Private AI Platforms
Private LLM, RAG and internal assistant platforms designed around enterprise data privacy, controlled access and local deployment.
Reference architecture
Private AI Architecture for Enterprise Knowledge
A reference architecture for approved knowledge ingestion, access-aware retrieval and governed model access in a privately deployable enterprise AI platform.
Architecture context
- Enterprise knowledge may be distributed across documents, service desks and operational procedures.
- AI-assisted search needs to respect existing access boundaries and data controls.
- Model and retrieval components should remain replaceable as requirements evolve.
Design constraints
- Sensitive documents remain within approved processing and storage boundaries.
- Access rules stay visible and manageable by the platform team.
- Deployment can target on-premises or private-cloud environments.
Reference architecture
- Approved-source ingestion feeds a private retrieval-augmented generation foundation.
- Metadata filters and access-aware retrieval apply source permissions at query time.
- A controlled model gateway provides logging, policy boundaries and usage review.
- Evaluation and observability workflows support retrieval and response-quality assessment.
Implementation considerations
- Start with bounded knowledge domains and map access rules before ingestion.
- Define evaluation cases for retrieval relevance, permissions and unsupported answers.
- Document ownership for source onboarding, model changes and governance review.
Expected operational characteristics
- Knowledge onboarding follows a repeatable, reviewable workflow.
- Platform logs make model access and retrieval behaviour observable.
- Components can evolve without coupling the platform to one model or retrieval implementation.
Related services
Secure Deployment & Governance
Secure architecture, identity controls, deployment guardrails and AI governance for enterprise systems that need auditability.