Private AI for ai engineers.

Evaluate the model on the failure cases that matter

Compare models, detect regressions and analyse performance by operating condition or input group.

Inputs: Approved datasets, task labels, experiment configurations and evaluation outputs.

From observation to completed task

Run controlled evaluations and create engineering tasks linked to model and data versions.

Document a model-evaluation experiment and its open questions

The supporting records include approved experiment notes, evaluation plans and result summaries.

Engineers reproduce results and check deployment suitability.

The systems involved

Experiment tracking, compute scheduling and permissioned evaluation data.

SOS AI configures the local models and tool permissions for this workflow. Actions in business software follow the authority you approve; uncertain cases and actions outside those limits go to the responsible person.

What a useful result must get right

Engineers check leakage, drift and task-specific risk; no benchmark-only deployment decision.