On-prem AI infrastructure
On-prem AI infrastructure.
Built around your business.
We plan and deploy models around existing servers, company knowledge, and business requirements. Give people useful AI while keeping control of where computation and data run.
What the work covers
Built around your business.
On-prem infrastructure planning
Assess existing servers, compute, storage, network, expected usage, and budget before committing to hardware. If private cloud is preferred, we scope it separately.
Models selected for the work
Evaluate suitable models on representative tasks. Size the infrastructure against quality, speed, and concurrent users rather than choosing a model by its name alone.
Your internal knowledge
Connect the agreed document sources, index the content, and return answers with references. Keep permissions and document versions part of the design.
A production handover
Document the architecture, access controls, update process, and operating responsibilities. Train the people who will use and manage the system.
A practical next step
Make the first project clear.
- Map one use case and the boundaries around its data.
- Evaluate models and compute requirements on representative work.
- Deploy, verify access, and train your team.
Before we start
Practical questions.
Can we use our existing servers?
We assess current compute, storage and network against the intended workload and the number of people using it. Models are evaluated on representative tasks before we recommend the hardware. The assessment determines what can be reused and where additional capacity may be needed; private cloud is scoped separately.
How will the system use our internal documents?
We agree which document sources to connect, where their content will be indexed, and who should be able to access it. Answers include references so people can check the source. Document versions, permissions, prompts, responses and logs are considered together when we map the system and its data boundaries.