The Power of Bridging Enterprise Experience and Frontier AI

The Power of Bridging Enterprise Experience and Frontier AI

For years, EPAM has helped enterprises put advances in AI to practical use. That work has deepened through our Applied AI practice and growing partnerships with leading frontier model developers, bringing increasingly capable models into the systems and workflows our customers rely on.

Over time, we have seen the priorities of model builders shift toward the challenges our customers face. Frontier labs increasingly need their models to do more than answer questions: they need them to complete complex enterprise work reliably. These are the systems and workflows EPAM has spent decades designing, integrating and maintaining. Now, convergence has led us to ask how our experience can contribute not only to utilizing these models, but also to improving their positive impact.

Why Enterprise Work Sets a Higher Standard

As frontier models have advanced, a specialized industry has emerged to support their development; supplying training data, expert feedback, evaluations and environments where models can safely practice tasks. This controlled ecosystem helps labs develop new capabilities, measure progress and identify weaknesses.

To-date, model training providers have often relied on crowdsourced specialists to generate and evaluate data in areas such as coding and scientific reasoning. But as model providers focus more on enterprise workflows, that approach becomes less sufficient on its own.

Consider an agent resolving a billing issue. It may need to pull customer and contract data from a CRM, reconcile that against billing and payment records, check entitlement or service information, follow approval rules and then update the appropriate case-management or support system. Success depends on completing the entire process correctly, not simply producing a convincing answer. Teaching and evaluating that work requires an understanding of how enterprise systems, processes and decisions fit together.

This is an enterprise intelligence gap, not simply a shortage of data. Closing it requires translating an understanding of how enterprises operate into fresh workflows and realistic training tasks and reliable measures of success. Often, advancement also requires architectural redesign.

Successfully bridging this gap is what prompted us to build EPAM’s Frontier AI business. We are bringing our enterprise engineering, domain expertise, evaluation and assurance capabilities into a dedicated practice, while continuing to develop the model-training capabilities needed to help accelerate frontier performance and meet the demands of AI-native enterprise work.

Turning Experience into Better Evaluations

This business builds on two complementary strengths: decades of enterprise engineering experience and an expert understanding of how frontier models behave in real deployments.

We are deepening that understanding through our deployment partnerships with frontier labs, including OpenAI, Google and Anthropic. With one of the largest AI engineering initiatives in the industry, EPAM has trained and invested in 3,000+ OpenAI-certified forward-deployed engineers (FDEs), 5,000+ Gemini-certified specialists and nearly 10,000 Claude-certified architects to-date.

These teams not only work directly with advanced models across complex enterprise environments but also help us build a grounded understanding of recurring deployment patterns: where models perform well, where they struggle and which process and infrastructure limitations repeatedly prevent agents from completing useful work.

Our domain experts then translate our field experience into the examples, evaluations and practice environments needed to improve performance and business impact. They help define what successful enterprise work looks like and turn that understanding into measurable feedback.

Building on an Existing Foundation

EPAM already delivers evaluations and cybersecurity services, and our gaming practice brings experience building simulated environments. Test.io adds an established global crowdtesting network and experience combining human expertise with automated and agentic quality assurance.

Frontier AI brings these strengths together while developing the additional capabilities model builders need. We offer expert-data production capabilities, including demonstrations and records of the actions and decisions involved in completing complex tasks. We are extending evaluation, assurance and adversarial testing to assess both performance and behavior under challenging or unexpected conditions.

Another priority is enterprise reinforcement learning environments: safe, simulated settings where agents can repeatedly attempt tasks and receive feedback. Building on existing environment work, we are developing scenarios that require coordination across multiple enterprise systems, with checks that establish whether the task was completed correctly.

Together, these capabilities turn enterprise expertise into the training and evaluation infrastructure needed to help agents perform complex work reliably.

Bringing Frontier Capabilities Back to the Enterprise

The value of this work extends well beyond frontier labs.

The same environments, evaluation methods and expert feedback can strengthen the agents we help enterprise clients build and deploy. Our goal is to develop these capabilities to meet the exacting standards of frontier model development, then apply that discipline across our broader client base.

That means helping clients reliably move from pilots to agents, earn users’ trust and deliver measurable business outcomes. It also means evaluating performance and economics together: delivering reliable business outcomes while reducing the cost of running AI, rather than defaulting to the largest model for every task.

These capabilities extend seamlessly across a range of model types: commercial frontier models, open-weight models that organizations can adapt, and smaller, task-specific models. They also support sovereign AI initiatives that require local-language expertise, relevant evaluations and control over where data is processed.

The feedback loop runs both ways: enterprise experience informs model improvement, and the capabilities developed at the frontier help make enterprise AI more effective, trustworthy and economical.

Connecting Experience with What Comes Next

We are building this practice collaboratively, alongside model developers, clients and research communities that are actively exploring new approaches to advance the frontier. This dialogue helps us understand what is working, what remains difficult and where our expertise can make the greatest contribution.

Frontier AI connects EPAM’s enterprise engineering heritage with the next generation of AI development. By building this bridge in both directions, we can contribute more to the partners advancing the technology and bring new, differentiated value to the clients putting it to work.

Talk to our Frontier AI team about making your models and agents more effective in the enterprise.