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Content Quality Dashboard & AI-Powered Audit Framework
- •Identified the absence of a systematic content quality measurement system and led the initiative to design and implement one.
- •The framework assessed content across three dimensions: Comprehensiveness, Accuracy, and Consumability.
- •Collaborated with the SEO and editorial teams to define metrics, scoring rubrics, and benchmarking methodology against competitors including Mayo Clinic, WebMD, and Apollo Pharmacy.
- •Scaled the audit process using AI-assisted evaluation and prompt-based workflows to handle audit volumes at pace.
The Challenge
- No standardised benchmark existed to measure the quality of drug content on the platform and the 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 350+ drugs per audit cycle.
- Benchmarked Tata 1MG content against 3 international competitor platforms.
Results
The content accuracy went up by 7% while the comprehensiveness increased by 8% for pareto business products.
350+ drugs audited per cycle using the structured framework.
Dashboard adopted as the internal quality standard across the medical affairs content team.
AI-assisted audit workflow reduced manual review time per drug by 60%.
Tech Stack
PythonExcelCanvaPrompt engineering (GenAI)MetabaseClaude CoworkChatGPTConfluence