Running AI on your own infrastructure
The objection to AI in professional services is rarely capability. It is that the material is confidential — client files, medical records, case documents, contracts under negotiation.
Running models on hardware you control removes that objection entirely.
What has changed
Open-weight models improved sharply. A model that runs on a single server now handles summarisation, extraction, classification and drafting at a quality that was cloud-only two years ago. For focused business tasks, the gap has narrowed to the point where it stops mattering.
What it takes
Hardware. A server with a modern GPU and adequate memory handles a small team comfortably. Smaller models run acceptably on a well-specified machine without one, if you accept slower responses.
A model matched to the task. Extraction and classification need far less capability than open-ended reasoning. Pick the smallest model that does your job well — it is faster and cheaper to run.
Somewhere to keep the documents. Usually a vector database, so material can be retrieved by meaning rather than keyword.
Ongoing maintenance. Models are updated, and someone has to test whether a new version is better for your specific task rather than in general.
The honest trade-offs
In favour: data never leaves, no per-token cost, no dependency on a vendor's pricing or roadmap, and it keeps working if the internet does not.
Against: capital cost up front, real maintenance effort, and the largest frontier models remain ahead for the hardest reasoning tasks.
Where it fits best
- Legal, medical, accounting and any regulated professional practice
- Companies with contractual restrictions on data residency
- Anywhere with high, steady volume, where per-token pricing compounds
- Public sector work with data localisation requirements
A sensible way in
Start with one task, one model and a machine you already have. Measure quality on your own material rather than trusting benchmarks — a model that scores well generally may perform poorly on your particular documents, and the reverse happens too.
We design and deploy private setups under AI implementation.