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What is Private AI?

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Private AI: bring AI to your data, not your data to AI

Most organisations do not lack AI ambition. They lack what it takes to run it in production. Private AI puts the models on infrastructure you control, under your own governance, so nothing sensitive leaves your perimeter.

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What is Private AI?

Private AI means running AI models and workloads entirely within an environment you control. Instead of sending your data to a public cloud service, the models come to you. Deployed on your own servers, your own infrastructure, under your own governance.

This covers the full AI stack: the language models themselves, the compute layer (GPUs), storage, and the software platform that ties it all together. Tools like Retrieval-Augmented Generation (RAG) let you connect AI directly to your own documents and data sources without anything leaving your perimeter.

At ITQ, we call this bringing AI to your data and not the other way around.

 

A typical Private AI stack looks like this

  • AI applications & use cases: chatbots, document assistants, process automation, video analytics.
  • AI models & orchestration: open-source or fine-tuned language models, managed within your environment.
  • AI platform & MLOps: pipelines, monitoring, and self-service tooling, all inside your perimeter.
  • GPU compute & infrastructure: NVIDIA-accelerated servers on-premises or in a private cloud.
  • Your data: stays here. Nothing leaves your environment.

The problem

AI is everywhere. Getting it into production is the hard part. Most initiatives stall between a proof of concept that works in a demo and an AI that actually runs, because the data is not ready, the architecture does not scale, and no one owns the platform underneath. For a lot of organisations the public route is off the table too: sending customer data, IP or regulated records to a public AI service is a risk they will not take. So the project sits between experiment and operation, and the control people think they have is thinner than it looks.

Our point of view

The blocker is rarely ambition. It is the conditions to run AI safely and keep it running: data you can actually use, an architecture that holds at scale, and an owner for the platform underneath. No hype, no endless pilots, just the work that gets AI into production. That is where ITQ starts, before any model or vendor.

Private AI means running the models and workloads inside an environment you control. Instead of sending your data out, you bring the models in: the language models, the GPU compute, the storage and the platform that ties them together. Retrieval-Augmented Generation connects AI to your own documents without anything leaving your perimeter.

We bring AI to your data, not your data to AI.

Public AI

Private AI
Where it runs: Public AI runs on someone else’s platform Private AI runs on infrastructure you control
Your data: Public AI sends your data out Private AI keeps it inside your perimeter
Cost at scale: Public AI bills you per token Private AI is infrastructure you can budget

How ITQ helps: choose your deployment

One promise, three deployment options. Which one fits depends on the stack you already run and where you want to take it. ITQ designs, validates and scales the platform with you, from a first proof of concept to full production.

VMware Cloud Foundation with Private AI Services

Best fit if you already run VMware or VCF. ITQ runs GPU-accelerated AI on the platform your teams already operate, with the same governance, tooling and automation as your VMs and modern apps.

Red Hat OpenShift with OpenShift AI

Best fit for container-first and Kubernetes teams. ITQ builds, serves and manages models with an open MLOps workflow on OpenShift, on-premises or in your private cloud.

SUSE AI

Best fit if you want an open-source-first, vendor-neutral stack. ITQ keeps you in full control of your models and data flows, with no lock-in to a single vendor.

Why Private AI

Why organisations choose Private AI:

  • Data stays yours: IP, customer data and trade secrets stay behind your firewall. No vendor trains on them and no vendor can read them.
  • Compliance you can prove: you control every layer of the stack, which is what makes GDPR, NIS2 and ISO 27001 achievable rather than assumed.
  • Security and control: you set the access policies, audit trails and governance. No shared tenants, no opaque processing of your data.
  • Predictable cost at scale: infrastructure you can budget, instead of per-token bills that climb with every user.
  • A real edge: fine-tune models on your own data and build capabilities that generic public tools cannot copy.

Who is it for?

Private AI is not for every organisation, and that is fine. It is worth a serious conversation if you recognise yourself in one of these profiles:

  • Regulated industries: healthcare, finance and government, where data protection is not optional.
  • IP-sensitive organisations: engineering, R&D and manufacturing, whose edge lives in proprietary designs and processes.
  • High-volume AI users: teams running AI at scale, where a private platform becomes cheaper than public per-token services.
  • AI builders: teams that want to build with AI, not just consume it.

Why choose ITQ?

  • 500+ data centers deployed and 50+ cloud-native platforms built for customers.
  • 1,200+ applications implemented.
  • 11 Red Hat certifications and a Golden Kubestronaut on the team.
  • NVIDIA-accelerated infrastructure; ISO 9001 and ISO 27001 certified.
  • Named expert and author: Johan van Amersfoort, Chief Evangelist.

Want to know how ITQ can help?

ITQ helps organisations design, validate, and scale Private AI solutions, from an initial Proof of Concept to a fully operational production platform. We bring AI to your data, not your data to AI. Ready for the next step? Discover how ITQ implements Private AI.

Discover our Private AI solution here
Johan van Amersfoort Chief Evangelist

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