Enterprise AIinside your control
Use AI with sensitive enterprise data inside organization-defined boundaries while retaining control over models, access, governance and operations.
Platform highlights
One governed AI utility for approved applications.
A shared platform for private model services, reusable APIs, governance and operations inside the customer-controlled environment.
Frequently asked questions
Does Private AI always mean on-premises deployment?
No. The key is the organization-defined data boundary, controls and operating model.
Does the Private AI Platform include customer-specific AI applications?
The base platform is a shared AI foundation; customer-specific applications should have separate scope and acceptance criteria.
Should hardware be purchased before assessment?
A better sequence is to assess use cases, governance, workload and architecture before committing to hardware so capacity matches real needs.
Private · Governed · Reusable · Operable
Customer-controlled
Runs inside the environment and data boundary defined by the customer.
Policy becomes control
Identity, registry, evaluation, approval and audit under one control model.
Shared AI services
Approved applications reuse gateway, retrieval, model and API services.
Built for operations
Monitoring, backup, recovery, release and model lifecycle after go-live.
Approved model catalogue
Model routing and controlled model changes without binding applications to one model.
Release control
Evaluate, approve, release and roll back versions through a managed process.
Included in the base platform
- Initial AI governance enablement
- AI Gateway and policy control
- Private model serving baseline
- Retrieval foundation components
- SSO/RBAC, audit, registry and release control
- Monitoring, backup, runbook and baseline assurance
Scoped separately
- Enterprise-wide AI policy or legal certification
- Customer-specific UI, workflow and business integration
- Data cleansing and knowledge-base configuration
- Fine-tuning / training
- Hardware, DR, 24x7 and vendor warranty unless included
- Application accuracy guarantee or independent audit
Keep the platform foundation separate from customer-specific AI applications
The Private AI Platform is the shared foundation for runtime, gateway, retrieval, governance and operations. Customer-specific applications remain separate workstreams.
Base Platform
AI gateway, policy control, private model-serving baseline, retrieval foundation, SSO/RBAC, audit, registry, release control, monitoring and runbook baseline.
Separate Workstream
Customer-specific UI, workflow, business integration, knowledge configuration, data cleansing and use-case accuracy acceptance.
Optional Profile
Hardware, DR, 24x7 support, fine-tuning/training and vendor warranty depend on benchmark and agreed scope.
Assess readiness before committing budget to hardware or a model
Review use cases, data boundary, governance, workload, integration and operating profile before designing the production platform.
Private AI Readiness Assessment
Governance baseline, target architecture, workload assumptions and solution blueprint.
Start an Assessment →Platform Foundation
Deploy shared AI platform, private model services, controls, monitoring and an acceptance baseline.
Discuss the Platform →Managed AI Operations
Monitoring, incidents, releases, recovery, capacity and continuous assurance after go-live.
Discuss Managed Operations →