EPAM

epam.com

EPAM is a technology consulting and engineering provider rather than solely an advisory firm or software vendor. Its service descriptions cover architecture, software development, deployment and ongoing operations. Its AI practice includes separately purchasable strategy work, generative-AI prototypes and products, enterprise AI/ML platforms and managed operations. EPAM also offers research services and the commercially licensed migVisor accelerator; these should be evaluated separately from implementation engagements. For procurement, the important distinction is whether an engagement ends with recommendations or a pilot, delivers a production system, or includes responsibility for running it. ^[1] ^[2] ^[3] ^[4] ^[5] ^[6] ^[7]

Services and delivery

Generative-AI assessment and strategy

Advisory. Advises enterprises on where and how to invest in AI. Deliverables include enterprise assessments, use-case prioritization workshops, operating models, investment analysis and implementation roadmaps. This advisory scope does not itself commit EPAM to implementation or operations. ^[3]

Generative-AI prototypes and product implementation

Implementation. Develops use cases, proofs of concept and production-ready minimum viable products, with roadmaps for scaling. Broader delivery scope includes AI products, LLM security, responsible-AI controls and operationalization through ML/LLMOps. ^[8]

Enterprise AI and machine-learning platform engineering

Implementation. Designs and deploys AI/ML platforms with target architecture, MLOps pipelines, model registries, feature stores, monitoring and governance. Applications include intelligent search, predictive models and recommendation engines, with assessment, build and optimization phases. ^[4]

Data and AI managed services

Managed services. Takes over ongoing data and AI operations, covering reliability engineering, incident frameworks, model and LLM observability, agentic remediation, cost optimization and platform upgrades. The offering describes a transition phase followed by 12–36-month contracts. ^[5]

Platform and product engineering

Implementation. Engineers software products and enterprise platforms across the development lifecycle. Implementation activities include architecture, agile development, DevOps, testing and product delivery. Product strategy, management and coaching are separate components of the broader offering. ^[2]

Cloud assessment and FinOps advisory

Advisory. Provides cloud portfolio assessment and FinOps advice to inform modernization and spending decisions. Cloud infrastructure advisory should be scoped as advice rather than assumed to include infrastructure implementation. ^[9]

Cloud modernization and cloud-native development

Implementation. Implements cloud modernization and cloud-native applications. This build scope is distinct from cloud portfolio advice and from ongoing operational support. ^[9]

Cloud operations

Managed services. Operates cloud environments with security, compliance, performance and cost optimization responsibilities. Buyers should establish the exact operational boundary and service levels separately from modernization delivery. ^[9]

Data platforms, governance and business intelligence

Implementation. Implements cloud data migration, master data management, data governance, data catalogs and analytical or self-service BI solutions. These are data technology services; they do not independently establish AI or machine-learning delivery. ^[10]

Managed cyber detection and response

Managed services. Provides continuous security monitoring and response through its cyber-defense offering. References to AI-driven tools do not, on their own, make this a customer AI engineering service. ^[11]

Cybersecurity incident response

Implementation. Supports breach containment and recovery through incident-response engagements. Buyers should agree response availability, escalation paths and recovery responsibilities before contracting. ^[11]

migVisor migration and modernization accelerator

Software product. Offers a commercially licensed tool suite for assessment, dependency mapping, code conversion, BI conversion and reconciliation. The product license is a separate purchase from any associated modernization implementation engagement. ^[7]

Research and insights

Research. Provides customer research, market forecasting, usability research, biometric testing and educational programs. This research offering is distinct from software or AI engineering delivery. ^[6]

Company details

Track record

For an unnamed European energy company, EPAM reports building a unified data platform and deploying churn-prediction models into production with AWS. The case names Amazon SageMaker, S3, Redshift and Glue, and reports more than 90% prediction accuracy after a year-long progression from proof of concept to production. The result is specific to this engagement. ^[12]

For Baker Hughes, EPAM reports building and piloting two generative-AI assistants for production and field engineers. Technologies included Amazon Bedrock, Anthropic Claude, Amazon Textract, Titan embeddings and OpenSearch. The assistants addressed operational questions, SQL generation, interactive plots and technical-document search. The documented stage was pilot or prototype, with production release or integration described as subsequent work. ^[13]

For Nelnet, EPAM describes a 12-week AI-enabled software-development pilot beginning in March 2025. Work included tool evaluation, environment integration, agent development, active coding, measurement and coaching. EPAM reports a measured 31% productivity improvement; that outcome should not be treated as a forecast for other development teams. ^[14]

For Liberty Global, EPAM documents development of the Horizon 4 entertainment platform, including mobile applications, microservices and back-office rearchitecture. The case lists AWS, Kubernetes, Kafka, Apache Spark and Adobe AEM, providing evidence of platform engineering beyond AI-specific projects. ^[15]

Scope and limitations

The case-study outcomes are EPAM’s reports, not independently audited findings. Production deployment, prototype completion and measured pilot results should be evaluated separately.
The churn-prediction case names Power BI in its body and QuickSight in its technology summary. The actual reporting stack requires confirmation.
The supplied evidence establishes advisory, implementation and managed-service offerings, but does not settle engagement-specific pricing, staffing, data residency, intellectual-property terms or support commitments.

Questions to ask

Which deliverables and acceptance criteria distinguish assessment, proof of concept, production deployment and operational handover in the proposed scope?
Who owns architecture, engineering delivery, model evaluation and production sign-off, and which relevant production references can be provided?
What data-residency, security and platform constraints apply, and how will model quality and operational behavior be evaluated?
What are the ownership and license terms for custom code, models, accelerators and third-party components?
For managed services, what are the transition obligations, support hours, incident-response SLAs, contract term and exit arrangements?

Overview