The gold standard enterprise Technology Ecosystem for Lifesciences

Leverage AI, NLP, automation and deep healthcare domain expertise to improve Commercial, R&D and Medical operations


This is the new gold-standard Enterprise Technology Ecosystem for life sciences—one that puts artificial intelligence, natural language processing, automation, and deep healthcare domain expertise at the heart of the modern Commercial, R&D, and Medical enterprises.

This is a revolutionary leap ahead of anything that presently exists within Commercial, R&D, and Medical Operations.

According to Gartner's Emerging Technology Trends 2018 Assessment, our platform is leading the way in life sciences in the democratized AI category.

We look at ourselves as transformation partners to the industry working at the exciting intersection of medical content, data, and technology, to bring lifesaving therapies faster, effectively, and more efficiently to patients.

Indegene next generation pharma commercialization
Emerging Technology Trends

Transforming Content and Data for Lifesciences

Some of the areas where we make our deepest impacts include driving reuse of content across a number of R&D, Clinical, Safety, Regulatory, Medical, and Commercial documents and industrializing data science by shortening time-to-data/time-to-insights through our enterprise data and analytics pipeline. We can bring significant acceleration to these transformations by curating and unlocking value from the data ecosystem.

A significant component of these accelerations involves managing "dark data" where modern technology coupled with strong domain expertise can transform the large amount of unstructured data in this ecosystem into computable models that underlie the accelerations we provide.

Our Intelligent Content approach enables global life sciences enterprises to get on an accelerated path of digital transformation, empowering them to create, curate, and manage all content in a centralized manner, automate content life cycles, and optimize processes while ensuring regulatory compliance and faster time to market.


Intelligent Content Management


Enterprise Data

Bringing Cross-disciplinary Teams and Capabilities Together

We bring about an innovative approach with our cross-disciplinary teams that include engineers, data scientists, clinical experts, and pharma subject matter experts along with core technical competencies in natural language processing, machine vision, machine learning, graph data modeling, and enterprise architecture. This approach has allowed us to create a portfolio of IPs that transform Commercial, R&D, and Medical businesses of our customers from the life sciences industry.

Our approach ensures that our customers see success in these transformations by taking advantage of well-tuned machine learning algorithms, best-in-class scalable cloud infrastructure and managed services, and strong domain expertise that result in fit-for-purpose solutions exactly addressing the industry needs.

Bringing cross disciplinary teams and capabilities together
Join our Technology Innovation Practice
 Join our Technology Innovation Practice

If you are looking to be at the intersection of cutting-edge technology and healthcare, Indegene is the de facto choice for healthcare innovators. Over the next decade, our talent will shape the known-life sciences technology universe. Are you interested in learning more?


Virtual Engagement and Account MGMT

Virtual Engagement and Account MGMT

Current content development and production processes are highly manual and siloed with no enterprise-level standardization, significant onshore coordination work, and lack of access to cross-market created content. This increases the time to market, decreases scalability, and substantially increases the overall expense of content creation and content management. In addition, the process of content discovery is complex.

How it Works

Virtual Engagement Manager (VEM), as the name calls out, manages content development and production engagements in an automated, optimized, and accessible manner. Automation of extensive manual processes such as requirement gathering, validation of inputs, pricing and timeline quotes, and asset tagging improves the efficiency and reduces the time to market substantially. At the same time, enterprise-level best practices playbook and asset socialization activities bring enterprise-level standardization, consistency, and accessibility. VEM's content cataloging features enable easier content discovery.

Data-Driven Intelligent Medical Content Creation

Data-Driven Intelligent Medical Content Creation

The current content creation process relies on creating every asset from scratch, thus, making the process slower, costlier, non-scalable and inconsistent, not to mention that every customer sees the same content. For each brand, when there is already so much existing content, it doesn't make sense to redo everything from scratch, thus each time. Our disruptive Virtual Content Creator (VCC) automatically creates several sub-assets in different formats from one master asset and auto localizes the assets for various regions in a faster and economical manner with a data-driven approach to generate relevant, personalized, and consistent content.

How it Works

VCC works by usage of advanced machine vision and machine learning concepts. Our machine vision system automatically slices the content into various logical blocks and classifies them based on several criteria. Our machine learning engine learns the relationships between the slices and how they are assembled in templates to automatically generate effective sub-assets that can be readily used.

 Indegene Intelligent Safety Suite

Indegene Intelligent Safety Suite




Voice to Text

Speech-to-text NLP engine for intelligent call-flow assistance ML-driven real-time structured data extraction in pre-defined templates

MI call center AE and PQC case intake

Unstructured to Structured Conversion

NLP/AI-driven data extraction and data population into structured fields from unstructured/semi-structured sources

Data Entry - ICSR, CDM, and DE for client-specific internal process

Data and Text Comparison Tool

Rule-based and AI-based text, data, table, and figure comparison

QC review/proofreading of clinical, regulatory, safety, and medical document

Literature Review Tool

Automated and configurable sourcing of literature articles from the databases; define and manage multiple search strategies; AI-driven literature article categorization, and deduplication technologies and NLP-based search

Data sharing and usability across different functional groups – clinical, regulatory, safety, and medical

Social Media Monitoring Tool

Technology-enabled social media monitoring, NLP-based data collection, AI-based extraction of product and related information from the posts

AE/PQC data collection sentiment analysis, consumer and prescriber perception and influencer engagement

 Intelligent Labels

Intelligent Labels

Labeling of pharmaceutical products is a complex and dynamic process requiring organizations to assemble information from multiple sources, consistently provide accurate information, comply with changing global regulatory guidelines, while ensuring that products are launched at the right time across markets. Generic authoring and content management applications that are tweaked to manage labeling content and life cycle operations are unable to achieve desired levels of effectiveness and efficiencies, resulting in wasted resources and high costs of compliance. Outsourcing to third parties further adds to project management overheads.

Intelligent Labels empowers global pharma enterprises to modernize their enterprise-wide labeling data and operations and transform the way labels are generated, contextualized, and published for consumption by patients and HCPs. Custom-built for global life sciences enterprises and augmented by AI and advanced analytics, Intelligent Labels drive content effectiveness, operational efficiency, and regulatory compliance across label life cycles.