Smart Data Quality (SDQ) Data Quality Co-Pilot

Generative AI trained on Saama’s proprietary DQ check library turns plain-language requests into tested, production-ready data-quality checks.

From plain language to production check

The Data Quality Co-Pilot removes the programming bottleneck from data-quality checks. Describe the DQ check you want in plain language, and SDQ writes the code and tests it automatically using generative AI trained on Saama’s proprietary historical DQ check data.

Because the Co-Pilot is grounded in years of real clinical DQ checks, it produces relevant, reliable logic — shortening the path from idea to production check and reducing reliance on specialized DQ programming.

Why teams choose the Data Quality Co-Pilot

No manual coding required

Plain-language descriptions become working checks.

Faster turnaround

Generation and testing happen in minutes, not days.

Trained on proprietary data

GenAI grounded in Saama’s historical DQ checks produces clinically relevant logic.

Auto-tested before approval

Generated code is validated automatically before it reaches production.

Minutes

From plain-language request to tested check

Generative AI trained on Saama’s proprietary historical DQ check data

How the Data Quality Co-Pilot works

The Co-Pilot guides a check from request to production while keeping a human in control.

Describe the check

A user explains the desired DQ check in natural language.

Co-Pilot generates the code

Generative AI drafts the corresponding check, with an option to explain the logic it produced.

Auto-test and approve

The check is automatically tested via dry run and unit testing; a reviewer approves it and pushes it to production.

Grounded in real DQ history. The Co-Pilot is trained on Saama’s proprietary library of historical DQ checks, so its suggestions reflect proven clinical data-quality logic rather than generic patterns.