social trading features

03

Create paper trading mode for practice

04

Develop advanced analytics for experienced traders

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