Indegene builds LLM Data Quality Agent on AWS

Indegene's Data Quality Agent is a response to a longstanding operational problem in life sciences data management: technical rules and business rules live in different places, so keeping them aligned across datasets and platforms is difficult. Traditional checks for nulls, data types, uniqueness, or referential integrity remain essential, but they cannot capture business-specific context such as product hierarchies, customer segmentation, or territory alignment. The agent uses two knowledge bases, one internal and one external, to ground its recommendations in enterprise standards and industry reference values.

The workflow keeps data stewards in control: they select a source, profile the data, review anomalies, approve or refine suggested rules, and only then does the agent generate SQL and Python code for execution. Indegene says approved rules go into a central repository for execution, monitoring, and audit. By embedding data quality inside the NEXT UDP platform rather than treating it as a stand-alone tool, Indegene aims to make quality a continuous capability rather than a periodic governance activity, which may matter as life sciences organizations scale AI and analytics on sensitive healthcare professional and patient data.