This working paper proposes a layered architectural framework for controlling, verifying, and contextualizing large language model outputs in high-stakes domains. Rather than introducing a new model or benchmark, the paper focuses on structural design principles for aligning model behavior with domain-specific constraints, verification requirements, and accountability mechanisms. The work is intended as a conceptual and governance-oriented contribution, suitable for discussion in AI governance, evaluation, and safety contexts.