Blog

Why do AI pilots fail to scale in life sciences regulatory teams? Based on our experience supporting AI adoption in regulatory environments worldwide, this article explains what successful teams do differently.

The promise of AI in pharma has been circulating for years. In 2025, the industry stopped talking about potential and started designing around it.

Yseop’s first CMC-focused offering automates the Quality Overall Summary (Module 2.3), a central component of regulatory submissions that pulls structured insights from Module 3.2.

The FDA has begun using generative AI to review drug submissions, signaling a major shift in regulatory review. Learn how this impacts pharma teams and what it takes to make your content AI-ready.

The AI landscape is evolving rapidly. While pre-configured workflows have powered automation for years, the next frontier lies in intelligent AI agents—systems that can plan, adapt and execute tasks dynamically rather than following static scripts.

See how Yseop Copilot is different from other Generative AI technologies across BioPharma and regulated industries.

See how Yseop Copilot is different from other Generative AI technologies across BioPharma and regulated industries.

In the high-stakes biopharma industry, the ability to produce accurate, compliant documents quickly can mean the difference between timely patient access to life-saving treatments and costly delays.

Explore how Yseop transitioned from AWS SageMaker to AWS Bedrock to overcome scalability challenges and enhance its generative AI capabilities. This strategic move accelerates innovation in regulatory document generation, empowering the pharmaceutical industry with cutting-edge, scalable solutions.