From Insight to Execution
Information is scattered across platforms, making it costly to gain clear insights.
Complicated trading processes and terms make it hard for beginners to get started.
Unable to watch the market all the time, beginners often miss opportunities.
New trader’s hope.
New trader’s struggle.
In the chaos of Web3.
AI brings clarity.
Cross-platform intelligence connects scattered information into a single clear view.
Structured clarity transforms complex data into clear and actionable insights.
Personalized understanding highlights the most relevant signals based on context and patterns.
Define AI Design Involvement
AI designed to guide.
Not decide.
AI is placed at the Informational level, it ensures compliance, keeps responsibility with the user, and still adds value.
Design through User Journey
Different stages.
Different strategies.
Friction Scenarios
Scattered information
High access cost
Diverse signals across dimensions
Hard to form judgment
Complex trading process
Overwhelming jargon
Hard to take first steps
Unclear sources of PNL
Hard to learn from past
User Goal
Understand market
Discover opportunities
Understand different perspectives clearly
Form reliable judgment
Understand trading steps
Smooth execution
Understand outcomes
Improve decisions
Business Target
User retention
Daily active users
Trade conversion
Trade completion rate
New user conversion
User retention
Long-term engagement
Design Strategy
Information aggregation
Structured analysis
Simplified guidance
Insightful reflection
AI Involvement
Level 2
Informational
Level 2
Informational
Level 2
Informational
Level 1
No Guidance
Step 1 Discovery: AI Market Daily
Quick scan.
Broad coverage.
AI Market Daily updates every morning and is featured on the homepage. It offers a cross-platform market overview and strengthens user stickiness.
A market briefing.
Engaging users daily.
By combining three dimensions into a single view, the overview guides users from quick scanning to deeper in-app engagement.
Market. Social Media. Assets.
All in one view.
Progressive display
Key market metrics, major coins, trending categories, and headline news are arranged hierarchically to present a layered view of market conditions.
Contextual linkage
Each module includes direct entry points to related in-app sections, guiding users to deeper content based on their interests.
Start from the market.
See the landscape unfold.
Visualized overview
See sentiment bias linked to keyword clouds for clarity.
Social sentiment reframed
Instead of labeling “opportunities,” social sentiment is framed as signals that draw attention and guide users to Alpha and Square for context while avoiding investment implications.
Zoom into social sentiment.
Detect signals in local segments.
End with asset relevance.
Extend with personalized context.
Completing the chain
From market signals and social sentiment to wallet and PnL, showing how external trends affect assets.
Selected with focus
Coins from holdings and watchlist, filtered to avoid repetition with market and social coverage.
Audio playback makes it easy to stay updated during commutes.
Floating windows allow seamless switching between Market Daily and other modules.
Not just reading.
Reading that adapts.
Step 2 Decision: AI Insight
Multi-dimensional views.
Deeper analysis.
Two ways in.
One clear destination.
List entry: Quick access through the market list delivers instant summaries for easy browsing.
Chart entry: The chart’s fixed entry allows deeper investigation, tailored for detailed exploration.
Path to understanding.
Information architecture matters.
Content is grouped into three categories: Positives, Risks, and Community Sentiment. However, core signals like Market Signal, Technical Signal, Whale Flow, and On Chain Activities are scattered across both Positives and Risks. The inconsistent classification makes it difficult to form a structured understanding.
Poor information architecture in many mainstream products results in fragmented content. Users remain confused and unable to make clear judgments even after reading large amounts of information.
Take Binance AI Insight as an example
Fragmented data distracts.
Content is organized by five signal types, each clearly split into Opportunities and Risks. Consistent structured framing helps users map insights by category and polarity easily, making complex information more digestible and actionable.
Clear categorization caters to different types of traders. Market participants, technical analysts, and long-term holders can each easily find what matters most to them.
Structured grouping guides.
Insights follow a structured flow, starting with summary conclusions and than expanding into detailed signals.
Red and green tags highlight risks and opportunities, while avoiding quantified intensity to prevent guidance risks.
For signals beyond numbers.
Qualitative tags, clarity in layers.
Signal as it is.
Keep neutral and objective.
For signals defined by data.
Visual design, precise attribution.
Visual form highlights signals, while transparent sourcing keeps interpretation unbiased.
Step 3 Execution: Smart Trade Assistant
Guided steps.
Seamless execution.
In uncertainty.
Restraint is clarity.
Trading carries weight where trust is fragile. AI steps in with context, never with direction.
Where hesitation begins.
Choosing an order type.
Complex explanation overwhelm beginners. To act with certainty, they face the pressure of reading and comparing every option.
Endless reading ends.
Clarity grows step by step.
Step 1
Users can tap the questionnaire entry when unsure of order type.
Step 3
The matched result is displayed clearly with brief explanation.
Step 2
Users Fill out the questionnaire based on trading intent.
Step 4
Users are guided back to the trading page to complete inputs, designed to assist completion rather than create a new flow.
At most 4 questions.
Decisions grow clear without AI involvement.
Breaking barriers of terms.
AI keeps trading seamless.
The AI entry is placed below the explanation, allowing instant follow-up when confusion remains.
The dialogue automatically includes original context, reducing explanation cost.
Technical terms often interrupt trading. Conversational AI gives contextual responses to resolve hesitation and keep the flow smooth.
AI is strictly trained not to provide investment advice,
protecting platform‘s trust
A contextual entry
keeps explanations seamless.
A flexible AI language style setting fits all levels of users.
Web3 users have very different levels of knowledge. Instead of relying on abstract labels, a live preview allows them to quickly see if the language style matches their understanding and avoid trial-and-error.
Step 4 Review: Structured Asset Page
Clear causes.
Clear outcomes.
From restraint to redesign.
Insight beyond AI.
True reflection
begins with restraint.
View Detail in Next Project
More Structured
Asset Page
Data structured for clarity and a seamless flow, aligned with user scenarios.
Progressive insights with clear attribution for better understanding.
Suggestions aligned with the user’s trading personality
More Insightful
Asset Report
More Personalized
Suggestions
In review, AI involvement can lead to misinterpretation, misleading patterns, or perceived bias. Profit and loss are deeply tied to personal behavior and remain highly sensitive. Errors here not only damage trust but also raise compliance concerns and compromise neutrality.