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Content Quality Dashboard & AI-Powered Audit Platform
- •Identified the absence of a systematic content quality measurement system and led the initiative to design and build Tata 1mg's AI-driven content quality platform from scratch.
- •The platform validates clinical content and regulatory classifications against source references, identifies gaps, and recommends corrections at scale for ePharma and eDiagnostics.
- •Combines automated audits, prompt-based evaluation workflows, and human review.
The Challenge
- No standardised benchmark existed to measure the quality of drug content on the platform, and quality was assessed subjectively.
- Manual audits of thousands of drug pages were unsustainable at scale without automation.
- Establishing consensus on what 'good' looks like across clinical accuracy, regulatory compliance, SEO value, and user readability simultaneously.
The Approach
- Defined a multi-dimensional scoring framework with weighted attributes across Comprehensiveness, Accuracy, and Consumability.
- Built an audit pipeline using Python for bulk content extraction and evaluation, with Flesch-Kincaid integrated for readability assessment.
- Deployed AI-assisted prompt-based evaluation workflows to scale audit capacity — enabling assessment of 500+ drugs per audit cycle.
- Benchmarked content against international competitor platforms.
Results
Increased drug safety and compliance from 63% to 95% across ePharma and eDiagnostics.
Reduced manual review effort by 60% through AI-assisted audit workflows.
Scaled reviews to 500+ drugs per cycle.
Improved content accuracy by 10%+ and increased comprehensiveness by 7% across the Pareto drug portfolio.
Tech Stack
PythonExcelCanvaPrompt engineering (GenAI)MetabaseClaude CoworkChatGPTConfluence