Private
AI Factory
Your own enterprise AI infrastructure — full control over data, compute and models. Design, deploy and operate on-premise or private cloud AI at any scale.
From design to a production-ready AI factory
Everything runs on your premises, in your perimeter
Architecture grows from one node to a cluster
Trusted by enterprise leaders
Pain points
Challenges we solve
The barriers between cloud dependency and AI infrastructure you own.
Restrictions on using public clouds for AI workloads due to security or regulatory requirements
Data localisation and protection requirements that prevent use of external AI services
Insufficient control over infrastructure, data access and model operations
No in-house expertise to launch and scale AI infrastructure at enterprise level
6-step methodology
How we deliver it
Our 6-step methodology takes you from infrastructure audit to a production AI factory under your full control.
Power, cooling, GPU cluster, storage and AI platform in one project.
Everything runs on your premises — nothing leaves your perimeter.
Designed for real workloads, not a lab demo.
Detailed assessment of existing compute, networking, storage and cooling to understand baseline and gaps
Design of the target AI infrastructure architecture: GPU cluster, storage, networking topology and AI platform
Selection of compute (NVIDIA DGX/HGX), storage systems, networking and management software aligned to workload requirements
Evaluation of power capacity, cooling systems, physical space and network connectivity requirements
Equipment delivery, physical installation, software configuration and integration with existing IT systems
Full system testing under production load, performance validation, documentation and team knowledge transfer
Interactive tool
Configure your AI infrastructure
Select workload type and organisational scale to get a recommended infrastructure specification.
Infrastructure configurator
Select your workload type and scale — see the recommended infrastructure tier
Workload type
Choose the primary workload for your AI environment.
Organisation scale
Select the scale that best matches your organisation.
Deliverables
What you get
Deployed AI infrastructure
Scalable AI compute cluster with full performance validation and monitoring
Full data control
All data, models and computations remain within your perimeter — no external dependencies
AI workload environment
Ready-to-use environment for inference, fine-tuning, training and development workloads of any scale
Documentation & runbooks
Complete technical documentation, operational runbooks and team training
Monitoring & alerting
Configured observability stack for GPU utilisation, temperature, storage and network metrics
Scaling roadmap
Architecture blueprint for future capacity expansion and new workload onboarding
Market benchmark
Cloud AI vs Private AI Factory: 3-year TCO
Total cost comparison for AI workloads. Adjust GPU-hours to see your breakeven point.
Why us
Why clients choose Noventiq
Enterprise-grade experience — practical expertise building production AI infrastructure for corporations and government agencies across industries
Full project lifecycle — we own every phase from architecture design through procurement, deployment and knowledge transfer
Modern platform expertise — deep knowledge of NVIDIA DGX/HGX, InfiniBand, NVIDIA AI Enterprise and Run.AI
Corporate & government sector — experience with strict compliance, security and localisation requirements
Technology
Tech stack
Timeline
Project timeline
Real results
Case studies
Large Kazakhstan university
AI research infrastructure for one of the largest universities in Kazakhstan — high-performance computing cluster for scientific workloads and AI experiments.
Telecom operator
AI development and commercial services infrastructure for a major telecom operator — private AI Factory enabling proprietary model development and internal service deployment.
Ministry of Digital Development
Participation in national AI infrastructure development for a Central Asian country's Ministry of Digital Development.
Frequently asked questions
Questions we hear most often
How long does it take to deploy a private AI factory?
A typical deployment takes 6 to 9 months, from architecture design to a production-ready environment.
Where is the data stored?
Entirely on your premises or in your private cloud — you keep full control over data, compute and models, with no public cloud dependency.
Can the platform grow later?
Yes. The architecture scales from a single node to a cluster, so capacity can be added as workloads grow.
Which hardware is used?
NVIDIA-based compute (DGX/HGX systems, A100/H100/GH200 GPUs) together with high-speed networking and NVMe storage.
Ready to build your AI Factory?
Get a personalised consultation on Private AI Factory for your organisation.