Knowledge Workloads
Support AI experiences grounded in approved enterprise data sources.
RAG and Vector Data
RAG and vector data for private AI.
Design retrieval and vector data patterns around approved data sources, access controls, and validated AI architecture.
Approved dataAI applications
RAG architecture planningIngest · index · retrieve
SourcesChunksVectorsPolicySearch
Customer-specific data governanceConnector and scale validation required
Retrieval patterns built around approved data sources.
RAG and Vector Data planning supports private AI use cases where data ingestion, indexing, access, and retrieval are validated.
- RAG architecture planning
- Vector data pattern definition
- Approved data source mapping
- Ingestion and indexing boundary planning
- Retrieval access-control design
- Storage and lifecycle considerations
- Connector validation planning
- Customer-specific data governance inputs
Where RAG fits
Use this capability only where the AI workload, data boundary, operating model, and validation scope are clear.
Sensitive Data
Keep retrieval patterns aligned with access and governance requirements.
Private LLM Endpoints
Connect validated retrieval flows to controlled endpoint architecture.
Architecture
Customer-specific data governance
Connector and scale validation required
Validation path
Each AI capability should move through assessment, design, and validation before publication or commitment.
Identify approved data
Classify sources, owners, access rules, formats, and lifecycle needs.
Shape retrieval flow
Define ingestion, indexing, retrieval, and governance boundaries.
Test connector and scale
Confirm connector behavior, retrieval flow, and scale assumptions.
Next step
Plan RAG architecture around approved enterprise data.
Start with your workloads, operating model, and control requirements.