01
Contracts, ontology, and synthetic data
Governed contracts and ontologies align with synthetic and ML-ready datasets so generation stays schema-safe and explainable.
Engine Architecture
From governed data to audited execution
Domain packages form a directed pipeline: contracts and synthetic data, state and optimization, business intelligence, then risk-gated action. pygubernator coordinates relays and composition so the stack behaves as one system.
02
Lifecycle and optimization
Deterministic state machines and optimization models turn policy and objectives into concrete, replayable decisions.
03
Rules, analytics, and features
Business rules, analytics, and engineered features translate raw state and data into decision-grade signals.
04
Risk, security, and controlled execution
Risk and security gates precede execution—the control layer that connects approved intent to the outside world.
Contracts, ontology, and synthetic data
Governed contracts and ontologies align with synthetic and ML-ready datasets so generation stays schema-safe and explainable.
Packages: pycharter, pycatalyst
Lifecycle and optimization
Deterministic state machines and optimization models turn policy and objectives into concrete, replayable decisions.
Packages: pystator, pyoptima
Rules, analytics, and features
Business rules, analytics, and engineered features translate raw state and data into decision-grade signals.
Packages: pyalbedo
Risk, security, and controlled execution
Risk and security gates precede execution—the control layer that connects approved intent to the outside world.
Packages: pyfortis, pyactuator
Explore the libraries
Each package owns one concern and ships independently. Open the ecosystem index for descriptions and links, or jump straight to the documentation.