Viserra: AI-Powered Stock Research & Screening
Time:
Sep 2025 (1 Week)
Role:
Solo Product Designer
Tools:
Lovable, Claude
Viserra is an AI-driven investment research experience designed to reduce the complexity of stock analysis for retail investors.
Overview
AI-powered stock research that turns complexity into clarity
Retail investors often spend hours navigating scattered dashboards and financial ratios just to evaluate a single stock.
To explore a better approach, I built a working AI-powered stock research prototype that integrates live financial APIs with LLM-based explanations. Users can screen stocks based on their goals and risk tolerance, retrieve real-time company data, and receive personalized insights explained in plain language.
The objective was not just to build a filtering tool, but to reduce cognitive load and help users move from research to confident decision-making faster.
💻 Frontend
Interactive UI
🗄 Backend
Database & Hosting
📊 Financial Data
Stock Price, Company Info
Alpha Vantage/FMP API
🧠 AI + LLM
Personalized Analysis
Problem
The Friction Behind Investment Decisions
Retail investors, especially beginners, face an overwhelming amount of data before making a decision. Even after hours of research, many remain uncertain about what truly fits their goals.
Time Intensive
Knowledge Gap
Complex financial metrics overwhelm beginners, making it unclear which signals truly matter.
Limited Alternatives
Human advisors are costly, while robo-advisors often lack transparency or personalization.
Opportunity
How might we help investors move from complex data to confident decisions faster?
Solution
AI-Powered Stock Research & Screening
💡Consideration:
Because this is an investment product, the system avoids direct recommendations. Instead, it surfaces stocks that match user-defined criteria and presents them as research inputs rather than prescriptions.
Reduce Research Time
Reduce Confusion
Plain-language explanations for complex financial metrics
Increase Transparency
Clear breakdown showing how each stock matches user preferences
Prototype
Questions about goals, risk comfort, industry preferences, and investing habits help identify which companies align with users’ expectations.
From a user-centric perspective, the system screens stock candidates, integrates API data, and leverages LLMs to generate a brief personalized analysis.
Key industry metrics are explained in plain language, helping users avoid complex financial jargon.
Users can click View Details to access a deeper layer of analysis, including extended explain of financial metrics, historical trends, and AI-generated insights.
The About the Company section provides background to help users understand company's business.
Users can save stocks they are interested in to their Watchlist, allowing them to revisit these candidates later with updated data.
Final Design
Visual Tone
Most stock research platforms rely on dark themes and high-contrast trading visuals. I intentionally chose a softer purple and pink palette to create a calmer, more approachable experience. The goal was to make financial research feel accessible.
Reflection
What I Learned from AI Prototyping