NEXT Technology Platforms

We trained some of the best-in-class NLP, ML classifiers and machine vision platforms to automate the process of life sciences content modularization, metadata generation, content comparison, mapping document lineage and new content creation. The different components of our platforms find applications in automated medical review and approval of documents, measurement of content effectiveness and analytics, agile reuse and repurpose of regulatory, medical, educational and promotional content, and rapid creation of new content.

Our platforms deliver to high levels of quality requiring humans to only supervise machine performance thereby enabling us to process large volumes of content faster and with lower headcount.

Envision a future where MLR reviews and approvals unfold within 24 hours of content creation. At Indegene, we are committed to this vision through our proprietary NEXT MLR Review Automation platform powered by advanced Agentic AI systems. Purpose-built for enterprise execution by MLR experts, the platform transforms the end-to-end MLR process (Creation, Review, and Process) by streamlining workflows, reducing review cycles, minimizing rework, and ensuring global and local compliance. With features like intelligent content suggestions, automated reference anchoring, local regulatory checks (e.g., ABPI, PAAB), and differentiated workflows, it accelerates review and approvals while empowering every stakeholder in the MLR process. Explore our brochure to learn how the NEXT MLR Review Automation platform can reduce time to market.

By automating the conversion of unstructured data and information to structured data, we successfully disrupted the extremely headcount-intensive process of pharmacovigilance as it relates to adverse event case intake and processing and its conversion into structured fields for a safety database.

This suite of platforms consists of components for the automatic monitoring and consumption of guidelines published by regulatory agencies in major markets such as the US FDA and EMA, and another for optimizing resource allocation to prioritize the timelines of submission of regulatory documents in different markets and hence maximize the business potential of new launches.

At the heart of omnichannel customer excellence and personalization of HCP and patient experiences lies the ability to ingest, organize and analyze diverse sets of data. We built a fit-for-purpose ecosystem to enable nimble and flexible data integration from internal and external sources such as CRM data, sales and marketing data, channel data, prescription data, real-world data such as medical claims and electronic medical records, and their conversion into analytics-ready datasets.

With our proprietary AI models, we enrich these data sources and leverage this augmented data to deliver advanced analytics and insights through technology-enabled modules for sales forecasting, omnichannel targeting and segmentation, customer cohort builders, channel mix optimization and recommendations for next best actions.

We developed easily configurable tools that integrate HEOR (Health Economics and Outcomes Research), medical claims, genomics, lab and other data sources to model disease states and their progression in a real-world patient population using graph-based predictive algorithms. Our patient cohort builder helps accelerate patient recruitment and enable fool-proof, evidence-based clinical trial design.

To help scale enterprise commercial and medical transformation for our clients and make the services delivered through our centers of excellence available to users across all markets, we developed a platform with in-built workflows catering to functions across the life sciences commercialization continuum - from regulatory content development to global product labelling, digital content development, omnichannel campaign design and execution. The platform enables global collaboration and eliminates multiple handovers during project execution resulting in speed, transparency and cost efficiency.