Private AI Infrastructure. Enterprise Control.
Build and operate secure AI platforms within your own infrastructure, with deployment architectures designed around security, governance and operational requirements.
Many organisations see the value of generative AI but cannot, or prefer not to, send sensitive data to public cloud services. TURKZEN helps keep data under the organisation's control by positioning large language models, RAG solutions and AI agents in on-premises, air-gapped or sovereign architectures. We combine our enterprise software and SAP experience with model serving, GPU infrastructure and identity management to embed AI into the business processes you already run. Every architecture is designed around the use case, data classification, security policies and operating model together.
Private & Sovereign AI is an expanding TURKZEN offering; architectures are tailored to the requirements of each project.
Data control
Models and data run within the infrastructure and network boundaries you define.
Governance by design
Access, audit trail and lifecycle rules are treated as part of the architecture.
Enterprise integration
AI applications connected to SAP, document repositories and internal systems.
End-to-end approach
From assessment and pilot to production deployment and operational support.
Private & Sovereign AI
Private & Sovereign AI
HPE Private Cloud AI
Solutions are designed around technologies including HPE Private Cloud AI, a turnkey platform for running AI on premises. Such platforms bring infrastructure and software layers together in a pre-validated stack.
Dell AI Factory with NVIDIA
The Dell AI Factory with NVIDIA approach brings server, storage, networking and software components together for enterprise AI workloads. Our architecture recommendations evaluate these components against project requirements.
NVIDIA AI Enterprise & NVIDIA NIM
NVIDIA AI Enterprise provides an enterprise software layer for AI development and deployment, while NVIDIA NIM offers microservices that serve models through standard APIs. We design the model serving layer around options that include these technologies.
NVIDIA GPU Computing
GPU selection for inference and fine-tuning workloads depends on model size, concurrent users and latency targets. We plan capacity by weighing these criteria together.
Red Hat OpenShift AI
Red Hat OpenShift AI enables model development and serving to be managed on an enterprise Kubernetes platform. For organisations already invested in OpenShift, our architectures consider it as an option.
Open models (Llama, Qwen)
Open-weight models such as Llama and Qwen can run inside the organisation, which gives meaningful flexibility for data control. We assess model choice together with licence terms, language support and hardware requirements.
Kubernetes, vLLM & enterprise model serving
High-throughput inference engines such as vLLM are widely used for scalable model serving on Kubernetes. We design the serving layer together with load balancing, monitoring and access control.
Why Private AI?
Data control
Prompts, documents and model outputs stay within the infrastructure boundaries you define.
Data residency
You decide in which country and facility your data is processed.
Regulatory and security requirements
The architecture is designed with your sector regulations and internal security policies in mind; compliance assessment is carried out together with your organisation.
Integration with internal systems
Connects to SAP, databases and document repositories while respecting existing authorisations.
Custom model deployment
Open models are selected to fit your needs and adapted with organisational data where required.
Predictable architecture and operations
Capacity, performance and cost are managed on planned infrastructure rather than usage-based uncertainty.
Deployment models
On-Premises Private AI
Models and applications run in your own data center, inside your corporate network.
Air-Gapped AI
Environments operated without external network connectivity, using offline update procedures.
Sovereign AI Architectures
Architectures where jurisdiction, operations and governance control remain within a defined country or organisation.
Hybrid Private AI
Sensitive workloads run in house, while approved workloads use controlled cloud resources.
Dedicated Enterprise GPU Infrastructure
Sized GPU server and cluster infrastructure dedicated solely to your organisation's workloads.
Sovereign AI is more than hosting models locally. It also covers which jurisdiction the data falls under, who operates the platform, how governance decisions are made and who holds control over data and models.
Platform architecture
A layered, auditable AI platform, from enterprise users down to GPU infrastructure.
- 1Enterprise Users
Employees and business units access AI capabilities through familiar interfaces and applications.
- 2Secure AI Portal / APIsAPI Gateway
All requests pass through a controlled portal and API gateway where usage policies are enforced.
- 3Identity & Access ManagementSSORBAC
Single sign-on and role-based authorisation are provided through the corporate identity provider.
- 4AI Applications / Agents / RAGRAGAgents
Assistants, agents and RAG pipelines generate responses using enterprise data in line with permissions.
- 5Model Serving LayerNVIDIA NIMvLLMLlamaQwen
Selected models are served through scalable inference engines via standard APIs.
- 6GPU InfrastructureKubernetesOpenShiftNVIDIA GPUs
GPU servers form a shared compute pool managed through container orchestration.
Data & connectors
- Vector Database
- Enterprise Connectors
- SAP
- SQL Databases
- Document Repositories
Observability
- Logging
- Audit Trail
- Monitoring
Enterprise use cases
SAP AI Assistant
Helps users query SAP data and transactions in natural language.
Finance & Procurement Assistant
Answers invoice, order and supplier questions based on enterprise records.
Enterprise Document Intelligence
Extracts, classifies and summarises information from documents.
HR Knowledge Assistant
Answers employee questions on leave, benefits and HR policies.
Internal Policy & Knowledge Search
Provides semantic search across procedures and policies, with source references.
Contract Analysis
Highlights key clauses, dates and obligations in contracts.
Customer Service AI
Gives agents response suggestions and summaries grounded in company knowledge.
AI-Powered Workflow Automation
Automates repetitive steps while keeping critical decisions under human approval.
Infrastructure options
Solutions designed around technologies including the following.
HPE Private Cloud AI
A platform offering a pre-validated infrastructure and software stack for on-premises AI, considered as an architecture option where it fits the project.
Dell AI Factory with NVIDIA
An approach that brings servers, storage, networking and software together for enterprise AI; solutions can be designed to include these components.
NVIDIA AI Enterprise
An enterprise software layer for model serving and AI workloads, evaluated together with NVIDIA NIM microservices.
Red Hat OpenShift AI
An option for organisations that want to manage the model lifecycle on an enterprise Kubernetes platform.
Open-source model serving stacks
Flexible, vendor-neutral model serving architectures built with open-source inference engines such as vLLM and Kubernetes.
Security & governance
- SSO / role-based access control (RBAC)
- Data encryption
- Network isolation
- Audit logging
- Model access control
- Offline deployment procedures
- Model and software lifecycle management
- Vulnerability and patch management
- Backup and disaster recovery
Private AI service packages
A three-stage approach, scoped to your needs rather than fixed public prices.
Private AI Assessment
Clarify use cases, data and security posture to define the right starting architecture.
- Use-case discovery
- Data and security assessment
- Architecture recommendations
- Initial infrastructure sizing
Private AI Pilot
Validate a selected scenario in a controlled environment with real users and enterprise data.
- Controlled proof of concept
- Model deployment
- RAG integration
- User testing
- Performance evaluation
Enterprise Private AI Deployment
Take the validated solution into production and operate it at enterprise scale.
- Production infrastructure
- Enterprise integrations
- Security hardening
- Monitoring
- Documentation
- Support options
Request a Private AI Assessment
Share your use case and deployment preference and we will come back with an architecture recommendation.
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