Compiled Intelligence is an AI-native systematic investment firm that transforms raw market events into real‑time, compiled decision systems for trading. By applying decades of quantitative mathematics and a runtime that operates without large language models, the platform continuously compiles, tests, and retains strategies, ensuring each experiment adheres to strict cost, risk, latency, and execution constraints. This approach turns research into a self‑reinforcing machine that compounds performance over time.
Funding
Funding not disclosed
Founders
Product
Problem
Systematic investment firms often rely on complex, loosely integrated models that can be slow, difficult to audit, and constrained by latency, risk, and execution limits, reducing their ability to act on real-time market information.
Solution
Compiled Intelligence addresses this by converting raw market events into a continuously updated market state and then applying decades of quantitative mathematics through compiled, bounded AI models that run without large language models in the decision loop. The compiled runtime is fast, replayable, and constrained by strict cost, risk, latency, capacity, and uncertainty parameters, ensuring reliable live execution. Research agents continuously generate, test, and retain strategies on the same constrained substrate, allowing successful experiments to become reusable primitives. This creates a self‑reinforcing research machine that compounds intelligence into capital over time.
Target Audience
Primary customers are institutional investors, hedge funds, and asset managers seeking systematic, AI‑driven trading solutions that combine speed, transparency, and rigorous risk controls.
Features
- Real-time ingestion of raw market events to produce a unified market state
- Compiled decision engine that executes AI‑derived signals without LLMs, guaranteeing low latency and deterministic behavior
- Built‑in constraints for cost, risk, latency, capacity, and execution uncertainty applied at every stage
- Automated research pipeline where agents generate, backtest, and compile strategies, with surviving experiments forming reusable building blocks
- Replayable execution environment enabling full auditability and verification of decisions
- Integration of mature quantitative techniques (filtering, control, calibration, optimization) within the AI‑native framework