Domino and EPAM Partner to Scale Life Sciences AI
The integration aims to modernize legacy systems to support GxP-validated AI models in complex regulatory environments.
Updated on Oct. 15, 2026 in Artificial Intelligence
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Domino Data Lab and EPAM have announced a strategic partnership to integrate AI governance platforms with enterprise systems for the life sciences sector. The collaboration focuses on streamlining the deployment of GxP-validated AI models within complex regulatory environments.
Why it matters
Life sciences firms face significant barriers to AI adoption due to legacy system fragmentation and strict governance requirements. This integration effort aims to modernize existing infrastructure to accelerate drug discovery, target identification, and regulatory submission processes.
The partnership combines the Domino Data Lab enterprise AI governance platform with EPAM systems engineering services. The teams will focus on modernizing legacy code and standardizing heterogeneous data sets to meet compliance standards.
The players
Domino Data Lab
An enterprise software company that provides a platform for AI governance and data science operations.
EPAM
A global firm specializing in complex systems engineering, digital product development, and IT consulting services.
The details
EPAM integration teams and forward-deployed engineers will embed directly with customer staff to refactor legacy IT architectures. By aligning these systems with Domino's governance layer, the collaboration seeks to ensure that AI models meet GxP—good practice, a set of quality standards required by regulators for life sciences products—compliance. The process aims to replace manual, fragmented data handling with automated, governed statistical computing environments.
Timeline
- 2026-10-15
Domino Data Lab and EPAM announced a strategic partnership.
The Tech Race
This partnership mirrors the industry-wide push to embed standardized governance layers into fragmented research environments. It competes with other attempts to unify enterprise AI stacks to solve the long-standing regulatory bottlenecks in drug development.
Life sciences organizations gain an integrated path to scale AI agents for discovery and regulatory tasks. The partnership is active now, targeting teams that need to modernize legacy statistical computing without sacrificing compliance.
The takeaway
The success of this collaboration will be measured by its ability to shorten regulatory submission timelines through modernized statistical computing. Watch for published case studies on the first GxP-validated model deployments resulting from this joint engineering effort.
Further reading
For broader trends in AI operationalization, visit the Artificial Intelligence section.
Source note: This article includes information reported by KMWorld.
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Do you believe the increased use of AI in medical research will improve healthcare outcomes?
