Private AI. Models that run where your data is allowed to live.
We set up open-weight language models on your own servers or private cloud, so confidential data never leaves your environment.
For banks, insurers, hospitals and public bodies with data-residency rules, and for any team handling data that cannot go to a third-party API.
Private AI on your own infrastructure
We size the hardware or cloud instance for your workload, choose and test suitable open-weight models, deploy an inference server with access control and logging, and connect it to your applications or agents. You get a cost model and a clear view of what the setup can and cannot do compared with hosted models.
- (01)Data stays in your environment
- (02)Sized for your workload
- (03)Access control and logging
- (04)Works with your apps and agents
Tools we use for this.
- vLLM
- llama.cpp
- Ollama
- Open-weight models
- Docker
- NVIDIA GPUs
We choose the stack that fits your requirements. Tool names describe our skills, not partnerships.
Setup.
1–2 weeks.
One clearly bounded piece of work, such as a system configured or a review completed, handed over ready to use.
Included
- (01)Workload and data-rule review
- (02)Hardware or cloud sizing
- (03)Model selection and evaluation
- (04)Inference server deployment
- (05)Access control and logging
- (06)Cost model and handover
Not included
- (01)Hardware purchase
- (02)Training models from scratch
- (03)Round-the-clock operations (available in Build & Support)
How it runs
- (01) · Week 1
Assess
Workloads, data rules, budget and latency needs.
- (02) · Week 2
Deploy
Install, secure and benchmark on your workloads.
- (03) · Weeks 2–3
Connect
Wire it to your applications and hand over.
Also available as: Workshop (½–2 days) · Build & Support (Staged, ongoing). Compare the formats.
You might also
look at these.
Are open-weight models good enough?
For many business tasks, such as extraction, classification, drafting and search, yes. For the hardest reasoning tasks, hosted frontier models may still be stronger. We test on your tasks before you commit.
Do we need to buy GPUs?
Not necessarily. Options range from a private cloud instance to on-premises hardware. We compare the costs for your volumes.
Is this cheaper than API calls?
At steady, high volumes it can be. At low volumes, hosted APIs are usually cheaper. The cost model shows where the line is for you.