ComplianceGate Routes LLM Queries for Regulated Industries

Deploying large language models (LLMs) in regulated industries presents significant challenges, particularly concerning data compliance and cost efficiency. User queries containing Personally Identifiable Information (PII) must not cross jurisdictional boundaries, and serving all queries through a single large model is often inefficient. ComplianceGate introduces a novel classifier-gated routing architecture that addresses these issues by design. This system places a trained encoder classifier before any LLM inference. This classifier evaluates each incoming query for its complexity and data sensitivity, then intelligently routes it to an appropriately sized dense model in the correct geographic location. PII-containing queries are directed to local endpoints before any LLM computation, structurally preventing data residency violations. Simple queries are routed to smaller, faster models, significantly reducing costs and latency. Evaluations show substantial reductions in latency and cost, alongside high classification accuracy, making pre-inference classification a practical path to compliance-by-design LLM deployment.