Shifting Dynamics in Life Sciences

Historically, the clinical trial process has been highly manual and slow to evolve, characterized by regulatory complexity, safety protocols, risk aversion and legacy infrastructure.

In recent years, however, we believe several factors have prompted a meaningful shift:

• The disruption of trial site access during COVID-19 pushed Clinical Research Organizations and their pharmaceutical sponsors to adopt digital tools to maintain trial continuity.
• An ever-increasing volume of clinical trial data has created larger and more complex, but well-structured datasets that are ideally suited for AI applications.
• Persistent cost and time pressures have intensified the need for scalable solutions that improve efficiency without compromising quality.

For CluePoints, these trends notably converged during the COVID-19 pandemic. The company played a critical role in supporting several high-profile COVID-19 vaccine trials, with its RBQM platform enabling remote oversight at scale, helping ensure data integrity and continuity under unprecedented constraints.

This work demonstrated both the resilience of CluePoints’ platform and its broader applicability in a rapidly changing environment. It underscored the potential for technology to materially improve how trials are managed—advancing both speed and quality—and laid a strong foundation for the company’s next phase of product development.
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CluePoints’ Approach to AI

While organizations continue to proceed with caution and widespread adoption remains gradual, we are seeing some pharmaceutical and biotech companies beginning to incorporate AI into key workflows. CluePoints has been at the forefront of this shift. The company has taken a pragmatic, targeted approach to applying AI, developing solutions designed to address real operational needs and deliver measurable value across the clinical trial lifecycle. These solutions include:

• Intelligent Medical Coding, which leverages deep learning to suggest standardized medical codes with high accuracy, significantly improving the speed and precision of a task traditionally handled by large clinical teams.
• Intelligent Query Detection, which applies large language models to help surface meaningful data inconsistencies while reducing the volume of false positives that require manual review.
• CluePoints has also embedded AI into its core RBQM platform, further facilitating the process of managing risks and signals surfacing during a clinical trial.

Importantly, these tools are designed to support—not replace—clinical teams, enabling faster, more informed decision-making while maintaining the highest standards of oversight and quality.
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Laying the Groundwork for Scalable AI

As more companies explore how to incorporate AI into their platforms, we believe CluePoints stands out not only for the solutions it brought to market, but for the way it approached building them. We see three key attributes that enabled CluePoints to effectively develop and scale AI solutions, which gained strong market traction:

• A strong technical foundation. CluePoints built its AI capabilities on top of a mature software platform and years of experience supporting clinical trials. Its access to large volumes of structured, standardized clinical trial data—gathered through long-standing customer relationships—provided an ideal base for training and applying machine learning models.
• Customer-driven development. In a sector where credibility is essential, CluePoints prioritized close engagement with customers throughout the product development process. Early collaborations with large sponsors not only provided valuable data but also helped validate new tools ahead of broader market rollout.
• Specialized, highly experienced talent. CluePoints built a lean but highly effective R&D team by hiring experts with deep experience in both life sciences and technical domains. This team brought a strong understanding of industry dynamics and a clear perspective on where AI could deliver real operational value. Working closely with product leaders and customers, they helped shape solutions that were both technically sound and directly relevant to market needs.
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Looking Ahead

While AI continues to evolve rapidly, we believe its role in life sciences will likely remain focused on areas where it can meaningfully improve accuracy, efficiency, and patient outcomes. In our view, some of the most promising use cases include automating manual clinical workflows, supporting patient recruitment and site selection, improving real-world evidence integration and analysis, and enhancing data interoperability across healthcare and research systems.