The Challenge
A top-20 pharmaceutical company was drowning in clinical trial data. Their data science team, understaffed and spread across 8 active trials, took an average of 14 weeks to process and analyze each trial phase’s data. Promising drug candidates were being delayed by data bottlenecks, not science.
Our Approach
We deployed a dedicated AI Squad combining AI agents, full-stack engineers, and operations engineers, embedding them directly within the R&D data team. We automated clinical data ingestion across different site formats, deployed machine learning models to detect early safety signals, and automated regulatory reporting preparation.
The Results
- 75% reduction in data processing time: From 14 weeks to under 4 weeks per trial phase.
- 3x more drug candidates evaluated: The freed-up capacity allowed the team to run parallel analyses they previously couldn’t resource.
- $200M in accelerated pipeline value: Getting viable candidates to the next phase faster translated directly to patent-protected market time.
- Zero regulatory findings: All AI-generated reports passed FDA audit without corrections.