Use Cases and Personas for AI Factory on Hybrid Manager v1.3

Overview

AI Factory on Hybrid Manager enables diverse organizational use cases through sovereign AI infrastructure. Organizations leverage these capabilities to build intelligent applications while maintaining complete control over data and models within their Kubernetes environments.

Key Use Cases

Internal Knowledge Management

Organizations deploy AI assistants that provide employees with natural language access to internal documentation, policies, and institutional knowledge.

Implementation Pattern

Financial services firms index compliance documentation, regulatory filings, and internal procedures into knowledge bases. Employees query these systems using conversational interfaces, receiving accurate answers with source citations while maintaining data sovereignty.

Technical Components

• Knowledge bases indexing SharePoint, Confluence, and database content
• LLM endpoints for query understanding and response generation
• Retrieval systems with role-based filtering for access control

See Knowledge Base Creation for implementation guidance.

Customer Service Automation

AI-powered customer support systems handle routine inquiries while escalating complex issues to human agents.

Implementation Pattern

E-commerce companies deploy assistants that access order systems, product catalogs, and return policies through secure APIs. These assistants resolve 60-70% of customer inquiries automatically while maintaining full audit trails for quality assurance.

Technical Components

• REST API tools for order management system integration
• Knowledge bases containing product documentation and FAQs
• Thread tracking for conversation history and escalation workflows

See Customer Service Agent Quickstart for a working example.

Enterprise RAG Systems

Retrieval-augmented generation applications ground LLM responses in organizational data, reducing hallucination while maintaining accuracy.

Implementation Pattern

Healthcare organizations implement RAG systems over clinical guidelines, research papers, and treatment protocols. Medical professionals receive evidence-based recommendations with citations to source materials, supporting clinical decision-making while ensuring traceability.

Technical Components

• Vector embeddings stored in PostgreSQL with pgvector
• Hybrid search combining semantic and keyword matching
• Reranking models for result optimization

See RAG Pipeline Documentation for detailed architecture.

AI-Enabled APIs

Organizations expose AI capabilities through secure APIs for integration with existing applications.

Implementation Pattern

Software companies provide intelligent features within their products by deploying model endpoints accessible through standard REST interfaces. These APIs handle tasks like document summarization, sentiment analysis, and content generation while maintaining predictable performance characteristics.

Technical Components

• InferenceServices exposing OpenAI-compatible endpoints
• API gateways with authentication and rate limiting
• Load balancing across model replicas for high availability

See Model Serving Guide for deployment instructions.

User Personas

Platform Engineers

Platform engineers manage AI Factory infrastructure, ensuring reliable model serving and optimal resource utilization.

Primary Responsibilities

• Configure GPU nodes and resource allocation
• Deploy and monitor InferenceServices
• Manage model library and registry connections
• Implement security policies and network configurations

Key Workflows

1. Provision GPU resources following GPU Setup Guide
2. Configure model registries per Model Library Documentation
3. Monitor inference performance using Observability Tools

Data Scientists

Data scientists leverage AI Factory to deploy models and build intelligent applications without managing infrastructure complexity.

Primary Responsibilities

• Create and optimize knowledge bases
• Configure retrieval strategies for RAG applications
• Evaluate model performance and accuracy
• Design assistant behaviors and tool integrations

Key Workflows

1. Build knowledge bases following Knowledge Base Management
2. Configure retrievers using Retriever Creation Guide
3. Deploy assistants through Assistant Configuration

Application Developers

Developers integrate AI capabilities into business applications through APIs and SDKs.

Primary Responsibilities

• Integrate model endpoints into applications
• Implement conversation management using threads
• Build custom tools for assistant capabilities
• Handle error conditions and fallback strategies

Key Workflows

1. Access model endpoints via KServe Integration
2. Implement conversation tracking using Thread Management
3. Create custom tools following Tool Development Guide

Business Analysts

Business analysts use Gen AI Builder's visual interfaces to create AI applications without coding expertise.

Primary Responsibilities

• Define assistant behaviors and knowledge domains
• Configure data source connections
• Test and validate assistant responses
• Monitor usage patterns and effectiveness

Key Workflows

1. Design assistants using visual builder interface
2. Connect data sources through configuration wizards
3. Test interactions and refine behaviors iteratively

Industry-Specific Implementations

Financial Services

Use Case: Regulatory compliance assistant analyzing transaction patterns and generating required reports.

Components

• Knowledge base of regulatory documentation
• Tools accessing transaction databases
• Audit logging for compliance tracking

Healthcare

Use Case: Clinical decision support system providing evidence-based treatment recommendations.

Components

• RAG over medical literature and guidelines
• Integration with electronic health records
• Strict access controls for patient data

Manufacturing

Use Case: Maintenance assistant diagnosing equipment issues using sensor data and technical manuals.

Components

• Knowledge base of equipment documentation
• Real-time data integration from IoT sensors
• Predictive maintenance model endpoints

Implementation Considerations

Scalability Planning

Different use cases require varying levels of scale:

• High-volume customer service: Multiple model replicas with auto-scaling
• Internal knowledge systems: Moderate scaling with caching optimization
• Specialized assistants: Single instances with dedicated resources

Security Requirements

Use cases determine security configurations:

• Public-facing applications: External ingress with authentication
• Internal systems: Cluster-local access with RBAC
• Regulated industries: Enhanced audit logging and encryption

Performance Optimization

Optimize based on use case characteristics:

• Real-time applications: Low-latency model serving with edge caching
• Batch processing: Higher throughput with larger batch sizes
• Interactive assistants: Balanced latency and throughput

Getting Started by Use Case

For Knowledge Management

1. Review Knowledge Base Concepts
2. Follow Data Source Integration
3. Implement Hybrid Search

For Customer Service

1. Start with Assistant Concepts
2. Configure Rulesets for behavior control
3. Deploy using Quickstart UI

For API Development

1. Deploy models via InferenceService Creation
2. Access endpoints following Python Client Guide
3. Implement monitoring per Observability Documentation

Additional Resources

Architecture References

• Gen AI Architecture

Implementation Guides

Related Documentation

• Hybrid Manager Overview
• Analytics Personas