The architecture describes how the system is structured across data, model execution, policy enforcement, governance, and explanation—ensuring all behavior is principle-aligned, transparent, auditable, and governable.
Each layer has a distinct responsibility: data, generation, evaluation, governance.
Model outputs are probabilistic. Policy enforcement is deterministic.
Every output must include reasoning, constraints, and traceability.
System behavior is not hardcoded—it is governed.
Critical actions are cryptographically verifiable.
User Input
↓
Context Builder
↓
Model Layer (LLM / DI Engine)
↓
Policy Engine (Corpus-aligned)
↓
Decision Engine
↓
Explanation Layer
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Output
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Logging & Audit
Eight distinct components work together to ensure governed, explainable decision-making.
Constructs structured input for the model by combining user input, relevant data, and system constraints.
Generates candidate outputs based on structured input. Probabilistic, pattern-based, derivative.
Evaluates model outputs against corpus-derived constraints. Deterministic and rule-based.
Selects and finalizes system output based on model score, alignment score, and violation penalties.
Ensures every output is explainable and traceable with reasoning, sources, and constraints.
Records all system activity for traceability, verification, and optional on-chain anchoring.
Manages system evolution, rule updates, proposals, and voting processes.
Provides structured access to all data classes while enforcing separation and provenance.
Every interaction follows this structured execution path.
The model generates.
The policy governs.
The system explains.
DATA → MODEL → POLICY → DECISION → EXPLANATION → AUDIT
↑
(probabilistic)
↓
(deterministic)
This architecture ensures that: