We orchestrate frontier AI models in parallel — Claude, Llama, Mistral — combined with Monte Carlo simulation, selecting highest-confidence signals through real-time consensus scoring.
No single model wins in every market regime. We run multiple frontier models simultaneously and let real-time performance determine which signals drive execution — a self-correcting system that adapts continuously.
Frontier LLMs — Claude, Llama, Mistral — run concurrently on Amazon Bedrock. Each model independently scores market sentiment, detects regime shifts, and generates directional forecasts. A rolling-accuracy weighted consensus determines final signal strength.
Stochastic volatility and jump-diffusion models generate 10,000+ scenario paths per second. The engine computes probability distributions, tail risk metrics, and optimal entry/exit boundaries across correlated asset classes.
Real-time VaR, CVaR, and maximum drawdown monitoring. Black-Litterman portfolio optimization with dynamic position sizing that adapts to volatility regimes and correlation breakdowns.
Sub-5ms signal-to-order pipeline with smart order routing, TWAP/VWAP execution algorithms, and real-time slippage monitoring. Pre-trade risk checks validate every order before submission.
Trades execute only when AI ensemble consensus and Monte Carlo simulation independently agree — eliminating single-model bias and reducing false signals.
Real-time market feeds, news, earnings, SEC filings, and alternative data via Amazon Kinesis.
Frontier LLMs on Bedrock independently evaluate conditions. Each model scores sentiment and forecasts direction.
Consensus signals feed into Monte Carlo engine. Only statistically significant expected-value scenarios pass.
Pre-trade risk checks, dynamic position limits, smart order routing with real-time P&L monitoring.
GreatLake Capital was founded on a thesis that no single AI model consistently outperforms across all market regimes. By orchestrating multiple frontier models in parallel and using real-time accuracy to weight signals, we build a self-correcting system that adapts faster than any individual model.
Our founding team combines production-grade software engineering experience at leading technology companies with rigorous quantitative methodology. Based in Boston, we leverage proximity to world-class research institutions and the financial industry.
We are onboarding a limited number of early partners. Request access to learn more about our quantitative approach and platform capabilities.
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