Building the Future of
Investment Intelligence
At B.D. Sterling, we're developing proprietary AI systems that augment human judgment with machine intelligence, enhancing research depth, accelerating insights, and optimizing portfolio decisions while maintaining rigorous fundamental analysis.
Prism AI
Our proprietary AI-powered document analysis tool designed specifically for investment research. Prism leverages a curated knowledge base to help our team synthesize financial data and generate deeper research insights.
10x Faster Research
Analyze what took days in minutes
KEY INSIGHT
Cloud segment margins expanded 400bps YoY, approaching breakeven for the first time. Capital expenditure guidance suggests continued infrastructure investment.
RAG Architecture
Vector Embeddings Store
Curated financial document library
Contextual AI Response
Grounded in proprietary data
RAG Knowledge System
Our Retrieval-Augmented Generation system creates a living knowledge base of financial intelligence. Unlike generic AI, our RAG model retrieves context from our proprietary research database to deliver precise, source-backed insights.
Curated Knowledge Base
SEC filings, reports, research
Growing Database
Continuously expanding library
Precision Retrieval
Semantic search accuracy
Source Attribution
Every insight is traceable
Why RAG Matters
Traditional AI models can hallucinate or provide outdated information. Our RAG system grounds every response in actual documents from our database, ensuring accuracy and enabling full auditability of AI-generated insights.
Portfolio Allocation Model
Our AI-driven allocation model combines modern portfolio theory with machine learning to optimize position sizing, manage risk, and maximize risk-adjusted returns.
Dynamic Position Sizing
ML models that adjust position sizes based on conviction levels, volatility regimes, and correlation dynamics across the portfolio.
Risk Optimization
Real-time Value-at-Risk calculations, drawdown protection triggers, and automated hedging recommendations.
Alpha Generation
Factor-based analysis identifying opportunities where fundamentals diverge from price, optimized for our investment style.
How It Works
Data Ingestion
Ingest real-time market data, fundamentals, and alternative data sources
Factor Analysis
Decompose returns into factor exposures and identify alpha opportunities
Optimization
Run convex optimization to maximize Sharpe ratio within risk constraints
Execution
Generate actionable allocation recommendations with confidence intervals
Suggested portfolio weights based on conviction, risk, and correlation analysis