EPAM Launches AI Training and Simulation Service for Complex Enterprise Work

EPAM is turning its experience building business software into a service for AI labs trying to make models handle complex enterprise work. The company launched its Frontier AI service on October 5, 2026, combining specialized data generation, model evaluation and simulated workflows. Its stated goal is more reliable execution of business tasks—not a new model or a newly announced lab partnership.

From general answers to specialized work

EPAM frames the offering around a distinction between broad model skills and the knowledge needed inside a business. General language and coding training draws on public data; specialized workflows, it argues, require vetted domain knowledge and the ability to carry out tasks across multiple steps.

The service brings together three kinds of work rather than selling access to a single model. EPAM says it will draw on its experience designing, integrating and maintaining enterprise systems to supply them:

• Specialized, high-fidelity data generation for model development.
• Model evaluation focused on performance in complex enterprise workflows.
• Custom reinforcement-learning environments, where feedback supports ongoing model improvement.

As frontier AI shifts from general experimentation to complex enterprise workflows, the demand has fundamentally moved from raw compute to specialized domain intelligence

Elaina Shekhter, EPAM Chief Strategy & Transformation Officer

A rehearsal space that also trains models

Simulation is a cornerstone of the launch. EPAM says its controlled virtual environments mirror enterprise systems and workflows, letting developers stress-test reasoning, tool use and interactions that unfold over several exchanges before putting a model into production.

Those environments serve two roles: checking how models behave and feeding results back into training. Reinforcement learning uses feedback to improve a model’s actions. EPAM describes a secure, closed-loop setup in which the simulated work supplies feedback for that learning process and subsequent model improvement.

To support its market case, the launch release cites Gartner’s April 2026 forecast that 99% of AI agent platform providers will offer simulation environments by 2028, up from fewer than 25% in 2026. That is a prediction about availability, not evidence that EPAM’s service has already improved model performance.

Existing lab ties underpin the launch

EPAM names Anthropic, OpenAI, Google and Microsoft as established partners. It presents those relationships as a foundation for the service, not new agreements announced with this launch.

The company also reports training investments covering nearly 10,000 Claude-certified architects, more than 3,000 OpenAI-certified forward-deployed engineers and more than 5,000 Gemini-certified specialists. EPAM’s argument is that familiarity with these model ecosystems, paired with firsthand knowledge of enterprise deployments, gives labs useful insight into where their models succeed and struggle.