The Challenge
The R&D department at a global pharmaceutical company needed to operationalize a growing portfolio of AI models - ranging from off-the-shelf LLMs to bio-specific and custom fine-tuned models trained on proprietary data.
However, they faced three major blockers:
- A secure VPC requirement, due to the sensitivity of clinical and research data.
- Lack of infrastructure to serve custom models at scale.
- Limited developer capacity - they couldn’t afford to divert engineering talent from core drug development efforts.
They wanted to move fast, remain compliant, and avoid building an in-house solution from scratch.
The Solution
Working with Doubleword, the team was able to rapidly deploy a suite of LLMs - including generic, domain-specific, and privately fine-tuned models - within their existing AWS and Snowflake environment.
Doubleword provided:
- A secure, compliant model-serving layer integrated into their VPC
- Persistent APIs that enabled seamless use of the models in internal tools
- A fully managed infrastructure setup, requiring zero internal DevOps effort
The deployment was complete in under six weeks, without disrupting existing workflows or compliance requirements.
The Impact
By removing infrastructure as a bottleneck, the R&D team was able to start using advanced AI models immediately in critical drug discovery workflows.
- Improved Accuracy: Researchers saw better performance using fine-tuned, domain-specific models
- Enhanced Compliance: Models were served entirely within their private cloud, satisfying all regulatory needs
- Higher Velocity: ML and research teams could focus on experimentation, not infrastructure maintenance
Today, this pharmaceutical leader is pushing forward drug discovery efforts faster - with no infrastructure trade-offs and no compromise on security.
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