Service / 07
Private AI
We deploy language models and AI pipelines that run entirely on infrastructure you control — on-premises or in your private cloud. You get capability comparable to hosted APIs, with data, weights, and logs inside your perimeter.
When you need this
The situations we are called into.
Legal, residency, or classification requirements prohibit sending data to external APIs.
Client contracts predate — and preclude — third-party AI processing of their data.
You need model behaviour frozen and reproducible for years, immune to provider deprecations.
Approach
How the engagement runs.
Requirements and threat model
Data classifications, isolation requirements, and the specific guarantees your auditors will ask about.
Model selection and benchmarking
Open-weight candidates benchmarked on your tasks and your hardware envelope — measured, not assumed.
Infrastructure build
Serving stack, orchestration, and monitoring deployed inside your perimeter, documented end to end.
Isolation review
Network paths, logging, and update mechanisms verified against the threat model before go-live.
Operate or transfer
A defined upgrade policy and either managed operations or full handover with training.
Deliverables
What you hold at the end.
Deployed private inference stack
Benchmark report on your tasks
Isolation and security review
Model upgrade policy
Operations training
Related research
The evidence behind this practice.
Benchmark · 2026
Private LLM inference on Apple-silicon clusters
Throughput, latency, and cost characteristics of quantised open-weight models served on commodity Apple-silicon hardware, measured against cloud baselines.
In preparation
Contact
Talk to our engineers.
Describe the situation you are in. The person who replies is the person who would do the work.