Microsoft Foundry Services and Cognitive APIs

EPAM’s Microsoft Foundry Services and Cognitive APIs implementation is designed for CIOs, CTOs, Heads of Data & AI, and engineering leaders who want to build a scalable, secure Enterprise AI platform on Microsoft Azure. This offer addresses key challenges such as fragmented AI adoption, lack of governance, disconnected data sources, and difficulties operationalizing AI at scale. By leveraging Microsoft Foundry services and Azure Cognitive APIs, organizations can accelerate AI innovation while maintaining enterprise-grade security, compliance, and operational control.

What You Will Receive

• Enterprise AI Platform on Microsoft Foundry: Design and deployment of a unified platform to build, deploy, and operate AI agents and applications with built-in monitoring and scalability.
• AI Governance & Security Framework: Implementation of identity, RBAC, policy enforcement, and compliance controls aligned with enterprise and regulatory requirements.
• Azure Cognitive APIs Integration: Enablement of AI capabilities such as language, vision, speech, and document intelligence through secure and governed API integration.
• Data Integration & AI Readiness: Establishment of a governed data foundation to support AI use cases across cloud, hybrid, and on-premises environments.
• Multi-Model & Agent Enablement: Access and orchestration of AI models from Microsoft and leading providers, integrated into enterprise workflows.

Typical Implementation Approach

Phase 1 — Discovery

• Assessment of current data, AI, and application landscape.
• Identification of high-value AI use cases and success metrics.
• Delivery of AI readiness assessment and platform vision.

Phase 2 — Design & Planning

• Architecture design for Microsoft Foundry-based AI platform and integration with Azure services.
• Definition of governance, security, and operating model.
• Creation of implementation roadmap, backlog, and prioritized use cases.

Phase 3 — Implementation

• Deployment of Microsoft Foundry landing zone and AI platform components.
• Integration of Azure Cognitive APIs, enterprise data sources, and business systems.
• Implementation of orchestration layers using Azure AI Search, Azure Kubernetes Service, and API Management.
• Delivery of infrastructure-as-code, CI/CD pipelines, monitoring, and observability setup.

Phase 4 — Support & Enhancement

• Post-deployment support, optimization, and scaling of AI workloads.
• Continuous improvement of AI agents, workflows, and retrieval strategies.
• Knowledge transfer, documentation, and operational runbooks.

Expected Outcomes

• Accelerated adoption of AI through a unified and governed platform.
• Improved semantic search and data retrieval using optimized indexing strategies.
• Secure and compliant AI operations with built-in governance and lifecycle controls.
• Reduced time-to-market for AI-driven applications and automation.
• Scalable foundation for enterprise-wide AI innovation and agent orchestration.