Allegra: An AI-Driven Fintech Interface Design Study
This is a design study, not a deployed product. Allegra is a concept project exploring how an AI-driven trading or fintech interface can be designed for retail-pro users. The deliverable was UI design and HTML samples — a proof of approach, not a launched financial product.
The design problem
Retail-pro fintech interfaces (TradingView, Interactive Brokers, ThinkOrSwim) optimize for power-user density: dozens of widgets, deeply customizable layouts, every pixel earning its place. AI-driven tools coming into this space (auto-trading bots, signal generators, portfolio assistants) typically attach a chat sidebar to one of these dense interfaces and call it AI-native. The result is bolted-on, not integrated.
The Allegra study explored what an AI-driven trading interface looks like when designed AI-first — with the assumption that most user interactions will be conversational queries, AI-generated suggestions, and one-tap actions, rather than manual chart navigation. The goal was an interface that respects the user’s existing mental models from charting tools while making the AI the primary co-pilot, not a sidebar feature.
Design decisions
The split: chart on left, AI workspace on right
50/50 split on desktop. The chart is the primary visual artifact — the AI doesn’t replace looking at price action, it accelerates interpretation. The right panel is where queries, AI explanations, signal cards, and action confirmations live. Mobile collapses this to tabbed views with the chart as default.
The action confirmation pattern
Every AI-suggested action (“buy 100 shares of X at limit $Y”) shows a structured preview before the user can execute: the order params, the AI’s reasoning, the inferred risk, the relevant historical context. The CTA to execute requires explicit user input — no auto-execution. The principle: AI accelerates analysis; the human commits the trade. This is non-negotiable for regulatory reasons and for user trust.
Risk visualization that’s actually honest
Position sizing, drawdown scenarios, and worst-case exposure are surfaced inline with every suggestion, not buried in a settings page. The disclosure language is plain (“if this trade goes wrong, your portfolio could lose 4%”) rather than regulatory boilerplate. This reflects a real engineering principle: regulatory disclosures that the user actually reads are more protective than legally-bulletproof ones they ignore.
The data density question
Pro traders want dense interfaces; new users get overwhelmed. The study explored progressive disclosure: a clean default view with one chart, one watchlist, the AI panel; the user adds widgets as they invest in the platform. The dense-by-default approach competitors take excludes new users; the simplified-default-with-progressive-disclosure approach lets the same product serve novice and pro.
What we built
- UI design system: ~80 components covering charts, watchlists, order entry, AI conversation, position management, account, settings
- HTML/CSS prototype of the main trading view (desktop) and the mobile experience
- Component samples for AI-suggestion cards, order preview, risk visualization, and the chat-driven query interface
- Specifications for accessibility (high-contrast mode, keyboard shortcuts, screen reader compatibility for charts)
What this study showed us
AI-driven fintech doesn’t need to look like a chatbot bolted onto a trading platform — the AI can be the workspace, with the chart as the primary contextual surface. Action confirmation patterns are the highest-trust design pattern in the AI-finance category, and they don’t have to feel friction-y if the preview itself is information-dense and useful. Regulatory honesty is a design choice, not a legal one; the language matters more than the legal team usually realizes.
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Design study by Sanya and the EtherLabz team. Allegra is a concept project for design exploration; not a deployed financial product. Nothing in this study constitutes financial advice or a regulated financial service.