Domino Data Lab and EPAM collaborate to hasten AI adoption in the life sciences space

Domino Data Lab, provider of a unified platform to build, scale, and govern enterprise AI, is partnering with EPAM, a leading global AI and digital transformation services company, pairing EPAM’s custom systems engineering expertise with Domino’s platform.

This partnership aims to help joint customers deploy GxP-validated AI models and governed enterprise analytics across R&D, according to the vendors.

Domino’s latest industry research identifies legacy system fragmentation, GxP revalidation demands, and governance hurdles as the primary barriers before AI initiatives can deliver impact. Domino provides the platform layer to bridge these gaps, while EPAM delivers the specialized domain engineering to integrate it into enterprise IT stacks, the companies said.

“Our strategic partnership with Domino marks a significant step toward AI-native transformation throughout the pharmaceutical industry, backed by EPAM's forward-deployed engineering (FDE) capabilities and industry experience across the life sciences value chain,” said Nir Kaldero, chief data and AI strategist at EPAM. “With Domino, we aim to generate meaningful value worldwide using our AI/RUN™ Transformation frameworks in a complex, dynamic business and technology environment.”

EPAM’s dedicated integration teams and FDEs work alongside Domino’s FDEs. Together, they embed directly with customer teams to accelerate deployment timelines, modernize legacy code, and ensure strict compliance across heterogeneous IT architectures.

“Life sciences leaders don’t want AI that only works for one pilot,” said Ricky Mann, chief solutions officer at Domino. “Pairing EPAM’s implementation experience with Domino’s platform lets leaders scale AI solutions enterprise-wide, not just within a single function.”

Together, EPAM and Domino will help leaders:

• Advance discovery and target identification: Improve candidate selection by running AI agents, LLMs, and machine learning pipelines against large-scale genomic datasets.
• Modernize statistical computing: Shorten regulatory submission timelines by giving biostatisticians and statistical programmers a governed environment to unify SAS, R, and Python, from analysis through TFL generation.
• Scale real-world evidence: Speed observational research by standardizing massive, heterogeneous real-world data sets on a secure analytics layer.

For more information about this news, visit https://domino.ai or www.epam.com.