Task and model evaluation
Compare suitable candidate models on an agreed set of permitted examples. Record useful answers, incorrect results and cases that need a person rather than selecting from a demonstration alone.
Private AI services
Plan the model service as part of a working application.
A focused scope can establish whether private deployment fits the task before committing to wider infrastructure.
Infrastructure and capacity planning
Assess the expected workload, concurrent users, response time and available compute. Include storage and the cost of operating the service alongside the initial setup.
Private application connections
Connect the model service to an agreed internal task. Define authentication, allowed actions and the data each part may receive.
Data-path review
Map prompts, retrieved documents, results, logs, backups and external calls. Identify retention and access decisions rather than assuming that a locally running model keeps every component local.
Updates and operational checks
Record the tested model and application configuration. Repeat the relevant quality and capacity checks before changing the model or serving software.
Failure handling and handoff
Define what the application does when the service is unavailable or cannot provide a useful result. Leave operating instructions, recovery limits and responsible roles with the deployment.
What needs to stay under your control?
Prepare the planning questions or discuss a first assessment.