Private and On-Premise AI
Private AI that keeps client data inside your walls
Private AI means AI models running on infrastructure you control, so prompts, documents and answers are never sent to a third-party model provider. Rhentech designs, builds and runs it for London firms whose client confidentiality, legal privilege or regulatory duties rule out public AI services for their most sensitive work.
30 minutes, free of charge. We respond within one business day.
- Data stays with youPrompts and documents never reach a public AI provider.
- Recommended only where it fitsWe advise cloud AI when it is the better answer.
- Run as a serviceMonitored, patched and backed up like the rest of your IT.
What does a private AI deployment include?
Seven workstreams, from the first assessment to day-to-day operation.
- 01
Workload and data assessment
We identify which AI workloads involve data that should not leave your control, and which can safely use a cloud service. Private infrastructure is recommended only where confidentiality, regulation or volume justifies it.
- 02
Open-weight model selection
An open-weight model is one whose trained weights are published for download and self-hosting, under a licence that sets the terms of use. We shortlist models such as the Qwen family on capability, licence and size, and test them on your own tasks before choosing.
- 03
Hardware sizing and placement
We size NVIDIA GPU servers to your models, users and response-time needs, and decide whether they sit in your office, your data centre or a private hosted environment. Where data allows, dedicated GPU cloud such as Runpod can carry testing or burst work.
- 04
Serving and integration
We run models with production serving software such as vLLM, or Ollama for smaller deployments, behind an internal interface your staff and systems can use. Access goes through your existing identities, so the same people who can open a file can ask AI about it.
- 05
Private search over your documents
Retrieval-augmented generation (RAG) is a method in which the AI first searches an approved document collection and then answers from what it found, with references. We build it over your precedents, policies and files, so answers cite your sources rather than the model's memory.
- 06
Security and isolation
Private AI is still a system to secure. We segment it on the network, restrict who can use it, log every request and response, and include it in monitoring, patching and backup.
- 07
Operations and optimisation
We monitor accuracy, performance and capacity, update models as better ones are released, and tune the system to the work it actually does. This can run as a monthly retainer, on its own or alongside managed IT.
How does a private AI project run?
Part of the Turnkey AI loop: discover, build, manage, review, then repeat.
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Discovery
We map the workloads, the data and the constraints, and set out the options: cloud, private cloud, on-premise, or a mix. You get a recommendation with the reasoning.
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Build and deploy
Hardware, serving, integration and private search are delivered in stages, each tested on your real tasks before staff rely on it.
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Manage and review
Monitoring, updates and optimisation, with a quarterly review of value, usage and risk.
Platforms we work with
- NVIDIA
- vLLM
- Ollama
- Qwen
- Runpod
- Microsoft Azure
- AWS
What do leaders ask about private AI?
Not answered here? Ask it on the call.
What is private or on-premise AI?
Private AI means AI models running on infrastructure you control, such as servers in your office, your data centre or a dedicated hosted environment. Prompts, documents and answers stay within that environment instead of going to a public AI provider. On-premise is the strictest form, with the hardware on your own site.
When is private AI better than a cloud AI service?
When the data is too sensitive to send to a third party, such as privileged legal material or health records, when contracts or regulators restrict where data may be processed, or when usage is high and steady enough that owning capacity costs less than paying per use. For occasional or experimental use, a well-configured cloud service is usually the better choice.
Are open-weight models good enough for professional work?
For many tasks, yes, particularly summarising, drafting, classification and answering questions over your own documents. We test candidate models on your real tasks before recommending one, and we tell you where a frontier cloud model would still do better.
How does private AI help with UK GDPR?
Keeping processing on infrastructure you control removes a third-party processor and any international transfer from the data flow. You still need a lawful basis, security and records under UK GDPR, which we build into the deployment, but the risk of data being retained or used for training by a provider is gone.
Who runs the system once it is live?
We do, unless you prefer to run it yourselves. Monitoring, updates, capacity and model upgrades can run as a monthly retainer, and the system can sit inside your Rhentech managed IT service.
Free consultation
Use AI on your most sensitive work, without letting it leave.
A 30-minute call with a senior consultant about your workloads and whether private AI is the right fit. We respond within one business day.
Or call 020 3886 3001 or message us on WhatsApp.
What happens next
- 01You tell us briefly what has landed on your desk.
- 02We respond within one business day to arrange a time.
- 03A 30-minute call with a senior consultant, and an honest view of what to do first.