Full-stack AI investment research platform aggregating 9 data sources with Gemini-powered analysis
Investment research is time-consuming and fragmented across multiple data sources. Analysts spend hours manually aggregating information, extracting financial data from unstructured sources, and performing technical analysis.
A comprehensive platform that automatically aggregates data from 9 sources, uses Gemini AI to extract structured financials from news articles, performs technical analysis, and generates investment-grade reports in minutes.
Multi-Agent AI System with 4 specialized agents: Data Gathering → Analysis → Synthesis → Report Generation with graceful degradation so system works even if AI fails
Phase 1: Foundation - API integration and data pipeline (9 sources including yfinance, NewsAPI, GitHub)
Phase 2: AI Agents - Gemini + FinBERT integration for financial extraction and sentiment analysis
Phase 3: Advanced Capabilities - Private company financial extraction from news, Hidden Signals analysis
Phase 4: Deployment - Docker optimization, HuggingFace deployment, multi-stage builds
Phase 5: Production - Testing with real companies, validation, cost optimization to $0/month
Transferable skills and capabilities beyond the technical implementation
Identified that investment research bottleneck was data aggregation across 9 fragmented sources taking 15 hours. Designed multi-agent architecture where each agent handles specific data source efficiently.
Built JSON extraction with regex fallback, retry logic with exponential backoff, graceful degradation so reports work without AI. Production systems need resilience patterns.
Hit Vercel 50MB serverless limit, pivoted to unified Docker deployment on HuggingFace. Understanding platform trade-offs crucial for production decisions.
Implemented explicit confidence levels (HIGH/MEDIUM/LOW) for different data sources. Free APIs have real constraints - designed around them with honesty rather than hiding limitations.