Private on-premise AI for organizations

What LLMLab.ee delivers

We clarify the use case, models, data volume, user count, and IT environment. Then we select the hardware, configure the software, and check the system before handover.

• Hardware and memory selected for the workload.
• Computer assembly, operating-system setup, and agreed local tools.
• A written quote and handover checklist covering known limits.

Data boundary and privacy

A local model can process input on the organization's hardware. The actual data path depends on software, integrations, and network configuration, so it is reviewed before handover.

• Air-gapped, certified, or regulated claims require a separately evidenced scope.
• Model licences, permitted uses, and data classification are agreed with the customer.
• Quote-request data is not used for model training.

Suitable workflows and limits

Typical workflows include private chat, document questions, coding assistance, and adapter tuning of smaller models. Model, context, concurrency, and performance are checked before quote.

• Multi-GPU or high-concurrency systems need separate architecture, power, and cooling review.
• Small workstations are not presented as full-model training systems.
• Mac, CUDA, and experimental eGPU limits differ.

Quote process

• Describe the workflow, model class, users, data volume, and environment.
• We clarify the requirement and send a quote covering configuration, availability, scope, price, and delivery.
• Payment and hardware ordering follow acceptance.