How a Leading Pharma Company Operationalized AI to Drive Innovation at Scale

Discover how a global pharmaceutical leader overcame fragmented data and slow AI adoption by building a unified, self-service platform – accelerating innovation across 265+ use cases.

Main Challenge

Our client, a leading pharmaceutical company, was experimenting with AI across multiple business functions. However, teams working on use cases faced major obstacles:

Data was scattered across various sources with inconsistent quality.
That same data had to be processed repeatedly by different teams.
Accessing both data and AI tooling was decentralized and time-consuming.
There was no shared framework, so for each use case they had to reinvent best practices for model development and deployment.

As a result, PoCs were slow to launch and rarely made it to production, limiting the business impact of AI initiatives. These inefficiencies and frustrations gave rise to the initiative “The Platform”.

This initiative set out to unify data sources and give teams self-service access to both data and AI tooling, eliminating delays and bottlenecks. It also provided ready-to-use capabilities for model development and deployment, embedding best practices directly into accessible pipelines.
The goal was clear: empower use case teams to go from setup to production faster, with everything they need, from data access to deployment tools, available from day one.

How We Helped

We designed and implemented comprehensive, reusable pipelines for model development (including hyperparameter tuning, design choice tracking, model versioning and management for reproducibility for GxP compliance) and model deployment, ensuring scalability, compliance, and efficiency from day one.

For model development, we supported multiple platforms including Databricks and AWS, covering a wide range of model types (from simple models to large deep learning models) across diverse data domains such as image modalities, clinical documents, and molecular data.

For model deployment, we designed an approach that scales from lightweight, serverless setups for POC demonstrations to high-throughput, real-time systems with high availability considerations. We focused on deployments to AWS infrastructure, with built-in support for champion/challenger models, lifecycle management, and rollbacks. We provided default configurations tailored to common patterns, while ensuring use case teams retained full control to customize as needed.

By distilling years of hands-on experience into these modular and accessible pipelines, we enabled hundreds of teams to move faster – building with confidence and deploying at scale.

The Impact

Faster Execution, Greater Scale

Visium’s contributions enabled a 30% faster execution of over 100 AI/ML POCs, while also driving a 50%+ increase in the number of solutions reaching production.

Widespread Adoption

The platform scaled to support 40,000 users, powering 265 use cases across the organization and connecting to over 80 unique data sources, transforming siloed experimentation into unified execution.

Recognized Innovation