Custom Model Fine-Tuning

Lamini lets a company fine-tune a large language model on its own documents and data rather than using a general purpose model as is. A Beam agent reads a newly approved training dataset, such as updated product documentation, from the connected storage system and starts the fine-tuning job in Lamini according to the schedule a team has set. It checks the job's status and notifies the team once training completes, along with a short summary of what changed. Requests to train on a new, previously unapproved data source are routed to a human before the job is allowed to start.

Private Model Hosting

Lamini keeps a fine-tuned model behind a private endpoint so a company's own applications can call it without exposing training data elsewhere. A Beam agent reads incoming requests from a connected application, applies the approved rate or usage rule, and forwards the request to the correct model version in Lamini. It records which version answered each request so results can be traced back later. A request to roll back to an older model version, or to retire one entirely, is routed to a human to confirm first.

Usage and Cost Reporting

Lamini records how much compute each model uses, which adds up across many requests into a meaningful cost. A Beam agent reads this usage data on a regular schedule, applies the team's approved budget threshold, and updates a shared cost sheet so finance does not have to check Lamini directly. When usage for a given model climbs well past its usual pattern, the agent flags the change rather than pausing the model itself. A human then decides whether to raise the budget or investigate the cause.