Clinical trial dropout is a persistent challenge, and one we address directly. Our patient dropout and site prediction solution uses historical data to cluster patients by risk level and predict dropout probability. By identifying at-risk participants early, trial teams can intervene proactively, keeping studies on track and protecting research investment.

PROMIS—our Patient-Reported Outcome Measurement Information System—uses a GenAI agent with text-to-speech capabilities to automatically capture patient information during hospital onboarding. It personalizes the intake experience, reduces manual inefficiencies and ensures patients feel heard from the start. It's generative AI in healthcare delivering real, human-centered value.

Our knowledge graph search use case leverages large language models to process massive volumes of enterprise data and generate interconnected knowledge graphs. For pharma, this means faster, more holistic decisions, eliminating delays caused by siloed information or hard-to-find niche expertise, which typically slow research and operational workflows.

We help teams work smarter, not harder. Our GenAI workflows enhance critical activities, improving overall process efficiency and individual productivity, drastically reducing time spent on manual, effort-intensive tasks. The result is a workforce that can redirect energy toward strategic, high-impact work rather than repetitive operational processes.

Administrative misalignment between payers and providers creates significant waste. Our GenAI solutions automate administrative tasks, align organizational goals and reduce friction across the payer-provider relationship. The outcome is improved care quality, better patient experience and a more efficient system, explored in depth in our blueprint for payer-provider collaboration.

Yes—and it's one of the most impactful applications we've seen. Our GxP document reviewer applies GenAI-driven automation to validation deliverables, significantly reducing the time and manual effort required to maintain audit readiness. Teams stay compliant without the operational burden that traditionally slows down quality and regulatory workflows.

Manual case intake is slow, inconsistent and error-prone. Our assisted case intake solution uses LLMs and NLP to automate case extraction, interpretation, recommendation generation and adverse event identification. This dramatically cuts processing time, reduces errors and frees pharmacovigilance SMEs to focus on higher-value, judgment-intensive work.

Comparing large regulatory guideline documents is one of the most manual and time-consuming tasks in the life sciences. Our guidelines comparison solution uses automation and a GenAI tool trained on similar documents to make the process faster, more consistent and far more reliable, reducing human error and accelerating regulatory readiness.

We do. Our client-specific AI Labs are designed for faster time-to-development, using tried-and-tested platforms to deploy GenAI use cases quickly and reliably. A global pharma leader recently partnered with us to launch a GenAI Lab—driving measurable innovation across research, drug development and user experience in a secure, compliant environment.

We work across leading cloud platforms—including AWS, Google Cloud and Azure—to deploy GenAI solutions at scale. Our partnerships include strategic collaborations with AWS and Google Cloud Gemini, and we've helped major health plans migrate to Azure and Snowflake, leveraging GenAI and advanced architectures to improve performance and decision-making.