How Indegene is modernizing and scaling enterprise data quality in life sciences

Indegene built an LLM-powered Data Quality Agent within its NEXT Unified Data Platform on AWS, using Amazon Bedrock, Strands Agents, and Amazon Bedrock AgentCore to generate context-aware data quality rules, validate them, and produce SQL and Python code under human-in-the-loop governance. The agent draws on internal enterprise and external reference knowledge bases via RAG to recommend technical and life sciences-specific business rules. Indegene reports the agent reduced rule definition and implementation effort from about 30 hours to about 4 hours per dataset and cut new table onboarding from 2–5 working days to 2–4 hours.