Became AI application builders.
Enterprise AI Accelerates Research for a Global Pharmaceutical Leader
A multi-phase program took a research organization from its first ML platform to secure generative AI used by nearly 2,000 researchers.
Client context
The work begins with the operating reality.
Client
A global pharmaceutical research organization with more than 100,000 employees and a focus on drug discovery and development.
EngagementBlueAlly designed and delivered a multi-phase AI program, from the organization's first enterprise machine learning platform to secure generative AI at research scale.
The challenge
What had to change
The organization saw AI's potential to speed discovery, but machine learning lived in isolated data science teams. The platform landscape was changing quickly, regulated research data had to stay protected, and there was no unified strategy across ML and generative AI work. Leaders needed AI that scientists would actually use, under enterprise-grade security and compliance.
The BlueAlly solution
How the work came together
The program ran in three phases. First, BlueAlly stood up the organization's first enterprise ML training and serving platforms in containerized environments that met pharmaceutical regulatory requirements. Second, it moved the organization to Amazon SageMaker, added data annotation with V7 and Labelbox, and put MLOps practices in place for reproducible experiments, version control and model governance. Third, it added experiment tracking with ClearML, formalized security for large language models (prompt injection, data extraction and AI security posture management), and led a program that let nearly 2,000 non-technical researchers build their own AI applications.
The results
What changed
GPU consumption rose 450 percent as research teams adopted AI. Researchers saw 30 to 60 percent faster execution in literature analysis, experimental design and data interpretation. The platform recorded more than 300,000 AI-powered interactions in its first month, and the program earned the client's highest internal innovation award.
Outcome highlights
What the client gained.
Literature analysis, experimental design and data interpretation.
Compute use rose with adoption across research teams.
AI-powered interactions in the first month after launch.
Related work
More outcomes in Data & AI
Data & AI
A 360° View of Customer Data for Better Decisions
The bank gained a trusted customer view for pricing and cross-sell decisions, delivered inside its rigorous security and compliance lifecycle.
Read the story
Data & AI
Student Success Reporting from District to Classroom
Educators can spot performance patterns and ask more precise questions about student needs, instruction, and demographic trends.
Read the storyHave a similar constraint?
We can start with the current state, the outcome, and the limits around time, risk, and budget.