Private AI for data engineers.

Make the analysis reproducible from input to result

Generate queries and analysis code, test data assumptions and compare forecasting or simulation results.

Inputs: Permissioned datasets, schema definitions, transformations and evaluation criteria.

From observation to completed task

Run approved jobs and publish a versioned analytical notebook for review.

Document a pipeline handover from approved engineering material

The supporting records include approved schema definitions, transformation notes and runbooks.

Engineers validate lineage and test pipeline behaviour.

The systems involved

Read-scoped databases, sandboxed execution and versioned analytical dependencies.

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

Check leakage, joins and uncertainty; numerical conclusions need reproducible calculations.