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Example Use Cases for AI Controller

This document provides example implementations showing how organizations can use AI Controller to strengthen security, establish governance, and improve efficiency when working with large language models (LLMs).

Note: These are illustrative examples to help you understand AI Controller's capabilities. Your specific implementation will depend on your organization's unique requirements, infrastructure, and goals. Use these examples as inspiration and adapt them to your needs.

Enterprise Governance and Security Examples

Example: Centralized LLM Access Control

Here's an example of how an organization might use AI Controller as a central gateway for managing all LLM interactions.

Sample Implementation Approach:

  1. Set up AI Controller as the central point for all LLM requests
  2. Connect to multiple providers like OpenAI, Anthropic, and others
  3. Create user groups that match your organization's departments
  4. Build rules that limit model access based on what each department needs
  5. Set up API keys for applications that need to integrate with LLMs
  6. Turn on comprehensive logging for audit purposes

Example Benefits in This Scenario:

  • One central point to manage all LLM access
  • Consistent security policies across different LLM providers
  • Complete audit trails of all AI interactions
  • Easy integration of new LLM services as they become available
flowchart TD
    Enterprise["Enterprise"]
    AIC["AI Controller (AIC)"]
    Providers["LLM Providers"]

    Enterprise --> AIC
    AIC --> Providers

    subgraph Enterprise
        direction TB
        A1[Marketing Department]
        A2[Engineering Department]
        A3[Research Department]
        A4[Customer Service]
        B1[Internal Applications]
        B2[Customer-facing Apps]
        B3[Development Tools]
    end

    subgraph AIC
        direction TB
        C1[Authentication]
        C2[Rules Engine]
        C3[Provider Management]
        C4[Request Routing]
        C5[Logging & Auditing]
        C6[Cache System]
    end

    subgraph Providers
        direction TB
        D1[OpenAI]
        D2[Anthropic]
        D3[Google]
        D4[Azure OpenAI]
        D5[Other Providers]
    end

    classDef enterprise fill:#5D8AA8,stroke:#333,stroke-width:1px,color:#fff
    classDef aic fill:#6A5ACD,stroke:#333,stroke-width:1px,color:#fff
    classDef providers fill:#3CB371,stroke:#333,stroke-width:1px,color:#fff
    classDef component fill:#D8BFD8,stroke:#333,stroke-width:1px,color:#000

    class Enterprise enterprise
    class A1,A2,A3,A4,B1,B2,B3 enterprise
    class AIC aic
    class C1,C2,C3,C4,C5,C6 component
    class Providers providers
    class D1,D2,D3,D4,D5 providers

Example diagram showing AI Controller as a central gateway - your architecture may vary

Example: Cost Management and Optimization

This example demonstrates one way to manage LLM costs while ensuring appropriate access levels.

Sample Cost Management Strategy:

  1. Configure AI Controller with strategic caching settings
  2. Create tiered access rules (example tiers):
    • Basic tier: Access to more affordable models like GPT-3.5
    • Advanced tier: Limited access to premium models (GPT-4, Claude)
  3. Set up usage reporting
  4. Track costs by department using API key monitoring

Potential Outcomes in This Example:

  • Reduced costs through smart caching of responses
  • Model assignment based on actual needs
  • Clear visibility into LLM usage patterns
  • Accurate cost allocation to departments or projects

Example: Supporting Multiple Applications

Here's how an organization might use AI Controller to manage several applications with different LLM needs.

Sample Multi-Application Setup:

  1. Deploy AI Controller as a central service
  2. Create separate API keys for each application
  3. Set up application-specific rules, for example:
    • Legal document review app: Access to legally focused models
    • Content creation app: Access to creative models
    • Code assistant app: Access to programming-specialized models
flowchart TD
    Enterprise["Enterprise"]
    AIC["AI Controller (AIC)"]
    Providers["LLM Providers"]

    Enterprise --> AIC
    AIC --> Providers

    subgraph Enterprise
        direction TB
        LegalApps["Legal Department"]
        MarketingApps["Marketing Department"]
        DevApps["Development Department"]

        subgraph LegalApps
            direction TB
            LA1[Document Review App]
            LA2[Compliance Assistant]
        end

        subgraph MarketingApps
            direction TB
            MA1[Content Creation App]
            MA2[Campaign Analysis Tool]
        end

        subgraph DevApps
            direction TB
            DA1[Code Assistant]
            DA2[Documentation Generator]
        end
    end

    subgraph AIC
        direction TB
        API["API Keys & Rules"]
        Routing["Request Routing"]
        Monitoring["Usage Monitoring"]
        Rules["Application-Specific Rules"]

        API --> Routing
        Rules --> Routing
        Routing --> Monitoring

        subgraph Rules
            direction TB
            LR[Legal Rules]
            MR[Marketing Rules]
            DR[Development Rules]
        end
    end

    subgraph Providers
        direction TB
        LP["Legal-focused Models"]
        MP["Creative Models"]
        DP["Code-specialized Models"]
        GP["General Purpose Models"]
    end

    classDef enterprise fill:#5D8AA8,stroke:#333,stroke-width:1px,color:#fff
    classDef department fill:#6A5ACD,stroke:#333,stroke-width:1px,color:#fff
    classDef aic fill:#3CB371,stroke:#333,stroke-width:1px,color:#fff
    classDef rules fill:#FF7F50,stroke:#333,stroke-width:1px,color:#fff
    classDef providers fill:#9370DB,stroke:#333,stroke-width:1px,color:#fff
    classDef component fill:#D8BFD8,stroke:#333,stroke-width:1px,color:#000

    class Enterprise enterprise
    class LegalApps,MarketingApps,DevApps department
    class AIC aic
    class Rules rules
    class Providers providers
    class API,Routing,Monitoring,LR,MR,DR component
    class LA1,LA2,MA1,MA2,DA1,DA2,LP,MP,DP,GP component

Example Benefits:

  • Unified management for diverse application needs
  • Smart routing to appropriate models
  • Simplified provider API key management
  • Combined usage tracking across applications

Sample Integration Scenarios

Example: Security-First Implementation

This example shows how organizations with highly sensitive data might deploy AI Controller in isolated environments.

Sample Air-Gapped Implementation:

  1. Deploy AI Controller in an air-gapped network
  2. Connect to on-premises LLM deployments
  3. Configure enhanced monitoring for data security
  4. Plan regular security audits

Potential Benefits in This Scenario:

  • LLM capabilities in high-security environments
  • Complete isolation from public networks
  • Total control over data flow
  • Compliance with strict security requirements

The security model of AI Controller supports various high-security implementations.

Example: Content Creation Workflow

Here's one way marketing teams might structure AI-assisted content creation.

Sample Content Creation Setup:

  1. Set up AI Controller with content-focused LLM providers
  2. Create specialized content creation rules with appropriate model access
  3. Build a review workflow application that integrates with AI Controller
  4. Configure content guidelines as system prompts
  5. Set up caching for consistent responses to similar queries

Example Outcomes:

  • Streamlined content creation process
  • Clear governance of content guidelines
  • Consistent brand voice
  • Efficient resource usage through caching

Example: Research and Development Environment

This example illustrates how research teams might use AI Controller for experimentation.

Sample Research Environment Configuration:

  1. Deploy AI Controller with connections to multiple advanced model providers
  2. Create specialized research team access rules
  3. Set up comprehensive logging for experiment tracking
  4. Configure monitoring dashboards for model performance comparison
  5. Schedule regular usage reports for research leadership

Potential Benefits for Research Teams:

  • Access to multiple LLMs through a single interface
  • Ability to compare different model outputs
  • Detailed tracking of experimental interactions
  • Proper oversight of research activities

Example: Educational Institution Implementation

Here's how a university might provide controlled LLM access to different departments.

Sample University Deployment:

  1. Deploy AI Controller as a campus-wide service
  2. Create department-specific rules, such as:
    • Research departments: Access to advanced models
    • Student labs: Access to educational models with usage limits
    • Administrative departments: Access to business-focused models
  3. Configure usage reporting for departmental cost tracking

Example Benefits for Educational Institutions:

  • Appropriate LLM access based on academic needs
  • Control over student usage to maintain educational integrity
  • Accurate cost allocation to departments
  • Centralized management for campus-wide access

Development and Model Management Examples

Example: CI/CD Pipeline Integration

This example shows one way development teams might integrate LLM capabilities into their workflows.

Sample DevOps Integration:

  1. Deploy AI Controller with code-specialized models
  2. Create dedicated API keys for CI/CD systems
  3. Set up specialized rules for code-related prompts
  4. Connect with version control systems
  5. Configure automated prompts for code review and documentation

Potential Developer Benefits:

  • AI-enhanced code quality and documentation
  • Controlled LLM access within workflows
  • Consistent application of code standards
  • Detailed records of AI-assisted development

Example: Model Evaluation Framework

Here's how AI teams might compare different LLMs systematically.

Sample Evaluation Setup:

  1. Deploy AI Controller with multiple model providers
  2. Create evaluation scripts that test models using standard benchmarks
  3. Configure detailed performance logging
  4. Set up model rotation for thorough comparison
  5. Build dashboards for tracking performance metrics

Example Outcomes:

  • Standardized evaluation across providers
  • Consistent testing methods
  • Detailed performance metrics
  • Data-driven model selection

Adapting These Examples to Your Needs

These examples are starting points. To implement AI Controller for your specific situation:

  1. Review these examples and identify patterns that match your needs
  2. Consider your unique requirements for governance, security, and performance
  3. Choose between Docker, Windows, or Linux installation based on your environment
  4. Start with a simple implementation and expand as you learn
  5. Define metrics that will demonstrate success for your use case

Remember: Every organization's needs are different. Use these examples as inspiration, but design your implementation based on your specific requirements.

Next Steps

Updated: 2025-05-27