Newtown, Pa. – October 05, 2026 -- EPAM Systems (NYSE: EPAM) has launched a strategic offering aimed at powering frontier AI models through high-fidelity data generation, model evaluation and custom reinforcement learning (RL) environments, targeting what it calls a critical industry bottleneck in enterprise AI deployment.

EPAM positions engineering heritage against shift from compute to domain intelligence

Elaina Shekhter, Chief Strategy & Transformation Officer at EPAM, said demand has moved from raw compute to specialized domain intelligence as frontier AI models shift from general experimentation toward complex enterprise workflows and multi-step agentic execution. The company is leveraging its multi-decade enterprise engineering background to build what it describes as a foundational intelligence layer for the AI ecosystem.

EPAM counts nearly 10,000 Claude-certified architects among its trained workforce

The offering is backed by EPAM's existing partnerships with Anthropic, OpenAI, Google and Microsoft. EPAM has trained nearly 10,000 Claude-certified architects, more than 3,000 OpenAI-certified forward-deployed engineers, and more than 5,000 Gemini-certified specialists to date, forming one of the largest AI engineering initiatives in the industry.

RL environments will simulate enterprise systems before production deployment

A core component of the service involves building controlled virtual RL environments that mirror complex enterprise systems, allowing developers to stress-test multi-turn interactions, reasoning and tool use prior to production. These environments provide closed-loop feedback for reinforcement learning and continued model improvement.

Gartner projects 99% of agent platforms will offer simulation tools by 2028

Gartner's April 2026 report, "Emerging Tech: AI Race: Simulation Supercharges Agent Evaluation and Self-Learning Loops for AI Agents,