Enterprise AI is creating new opportunities for life sciences organizations to improve decision-making across commercial, medical, market access, and other business functions. But the quality of every AI model, dashboard, and business insight depends on one thing: trusted data. As enterprise data grows across multiple platforms, pipelines, and business domains, maintaining consistent data quality has become increasingly difficult. Manual data quality rule creation, fragmented governance, and evolving business definitions make it challenging to scale data quality across the enterprise.

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. The agent generates context-aware data quality rules, validates them, and produces ready-to-run SQL and Python code under centralized, human-in-the-loop governance. Together, these capabilities help organizations improve data quality at enterprise scale while reducing the effort required to build and maintain data quality programs.

Indegene Limited (BSE: 544172, NSE: INDGN) is the strategic operating partner to the life sciences industry, orchestrating commercial, medical, R&D, and safety operations across the product lifecycle. Trusted by the world’s top 20 biopharma companies, Indegene helps run complex, highly regulated workflows at global scale. Indegene combines deep life sciences expertise with AI-embedded platforms and governed operating models to improve speed, consistency, and compliance across mission-critical operations. By integrating technology into how work is executed and scaled, Indegene helps life sciences organizations deliver measurable outcomes in regulated environments.

Most organizations have established data quality processes but keeping them effective is becoming increasingly difficult as enterprise data ecosystems evolve.

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