Private AI for quantitative analysts.

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 an approved research experiment and its assumptions

The supporting records include model documentation, code comments and authorised result summaries.

The analyst independently validates models and statistical claims.

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.