Mobius: AI-Powered Market Data Querying Project

Mobius is an AI-powered data querying platform designed to enhance the way investors, corporate executives, and data analysts interact with financial market trends. The project's goal was to improve the accuracy, clarity, and usability of graph-based results, ensuring that users receive insightful and actionable data visualizations when querying market trends.

Problem Statement

Financial professionals rely on data-driven insights to make informed decisions, but traditional querying tools often produce cluttered or unclear graph results. Users needed a solution that:

• Provides intuitive, well-structured data visualizations.
• Supports natural language queries with high accuracy.
• Integrates seamlessly into existing investment and analytics workflows.

Challenges

• Graph Accuracy & Readability – Existing solutions struggled to present complex financial data in an intuitive way.
• Natural Language Querying – Users needed a seamless way to ask market-related questions without relying on complex SQL or code.
• Performance & Scalability – The system had to process large datasets quickly while maintaining real-time responsiveness.
• User Experience (UX) Optimization – Balancing a feature-rich UI with ease of use was critical.

Research & Planning

We conducted user research with Wall Street investors, corporate executives, and data analysts to understand their workflow pain points. The key takeaways:

• Users preferred dynamic, customizable dashboards.
• Graphs needed to be interactive, allowing drill-downs into specific data points.
• AI-driven query recommendations would help users refine their searches.
• Dark mode and accessibility improvements were essential for prolonged usage.

Key Research Methods

• User Interviews: Conducted with 15+ finance professionals.
• Competitive Analysis: Studied existing solutions like Bloomberg Terminal and Eikon.
• Usability Testing: Iterated on UI/UX designs based on user feedback.

Designing the Solution

1. Enhanced Data Visualization

• 📌 Solution: Redesigned graphing components for improved clarity, interactivity, and customization.
• Implemented drill-down functionality for deep data insights.
• Designed a modular graphing system to support various financial metrics.
• Used color-coded trend indicators to highlight key patterns.

2. AI-Optimized Querying

• 📌 Solution: Integrated NLP (Natural Language Processing) for smarter question interpretation.
• Query Autocomplete: Suggested queries based on past searches.
• AI-driven Insights: Highlighted key market movements.
• Context-aware filtering: Refined results based on user preferences.

3. Scalable Performance Architecture

• 📌 Solution: Optimized backend to support high-frequency data retrieval.
• Indexed data storage to speed up queries.
• Real-time processing using AI-driven caching mechanisms.
• Cloud-based scaling for handling large datasets seamlessly.

4. User-Centric Interface & Customizable Dashboards

• 📌 Solution: Built an intuitive UI with customizable dashboards.
• Drag-and-drop widgets for personalized financial analysis.
• Dark mode & high-contrast UI for better readability.
• One-click export for easy reporting.

Results & Impact

• ✅ Faster Query Processing – Optimized AI-driven backend improved response times.
• ✅ Increase in User Engagement – Interactive graphs and query refinements led to more usage.
• ✅ Higher Accuracy in Financial Insights – AI-driven recommendations provided better investment strategies.

Key Takeaways

• AI-driven NLP transforms data querying, making it more accessible.
• Clear and interactive data visualizations enhance decision-making.
• Performance optimization is crucial for handling large-scale financial datasets.

Final Thoughts

Mobius seamlessly connects AI-driven data querying with financial market insights, delivering an intuitive, efficient, and user-friendly experience for investors and analysts. I thoroughly enjoyed this project, especially creating data-rich prototypes and experimenting with various graphing libraries to refine visual representations. Testing and iterating on different approaches allowed me to enhance user understanding and engagement, making complex data more accessible and actionable.