Establish the baseline
We define the task, gather representative evaluation cases, and measure the existing model. That gives your team a concrete basis for deciding whether adaptation is justified.
Prepare the right data
Training examples need clear provenance, appropriate permissions, and consistent quality. We assess the dataset, separate training from evaluation, and document its limits.
Adapt with intent
Depending on the task, the work may include supervised fine-tuning, preference-based methods, or parameter-efficient adaptation. Quality, latency, and operating cost are considered together.
A model your team can manage
Model versions, evaluation results, deployment choices, and rollback procedures stay visible. We help your team compare the result with the baseline and make an informed release decision.