As large language models (LLMs) diversify across modalities, capabilities, and cost profiles, the problem of \emph{intelligent request routing}---selecting the right model for each query at inference time---has become a critical systems challenge.
We present \textbf{\sysname{}}, a signal-driven decision routing framework for Mixture-of-Modality (MoM) model deployments.

The architecture follows two complementary Shannon-inspired views.
In the information-theoretic regime, signal extraction reduces the entropy of ``which model?'' by distilling routing-relevant information from raw queries.
In the Boolean-algebraic regime, the decision engine composes functionally complete routing policies from signal conditions.

The central innovation is \emph{composable signal orchestration}: thirteen heterogeneous signal types---spanning sub-millisecond heuristics and neural classifiers for semantics, safety, and modality---are composed through configurable Boolean decision rules into deployment-specific routing policies, so that fundamentally different scenarios (multi-cloud enterprise, privacy-regulated, cost-optimized) are expressed as different configurations over the same architecture.

Matched decisions drive \emph{semantic model routing} via thirteen selection algorithms, while per-decision plugin chains enforce safety constraints including a three-stage \emph{HaluGate} hallucination detection pipeline and a lightweight episodic memory system with \emph{ReflectionGate} for personalized multi-turn context.
A typed neural-symbolic DSL specifies these routing policies and compiles them to multiple deployment targets, enabling configuration-first adaptation without code changes.
Together, these components show that composable signal orchestration enables a single framework to serve diverse deployment scenarios with differentiated cost, privacy, and safety policies.
