Artificial intelligence · Artificial Intelligence
Private AI on your own infrastructure
Models running inside your perimeter, for when the information cannot leave towards any cloud.
- Deployment
- On-premise or private cloud
- Models
- Open models, tuned on your data
- Duration
- 8 to 14 weeks
- Data
- Never leaves your network
The problem
There is information that simply cannot travel: medical records, case files, intellectual property, defence data. For those cases the model goes to where the data is, and not the other way round.
What it includes
Tuned open models
Selection and tuning of models that run on your hardware, with quality sufficient for the specific use case.
Honest sizing
We tell you what can be achieved with the infrastructure you have and what requires investment, without over-promising.
Internal vector store
Your knowledge indexed inside the perimeter, without sending fragments to external services.
Operation and updating
Model update cycle, performance monitoring and backup, like any other critical service.
Hybrid mode
Where appropriate, the sensitive part is processed locally and only the anonymised part queries larger models in the cloud.
How we work
- 01EvaluationWhat the use case demands and what your infrastructure allows.
- 02Proof of conceptMeasurement of quality and speed with your real data.
- 03DeploymentInstallation, hardening and backup.
- 04OperationMonitoring, updating and handover to the team.
Deliverables
| Document | Length |
|---|---|
| Platform deployed Documented and hardened. | included |
| Quality report Comparison against cloud alternatives. | included |
Local AI is not always the right answer. It costs more and performs worse than a larger model in the cloud. It is justified when the sensitivity of the data or a regulatory obligation demands it, and in the assessment we tell you frankly whether that is your case.