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The Tech Marketer > Blog > White Paper > Ganit Pharma capabilities
White Paper

Ganit Pharma capabilities

Helping organizations compete on data & AI by focusing on consumption over creation

Last updated:
3 years ago
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Ganit Pharma: Revolutionizing the Pharmaceutical Industry with Data and AI

Ganit, a profitable, pre-Series A funded company, is making significant strides in the pharmaceutical sector with its advanced data and AI capabilities. With a global clientele and a 350+ strong team of experts, Ganit’s solutions are reshaping the industry.

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Ganit Pharma: Revolutionizing the Pharmaceutical Industry with Data and AIOh hi there 👋It’s nice to meet you.Sign up to receive awesome content in your inbox, every week.

Ganit’s Lifesciences Capabilities: Ganit’s prowess in life sciences is evident in their diverse offerings:

  1. Sales & Marketing, Commercial Effectiveness: Specializing in call planning, HCP omni-channel analytics, and commercial spend optimization, Ganit enhances salesforce effectiveness and marketing strategies.

  2. Revenue Management: The company excels in managing supply chain complexities, like diagnosis and prevention of supply failures and rebate analysis.

  3. Supply Chain & Logistics: Ganit addresses crucial areas such as supply and demand planning, serialization analytics, and network optimization.

  4. R&D, Regulatory & Others: The company’s expertise extends to infection prediction, regulatory reporting, drug discovery, and safety analytics.

Ganit’s Impactful Projects:

  • Salesforce Planning & Incentives: Ganit has developed tools for sales target planning, leading to significant time savings and improved tracking.
  • Pricing Analytics: By optimizing discount strategies, Ganit has increased contract prices and identified price-sensitive distributors.
  • Real-World Evidence (RWE) Analytics: Analyzing claims data to identify patient journeys and potential patient pools for specific drugs.

Ganit’s MLOps Practices: Ganit’s MLOps practices streamline the process of deploying and maintaining ML models, ensuring faster deployment, improved collaboration, enhanced reproducibility, scalability, and continuous improvement. This approach has led to significant reductions in model development efforts and improvements in operational efficiency.


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