Run AI on your own network
Public AI services are not appropriate for every workload. On-premises AI lets an organization use modern models while keeping direct control over the infrastructure and the data. We design and deploy local AI systems based on the work you actually need done.
On-premises does not have to mean massive infrastructure
The phrase conjures a room full of GPUs. In practice, useful on-premises AI runs on:
- Existing servers, in the hours they are not busy
- Workstations, including ones bought for something else
- Dedicated GPU systems, when the workload earns one
- Virtualized environments you already operate
- Small departmental appliances
- Small clusters, for the genuinely large cases
The correct answer depends on workload volume and expected turnaround time, and those two numbers move the requirement enormously. We work out what is actually necessary before anybody buys equipment.
A great many first projects need nothing purchased at all.
What "entirely local" actually requires
Running on your own hardware is the easy half. The part that gets missed is everything around it — and it is where a promising pilot usually stops being promising:
- Model storage and versioning
- Job queues and scheduling
- Failure handling and retries
- Storage for intermediate output
- Search and vector indexes
- Backup for the derived data
- Monitoring and alerting
- Update and patch process
- Access control
- Integration with existing systems
None of it is exotic. All of it is the difference between a demonstration and something the organization can rely on next year.
Everything inside the boundary
In a fully on-premises build there is no step in the diagram that leaves — including during development, and including the model weights themselves.
- ingest file shares, intranet, wiki Runs on your network
- extract + OCR Runs on your network
- embed local embedding model Runs on your network
- index vector + full text Runs on your network
- answer local language model Runs on your network
- cite sources back to the original Runs on your network
The trade you are making
On-premises buys you control and predictable cost. What it costs you is access to the very largest models, and the convenience of somebody else handling the operations.
For most of the workloads on this site that is a good trade, because most of them do not need a frontier model — extraction, OCR, transcription, classification and search are all well served by models you can host. For an interactive assistant being judged against a consumer chatbot, the trade is harder, and we will say so rather than let you find out after the purchase order.
Hybrid is not a failure
If one part of the work genuinely benefits from a larger model and that part happens to involve nothing sensitive, sending only that part out is not a compromise of the principle — it is the principle, applied.
Deployment options covers how the boundary gets drawn and enforced.
Design the environment before buying anything
Sometimes the right answer is a GPU server. Sometimes it is a workstation. Sometimes it is hardware you already own. We will tell you which.