Provider recordAI implementation partnerAI consultancyNot claimed

EPAM Systems

AI advisory, platform engineering and implementation for enterprise workflows.

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Quick reference

Service details

Service information · see individual claim labels below.

Service coverage: Not stated
Task: Automate business workflows
Industries: Not stated
Delivery mode: AI Implementation
Pricing model: Not stated
Headquarters: Not stated
Languages: Not stated in the sources reviewed

Record summary18 facts documented · sources reviewed Oct 9, 2026

Provider-reported 4Not stated 14Independently corroborated 0

No fact on this record has independent corroboration yet. Provider statements remain labeled, and gaps stay visible.

01 · At a glance

What this provider does

EPAM Systems describes AI services that connect business assessment with platform and product engineering. Its generative AI offering includes proofs of concept, minimum viable products and support for operational adoption. Separate advisory and responsible AI services address project prioritization, data foundations and governance. The combination is relevant to organizations commissioning a business-system change that requires both software delivery and changes in how teams work.

Choosing the first implementable use case

EPAM’s advisory service begins with an enterprise assessment and workshop. It describes examining available business and industry data, identifying where information is stored and used, and prioritizing a portfolio of possible AI projects. The work is intended to connect an opportunity with the data and organizational changes needed to implement it.

Its roadmap approach considers a future operating model, then works backward to a practical starting point. Data curation and knowledge management are part of the preparation. The service also includes engineer onboarding and training around AI-assisted work, with productivity measures used to evaluate adoption.

Sources: EPAM — Generative AI Advisory ↗ (provider)

Moving from an experiment into a working system

For a selected use case, EPAM describes developing a proof of concept with foundational data capabilities, followed where appropriate by an MVP and a scaling roadmap. This separates feasibility work from the larger task of creating an operating platform. Model research and experimentation inform recommendations about language models and custom solutions.

The broader delivery offering covers data platforms, AI products and operating-model changes. EPAM identifies machine learning operations and LLMOps alongside organizational change management as part of operationalizing the result. The service page supports this lifecycle scope; a particular engagement still needs its own deliverables and support arrangement.

Sources: EPAM — Generative AI Services ↗ (provider)

Customer service in an existing operation

EPAM’s published 1&1 case describes replacing parts of a telecommunications customer-service workflow with voice-enabled agents. The account says EPAM embedded specialists in the business, ran stakeholder workshops and developed the transformation and implementation approach. Microsoft Azure supplied cloud and AI capabilities.

The technical account identifies DIAL as a common API access layer for language models, together with agent evaluation and conversation management in EPAM’s delivery approach. This illustrates how model access, workflow design and organizational participation fit together. It remains a provider-published example, not independent confirmation of performance or a forecast for another buyer.

Sources: EPAM — 1&1 Agentic AI Customer Service Case ↗ (provider)

Assessing governance as part of the work

EPAM offers responsible AI assessment focused on enterprise governance, policies and risk management. Its page describes reviewing systems as well as advising on how they are developed. Examples include assessing an AI document-processing application and examining data-licensing concerns in a consumer-facing system.

These examples indicate an assessment-and-remediation service rather than a certification attached to every implementation. For a project involving sensitive data or consequential decisions, this work can be scoped alongside engineering so that technical changes and organizational responsibilities are considered together.

Sources: EPAM — Responsible AI Assessment and Services ↗ (provider)

Buyer fitTeams planning a substantial AI implementation within existing enterprise systems. Smaller businesses should confirm minimum engagement size, budget, access requirements and post-launch ownership before commissioning work.

02 · Claims and sources

Every fact, with its label.

A source link shows where a statement came from. It does not by itself confirm delivery.

Delivery

6 facts

End to end

Provider-reported

Describes proofs of concept, MVP delivery, data platforms and operational adoption.

EPAM — Generative AI Services ↗