Indegene built an element-level framework to score how faithfully global promotional and medical content is localised.
The company compared global and local assets by hand, lacked visibility into deviations and could not scale as its portfolio grew. Indegene deconstructed assets into visual and textual elements, mapped them, classified changes as Reuse, Adapt or Net-New and produced integrity scores. The framework assessed 5 brands, 250+ assets, 10,000+ content units and 25+ markets; AI-powered deviation detection is named only as a future capability.
Indegene is a healthcare commercialization and technology services firm, used by pharma for AI-assisted content and campaign creation, omnichannel HCP engagement and commercial analytics
This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.
Published by Indegene on its own site — read the original case study on indegene.com ↗
Note: Page names the Cortex platform only in the footer; AI features are described as future work.
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