We help organizations unlock their most important data to build the future of health
Explore Cases
Explore some of the stories that make us proud, the collaborations our partners value the most, and how we turn data and AI opportunities into meaningful progress - together
OUR DISCIPLINES
How can we help?
We want to be part of your data and AI journey - from strategic direction to scalable technology.
We adapt to your context, tailor the right approach to advise with care, and infuse new energy to your team. Together, we design, build, and transform solutions that last.
Setting the direction
From actionable data and AI strategies to data architecture designs and omnichannel orchestration, we help organizations set a clear course for lasting impact.
Making it real
From advanced AI/ML models and scalable enterprise data platforms to predictive analytics tools and decision-support applications, we help our partners turn strategy into working solutions.
Bringing it to life
From operating model redesigns and governance frameworks to capability-building programs and adoption roadmaps, we help our partners in making change last.
From diagnostics to mental well-being, we partner with innovators to shape a more holistic health experience - one built on data, trust, and collaboration.
Trusted by organizations
pioneering health
As a trusted partner to organizations transforming quality of life for millions, we work side by side to shape the future of health.
Insights
From strategy to technology, we’re continuously exploring and learning what can make a real difference in the health industry - not just in systems and structures, but also for the people working every day to create better health journeys.
In this article we give our point of view on how healthcare organizations can design a scalable content engine with local flexibility for impactful customer engagement - and how to succeed where other organizations typically fail.
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In this perspective on Build vs Buy in AI, we share how to move beyond a buy-only mindset by designing AI around your processes and competitive edge. We explore why off-the-shelf works well in domains where correctness and compliance matter most, why you need more ownership in use cases that drive differentiation, and how a hybrid approach - buying the base and building the system around it - keeps operations stable while still enabling you to stand out.
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In this four-part series, “Mastering CX in Pharma”, we aim to share our perspective and approaches on how to succeed in designing a winning customer experience. We will deep dive into engagement model design, data as a key enabler, how to design an operating model that supports your strategic ambitions, and the role of change management.
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In this four-part series, “Mastering CX in Pharma”, we aim to share our perspective and approaches on how to succeed in designing a winning customer experience. We will deep dive into engagement model design, data as a key enabler, how to design an operating model that supports your strategic ambitions, and the role of change management.
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In this four-part series, “Mastering CX in Pharma”, we aim to share our perspective and approaches on how to succeed in designing a winning customer experience. We will deep dive into engagement model design, data as a key enabler, how to design an operating model that supports your strategic ambitions, and the role of change management.
Click to read more
In this four-part series, “Mastering CX in Pharma”, we aim to share our perspective and approaches on how to succeed in designing a winning customer experience. We will deep dive into engagement model design, data as a key enabler, how to design an operating model that supports your strategic ambitions, and the role of change management.
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Sign up for the whitepaper and learn how Agentic AI is set to disrupt pharma’s commercial engagement - and why acting now is key to staying ahead. Explore how a strategic approach to AI adoption can help pharma scale impact, optimize operations, and unlock lasting value.
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AI is revolutionizing the pharmaceutical industry, improving efficiency in drug discovery, clinical development, and customer engagement. Key applications like content creation, insights extraction, and predictive analytics streamline operations. To maximize AI’s potential, a strategic, holistic approach is crucial, avoiding siloed efforts and aligning with business goals for scalable success.
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AI has the potential to revolutionize the pharmaceutical industry, but success hinges on selecting the right use cases. Without a structured approach, companies risk spreading resources too thin and missing out on scalable, impactful solutions. This article explores a strategic framework for evaluating and prioritizing AI projects, ensuring they align with corporate goals and deliver maximum value
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Successfully implementing AI in pharma requires building a strong foundation across people, processes, and platforms.Intellishore’s approach involves assessing organizational readiness, identifying maturity gaps, and creating a roadmap to address these challenges. By focusing on targeted initiatives, organizations can enable AI innovation and achieve sustainable, long-term success.
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Successfully implementing AI in the pharma industry requires a strategic focus on Organizational Change Management (OCM) to ensure adoption and lasting value. Key elements include a clear communication plan, targeted training, and ongoing support to embed AI into everyday processes. By addressing these factors, pharma companies can fully harness AI's potential and drive sustainable business impact
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Clinical Decision Support (CDS) tools have tremoundous potential to revolutionize all steps of the patient journey. Read the white paper to discover how CDS tools can help address critical challenges in healthcare, such as information overload, care-giver shortages, and misdiagnosis.
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Clinical Decision Support (CDS) tools have the potential to transform healthcare by reimagining patient journeys and addressing critical inefficiencies in health care delivery. This white paper explores the key considerations for successfully developing and executing a CDS solution if you are a Pharma or a Medtech Company.
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Pharma sales is undergoing a major transformation. What began as a numbers game - more reps, more reach - is now a precision sport powered by data and AI. Discover how the role of the rep is evolving from high-touch to high-tech, and how AI agents are set to become the ultimate sales force multiplier.
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For us, progress doesn’t come from tech alone. It happens when the right people have the right data, strategy, and tools to act. That's why we build for adoption, blending data and AI with strategy and human insights to solve problems that matter.
WEBINAR
As patents expire, launches fragment and competitive pressure intensifies, forecasting accuracy has become a strategic advantage. In this webinar, we'll show how our ML forecasting engine helps healthcare companies make better decisions from launch and competition to loss of exclusivity.
Date: 31st of March
Time: 11:00 - 12:00 am CET
Loss of exclusivity (LoE) is accelerating across major therapeutic areas. Between 2025 and 2030, more than $230B in pharma revenue will go off patent, pushing many companies toward an imminent patent cliff and forcing more deliberate, data-driven end-of-lifecycle planning. At the same time, true commercial white space is becoming harder to find, and new launches are increasingly smaller, more segmented, and more competitive.
Leading organizations are responding by treating forecasting as a learning system, not a one-off exercise.
In this webinar, we'll share a real-world case study where we built a drivers-based machine learning forecasting engine to predict LoE erosion for a neurology asset. Rather than relying on a handful of hand-picked analogues, the model learns from hundreds of historical events and external market drivers to generate forecasts that are more accurate, explainable, and scalable.
While the case focuses on LoE, the same ML engine can be applied across the product lifecycle - from forecasting launches to modeling branded competition and generic or biosimilar uptake.
What you’ll take away
- Why traditional analogue-based forecasting often breaks down
- How a drivers-based ML approach improves erosion predictions and transparency
- How better forecasts support earlier, more confident de-investment and resource allocation decisions
- How the same modeling framework can be reused across LOE, launches, and competitive market simulations
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