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Enterprise management systems have historically served as an externalized Master (in McGilchrist's sense): they hold the memory of business acts and their relational logic, but through a procedural paradigm dating to Pacioli (1494) marked by redundancy, rigidity, vulnerability, and reliance on external verification. Until now, humans have supplied the missing Emissary function, correcting inconsistencies through judgment the system itself lacks. Generative AI represents the first externalization of the Emissary, but lacking access to a Master, it invents data to produce coherent output. Rather than equipping AI agents to reconstruct an internal World Model at each query — a computationally expensive approach — this work proposes building an explicit, external Master accessible to generative AI. We formalize this as Modelo G, an algebraic framework separating a declarative Concrete System, which encodes business entities, documents, data, and rules as a relational structure, from an Abstract System, the generative AI, which acts as a grammatical translator interpreting natural language and documents against that structure without needing domain-specific training. Because logic resides in the Concrete System rather than in the model or the prompt, governance, operation, and audit collapse into a single continuous cycle, reversing the usual growth of the gap between formal rules and actual practice. Developed and validated in the domain of enterprise management systems, the model generalizes to other domains of structured information, and achieves through external declarative structure what architectures such as DeepSeek's Engram pursue internally: separating static knowledge from reasoning to avoid its constant reconstruction.