Infrastructure should be sized for the work it will perform. Employee count alone does not describe an AI workload. The relevant factors include the model, request size, document processing volume, simultaneous use and the response time your workflow needs.

Establish a realistic workload

We separate interactive requests from background work, such as indexing documents or processing a batch of files. That makes it easier to understand when workloads compete for resources and which delays are acceptable.

We also review your existing environment. A project may be able to use available capacity, or it may need a dedicated deployment. The recommendation makes those assumptions explicit.

Test the options before committing

Where candidate environments are available, we run representative scenarios and compare their results. The assessment includes application quality as well as throughput and response time. A faster configuration is useful only if it supports the workflow you need.

The specification describes the intended workload, operating requirements and constraints to revisit if usage grows. It gives your infrastructure team or supplier a clear basis for preparing the environment.

Keep procurement responsibilities clear

We provide planning, testing and a specification. You arrange equipment purchase, physical installation, hardware warranty and hardware servicing with your chosen supplier. Our implementation work begins on a prepared environment and covers software deployment, configuration and operation within the agreed scope.

The next step can be private model deployment or a complete AI implementation.

What you receive

A configuration specification with the rationale for each requirement
Results from agreed workload tests
Deployment prerequisites and capacity assumptions