Model Governance Layer

The Challenge: Generalist LLMs in a Specialized World

Modern LLMs, while powerful, often lack the specific context of your users, their roles, your organization's operational goals, and the implicit or explicit rules of engagement. The Model Governance Layer (MGL) by Flux Inc. bridges this critical gap, turning capable but generic AI into truly effective, organizationally-aware partners.

Intelligent and Aware AI Operations

The MGL is a sophisticated intermediary layer, compatible with OpenAI and Ollama APIs, that sits transparently between your users and your chosen LLMs. It intelligently intercepts requests, enriches them with vital organizational context, and ensures that LLM interactions are always aligned with your policies and objectives through bidirectional context-based content control.

How the Model Governance Layer Works:

1. Contextual Enrichment: Gathers user identity, group memberships, and other relevant attributes (potentially integrating with systems like Active Directory).
2. Policy Application: Applies pre-configured rules, preferences, and policies tied to the specific user and operational context.
3. Request Decoration: Modifies the outbound request to the LLM, imbuing it with the necessary context and instructions.
4. LLM Interaction: Forwards the enhanced request to any compatible local or remote LLM.
5. Response Governance: Intercepts the LLM's response, evaluates it against established governance rules, and amends it if necessary (e.g., to exclude restricted information or ensure adherence to stylistic guidelines) before delivering it to the user.

This ensures your LLM operates as a knowledgeable, compliant, and effective extension of your team.

Key Capabilities & Benefits

The Model Governance Layer empowers you to:

• Develop Organizationally-Aware LLMs: Enable your AI to understand and adapt to specific organizational structures, roles, expertise, preferences, and cultural norms.
• Implement Robust Context Management: Define, store, and dynamically retrieve various layers of context (user, session, topic, organization) to inform LLM behavior and tool usage.
• Establish a Flexible Governance Framework: Clearly distinguish between firm rules (what the LLM must or must not do/say/use) and adaptable preferences (how the LLM should ideally behave or prioritize information/tools), all tied to user context.
• Dynamic Tool Management: Control which LLM tools and capabilities are available during any interaction, based on user role and context. Define the scope of each tool (e.g., limiting a data query tool to specific datasets for certain roles).
• Enable Conversational Configuration: Allow authorized administrators and managers to define and modify rules, preferences, user roles, and tool access policies through intuitive natural language interactions.
• Ensure Persistent and Evolving Understanding: Maintain a comprehensive audit trail and allow the system's behavior to be refined over time, handling conflicting rules or preferences through clarification dialogues.
• Prioritize Information and Actions: Equip the system to understand the relative importance of different information, instructions, or potential tool usage based on established context and governance.

By transforming generalist LLMs into highly specialized and contextually aware assistants, the Model Governance Layer helps you leverage the full power of AI, safely and effectively, within your specific operational environment.