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:
- Set up AI Controller as the central point for all LLM requests
- Connect to multiple providers like OpenAI, Anthropic, and others
- Create user groups that match your organization's departments
- Build rules that limit model access based on what each department needs
- Set up API keys for applications that need to integrate with LLMs
- 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:
- Configure AI Controller with strategic caching settings
- 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)
- Set up usage reporting
- 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:
- Deploy AI Controller as a central service
- Create separate API keys for each application
- 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:
- Deploy AI Controller in an air-gapped network
- Connect to on-premises LLM deployments
- Configure enhanced monitoring for data security
- 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:
- Set up AI Controller with content-focused LLM providers
- Create specialized content creation rules with appropriate model access
- Build a review workflow application that integrates with AI Controller
- Configure content guidelines as system prompts
- 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:
- Deploy AI Controller with connections to multiple advanced model providers
- Create specialized research team access rules
- Set up comprehensive logging for experiment tracking
- Configure monitoring dashboards for model performance comparison
- 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:
- Deploy AI Controller as a campus-wide service
- 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
- 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:
- Deploy AI Controller with code-specialized models
- Create dedicated API keys for CI/CD systems
- Set up specialized rules for code-related prompts
- Connect with version control systems
- 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:
- Deploy AI Controller with multiple model providers
- Create evaluation scripts that test models using standard benchmarks
- Configure detailed performance logging
- Set up model rotation for thorough comparison
- 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:
- Review these examples and identify patterns that match your needs
- Consider your unique requirements for governance, security, and performance
- Choose between Docker, Windows, or Linux installation based on your environment
- Start with a simple implementation and expand as you learn
- 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