Indegene deploys a Data Quality Agent on the AWS platform
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Indegene has built a large language model-driven Data Quality Agent within its AWS-based NEXT Unified Data Platform (NEXT UDP) to generate context-aware data quality rules, perform validation, and output directly runnable SQL and Python code. The Agent adopts centralized human-in-the-loop governance: only high-confidence, low-risk rules are automatically approved, while critical rules and business rules must be reviewed by subject matter experts (SMEs). Indegene says prompts and outputs are not used to train models and are not shared with model providers. Indegene says it will subsequently add AI-assisted remediation suggestions on this basis to help users address data quality checks that fail.
AI overview based on source reports · Updated 2 days ago
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- AWS Machine Learning BlogHow 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.
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