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TutorialsAWS Machine Learning BlogFrom the source· Bhagyashree Chandak·· 3 hours ago

How Indegene Scales Enterprise Data Quality with an LLM Agent

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

Image: AWS Machine Learning Blog
Who
AWS
What
explained Indegene’s LLM-powered Data Quality Agent on NEXT UDP
Quote
This post explores how Indegene built a production-grade, large language model (LLM) powered Data Quality Agent within its NEXT Unified Data Platform (NEXT UDP) on AWS.

What happened

AWS and Indegene describe a Data Quality Agent built into Indegene’s NEXT Unified Data Platform on AWS. It uses metadata, profiling, anomaly results, existing rules, and internal and external knowledge bases to recommend checks and generate SQL and Python for human review. Indegene reports that deployments reduced rule creation effort from about 30 hours to about 4 hours per dataset, while new table onboarding takes 2–4 hours instead of 2–5 working days.

Read next

See the full storyAWS Describes Indegene’s LLM Data Quality Agent on NEXT UDP

The story so far

Indegene built an LLM-powered Data Quality Agent into its NEXT Unified Data Platform on AWS. It recommends data checks and generates SQL and Python for human review, using metadata, profiling and anomaly results, existing rules, and internal and external knowledge bases. Indegene reports that the agent reduced rule creation from about 30 hours to about 4 hours per dataset, and cut new table onboarding from 2–5 working days to 2–4 hours.

AgentsDeployment engineeringTutorials#Tutorials and practice#Agent#RAG#Deployment and engineering

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