Executive summary
Project Rehoboam
A large-scale AI simulation and prediction engine designed to model outcomes, anticipate risks, and guide decision-making at scale. Inspired by systemic simulations, powered by high-performance backends and LLM orchestration.
Objectives
Investigate whether systemic simulations combined with LLM-driven orchestration could forecast and stress-test real-world decisions.
Market & Opportunity
Market size: Multi-billion dollar AI forecasting/simulation market
Industry trends: Systemic modeling, AI orchestration, distributed compute systems
Competitive landscape: Limited — closest parallels are enterprise analytics or think tank simulations
Target audience: Governments, enterprises, researchers, think tanks
Why now: Explosion of LLM capability and demand for decision-support systems at scale
Approach
Designed a scalable architecture with Rust servers, QUIC-based distributed networking, real-time data pipelines, and modular LLM simulation nodes, capable of running predictive scenarios across multiple domains.
- Predictive LLM modules
- Simulation pipelines
- Rust-based backend
- QUIC protocol networking
- Modular orchestration via MCP
Challenges
Balancing prediction accuracy vs. interpretability, building high-performance infrastructure, integrating real-time external data without bottlenecks.
Results
Delivered a comprehensive PRD and architectural framework for a next-gen simulation AI system, establishing the foundation for large-scale deployment.
Funding & Partner Impact
N/A
Valuation: $3.1m
Credibility
Founder story
Inspired by the concept of “macro-brains” from science fiction, Kevin reimagined how real predictive AI could function—transparent, modular, and built with rigorous infrastructure instead of black-box models.
Scalability & Defensibility